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DIKWP-TRIZ：purpose driven integration of DIKWP

已有 864 次阅读 2023-11-14 17:41 |系统分类:论文交流

DIKWP-TRIZ：purpose driven integration of data, information, knowledge, and wisdom forming invention and creation methods

November 2023
DOI:
10.13140/RG.2.2.22483.35360

Traditional Invention and Innovation Theory 1946-TRIZ Does Not Adapt to the Digital Era

-Innovative problem-solving methods combining DIKWP model and classic TRIZ

Purpose driven

Integration of data, information, knowledge, and wisdom

Invention and creation methods:

DIKWP-TRIZ

(Chinese people's own original invention and creation methods:DIKWP - TRIZ)

Prof. Yucong Duan

DIKWP-AC Artificial Consciousness Laboratory

AGI-AIGC-GPT Evaluation DIKWP (Global) Laboratory

(Email：duanyucong@hotmail.com)

Introduction

With the development of society and the advancement of science and technology, innovation and problem solving have become key elements in promoting social progress and development. However, in the face of increasingly complex problems and challenges, traditional problem-solving methods are often difficult to cope with. Therefore, a comprehensive approach is needed to help us better understand problems, discover innovative solutions, and enhance the effectiveness and efficiency of problem solving.

The DIKWP-TRIZ approach is an innovative problem solving methodology that blends the DIKWP model with the classical TRIZ approach.The DIKWP model provides an integrated framework of data, information, knowledge, wisdom and purpose, emphasising the transformative process of information and the driving role of purpose. The classical TRIZ approach, on the other hand, focuses on resolving contradictions and conflicts, applying innovative principles and tools to discover non-traditional solutions.

By merging the DIKWP model with the classical TRIZ approach, the DIKWP-TRIZ approach provides comprehensive, innovative and effectual solutions to problems. It helps people to analyse and understand problems from multiple levels and dimensions, transform data into meaningful information, transform information into valuable knowledge, and find better solutions through innovative thinking and solution generation. At the same time, the DIKWP-TRIZ approach emphasises the evaluation and optimisation of solutions to ensure their feasibility and effectiveness.

This book will explore in detail the principles, methods, architecture and key steps of the DIKWP-TRIZ methodology. We will delve into the key steps of Problem Modelling, Knowledge Acquisition, Innovative Thinking, Solution Generation, Solution Evaluation and Optimisation to help readers better understand and apply the DIKWP-TRIZ methodology. By combining the DIKWP model with the classical TRIZ approach, the DIKWP-TRIZ methodology provides a powerful tool to drive innovation and problem solving.

1. Introduction to Related oncepts

1.1 DIKWP

Data can be understood as a figurative representation of what we perceive as "the same" semantics. Data usually represents a concrete fact or observation with a specific semantic meaning behind it. When working with data, we often look for and extract the same semantics and unify them into a single concept. For example, if we see a flock of sheep, although each sheep may have a different size, colour, gender, etc., we will group them into the concept of "sheep" because they share our semantic understanding of the concept of "sheep".

Information corresponds to the semantic expression of "difference" in cognition. Information usually refers to knowledge or data about the environment or an object that we acquire through our senses and observations. When processing information, we identify and categorise intrinsic differences in the input data. For example, in a car park, although all cars can be classified under the concept of "car", each car has its own special characteristics, such as make, model, colour, etc., which are all information.

Knowledge corresponds to the semantic meaning of "integrity" in cognition. Knowledge is the understanding and interpretation of the world that we gain through information. In processing knowledge, we abstract complete concepts or patterns through observation and learning. For example, we learn from observation that all swans are white, which is a complete understanding of the concept of "swan" that we gain from gathering a lot of information.

Wisdom (Wisdom) corresponds to information on ethics, social morality, and human nature, and is a high level of understanding, synthesis, and application of knowledge and information. When dealing with Wisdom, we integrate this information and use it to guide decisions. For example, when faced with a decision-making problem, we take into account all aspects of ethics, morality, and feasibility, not just technology or efficiency.

Purpose can be understood as a dichotomy (input, output), where both input and output are DIKWP content. Purposes represent our understanding of a phenomenon or problem (input) and the goal we wish to achieve by processing and solving the phenomenon or problem (output). When processing purposes, the AI system processes the input DIKWP content according to its predefined goal (output), and by learning and adapting, makes its output converge to the predefined goal.

1.2 TRIZ

TRIZ is an acronym for the Russian phrase "Теория решения изобретательских задач", and the full English name is "Theory of Inventive Problem Solving". It is a methodology for solving complex problems and innovative design challenges that was originally developed in 1946 by Soviet inventor and scientist Genrich Altshuller and his colleagues, and has been continuously developed and refined since then.

TRIZ is based on the idea that the laws of creative problem solving are universally applicable and that these laws can be identified by analysing a large number of patents for inventions.The aim of TRIZ is to help problem solvers anticipate the direction of technological systems and to find innovative solutions that break through traditional ways of thinking and technological barriers.

The TRIZ methodology includes a variety of tools and concepts, including:

Problem analysis tools:

l Functional Analysis: Identifies all the components in a system and the relationships between them.

l Problem formalisation: converting practical problems into standard problems.

Principles and modalities of problem solving:

l Principles of Invention: 40 Universal Principles for Generating Innovative Ideas to Solve Problems.

l Contradiction Matrix: Used to resolve technical contradictions in inventive problems by converting problem descriptions into standard parameters and using pre-defined solutions.

l Matter-Field Analysis: Using the concepts of matter and fields to improve systems or solve problems.

Innovation process:

l ARIZ（Algorithm for Inventive Problem Solving）：A structured problem solving process designed to systematically guide users from problem description to solution creation.

Predictive tools:

l Laws of technological system development: describes the general patterns and trends that systems follow as they develop over time.

l S-curve analysis: assessing the maturity of technology systems and the potential scope for development.

TRIZ is widely used in the fields of product design, engineering, and problem solving. It encourages innovators to go beyond the boundaries of existing knowledge and solve problems through novel approaches.A central concept of TRIZ is that innovation often involves resolving contradictions in a system, known as technical and physical contradictions. A technical contradiction is a situation where some parts of a system need to be improved in some way, but this improvement may harm other parts of the system. Physical contradictions are when the same part or characteristic needs to be in a different state under different conditions.

The strength of the TRIZ methodology lies in the fact that it provides a systematic process of innovation, a process that helps to accelerate and guide innovative activity by analysing and applying previous solutions to similar problems. Although it was originally developed to solve engineering and technology problems, the principles and tools of TRIZ have been applied in other fields such as business, management and the social sciences.

1.3 Purpose-driven approach to fusion of data, information, knowledge, and wisdom for invention and creation:DIKWP-TRIZ

Abstract

This chapter proposes an innovation methodology adapted to the digital information age: DIKWP-TRIZ.DIKWP-TRIZ integrates data, information, knowledge, wisdom and prediction, introduces humanistic purpose as the core motivation for invention, and emphasises value orientation and cognitive progress of innovation. By combining complete data processing and consistent information updating, DIKWP-TRIZ reinforces efficiency and comprehensiveness, thus overcoming the limitations of traditional TRIZ methods in the digital age, especially in a country like China where technological innovation is rapidly emerging.

Keywords: TRIZ, DIKWP, invention, digital information age, purpose-driven, human-centred TRIZ, value TRIZ, cognitive TRIZ, complete TRIZ, consistent TRIZ, efficiency TRIZ

1. Introdution

With the rapid development of global information technology, especially in China, the context and requirements of technological innovation have changed fundamentally. This change requires us to revisit and improve the traditional innovation methodology TRIZ.In order to adapt to the challenges of the digital information age, this paper proposes DIKWP-TRIZ-a new innovation methodology that integrates data, information, knowledge, wisdom, and purpose.

2. Limitations of Traditional TRIZ

Traditional TRIZ is a methodology constructed on the basis of a large number of case studies of technological innovation. Although it has achieved great success historically, several of its limitations are gradually emerging in today's digital information age:

l Relying on historical cases does not allow for a quick response to emerging technologies;

l Lack of deep dive and application of data;

l Ignores the humanistic purpose and values of the innovation process;

l Insufficient to support the integration of knowledge across disciplines;

l Information is not updated quickly and consistently enough;

l Inefficient and difficult to respond to complex and changing issues.

l Material-field analysis: Utilizing the concepts of material and field to enhance systems or address problems.

Innovation process:

l ARIZ（Algorithm for Inventive Problem Solving）：A structured problem-solving process designed to systematically guide users from describing a problem to creating solutions.

Prediction tool：

l The laws of technological system development: Describes the general patterns and trends that systems follow over time in their development.

l S-curve analysis: Assessing the maturity and potential development space of a technological system.

TRIZ is widely applied in fields such as product design, engineering, and problem-solving. It encourages innovators to surpass the boundaries of existing knowledge and address problems through novel approaches. A core concept of TRIZ is that innovation often involves resolving contradictions within a system, known as technical contradictions and physical contradictions. Technical contradictions refer to situations where improving certain parts of a system may potentially harm other parts. Physical contradictions involve the need for different states of the same component or feature under different conditions.

The strength of the TRIZ methodology lies in providing a systematic innovation process. This process, through the analysis and application of methods previously used to solve similar problems, helps accelerate and guide innovative activities. Although initially developed to address engineering and technical issues, the principles and tools of TRIZ have been applied in other fields such as business, management, and social sciences.

1.3 Purpose-driven integration method for the synthesis of data, information, knowledge, and wisdom in invention and creation: DIKWP-TRIZ

Abstract

This chapter introduces an innovative methodology adapted to the digital information era: DIKWP-TRIZ. DIKWP-TRIZ integrates data, information, knowledge, wisdom, and prediction, introducing human purpose as the core driving force for invention and creation. It emphasizes the value orientation and cognitive progress of innovation. By combining comprehensive data processing and consistent information updating, DIKWP-TRIZ strengthens efficiency and comprehensiveness, overcoming the limitations of traditional TRIZ methods in the digital age. This is particularly crucial in a rapidly emerging country for technological innovation like China.

Keywords：TRIZ, DIKWP, invention and creation, digital information era, purpose-driven, human-centric TRIZ, value-oriented TRIZ, cognitive TRIZ, comprehensive TRIZ, consistent TRIZ, efficient TRIZ.

1. Introduction

With the rapid development of global information technology, especially in China, there has been a fundamental shift in the context and requirements of technological innovation. This transformation calls for a reexamination and improvement of traditional innovation methodologies like TRIZ. To meet the challenges of the digital information era, this paper proposes DIKWP-TRIZ—a novel innovation methodology that integrates data, information, knowledge, wisdom, and purpose.

2. Limitations of traditional TRIZ

Traditional TRIZ is a methodology constructed based on the analysis of numerous cases of technological innovation. Despite its significant historical success, several limitations are becoming apparent in today's digital information era:

l Dependency on historical cases hinders quick responses to emerging technologies.

l Lack of in-depth exploration and application of data.

l Overlooking human purpose and value orientation in the innovation process.

l Insufficient support for interdisciplinary knowledge integration.

l Information updates are not sufficiently rapid and consistent.

l Inefficiency, making it challenging to address complex and dynamic problems.

3. The theoretical framework of DIKWP-TRIZ.

The theoretical framework of DIKWP-TRIZ is built upon the following core components:

(1) Human-centric TRIZ (purpose TRIZ): In the innovation process, human purposes and needs are driving forces. DIKWP-TRIZ emphasizes the priority of user needs and design purposes, ensuring that solutions effectively address users' pain points.

(2) Value-oriented TRIZ (Wisdom TRIZ): Through intelligent decision-making and prediction, DIKWP-TRIZ enhances the value orientation of solutions, emphasizing the maximization of social and environmental benefits.

(3) Cognitive TRIZ (Knowledge TRIZ): DIKWP-TRIZ advocates for extensive knowledge acquisition and deep integration, enhancing the depth and breadth of innovation through interdisciplinary knowledge fusion.

(4) Comprehensive TRIZ (Data TRIZ): DIKWP-TRIZ utilizes big data and data analysis technologies to support decision-making, ensuring the comprehensiveness of data and comprehensive consideration of solutions.

(5) Consistent TRIZ (Information TRIZ): DIKWP-TRIZ values the consistency and timeliness of information, maintaining the synchronous updating of knowledge and information through information technology.

(6) Efficient TRIZ (DIKWP Transformation TRIZ): By optimizing the transformation process of each element of DIKWP, DIKWP-TRIZ improves the efficiency and responsiveness of the innovation process.

4. The practical application of DIKWP-TRIZ

4.1 Case Analysis: Application of DIKWP-TRIZ in Mainland Chinese Enterprises

Taking a manufacturing company in mainland China as an example, the company faced a series of challenges in the product design and manufacturing process. By applying the DIKWP-TRIZ method, the company successfully addressed the following issues:

At the data level: By collecting and analyzing a vast amount of market data and user feedback, the company identified pain points and improvement opportunities in the product's usage.

At the information level: Based on the results of data analysis, the company categorized and organized information, discovering a design bottleneck in a specific stage that hindered the product's performance improvement.

At the knowledge level: Through research on existing technologies and knowledge, the company identified a new material and manufacturing process that could address the product design bottleneck and enhance product performance.

At the wisdom level: Applying wisdom-level thinking, the company comprehensively considered factors such as technology, cost, and market demands. It formulated an innovative plan and flexibly adjusted and optimized strategies during the implementation process.

At the purpose level: The company's purpose was to enhance product performance through innovative improvements, increase market competitiveness, and achieve higher profits and returns.

4.2 Methodology Implementation: Implementation Strategies and Steps of DIKWP-TRIZ

DIKWP-TRIZ Implementation Strategy: Integrating the cognitive levels of DIKWP with the innovative methods of TRIZ to construct a framework for innovative thinking, making problem-solving and the innovation process more systematic and targeted.

Steps of DIKWP-TRIZ:

At the data level: Collect and analyze data related to the problem to understand the basic situation and influencing factors.

At the information level: Categorize and organize data to discover patterns and regularities.

At the knowledge level: Based on the analysis of information, extract relevant knowledge and technology, search for existing solutions, or engage in technological innovation.

At the wisdom level: Consider factors such as technology, economics, and the market comprehensively. Formulate innovative strategies and plans.

At the purpose level: Clearly define the goals and purposes of innovation and develop an implementation plan and measures.

4.3 Effectiveness Assessment: Efficiency Improvement and Value Growth with DIKWP-TRIZ.

Through comparative analysis of the application of DIKWP-TRIZ and traditional TRIZ, the following conclusions can be drawn:

Efficiency Improvement: By combining cognitive levels with innovative methods, DIKWP-TRIZ makes enterprises more systematic and targeted in problem-solving and the innovation process, thereby enhancing the efficiency of problem-solving.

Value Growth: DIKWP-TRIZ, by fully leveraging data, information, and knowledge, helps companies discover and create new solutions, leading to the following aspects of value growth:

Enhanced Innovation Capability: The DIKWP-TRIZ method enables companies to systematically analyze and understand problems, creating new solutions by comprehensively applying different levels of cognitive content, thus enhancing the innovation capability of the enterprise.

Improved Problem-Solving Efficiency: The structured problem-solving framework provided by DIKWP-TRIZ allows companies to address problems more targetedly, thereby improving the efficiency of problem-solving.

Cost Reduction: DIKWP-TRIZ, by utilizing existing data, information, and knowledge, helps companies avoid redundant work and resource wastage in the innovation process, thereby reducing costs.

Increased Product Competitiveness: DIKWP-TRIZ assists companies in discovering and solving problems in the product design and manufacturing process, enhancing product performance and quality, and strengthening product competitiveness.

Strategic Decision Support: DIKWP-TRIZ not only focuses on technological and product-level innovation but also considers factors at the wisdom level, such as market demands and cost-effectiveness. This enables companies to make more comprehensive and scientific strategic decisions.

5. Conclusion

As an emerging invention and creation method, DIKWP-TRIZ is better suited to the requirements of the digital information era. It not only emphasizes the comprehensiveness, depth, and foresight of innovation but also values human-centric purpose, value orientation, and cognitive progress. Against the backdrop of rapid technological innovation in China, DIKWP-TRIZ demonstrates significant potential and practical value, offering a new perspective and tools for the global technology innovation field.

2.DIKWP-TRIZ Methodology

2.1 Crossing Knowledge Boundaries: Methodological Exploration of Integrating TRIZ and the DIKWP Model.

Abstract

This chapter aims to explore the new application of the traditional technical problem-solving method—TRIZ methodology within the framework of the DIKWP model in modern information management. Through a thorough analysis of the relationships at each level between TRIZ and DIKWP, this chapter proposes a comprehensive model to guide the entire process from data collection to achieving innovation goals. The novel perspective of this methodology not only enhances the adaptability and depth of TRIZ in the information age but also promotes the efficiency of the transformation from theory to practice.

1. Introduction

In the face of increasingly complex technological challenges, the methodology of innovation and problem-solving has become a crucial factor in leading success. The TRIZ methodology, as a classic innovation theory, has proven to be remarkably effective in addressing various engineering problems. However, with the development of information technology, traditional TRIZ needs to keep pace with the times and adapt to the problem-solving environment in the context of informatization. The DIKWP model provides a comprehensive theoretical framework for the hierarchical processing of information. This paper aims to integrate the TRIZ methodology with the DIKWP model to construct a new methodological framework that caters to current and future technological innovation needs.

2. Foundations of the TRIZ Methodology

The TRIZ methodology was proposed by Soviet scientist Genrich Altshuller. Its core idea is to standardize the problem-solving process, forming a systematic set of tools and methods. Core concepts in TRIZ, including the contradiction matrix, 40 inventive principles, and the laws of evolution of technical systems, are designed to guide innovators to surpass the limitations of traditional thinking, identify the root causes of problems, and propose innovative solutions.

3. Overview of the DIKWP Model

The DIKWP model consists of five levels: Data, Information, Knowledge, Wisdom, and Purpose, reflecting the transformation process of information from its raw form to final decision-making. Widely applied in management and information science, the model plays a crucial role, especially in the design of knowledge management and decision support systems.

4. Mapping Traditional TRIZ to DIKWP

(1) Data and TRIZ

The technical problem identification stage in TRIZ maps to the Data level in DIKWP, focusing on collecting relevant technical parameters, performance data, and other basic information.

(2) Information and TRIZ

Transforming TRIZ's problems and technical contradictions into structured information is like the role of the Information level in DIKWP, involving data processing, classification, and interpretation.

(3) Knowledge and TRIZ

TRIZ's solutions and inventive principles constitute the Knowledge level, corresponding to the Knowledge level in DIKWP. This level represents the accumulation of knowledge about a deeper understanding of the problem and methods for its resolution.

(4) Wisdom and TRIZ

Within the TRIZ framework, selecting appropriate solutions requires wisdom, directly related to decision-making in the Wisdom level of DIKWP.

(5) Purpose and TRIZ

TRIZ's innovation goals align with the Purpose level in the DIKWP model, emphasizing the driving force and ultimate goal of the entire innovation process.

5. Optimization of TRIZ Methodology and Integration with the DIKWP Framework

(1) Optimization Transformation from Data to Information

Precise analysis of data within the TRIZ framework ensures that the Information level accurately reflects the actual state of the problem.

(2) Reinforcement Transformation from Information to Knowledge

Strengthening the transformation from the Information level to the Knowledge level using TRIZ tools, integrating different sources of information within the DIKWP framework to enhance the depth of solutions.

(3) Intelligent Application of Knowledge

Emphasizing the transformation from Knowledge to the Wisdom level, the TRIZ methodology in this process highlights the importance of principle and strategy selection.

(4) Clarification and Guiding Role of Purpose

Clearly defining the Purpose level emphasized by both DIKWP and TRIZ guides the selection of innovative tools and principles, ensuring efficiency and directionality.

6. Conclusion

The integrated methodology proposed in this section, combining TRIZ with the DIKWP model, demonstrates how to more efficiently handle data, information, knowledge, wisdom, and purpose in the era of information technology, enhancing the capability for technological innovation. This interdisciplinary fusion of methodologies not only provides important guidance for practical technological innovation but also offers a new perspective for theoretical development and application.

2.2 DIKWP-TRIZ Methodology: Building an Innovation Model Based on the Data-Information-Knowledge-Wisdom-Purpose Framework.

Abstract

TRIZ, as a mature methodology for problem-solving and innovation, is effective in providing systematic tools and principles for addressing problems. This technical report introduces a new model based on TRIZ—the DIKWP-TRIZ methodology. This model incorporates data, information, knowledge, wisdom, and purpose into the innovation process. Through a comprehensive analysis and application across these five dimensions, DIKWP-TRIZ aims to enhance the human-centric value, cognitive depth, and efficiency of TRIZ to adapt to contemporary rapidly changing technological and societal demands.

Introduction

Traditional TRIZ methodology promotes innovation through a series of tools and principles. However, with the rise of information technology and big data, traditional TRIZ needs to expand its scope to better handle data and information while enhancing sensitivity to user needs and predictive capabilities. DIKWP-TRIZ emerges in response to this context.

In constructing and elucidating the comprehensive theoretical framework of DIKWP-TRIZ, it is crucial to recognize that it is not just a set of innovation tools in the technical or engineering domain but rather a composite innovation approach that integrates human perspectives, values, knowledge cognition, data analysis, and information processing capabilities. The following exploration delves into the DIKWP-TRIZ model from each dimension, anticipating its application in future innovation practices.

Human-Centric TRIZ (Purpose TRIZ)

At the core of DIKWP-TRIZ as a human-centric innovation tool is the placement of human purpose and needs at the forefront of technological innovation. This concept recognizes that technology is not a spontaneously existing entity but is designed and developed to address human problems and fulfill human needs. The trajectory of technological development should be guided by human purpose and needs rather than solely driven by the inherent logic of technology. Therefore, Human-Centric TRIZ demands innovators to continually return to the essential needs of humanity, thereby driving technological progress in a direction favorable to human development.

Value TRIZ (Wisdom TRIZ)

Value TRIZ in the DIKWP-TRIZ framework embodies the deep-level application of wisdom. It not only focuses on the functionality and efficiency of technological innovation but also emphasizes the value-oriented and ethical responsibility of innovation. This requires innovators to have foresight and consider the impact of technology on society, the environment, and the future of humanity when facing technological choices and decisions. Value TRIZ emphasizes a global perspective, showcasing wisdom that reflects the highest human values and ethical principles in the innovation process.

Cognitive TRIZ (Knowledge TRIZ)

Cognitive TRIZ in the DIKWP framework emphasizes the integration and application of knowledge. It goes beyond simple processing of data and information and involves a deep cognitive process that includes the refinement, systematization, and application of knowledge. It requires innovators to understand and apply the principles of cognitive science, transforming complex knowledge into formats that are easy to understand and operate, thereby enhancing the quality and adaptability of technological solutions.

Complete TRIZ (Data TRIZ)

In Complete TRIZ, data is more than just numbers and statistics; it represents a profound insight and understanding of the real world. The DIKWP-TRIZ framework views data as a key to discovering problems and opportunities, advocating for comprehensive data analysis to explore innovation possibilities. This approach requires innovators to handle and analyze various types of data, including qualitative, quantitative, structured, and unstructured data, in order to uncover valuable information.

Consistent TRIZ (Information TRIZ)

Information TRIZ focuses on the accuracy, consistency, and timeliness of information. The DIKWP-TRIZ model considers effective information flow as indispensable in the innovation process. It emphasizes establishing and maintaining a stable, reliable information system to ensure error-free communication among team members, decision-making based on the most accurate information, and a clear and unified understanding of innovation goals and progress among all stakeholders.

Efficiency TRIZ (DIKWP Transformation TRIZ)

Efficiency TRIZ is the ultimate goal of the DIKWP-TRIZ model, aiming to enhance innovation efficiency through process optimization. This concept involves seamlessly connecting the elements—data, information, knowledge, wisdom, and foresight—ensuring that every step from problem identification to solution implementation is efficient and unobstructed. This includes not only improving individual work efficiency but also optimizing collaboration and communication processes among teams.

The DIKWP-TRIZ methodology is a highly comprehensive innovation model that tightly integrates the five core elements: data, information, knowledge, wisdom, and purpose. The following is an in-depth discussion of these five elements.

1. Data Analysis and Transformation (Data)

Data serves as the starting point for the innovation process. In the DIKWP-TRIZ methodology, the role of data analysis is significantly expanded. This not only includes traditional quantitative analyses, such as market share, growth rates, or technical performance parameters, but also encompasses unstructured data like user reviews, social media trends, forum discussions, and more. By applying advanced data mining techniques, natural language processing (NLP), and machine learning, valuable insights and trends can be extracted from vast amounts of unstructured data.

Furthermore, these data analyses are not static but constitute a dynamic process. Through continuous monitoring and analysis of data, innovators can timely capture new dynamics in market changes and technological developments. This real-time data analysis provides the DIKWP-TRIZ methodology with a feedback mechanism, assisting innovators in maintaining agility and adaptability throughout the entire innovation process.

2. Information Integration and Decision Support (Information)

After data is collected and analyzed, the next step is to transform this data into meaningful information. Information integration plays a crucial role in the DIKWP-TRIZ methodology as it directly impacts the quality and efficiency of decision-making. Innovators need to use advanced information management systems for integrating this data, which may involve database management, data warehouses, and online analytical processing (OLAP).

The purpose of information integration is not just to simplify information but to reveal patterns, trends, and correlations behind the data, providing a foundation for innovative decision-making. Effective information integration ensures that the innovation team has a shared, consistent information view, which is crucial for maintaining synchronization among team members and a common understanding of innovation strategies.

3. Knowledge System and Innovation Principles (Knowledge)

The establishment of a knowledge system involves transforming information into actionable knowledge, encompassing knowledge management and the application of innovation principles. In the DIKWP-TRIZ methodology, building a knowledge system goes beyond archiving historical data; it involves the systematic and structured organization of experience and wisdom gained from innovation practices.

This requires innovators to deeply understand and learn from previous innovative cases, principles, and patterns, translating this experience into organizational knowledge assets. By systematizing these knowledge systems, innovators can identify problems more quickly and generate innovative solutions more effectively.

4. Wisdom Decision-Making and Value Orientation (Wisdom)

Wisdom in the DIKWP-TRIZ methodology refers to going beyond data, information, and knowledge to engage in deeper thinking and decision-making. Wisdom decision-making considers the extensive impact of innovative activities on society, culture, and the environment, seeking balance and harmony in decision-making. This means that innovators need to not only analyze data and information but also consider how their innovation aligns with social responsibility, sustainable development, and human values.

Wisdom decision-making requires innovators to possess interdisciplinary knowledge, profound insight, and a strong ethical awareness. In the DIKWP-TRIZ methodology, the application of wisdom helps ensure that technological innovation not only meets current needs but also paves the way for future challenges.

5. Purpose Recognition and Goal Orientation (Purpose)

Purpose recognition is a core component of the DIKWP-TRIZ methodology, emphasizing the need to clearly and understand the deep-seated purpose of innovative activities. This is not only about defining the problem and choosing solutions but also about ensuring the consistency of innovative activities with broader societal goals.

At this stage, innovators need to identify and articulate the values and principles behind innovation goals, ensuring that these goals align not only with the organization's strategy but also with the long-term interests of society. This profound understanding of purpose can help innovators make foresighted and enduring decisions in complex decision-making environments.

Application in Practical Context

The DIKWP-TRIZ methodology is not a standalone theoretical framework; it requires close integration with practical innovation activities. When applying this methodology, organizations can follow these steps:

(1) Conduct continuous monitoring of market and technological trends, updating data analysis in real-time.

(2) Utilize advanced information technology for data integration, ensuring the accuracy and timeliness of information.

(3) Establish a knowledge management system, integrating knowledge resources within and outside the organization for easy sharing and utilization.

(4) Incorporate considerations of ethics and values into decision-making, ensuring that innovative activities align with the long-term interests of the organization and society.

(5) Clearly define the deep-seated purpose of innovation, ensuring that each innovative activity is goal-oriented.

Through this methodology, innovators can ensure that their innovative activities are built on a solid foundation of data, while also being thoughtful and value-oriented. The DIKWP-TRIZ methodology provides a comprehensive framework to help organizations stay ahead in rapidly changing markets and play an active role in society.

The DIKWP-TRIZ methodology tightly integrates the five dimensions of data, information, knowledge, wisdom, and purpose, forming an iterative and evolving application process. The following provides a more detailed expansion of each stage:

Stage One: Requirement Analysis and Data Collection

In this stage, the key is to understand the true nature of the problem and requirements. This involves communication with stakeholders such as customers, suppliers, partners, and internal team members to gather their needs and expectations for products, services, or processes. Data collection extends beyond market data or technical parameters, including aspects such as user experience, operational habits, and even regulatory policies.

l Utilize methods such as surveys, one-on-one interviews, focus groups, and market research to collect data.

l Gather information on technological trends and competitors using tools like patent analysis and industry reports.

l Collect usage data through sensors, log files, online tracking, and other means.

Stage Two: Information Refinement and Requirement Transformation

Once data is collected, it needs to be analyzed and processed to become useful information. Advanced data analysis methods and tools, such as statistical analysis, predictive models, and clustering analysis, are employed.

l Use data mining techniques to refine user behavior patterns and market trends.

l Employ text analysis tools to process user feedback and social media data.

l Utilize data visualization methods to aid in understanding and sharing information.

Stage Three: Knowledge Application and Solution Generation

The core of this stage is to use existing knowledge resources to generate solutions. Knowledge sources can be internal experiences shared within the organization or external professional papers and case studies.

l Apply principles and patterns from innovation methodologies like TRIZ to simplify complex problems and find solutions.

l Combine industry best practices to ensure the practicality and effectiveness of the solutions.

l Explore solutions from other domains through analogy and interdisciplinary thinking.

Stage Four: Wisdom Evaluation and Value Judgment

Innovation should not only be technically feasible but also socially acceptable and ethically viable. The application of wisdom lies in transforming technological innovation into societal value.

l Conduct cost-benefit analysis, risk assessment, and environmental impact evaluation.

l Consider cultural sensitivity, user acceptance, and potential societal reactions.

l Use ethical frameworks to guide solution selection, ensuring innovation aligns with moral and societal standards.

Stage Five: purpose Implementation and Solution Refinement

The ultimate goal is to ensure that the innovative solution achieves its intended purpose, meets stakeholders' needs, and has long-term sustainability.

l Collaborate with all stakeholders to ensure the implementation aligns with strategic goals.

l Conduct prototyping, testing, and feedback loops to optimize the solution.

l Establish an implementation plan, including a timeline, budget, and resource allocation.

Through these five stages, the DIKWP-TRIZ methodology ensures that each step of the innovation process is built on a solid foundation of data while emphasizing practical feasibility, social value, and long-term purpose. The successful application of this process relies on continuous iteration and improvement, as well as mechanisms for internal communication and knowledge sharing within the organization.

Conclusion

The construction of the DIKWP-TRIZ methodology brings a fresh perspective and tools to the traditional TRIZ methodology. By more comprehensively integrating data, information, knowledge, wisdom, and purpose, it enhances the comprehensiveness and depth of innovative activities. In the contemporary society of rapid information technology development, DIKWP-TRIZ provides a more flexible and comprehensive approach to address increasingly complex and dynamic innovation challenges. Through the in-depth exploration above, we can see that the DIKWP-TRIZ model, as an innovation methodology system, is multidimensional and all-encompassing. It not only focuses on technological innovation itself but also emphasizes the role of people, values, knowledge application, data analysis, and information processing in the innovation process. The goal of the DIKWP-TRIZ model is to establish a sustainable innovation ecosystem, promoting the synchronous evolution of innovation and societal needs, ultimately achieving the harmonious unity of technological innovation and human development. In future applications, with the continuous advancement of technology and the increasing complexity of societal needs, the DIKWP-TRIZ model is expected to become a crucial tool guiding innovation practices. It will assist innovators in finding the right direction, making valuable decisions, and realizing the maximum potential for innovation in an ever-changing environment.

2.3 DIKWP-TRIZ: Constructing a Comprehensive Innovation Methodology

Abstract

In the evolving knowledge economy era, TRIZ, as a mature innovation methodology, has facilitated numerous technological innovations. However, facing increasingly complex societal and technological challenges, traditional TRIZ methods need further expansion of their theoretical and applied frameworks to meet the demands of the new era. The DIKWP-TRIZ model is proposed in this context, integrating data, information, knowledge, wisdom, and purpose into the innovation process. The aim is to create a more humane, value-driven, cognitively enhanced, comprehensive, consistent, and efficient innovation methodology.

Keywords：Human-centric TRIZ, Value TRIZ, Cognitive TRIZ, Comprehensive TRIZ, Consistent TRIZ, Efficiency TRIZ, DIKWP-TRIZ

Introduction

With the rapid development of information technology and cognitive science, innovation is no longer confined to technological breakthroughs alone. It now requires a more comprehensive consideration of human needs, value orientation, cognitive abilities, and the effective utilization of data and information. The DIKWP-TRIZ model is human-centric, emphasizing a value-driven approach, enhancing the application of knowledge and cognition, ensuring the integral utilization of information and data, and simultaneously improving the efficiency of the entire innovation transformation process.

In the swiftly evolving societal and technological landscape of today, innovation methodologies need to continually evolve to adapt to new challenges and opportunities. TRIZ, as a mature innovation tool, has assisted numerous organizations and individuals in solving complex technical and managerial problems. However, with the advent of the digital age and shifts in human values, there is a need for further development of traditional TRIZ. Against this backdrop, the DIKWP-TRIZ model emerges, aiming to provide a more comprehensive and human-centered innovation framework.

Human-Centric TRIZ (Purpose TRIZ)

As a human-centered innovation tool, the DIKWP-TRIZ model reshapes the starting point and focus of traditional TRIZ. It no longer merely focuses on solving technical problems but places human purpose and needs at the core. In this model, the dimension of purpose extends beyond the simple pursuit of technical performance, reaching into the profound integration of technology with human life. It explores how technology can improve people's work and living quality, enhance social welfare, and even address more fundamental societal issues.

For example, when designing a new mode of transportation, the application of Purpose TRIZ involves not only considering its speed and efficiency but also delving into how this mode of transportation adapts to human behavior, aligns with cultural customs, and enhances the overall user experience while ensuring safety. In this process, human needs, habits, and expectations drive the direction of technological innovation, requiring innovators to possess interdisciplinary knowledge and a deep understanding of user needs.

Value TRIZ (Wisdom TRIZ)

On the dimension of Value TRIZ, the application of wisdom becomes crucial. It requires innovators not only to innovate technically but also to think and break through at the level of values. Innovation should not be solely for the sake of innovation but should serve broader social values and long-term human well-being. This demands innovators to have foresight, considering the potential impacts of technological solutions on society, culture, and the environment from the outset.

Guided by Wisdom TRIZ, a technological solution, when being designed, will comprehensively consider efficiency, cost, sustainability, and its potential impact on social structures. For example, when developing new energy technologies, innovators need to consider not only their energy efficiency but also evaluate their environmental impact, how they adapt to and promote sustainable lifestyles, and their role in global energy politics. Wisdom TRIZ requires innovators to stay at the forefront of technology while maintaining ethical foresight, ensuring that technological development brings positive impacts to society.

Cognitive TRIZ (Knowledge TRIZ)

Cognitive TRIZ emphasizes the role of knowledge in the innovation process, not merely as an accumulation of information but as a crucial element for understanding and solving complex problems. In this model, knowledge management and application serve as a bridge connecting raw data and deep wisdom. It facilitates the transformation from information to knowledge and further to wisdom, helping innovators better grasp and utilize complex information flows.

By applying principles from cognitive psychology, Cognitive TRIZ enables innovators to better understand users' thought patterns and decision-making processes, designing products and services that align with users' cognitive habits. This means that in the innovation process, attention should be given not only to the advanced features of technology but also to how these features are understood and adopted by users. For example, when developing a new mobile application, Cognitive TRIZ will guide innovators to thoroughly research how users interact with the app and how they process information, resulting in the design of an interface that is both efficient and user-friendly.

Integrated TRIZ (Data TRIZ)

In the digital age, data has become a new factor of production and a crucial resource for innovation. Integrated TRIZ recognizes this and places data analysis and mining at the core of the innovation process. In this model, data is not just a collection of numbers and facts but a deep resource that can reveal the essence of problems and guide innovative directions.

The DIKWP-TRIZ model views data as a dynamic and developable asset, emphasizing the process of extracting information from data, refining knowledge from information, and generating wisdom from knowledge. This requires innovators to have data analysis skills, enabling them to process and analyze large quantities of quantitative and qualitative data to discover potential problems and opportunities. For instance, during market research, innovators analyze consumer behavior data, market trends, and competitor information to ensure that the development of new products meets market demands and remains competitive.

Consistent TRIZ (Information TRIZ)

Information TRIZ focuses on information consistency and accuracy. In the DIKWP-TRIZ model, there is an emphasis on optimizing the flow of information in the innovation process, ensuring that information is consistent and reliable at every stage of the innovation chain. In practical application, this means that innovation teams need to establish an effective mechanism for information sharing and communication, ensuring that information does not become distorted or delayed across functional and interdisciplinary teams.

For example, within a multinational corporation, different departments may interpret information differently due to geographical and cultural differences. Information TRIZ addresses this by establishing standardized information transmission protocols and tools to help all parties accurately understand the content of information, thereby avoiding decision-making errors caused by misunderstandings. Additionally, it emphasizes information security and the protection of intellectual property, ensuring that innovative activities take place in a secure and reliable environment.

Efficiency TRIZ (DIKWP Transformation TRIZ)

Efficiency TRIZ focuses on the transformation efficiency between DIKWP elements, striving for lean and rapid responses in the innovation process. In this model, every step, from data collection, information processing, knowledge formation, to the application of wisdom and the realization of purpose, is carefully designed to eliminate unnecessary steps and redundant operations.

This requires innovators to not only focus on the results of innovation but also on the innovation process itself. By applying lean methods and principles of continuous improvement, Efficiency TRIZ helps organizations shorten the time from concept to market while enhancing the quality of innovation. For example, in the product development process, Efficiency TRIZ can guide teams through rapid prototyping and testing, maintaining innovation quality while improving response speed and the efficiency of innovation implementation.

Conclusion

The DIKWP-TRIZ model is a comprehensive innovation methodology that provides a new solution framework for innovators facing complex challenges by integrating the management and application of data, information, knowledge, wisdom, and purpose. This model not only enhances the effectiveness of traditional TRIZ but, more importantly, emphasizes human-centered, value-driven, cognition-enhancing, integrity and consistency, and efficiency improvement, providing guidance for innovation activities in modern society.

In summary, the DIKWP-TRIZ model offers a holistic perspective, bringing the innovation process closer to human needs, promoting the realization of societal value, harnessing the power of cognition and knowledge, ensuring the efficient utilization of information and data, and ultimately achieving efficient and high-quality innovation outcomes in the complex modern societal environment.

2.4 Complete TRIZ (Data TRIZ): Integrating big data analytics to enhance decision-making in the innovation methodology.

Abstract

In the era of information explosion and big data, the traditional technological innovation methodology TRIZ has encountered new challenges and opportunities. Complete TRIZ (Data TRIZ) under the DIKWP-TRIZ framework extends and strengthens traditional TRIZ tools using big data technology, providing data-driven support for innovation decisions. This article elaborates on how Data TRIZ ensures data comprehensiveness and considers solutions from multiple perspectives in the decision-making process. It explores the theoretical model, implementation strategies, and practical applications of Data TRIZ.

Keywords：Complete TRIZ, Data TRIZ, DIKWP-TRIZ, big data, data analytics, innovation methodology, decision support.

Introduction

As technology advances and market demands become more volatile, innovation activities in businesses have become increasingly complex and challenging. In this context, leveraging the growing data resources to assist decision-making has become a key factor in enhancing the competitiveness of enterprises. This article introduces Data TRIZ, which integrates big data analytics with TRIZ to improve the quality of decisions and the success rate of innovative solutions. The article first reviews the basic principles and development of TRIZ, then explores in detail the theoretical framework and implementation strategies of Data TRIZ, and illustrates its utility and potential through case analyses in practical applications.

Theoretical Foundation and Historical Background:

Traditional TRIZ methodology, with its unique problem-solving framework and tools, has been widely applied to address contradictions in technology and engineering. With the advent of the big data era, TRIZ methodology needs to be combined with data analysis to adapt to the new innovation environment. The concept of Complete TRIZ is not just about finding solutions but ensuring the comprehensiveness and adaptability of solutions through data analysis. This section will provide a detailed overview of the development of TRIZ and the theoretical foundation of Data TRIZ.

Key Features of Data TRIZ:

Data-Driven Innovation: Describes how Data TRIZ captures market trends and technological evolution through real-time data analysis.

Comprehensive Consideration: Expounds on how Data TRIZ ensures a multi-dimensional and multi-angle approach to problem-solving, avoiding local optimization and overlooking overall benefits.

Decision Support Systems: Discusses how Data TRIZ constructs and utilizes decision support systems for more effective problem analysis and solution generation.

Data Acquisition and Processing: This section will explore the strategies and tools of Data TRIZ in acquiring and processing data. This includes the sources of data, the importance of data cleaning and preprocessing, and how valuable information can be extracted through data mining techniques.

Development and Validation of Solutions: In this section, we will delve into how Data TRIZ is employed to develop innovative solutions. This includes using models and algorithms to predict the effectiveness of solutions, as well as how to proceed with subsequent validation and iterative improvements.

Case Studies: Selecting several successful cases related to Data TRIZ for analysis to demonstrate its specific applications in different fields and problem-solving scenarios.

Challenges and Prospects: Discussing the challenges that may be encountered in the implementation process of Data TRIZ, such as data quality control, privacy and security issues, and the integration challenges of cross-disciplinary knowledge. Additionally, providing a forward-looking analysis of the potential role Data TRIZ may play in future technological innovations.

We can compare traditional TRIZ and DIKWP-TRIZ (including Data TRIZ or Complete TRIZ) from multiple perspectives. The following is a detailed comparative analysis table:

dimension	Traditional TRIZ	DIKWP-TRIZ
Theoretical foundation	Based on the laws of development of technological systems, including the 40 Principles of Invention, the Contradiction Matrix, and more.	Combines modern theories such as data analysis, knowledge management, and information science to extend the tools and methods of traditional TRIZ.
Innovative strategy	Resolving technological contradictions and the evolution of technological systems.	Includes broader problem-solving strategies for dealing with data, information, knowledge, wisdom, and prediction.
Knowledge management	Focus on utilizing historical invention cases and technical conflict resolution.	Emphasize full life-cycle management of knowledge and integration of multi-source data and information for knowledge innovation.
Data application	The data has limited application and relies heavily on invented cases.	Big data analytics is at the core for pattern recognition, prediction and supporting innovative decision making.
Tools and methods	Contradiction Matrix, Principle of Invention, Matter-Field Analysis, etc.	This includes traditional tools, as well as data analysis software, algorithms, knowledge maps, and simulation models.
Decision-making support	Based primarily on heuristics and rules of thumb.	Combine heuristics with data-driven decision making using algorithms and machine learning support.
Forecasting methods	Technical trend analysis, S-curve analysis.	Predictive models that incorporate data analytics, such as time series analysis, machine learning forecasting, etc.
Interdisciplinary applications	Mainly for engineering and technical issues.	The applications are broader and include fields such as business, management, and social sciences.
Collaborative models	Teamwork, relying on individual or group expertise.	Emphasize open innovation, cross-border cooperation, collaborative work and group wisdom.
Sustainability	Can be applied to environmental and social issues, but is not the primary focus.	Emphasize the social and environmental benefits of innovation for sustainable development.
Learning and training	Emphasis is placed on learning TRIZ principles and case studies.	In addition to TRIZ knowledge, training in data analysis, information management and other multifaceted skills is required.
Difficulty of implementation	Relatively simple and relies heavily on specialized training.	More complex, requiring interdisciplinary knowledge and data processing skills.

This table covers all aspects, from theoretical foundations to implementation difficulties, and shows the main differences between traditional TRIZ and DIKWP-TRIZ in terms of methodology, tools, and fields of application.DIKWP-TRIZ and Data TRIZ particularly emphasize the role of data and knowledge in the innovation process, reflecting the impact and expansion of modern information technologies on traditional innovation methods.

Conclusion

Data TRIZ as a cutting-edge innovation methodology combines the power of big data with the systematic nature of TRIZ. By integrating data analysis in the decision-making process, Data TRIZ not only enhances the comprehensiveness of solutions, but also accelerates the innovation process and gives enterprises an edge in competition. However, in order to realize the potential of data TRIZ, companies need to invest resources to overcome the associated challenges and continuously optimize their innovation processes.

2.5 Consistent TRIZ (Information TRIZ): Synchronizing Knowledge and Information in the DIKWP-TRIZ Framework

Abstracts

In rapidly evolving technological fields, consistency and real-time availability of information are at the heart of the innovation process. Consistent TRIZ (Information TRIZ) is a key component of the DIKWP-TRIZ methodology, which utilizes advanced information technology to guarantee the simultaneous updating of knowledge and information in order to enhance the quality of decision making and innovation activities. This paper discusses the theoretical foundations of consistent TRIZ, its implementation framework, and its importance in technological innovation.

Keywords: consistent TRIZ, information TRIZ, DIKWP-TRIZ, information consistency, knowledge synchronization, technological innovation

Introduction

In the current era of explosive information growth and increasingly shortened knowledge update cycles, ensuring the consistency and timeliness of information is a challenge faced by enterprises and research institutions. As a part of the DIKWP-TRIZ system, Consistent TRIZ not only extends traditional TRIZ, but also incorporates dimensions of data, information, knowledge, wisdom, and prediction, striving to improve the efficiency and effectiveness of information processing in the innovation process. Consistent TRIZ focuses on information consistency, providing a theoretical basis and practical guidance for the application of information technology in innovation.

Theory base and implementation framework

The theoretical basis of Consistent TRIZ considers the consistency of information to be the key to improving the quality of innovation. Real-time information updating and sharing mechanisms can improve the efficiency of teamwork and decision-making, and information technology is an important means to achieve this goal.

The implementation framework mainly includes:

Real-time information updating: Utilizing cloud computing, distributed database and other technologies to ensure that team members have access to the latest information.

Information sharing mechanism: Build an efficient information sharing platform to promote knowledge dissemination and information transparency.

Information quality control: Set up information review and verification processes to ensure the accuracy and credibility of information.

Technology monitoring and trend analysis: Use big data and machine learning to monitor market and technology trends and provide data support for decision-making.

Application of Information Technology

Consistent TRIZ exhibits the following characteristics in the application of information technology:

l Integrated system: Using ERP, CRM, SCM and other integrated technologies to promote real-time sharing and updating of information inside and outside the organization.

l Big Data Analytics: analyze real-time data on markets, customers, product usage, etc. through big data technologies to assist organizations in grasping innovation opportunities.

l Artificial wisdom (AI): applying AI, especially machine learning and natural language processing, to recognize patterns, predict trends, and provide decision support from large data sets.

l Internet of Things (IoT): collect real-time data through IoT devices to optimize product performance and innovate based on user usage patterns and feedback.

Discussion

The application of consistent TRIZ is not limited to improving the efficiency of information flow, but also includes ensuring the quality of information and the effectiveness of innovative decision-making. The integration and application of information technology provides a mechanism that not only facilitates the collaborative work of cross-functional teams, but also strengthens an organization's responsiveness to changes in the external environment. On this basis, Consistent TRIZ further strengthens the problem-solving and forecasting functions of traditional TRIZ, making it more adaptable to the requirements of the information age.

The following is a comparative analysis table between traditional TRIZ and Consistent TRIZ (Information TRIZ). This table is intended to reflect the main differences between the two in terms of concepts, tools, processes, and goals.

features	Traditional TRIZ	Consistent TRIZ (Information TRIZ)
Basic concept	Resolving technological contradictions and regularities in the development of technological systems.	Based on the resolution of technological contradictions, it emphasizes the consistency of information and the use of information technology to improve the efficiency of the decision-making and innovation process.
Core Tools	Contradiction Matrix, 40 Principles of Innovation, Functional Analysis, Object Field Analysis, etc.	In addition to the use of traditional TRIZ tools, information technologies such as ERP, CRM, SCM, big data analytics, and artificial wisdom are also integrated.
Data and information processing	Relies heavily on expert experience and historical case studies.	Real-time data is processed using big data, AI and other methods to enable instant updating and sharing of information.
Knowledge Synchronization	Knowledge transfer is more static, relying mainly on individual and document dissemination.	Synchronized updating of knowledge and information, relying on information systems for real-time synchronization.
Decision support	Rely on the solutions provided by TRIZ tools and methods.	Provide more accurate decision support based on real-time analyzed data and information.
Collaborative approach	Mostly offline teamwork, through meetings, discussions, etc.	Support online collaboration platform to realize real-time information exchange and collaboration among team members.
Innovation process	Performs structured problem analysis, solution generation, and evaluation in accordance with TRIZ theory.	Processes are more dynamic, enabling the innovation process to respond instantly to changes in the external environment through continuous data input and analysis.
Field application	Primarily for engineering and technical problem solving.	Spanning engineering and technology, it also includes business wisdom, market analytics, customer relationship management, and many other areas.
Efficiency and effectiveness	Efficiently solving technological contradictions, the effectiveness of innovation depends on the user's mastery and application of the TRIZ method.	Increased efficiency of information processing, improved quality of decision-making and promotion of faster innovation cycles.
Learning and training	Focuses on TRIZ principles and tools.	In addition to TRIZ principles and tools, knowledge and application of relevant information technology is required.
Sustainability	Promoting long-term innovation by resolving technological conflicts.	Continuous innovation management and optimization through IT integration.
Predictive capacity	Innovation forecasting using technology system evolution laws.	Combines real-time market and technology trend analysis to provide more accurate forecasting capabilities.
challenges	The learning curve is steep and requires deep expertise.	Processing and analyzing large amounts of data requires specialized data science skills.

This table provides an overview of how Consistent TRIZ builds on traditional TRIZ and incorporates the power of information technology to provide modern businesses with a new approach to solving complex problems and innovation.

Conclusion

Consistent TRIZ, as a part of DIKWP-TRIZ, provides a powerful theory and tool for technological innovation in the information age. By integrating advanced information technology, it not only enhances the ability to synchronize the updating of knowledge and information, but also provides support for rapid, high-quality innovation decisions. As technology continues to advance, Consistent TRIZ will continue to evolve and provide innovative solutions for companies and research organizations to face new challenges.

2.6 Cognition TRIZ (Knowledge TRIZ): an innovation methodology for interdisciplinary knowledge integration

Abstracts

In the context of knowledge-based economy, innovation has become the core driving force for development. Cognitive TRIZ (Knowledge TRIZ) methodology, as an important part of DIKWP-TRIZ, highlights the central role of knowledge integration and interdisciplinary collaboration in technological innovation. The purpose of this paper is to analyze the theoretical foundation of cognitive TRIZ, its implementation mechanism, and to demonstrate, through case studies, how the depth and breadth of innovation can be enhanced through deep knowledge integration and how to promote the cross-fertilization of innovations between different fields.

Keywords: cognition TRIZ, knowledge TRIZ, DIKWP-TRIZ, innovation methodology, interdisciplinarity, knowledge integration

1. Introduction

In an era of globalized competition and rapid technological change, firms and research institutions are increasingly dependent on innovation to maintain competitiveness. The importance of knowledge as a core resource is becoming increasingly prominent, but at the same time, the phenomenon of knowledge silos poses a challenge to comprehensive innovation. As part of the DIKWP-TRIZ methodology, Cognitive TRIZ strives to improve the quality and efficiency of innovation by systematically integrating multi-domain knowledge. In this paper, we will explore the theoretical framework, strategies and methods of cognitive TRIZ, and analyze them through examples to reveal their efficacy in practical applications.

Theoretical foundation

Cognitive TRIZ is based on several key theoretical assumptions:

Innovation as a process of knowledge transformation: Cognitive TRIZ assumes that innovation is not only about inventing something new, but more importantly about transforming existing knowledge into new forms or applications.

Importance of interdisciplinary knowledge integration: this theoretical assumption suggests that interdisciplinary knowledge integration is a key step towards deep innovation.

Knowledge diversity and complementarity: this hypothesis states that knowledge diversity and complementarity across disciplines can foster more creative problem solving.

The central role of knowledge management and sharing: Cognitive TRIZ emphasizes that effective knowledge management and sharing mechanisms are the cornerstone of sustained innovation activity.

Strategies for knowledge acquisition and integration

To achieve effective knowledge integration, Cognitive TRIZ proposes the following strategies:

Build interdisciplinary teams: Create an environment for knowledge exchange and integration by assembling experts from different disciplinary backgrounds.

Build knowledge maps: Use knowledge maps to identify and correlate key knowledge points in different domains to facilitate information sharing and connectivity.

Promote open innovation: Create an open innovation platform to encourage internal and external knowledge exchange and integration.

Development of knowledge databases and information systems: building specialized knowledge repositories and information systems to support the storage, retrieval and reuse of knowledge.

Approaches to Interdisciplinary Knowledge Integration

To promote interdisciplinary knowledge integration, Cognitive TRIZ recommends the following methods:

Problem reframing: revisit and redefine problems in an interdisciplinary context to open up new ideas and solutions.

Utilizing analogies and metaphors: Connecting knowledge and ideas from different disciplines through analogies and metaphors to facilitate the activation of innovative thinking.

Technology transfer: Promote technology transfer between different disciplines and fields to find new application possibilities.

Case Studies

Through case studies in specific fields such as high-tech materials R&D and biotechnology innovation applications, this paper demonstrates how cognitive TRIZ can facilitate the integration of interdisciplinary knowledge, and in this way promote technological breakthroughs and product innovation. The case studies will highlight the specific steps and implementation effects of knowledge integration, revealing the value of Cognitive TRIZ in practice.

Below is a table of comparative analysis between traditional TRIZ and cognitive TRIZ (with DIKWP-TRIZ as part of it):

Features / Methodology	Traditional TRIZ	Cognitive TRIZ (Knowledge TRIZ)
theoretical foundation	Based on the Invention Principle, the Contradiction Matrix and the Law of Innovation.	In addition to including all the theoretical foundations of traditional TRIZ, knowledge management theory and the concept of interdisciplinary integration have been added.
innovative strategy	Resolve technical and physical conflicts and use standard solutions.	The innovation strategy encompasses the traditional TRIZ approach and integrates a wider range of knowledge and information sources, pushing for interdisciplinary collaboration.
Knowledge transformation	The main focus is on the translation of technical and engineering knowledge.	Focuses on the translation of knowledge across domains and emphasizes the integration of knowledge from different disciplines and domains.
Problem solving	Focus on specific, well-defined technical issues.	Emphasis is placed on redefining the problem and utilizing a broader body of knowledge to solve it.
knowledge management	Not a TRIZ focus, heavy on the application of innovative technologies.	An important component that emphasizes the collection, integration and sharing of knowledge.
Team Composition	Usually consists of engineers or technical experts.	Encourage teams with multidisciplinary backgrounds, including engineers, designers, marketing specialists, etc.
Tools and techniques	Use tools such as the Contradiction Matrix, Principles of Physics, and Principles of Innovation.	In addition to traditional TRIZ tools, these include knowledge maps, analogical reasoning, and information systems.
Depth and breadth of innovation	Concentrate on upgrading the performance of existing technology systems.	Seek to develop new areas of application and markets through knowledge fusion.
Difficulty of implementation	Relatively easy to grasp and oriented to specific technical problems.	Implementation is more complex and requires extensive knowledge and a high level of collaborative capacity.
suitability	The main applications are in the field of engineering and technological innovation.	Applicable to a wider range of fields, including business, design, social innovation, etc.
Case Study	Usually focuses on engineering issues and technology improvements.	Covers a wider range of cases, including innovative solutions to non-technical problems.
Flow of knowledge	Relatively closed, flowing mainly within specific areas.	Open and dynamic, emphasizing the flow and use of knowledge across borders.
Forecasting and trend analysis	Trends and patterns based on technology evolution.	Incorporate analysis of market and social development trends in conjunction with technology trends.
value-oriented	The main focus is on technology value and efficiency gains.	At the same time, it focuses on the comprehensive value enhancement of social, environmental and economic benefits.

Conclusion

Cognitive TRIZ (Knowledge TRIZ) provides a comprehensive framework to facilitate the acquisition, integration and application of knowledge. By facilitating interdisciplinary collaboration and knowledge integration, innovators are able to broaden their thinking and find more effective solutions. With the development of knowledge management technologies and the complexity of the innovation environment, cognitive TRIZ will play an increasingly important role in helping organizations achieve sustainable innovation.

2.7 Value TRIZ (Smart TRIZ): Enhancing the social and environmental value of innovative solutions through smart decisions and predictions

Abstracts:

Value TRIZ (Smart TRIZ) is an innovation framework that combines traditional TRIZ methodology with modern smart decision-making processes to enhance the social and environmental value of technology and product innovation. The framework emphasizes intelligent decision-making and forecasting in the innovation process through the efficient integration of Data, Information, Knowledge, and Wisdom (DIKWP) methodology to ensure that solutions meet current needs while delivering positive social and environmental benefits for the future.

Keywords: value TRIZ, wisdom TRIZ, social value, environmental benefits, DIKWP-TRIZ, intelligent decision making, technological innovation

1. Introductory

The goal of technological innovation is no longer just to achieve commercial success and technological breakthroughs, but is increasingly entrusted with the important task of ensuring social sustainability and environmental friendliness. The Value TRIZ (Wisdom TRIZ) framework has emerged, aiming to guide innovation activities to be more socially responsible and environmentally friendly through intelligent decision-making and prediction. This paper explores how Value TRIZ integrates DIKWP resources to promote the maximization of social and environmental values through systematic and creative thinking in TRIZ.

2. Theoretical Foundations of Value TRIZ (Wisdom TRIZ)

Value TRIZ is a new innovation methodology based on traditional TRIZ principles by expanding and integrating wisdom level considerations. It emphasizes the following core elements:

(1) Connotation of Wisdom Decision Making: In the innovation process, wisdom decision making implies not only analyzing problems and generating solutions, but also considering their long-term impacts, social values and environmental benefits.

(2) The role of systematic forecasting: Systematic forecasting helps innovators to understand the trend of technological development and foresee possible future needs and environmental changes, so that they can make forward-looking decisions when designing solutions.

(3) Maximization of social and environmental benefits: Innovation not only pursues economic benefits, but also enhances social well-being and protects environmental resources, which is the goal pursued at the core of Value TRIZ.

3. Value TRIZ ( Wisdom TRIZ ) operation process

(1) Problem Identification and Definition: In value TRIZ, problem identification should be based not only on technology and market needs, but also combined with social trends and environmental requirements.

(2) Innovative design of solutions: When designing solutions, the innovative principles of TRIZ are utilized to guide thinking and combined with wisdom-level forecasting tools, such as scenario analysis and the Delphi method, to ensure the comprehensiveness of the solutions.

(3) Value assessment of the solution: During the solution design stage, the possible social and environmental impacts of the solution are assessed through value engineering, life cycle assessment and other methods.

(4) Implementation and feedback of the final solution: after implementing the solution, continuously track its social and environmental benefits, and adjust and optimize the solution through feedback.

4. DIKWP-TRIZ in promoting value-based innovation

4.1 Data and Information in Value TRIZ:

In value TRIZ, data and information play a key role in helping firms collect and analyze environmental and social indicators related to innovation. By effectively collecting and interpreting data, firms can gain insights into market demand, consumer behavior, competitor situation, and other information to support innovation decisions.

In the DIKWP-TRIZ methodology, the collection and analysis of data is reflected in the first level of the "data" phase. Enterprises need to collect data related to innovation goals, which can come from market research, user feedback, industry reports and other sources. By analyzing and processing the data, companies can identify shortcomings in existing products or services and discover potential opportunities for innovation.

The application of data is not limited to internal data; external data is also an important part of Value TRIZ. Enterprises can monitor the socio-economic environment, technological development trends, changes in regulations and policies, and other information to grasp the pulse of the times and adjust their innovation strategies in a timely manner. For example, an environmental technology enterprise can collect data on environmental pollution and sustainable development to guide its innovation direction and product design.

In addition to data, the processing and organizing of information is also an important part of value TRIZ. In the DIKWP-TRIZ methodology, the organization and classification of information is reflected in the second level of the "information" stage. Enterprises need to categorize, integrate and interpret the collected data to discover patterns and regularities. Through the processing of information, enterprises are able to identify the essence of the problem and the breakthrough, providing a theoretical basis for subsequent innovation decisions.

4.2 Integration of knowledge and wisdom

Knowledge management and wisdom level decision support systems play an important role in DIKWP-TRIZ. Integration and utilization of knowledge can help enterprises better respond to challenges and solve problems, thus realizing value innovation.

In the DIKWP-TRIZ methodology, the integration of knowledge is reflected in the third level of the "knowledge" stage. Companies need to analyze information to extract knowledge and experience related to innovation goals and to find innovative principles and methods for solving problems. This knowledge can come from the experience of experts within the enterprise, technical literature, patent databases, and so on. By integrating knowledge and experience from different fields, enterprises can broaden their innovative ideas and discover more solutions.

Decision making at the wisdom level is reflected in the fourth level of the DIKWP-TRIZ methodology. In DIKWP-TRIZ, the Wisdom Level emphasizes the comprehensive consideration of multiple factors, such as technology, economy, and market, to formulate solutions and make decisions. By utilizing the decision support system at the wisdom level, companies can fully consider the relationship and trade-offs between different factors and develop more practical solutions.

Decision making at the wisdom layer can be supported by technology tools such as artificial wisdom, big data analytics and simulation. Enterprises can use big data analytics to mine and analyze massive amounts of data to reveal potential correlations and trends. At the same time, the wisdom layer can also use simulation and emulation technologies to assess the effectiveness and risks of different innovative solutions. By simulating and testing different scenarios, companies can better understand their potential impact and make informed choices when making decisions.

The integration of knowledge and wisdom in DIKWP-TRIZ can enhance the quality of decision-making and the effectiveness of innovation. By integrating and utilizing a variety of knowledge resources, companies can gain a more comprehensive understanding of problems and needs, leading to more practical solutions. At the same time, with the help of the decision support system in the wisdom layer, enterprises can make decisions based on comprehensive information and analysis that are consistent with their innovation goals, thus improving the success rate of innovation and market competitiveness.

5. Discussion

Through an in-depth discussion of the theory and practice of Value TRIZ, we analyze its applicability in different industries and fields, as well as the challenges and opportunities in a globalized and rapidly changing market environment.

Below is a comparative analysis table that describes in detail the differences between traditional TRIZ and value TRIZ (wisdom TRIZ):

features	Traditional TRIZ	Value TRIZ (Wisdom TRIZ)
Core concepts	Addressing technological contradictions and innovation	Combining technological innovation with maximization of social and environmental values
Focus	Technological innovation, problem solving	Total value innovation, including social and environmental benefits
Basis for decision-making	Technical parameters, functional models, physical contradictions	Integration of technical, economic, social, environmental and other multidimensional factors
The Driving Force for Innovation	技术系统的内在矛盾	社会需求、环境挑战与技术进步的动态平衡
Solution Evaluation	功能最优化、成本效益	综合评价解决方案的长期价值、生命周期成本、社会响应和环境影响
Forecasting methodology	技术系统演化的规律性	基于情景规划、德尔菲法等对未来社会环境趋势的预测
knowledge management	依赖专利信息、科学原理、技术知识库	整合跨学科知识、专家智慧、实时数据和信息分析
Tools and methodologies	Contradiction Matrix, Physical Contradiction Diagram, Principles of Innovation	Add value process mapping, life cycle analysis, system dynamics modeling, etc.
Scope of Solutions	Improvements at the technical level	Technological innovation and the integration of socio-environmental-economic systems
user participation	User requirements as input for problem definition	User requirements are directly involved in the solution iteration process
sustainability	Indirect considerations (through technical efficiency gains)	Direct consideration (as a core evaluation indicator and design principle)
Responding to complexity	Solving complex technical problems	Integrated Strategies for Coping with Technological, Social, and Environmental Complexity
Implementation process	Focus on technology development and design	Balancing technological innovation with socially responsible implementation processes
Case and empirical studies	Specializing in technology innovation case studies	Introduction of social innovation and environmental protection case studies
Training and education	Technical innovation training for professional engineers and technicians	Interdisciplinary teamwork, including designers, engineers, sociologists, environmental scientists, etc.
Assessment of indicators	Technical performance, cost, speed	Sustainability indicators, social impact assessment, environmental impact assessment
Feedback mechanisms	Focus on technology validation and testing	Including market feedback, social impact assessment and environmental impact tracking
value-oriented	Market and technology value oriented	Oriented towards global and long-term social and environmental values
paradigms	Technical problem-solving paradigm	Socio-technical systems integration paradigm

This table provides a detailed comparative view of the differences between traditional TRIZ and Value TRIZ (Wisdom TRIZ) on a number of dimensions, including philosophy, focus of attention, and decision-making rationale. In practical applications, these two methodologies can complement each other to form a more comprehensive innovation toolset, depending on the specific situation and objectives.

6. Conclusion

Value TRIZ (Wisdom TRIZ) provides a new perspective to help innovators maximize social and environmental benefits while pursuing technological innovation. Through wise decision-making and forecasting, DIKWP-TRIZ ensures that solutions are able to meet future challenges and contribute to the achievement of the SDGs.

2.8 Human-centered TRIZ (Purpose TRIZ): A Methodology for Innovation Based on User Needs and Design purposes

Abstracts

In the rapidly developing digital information era, technological innovation should not only solve technical problems, but also pay attention to user needs and design Purpose. This paper discusses in depth the human-centered TRIZ (Purpose TRIZ) component of the DIKWP-TRIZ methodology, and explains the importance of taking user's purpose as the core driving force of the innovation process. Through specific case studies and theoretical discussions, the paper demonstrates the effectiveness of Purpose TRIZ in ensuring that solutions truly fit user needs and pain points.

Keywords: human-centered TRIZ, Purpose TRIZ, user needs, design purpose, DIKWP-TRIZ, technological innovation, innovation methodology

1. Introduction

With the increasingly personalized and demand-driven nature of technological innovation, how to ensure that innovation activities can closely focus on the actual needs and design purposes of users has become an urgent problem. Human-centered TRIZ (purposeal TRIZ), as an important part of the DIKWP-TRIZ methodology, proposes a new innovation methodology that aims to take user needs and design purpose as the starting point and destination of innovation, with a view to achieving the best innovation results.

2. Theoretical Foundations of Human-Centered TRIZ (purposeal TRIZ)

Human-centered TRIZ (purposeal TRIZ) is based on the concept of user-centered design, which puts human needs and purposes at the center of the entire innovation process. It is not just a technology- or function-driven innovation model, but also a methodology that deeply understands and satisfies the intrinsic needs of users. On this basis, purpose TRIZ emphasizes the following core elements:

2.1 In-depth exploration of user needs: Utilizing various research methods (e.g., interviews, observations, questionnaires, etc.) to deeply understand the potential needs of users.

2.2 Clear Expression of Design Purpose: Ensure that the design team's innovation purpose is highly consistent with user needs, and maintain this consistency throughout the innovation process.

2.3 User validation of the solution: Continuously validate and iterate the design through prototype testing, user feedback, etc. to ensure the effectiveness of the solution.

3. Purpose-Oriented Innovation Process in DIKWP-TRIZ

(1) Problem definition: In the DIKWP-TRIZ framework, the first step is to define the problem based on user needs. This requires innovators to go deep into the user's environment and understand their actual application scenarios and needs.

(2) Solution design: The solution design should directly respond to user needs. In this stage, innovators should utilize the analysis tools and principles in DIKWP-TRIZ to design solutions.

(3) User testing and feedback: After the solution is initially shaped, user testing must be conducted. User feedback will directly influence the iteration and optimization of the solution.

(4) Final Solution Development: Ensure that the final product or service meets the user's needs while also reflecting the innovator's design purpose.

4. Strategies for Integrating Purpose TRIZ with User Requirements

4.1 Application of Design Thinking in purposeal TRIZ

Design thinking is a user-centered approach to innovation that focuses on understanding and empathizing with users to meet their needs and expectations. Incorporating design thinking in the purpose TRIZ methodology can help innovators capture and understand user needs more comprehensively, leading to more valuable innovations.

In the DIKWP-TRIZ methodology, the application of design thinking can be carried out on several levels. First, design thinking focuses on understanding users in depth and obtaining insights into user needs through methods such as user research, user interviews, and user stories. These methods can help innovators understand users' expectations, problems, and challenges, which can guide the direction and goals of innovation.

Second, design thinking focuses on thinking about problems and solutions from the user's perspective. In purpose TRIZ, innovators can use design thinking tools and techniques, such as persona portraits and scenario simulations, to put themselves in the user's environment and gain a deeper sense of the user's needs and experiences. In this way, innovators can better understand the problems faced by users and provide innovative solutions that better meet their expectations.

Finally, design thinking emphasizes iteration and rapid prototyping. In purpose TRIZ, innovators can transform innovative ideas into the form of actual products or services through rapid prototyping and feedback and testing with users. By interacting with users, innovators can identify problems and opportunities for improvement early on, so that they can continuously optimize their innovative solutions and bring them closer to user needs.

4.2 Selection and use of innovation tools

The selection of tools and methods suitable for user-centered innovation is also key in the purpose TRIZ approach. Innovation tools can help innovators think about problems from the user's perspective and provide specific frameworks and methods to guide the innovation process.

For example, empathy map is a commonly used tool to help innovators understand the thinking, feelings, expectations and needs of users in a user-centered way. By drawing empathy maps, innovators can gain a deeper understanding of users' emotions and experiences, thus providing more accurate directions and goals for innovation.

Another important tool is the customer journey map, which describes the stages and touchpoints in a user's interaction with a product or service. By mapping the user journey, innovators can gain a clearer understanding of the needs and experiences of users at different stages, and identify the pain points and opportunities, so that they can target innovative solutions.

In addition to empathy maps and user journey maps, there are other tools and methods, such as user profiling, scenario simulation, user stories, etc., that can help innovators better understand and meet user needs. Innovators can choose the appropriate tools and methods to guide the innovation process and ensure that user needs are fully considered and synthesized according to specific innovation projects and goals.

4.3 Mechanisms for purposeal TRIZ to operate in multidisciplinary teams

The purpose TRIZ methodology emphasizes interdisciplinary collaboration and integrated innovation to understand and interpret user needs from a more holistic perspective. In multidisciplinary teams, the DIKWP-TRIZ methodology supports and facilitates interdisciplinary collaboration, providing richer perspectives and resources for purpose-driven innovation.

First, the purpose TRIZ methodology encourages multidisciplinary team members to work together in user research and requirements analysis. Experts from different disciplines can provide different insights and analyses from their own perspectives, resulting in a more comprehensive and accurate portrait of user needs. For example, HCI experts can focus on the ease of use of the user interface, engineers can focus on the technical feasibility, and marketing experts can focus on the market demand and business model. Through the cooperation of multidisciplinary teams, the limitations of a single perspective can be avoided to better understand and satisfy user needs.

Second, the purpose TRIZ method provides a set of shared innovation tools and methods that can facilitate communication and collaboration among multidisciplinary teams. Team members from different disciplines can use the same tools and methods and participate in the innovation process together. Such harmonized tools and methods can provide a common language and framework that helps team members communicate and collaborate better. For example, using a user journey diagram as a common tool, team members can label different information and insights on the same diagram to collaboratively analyze user needs and design innovative solutions.

In addition, the purpose TRIZ methodology encourages multidisciplinary teams to collaborate and innovate across boundaries. During the innovation process, team members can inspire and learn from each other, integrating knowledge and experience from different disciplines across borders. For example, engineers can draw on psychological principles to design more user-friendly product interfaces, and designers can draw on engineering principles to improve product reliability and maintainability. Through cross-border cooperation, more creative and innovative solutions can be generated to better meet user needs.

Finally, the purpose TRIZ approach advocates a multidisciplinary background and integrative capabilities for innovators. In a multidisciplinary team, team members can enhance their integrative capabilities through learning and training to better understand and apply knowledge and methods from different disciplines. Innovators can improve their ability to understand and grasp user needs by learning about psychology, human-computer interaction, engineering, marketing and other related disciplines. At the same time, they can also work together to improve their comprehensive innovation capabilities through communication and cooperation with team members, and make greater contributions to purpose-driven innovation.

5. Challenges and responses to the practice of purposeal TRIZ

The Purpose-TRIZ method, as a user-centered approach to innovation, faces a number of practical challenges. These challenges include the ever-changing nature of user needs, balancing user needs with technical feasibility, and understanding needs across cultures and geographies. To effectively address these challenges, there are a range of countermeasures that innovators can take, incorporating the principles and tools of the DIKWP-TRIZ methodology, to meet user needs and realize valuable innovations.

5.1 Changing user needs

User needs are dynamic and changing and are influenced by a variety of factors such as technology, market and society. This places a demand for flexibility and rapid iteration on the purpose TRIZ methodology. Below are several responses to meet this challenge:

a. Continuous user research: Innovators should maintain close contact with users and conduct continuous user research and feedback collection. By observing user behavior, conducting user interviews and surveys, etc., they can understand the changes and evolutionary trends of user needs. This allows for timely adjustments to the innovation direction and goals to keep the innovative solution in line with user needs.

b. Agile Development Methods: Adopting agile development methods can help innovators iterate and improve more quickly. By dividing the innovation process into short iteration cycles, such as Sprint in the Scrum method, innovators can make adjustments and improvements faster based on user feedback. This allows for a timely response to changes in user needs and ensures continuous optimization of the innovative solution.

c. Innovation ecosystem: Establishing an innovation ecosystem can help innovators keep close contact with the external environment and obtain timely information about changes in user needs. The innovation ecosystem can include partners, user communities, industry experts and other types of participants. By working with these participants, innovators can share resources and information, keep abreast of changes in user needs, and obtain support and feedback during the innovation process.

d. Data-driven innovation: Using data analysis and AI technology, innovators can gain a deeper understanding of changes in user behavior and needs. By analyzing user data, market trends, competitive wisdom and other information, innovators can discover the changing patterns and trends of user needs and make corresponding innovation adjustments accordingly. Data-driven innovation can provide more objective and accurate insights into user needs and help innovators better respond to the constant changes in demand.

5.2 Balancing User Needs and Technical Feasibility

a. Technology assessment and validation: Before starting the innovation process, innovators should conduct technology assessment and validation to determine whether the selected technology can realize user needs. By assessing the maturity, feasibility, and reliability of the technology, innovators can better understand the limitations and potential of the technology and thus make more informed decisions during the innovation process.

b. Multidisciplinary Collaboration: Establishing multidisciplinary teams that bring together experts from different fields can help innovators better balance user needs and technology feasibility. Through interdisciplinary collaboration, knowledge and experience from different perspectives, such as technologists and user experience experts, can be combined to evaluate and make decisions in a more holistic manner. Multidisciplinary collaboration can facilitate innovators' understanding of the interplay between technology and user needs to find a better balance.

c. Models for innovation decision-making: DIKWP-TRIZ provides a systematic approach to assessment and decision-making. Innovators can utilize these models and tools to quantitatively assess and compare user needs against technological feasibility. By building innovation decision models, innovators can combine subjective and objective factors to make decisions in a data-driven manner, thus better balancing user needs and technical feasibility.

d. Innovation management methods: Adopting appropriate innovation management methods can help innovators better balance user needs and technical feasibility. For example, design thinking methods can guide innovators to prioritize user needs and verify technical feasibility through iteration and rapid prototyping. Agile development methods can help innovators balance requirements and technology in rapid iterations. By applying these innovation management methods wisely, innovators can better manage the relationship between user needs and technical feasibility.

5.3 Requirements Understanding Across Cultures and Geographies

a. Localized Research and Design: Cross-cultural and geographic requirements understanding requires localized research and design. Innovators should communicate and collaborate intensively with local users and stakeholders to understand their culture, values, and habits. By working with local users, innovators can better understand their needs and preferences and incorporate these factors into the design of innovative solutions.

b. Diverse Teams: Building diverse teams can help innovators better understand and meet cross-cultural and geographic needs. Team members from different cultural backgrounds and geographies can provide innovators with different perspectives and insights. Through diverse teamwork, innovators can better capture the differences in needs across cultures and geographies and design innovations accordingly.

c. Flexible use of design language and symbols: Flexible use of design language and symbols is key in cross-cultural and geographical needs understanding. Innovators should avoid using symbols and languages that are only meaningful in a particular culture or region, and instead use more universal and accessible design elements. By utilizing universal design languages and symbols, innovators can better transcend cultural and geographic differences and make innovative solutions more accessible to various user groups.

Conclusion

By placing user needs and design purpose at the center, human-centered TRIZ (purposeal TRIZ) provides a new perspective and approach to technological innovation. It emphasizes the understanding of the deeper needs of users, the clear expression of the innovation purpose, and the precise matching of the solution to the user's needs.This component of the DIKWP-TRIZ methodology not only strengthens the user-orientation of the innovation, but also significantly improves the market adaptability and success of the innovative solution through the real sense of user participation. This human-centered innovation methodology is especially important in the digital information age.

2.9 Efficiency TRIZ (DIKWP Transformation TRIZ): Improving Innovation Efficiency through Optimization of DIKWP Transformation Processes

Abstracts

With the development of information technology and the increase of complex problems, the efficiency of the innovation process becomes crucial. Efficiency TRIZ (DIKWP Transformational TRIZ) is an extension and refinement of the traditional TRIZ theory, which integrates the concepts of Data, Information, Knowledge, Wisdom, and purpose to form a comprehensive innovation framework. This paper explores how the transformation process between the elements of DIKWP can be optimized to increase the efficiency and responsiveness of the entire innovation process, leading to a more efficient innovation ecosystem in modern organizations.

Keywords: efficiency TRIZ, DIKWP model, innovation efficiency, transformation process, systems thinking, predictive analytics

Introduction

In the current rapidly changing market and technological environment, organizations face increasingly complex challenges that require rapid response and efficient innovation. The Efficiency TRIZ (DIKWP Transformational TRIZ) methodology, by refining and optimizing the transformation steps from data to prediction, not only emphasizes the importance of innovation efficiency, but also provides a systematic path to achieve this goal. In this paper, we will discuss the theoretical foundations, the optimization of the transformation process, the case applications and their impact on organizational innovation capability.

1. theoretical foundation

The traditional TRIZ theory was proposed by Soviet inventor and scientist Genrich Altshuller in the mid-20th century, which summarizes the universal principles and patterns of innovative design based on large-scale analysis of patents. Efficiency TRIZ is a further development on this basis, which integrates the DIKWP concept into the TRIZ system to form a more complex problem-solving framework.

2. DIKWP elements and innovation efficiency

According to the DIKWP-TRIZ methodology, the key to innovation efficiency lies in the effective transformation of five elements: data, information, knowledge, wisdom and prediction. Data are raw facts and figures, information is the result of data after processing, knowledge is the way in which information is understood and applied, wisdom is the deeper understanding and utilization of knowledge, and prediction is the ability to predict future knowledge and trends.

3. Optimizing the DIKWP conversion process

To improve innovation efficiency, the key to optimizing the DIKWP transformation process is to reduce friction and time delays at all stages of the process, from data to predictions. This requires building a system in which each link is highly automated and intelligent to facilitate the rapid transformation of information and knowledge flows.

(1) Transformation of data into information

At this stage, it is important to collect large amounts of raw data through automated tools and technologies (e.g., sensor technology, the Internet, the Internet of Things) and transform it into useful information through data cleansing and preprocessing. For example, machine learning algorithms can discover patterns in customer behavior data to provide basic information for market trend analysis.

(2) Transformation of information into knowledge

This step involves analyzing and interpreting information and transforming it into applicable knowledge. Here, the role of knowledge management systems and collaboration tools is critical, helping team members to share insights and merge information into structured knowledge in the organizational knowledge base.

(3) Transformation of knowledge into wisdom

Wisdom is a deep level of understanding and judgment of knowledge, which usually requires extensive experience and intuition. At this stage, decision support systems (DSS) and expert systems can help simulate expert thinking and transform knowledge into insightful wisdom that can guide complex decision-making processes.

(4) Transformation of wisdom to purpose

Ultimately, forecasting involves the ability to anticipate the future, which is achieved by analyzing past and present data, information, knowledge and wisdom. Here, trend analysis tools, simulation software and prediction market tools are indispensable in identifying possible future changes and new opportunities.

4. Case Studies

Case studies are one of the most important ways to gain a deeper understanding of the role and effectiveness of efficiency TRIZ in practical applications. By analyzing cases from different industries, we can better understand how TRIZ can help companies improve efficiency, optimize products and services, and enhance market competitiveness. In this paper, we will discuss two case studies, one in the automobile manufacturing industry and the other in the healthcare field.

In the automotive manufacturing case study, an automotive manufacturer used the DIKWP methodology to improve its product development process. By integrating real-time collection and analysis of sensor data, the company was able to transform the data into useful information about the performance of the vehicle and further transform this information into knowledge for future product improvements. Through wise decision-making and predictive analytics, the company has successfully predicted market trends for electric vehicles and adjusted its product line and technology roadmap accordingly.

The widespread use of sensor technology enables automakers to monitor and collect vehicle performance data in real time, including battery status, energy utilization efficiency, driving range, and more. By analyzing and mining this data, manufacturers are able to gain in-depth insights into vehicle performance, which can be translated into concrete measures for product improvement. For example, by analyzing EV battery usage and charging patterns, manufacturers can identify key factors in battery life and adjust charging strategies or develop more efficient battery technologies accordingly.

In addition, by analyzing forecasts of market demand and trends, automakers can make adjustments in advance to accommodate future market changes. By collecting and analyzing market data, consumer feedback, and competitive wisdom, manufacturers can identify potential needs and trends and adjust product lines and technology roadmaps accordingly. For example, against the backdrop of a rapidly growing market for electric vehicles, the automaker adjusted its product strategy in a timely manner by accurately predicting the market demand and technology trends for electric vehicles and increasing its investment in R&D and production of electric vehicles, thereby maintaining its competitive edge in a highly competitive market.

Another case study is an application in the medical and healthcare field. In this case study, a healthcare organization used big data and AI algorithms to improve the accuracy of disease diagnosis by analyzing patient data. The healthcare organization collects and integrates a large amount of patients' clinical data, medical imaging data, and genomics data, and by analyzing and mining this data, it is able to gain deep insights into disease patterns and trends, which can be transformed into wisdom about diagnostic knowledge and treatment strategies.

By analyzing patient data, healthcare organizations are able to identify potential disease patterns and risk factors, thereby improving the early diagnosis rate and accuracy of diseases. For example, in the field of cancer diagnosis, medical institutions can use big data and machine learning algorithms to analyze patients' genomics data, clinical records and medical imaging data to assist doctors in making accurate diagnoses of tumor types and grading. By comparing and analyzing data from a large number of cases, medical institutions can discover patterns and laws hidden behind the data to further optimize the diagnostic process and improve accuracy and efficiency.

In addition, by mining and analyzing patient data, healthcare organizations are able to predict patients' health trends and disease development. Based on the analysis of large-scale datasets, organizations can build models to predict a patient's disease risk, disease progression, and treatment outcomes. This predictive information can help physicians develop personalized treatment plans for early intervention and management of patients' health conditions.

By applying the Efficiency TRIZ methodology, healthcare organizations can better leverage big data and AI technologies to transform massive amounts of patient data into useful knowledge and wisdom. This transformation process involves data collection, cleansing, integration, analysis, and model building, and needs to be done in a way that protects patient privacy and data security. However, once an effective data analysis and mining system is in place, healthcare organizations can make full use of the data to improve the accuracy of disease diagnosis, optimize treatment strategies, and predict patient health trends, thereby improving clinical decisions and healthcare outcomes.

In conclusion, through the above two case studies, we can see the value and potential of efficiency TRIZ in the application of different industries. Whether in automotive manufacturing or healthcare, TRIZ methodologies and tools can help companies and organizations better leverage data and knowledge, optimize products and services, and improve efficiency and competitiveness. With the continuous development of big data and artificial wisdom technologies, the role of efficiency TRIZ in practical applications will become more and more important, bringing more opportunities and challenges to enterprises and organizations.

Below is a detailed comparative analysis table of traditional TRIZ and DIKWP-TRIZ methodologies:

features	Traditional TRIZ	DIKWP-TRIZ
fundamental definition	A theoretical framework based on creative problem solving to innovate by resolving contradictions in technological systems.	Expand TRIZ to include data, information, knowledge, wisdom, and purpose, emphasizing the holistic and purpose-oriented nature of innovation
main objective	Resolve technical conflicts and find innovative solutions.	Information Integrity, Efficiency, Consistency and Purposefulness in the Innovation Process.
body of knowledge	40 Inventive Principles, Technical Contradiction Matrix, Physical Field Analysis, etc.	Data Processing, Information Management, Knowledge Integration, Wisdom Application and PurposeOrientation.
methodology	Paradox Analysis, Application of Innovative Principles, and Ideal Final Result (IFR).	DIKWP's integrated applications use data as a foundation and information as a tool to realize purpose through knowledge and wisdom.
Decision support	Relies heavily on expert experience and intuition.	Data-driven decision making, information system support, knowledge base application, intelligent algorithmic assistance.
Technical applicability	Better suited to solving known technical conflicts and problems.	Widely used for exploratory innovations for unknown and uncertain problems.
Role of data and information	Ancillary, mainly for problem definition and solution validation.	core as a cornerstone for identifying problems, shaping solutions, and guiding practice.
Forecasting methodology	Trend analysis and technology evolution paths.	Combine real-time data analysis, market dynamics and user feedback to make dynamic predictions and adjustments.
Application of knowledge and wisdom	Used through expert systems and analogical reasoning.	Deep application through knowledge management systems, artificial wisdom and machine learning.
Manifestation of purpose	Indirectly, this is reflected in the purpose of reaching technological innovations.	It is directly reflected in every aspect of the innovation, ensuring that every step serves the ultimate purpose.
Tools and techniques	Contradiction Matrix, Object Field Analysis, Principle of Invention, etc.	Data analysis tools, knowledge management systems, intelligent decision support, purpose recognition tools, etc.
Cases and implementation	Mature cases are concentrated in the mechanical, engineering and technology sectors.	Cross-domain applications, including business, education, healthcare, social innovation, etc.
Sustainability and adaptation	Tools and techniques need to be regularly updated to keep up with technological developments.	Adapt to change naturally through continuous data updates and knowledge iterations.
Collaboration and teamwork	Focus on individual experts and the creativity of designers.	Promote interdisciplinary teamwork and collective wisdom.
Scope of Application	Mainly used in technical and engineering fields.	Applicable to a wider range of fields, including business, education, healthcare, etc.
user interaction	Often relies on the collaboration of individuals or small teams.	Encourage wider collaboration across sectors and organizational boundaries.
Updating and adaptation	Relatively static and infrequently updated.	Dynamic and adaptive, continuously updated to adapt to environmental changes.
Forecasting and modeling	Relies heavily on trend analysis and expert assessments.	Utilize advanced predictive modeling and simulation techniques to incorporate purpose for future planning.
Effectiveness evaluation	Solution-based effectiveness and technological innovation.	Includes effectiveness of solutions, optimization of decision-making processes, and long-term impact and alignment of objectives

This table summarizes the main differences between traditional TRIZ and DIKWP-TRIZ, which is based on data, information, knowledge, wisdom, and purpose. While traditional TRIZ focuses on resolving technical conflicts, DIKWP-TRIZ looks at the flow and application of information and knowledge throughout the innovation process, and ultimately at the overall strategy for realizing the organization's or individual's purpose.

5. Conclusion

Efficiency TRIZ (DIKWP Transformational TRIZ), as an innovation methodology, is centered on significantly improving the efficiency and responsiveness of the decision-making and innovation process by optimizing the transformation process of data, information, knowledge, wisdom, and prediction. It is not only a theoretical framework, but also provides a series of tools and methods to realize these transformational processes. Through practical examples, we can see how Efficiency TRIZ works in different industries to help organizations gain an edge in a competitive market.

As technology continues to advance and information technology improves, Efficiency TRIZ will continue to evolve and improve, providing a more efficient path to innovation for organizations and businesses around the world.

3. The DIKWP-TRIZ approach: an innovative problem-solving approach synthesizing the DIKWP model and classical TRIZ

Abstracts

DIKWP-TRIZ is an innovative problem-solving methodology that blends the DIKWP model with the classical TRIZ approach.The DIKWP model provides an integrated framework of data, information, knowledge, wisdom, and purpose, emphasizing the transformative process of information and the driving role of purpose. The classical TRIZ approach focuses on resolving contradictions and conflicts, applying innovative principles and tools to discover non-traditional solutions.

The DIKWP-TRIZ methodology combines the strengths of the DIKWP model and the classical TRIZ methodology by focusing on problem solving and taking the key steps of problem modeling, knowledge acquisition, innovative thinking, solution generation, solution evaluation and optimization at different levels and dimensions. By transforming the data, information, knowledge, wisdom and purpose in the DIKWP model into innovative thinking and solutions, the DIKWP-TRIZ methodology is able to provide comprehensive, innovative and effectual solutions to problems.

The application of DIKWP-TRIZ has a wide range of potential for innovation and problem solving in various fields and industries. It helps people to understand and solve problems from more dimensions and perspectives, promoting innovation and improving the effectiveness and efficiency of solutions. By integrating the DIKWP model and the classical TRIZ approach, the DIKWP-TRIZ approach provides us with a comprehensive thinking and tools to promote innovation and problem solving.

Keywords: dikwp model, classical triz, innovation, problem solving, methodology, principles, architecture, steps

Introduction

The DIKWP-TRIZ approach is an innovative problem solving method that integrates the DIKWP model and the classical TRIZ approach.The DIKWP model provides an integrated framework of data, information, knowledge, wisdom, and purpose, emphasizing the transformation process of information and the driving role of purpose. The classical TRIZ approach, on the other hand, focuses on resolving paradoxes and conflicts, applying innovative principles and tools to discover non-traditional solutions.

By merging the DIKWP model with the classical TRIZ approach, the DIKWP-TRIZ methodology is able to provide comprehensive, innovative and effectual solutions to problems. It helps people analyze and understand problems from multiple levels and dimensions, transform data into meaningful information, transform information into valuable knowledge, and find better solutions through innovative thinking and solution generation. At the same time, the DIKWP-TRIZ approach emphasizes the evaluation and optimization of solutions to ensure their feasibility and effectiveness.

In this chapter, we will explore in detail the principles, methods, architecture and key steps of the DIKWP-TRIZ methodology. We will delve into the key steps of problem modeling, knowledge acquisition, innovative thinking, solution generation, and solution evaluation and optimization to help readers better understand and apply the DIKWP-TRIZ methodology. By combining the DIKWP model with the classical TRIZ approach, the DIKWP-TRIZ methodology provides us with a powerful tool to drive innovation and problem solving.

DIKWP-TRIZ is an innovative problem-solving approach that blends the DIKWP model with the classical TRIZ method. The principles, methods, architecture and key steps of DIKWP-TRIZ are described below:

3.1 Principles of DIKWP-TRIZ

(1) DIKWP model

Data can be understood as a figurative representation of what we perceive as the "same" semantics. Data usually represents a materialized fact or observation with a specific semantic meaning behind it. When working with data, we often look for and extract the same semantics, unifying them into a single concept. For example, if we see a flock of sheep, although each sheep may have a different size, color, gender, etc., we will group them into the concept of "sheep" because they share our semantic understanding of the concept of "sheep".

Information is the semantic representation of the cognitive "difference". Information usually refers to knowledge or data about the environment or an object that we acquire through our senses and observations. When processing information, we identify and categorize the intrinsic differences in the input data. For example, in a parking lot, although all cars can be categorized under the concept of "car," each car has its own specific characteristics, such as make, model, color, etc., which are all information.

Knowledge corresponds to the semantics of "completeness" in cognition. Knowledge is the understanding and interpretation of the world that we gain through information. In processing knowledge, we abstract complete concepts or patterns through observation and learning. For example, we learn from observation that all swans are white, which is a complete understanding of the concept of "swan" that we gain from gathering a lot of information.

Wisdom corresponds to information about ethics, social morality, human nature, etc. It is a high level of understanding, synthesis and application of knowledge and information. When dealing with wisdom, we integrate this information and use it to guide decisions. For example, when faced with a decision-making problem, we take into account all aspects of ethics, morality, and feasibility, not just technology or efficiency.

Purpose can be understood as a dichotomy (input, output) where both input and output are DIKWP content. Purpose represents our understanding of a phenomenon or problem (input) and the goal we wish to achieve by processing and solving that phenomenon or problem (output). When processing the purpose, the AI system processes the input DIKWP content according to its predefined goal (output), and by learning and adapting, makes its output converge to the predefined goal.

(2) The classical TRIZ method

TRIZ is an acronym for the Russian phrase "Teoriya Resheniya Izobreatatelskikh Zadatch", which in English is known as "Theory of Inventive Problem Solving". It is a methodology for solving complex problems and innovative design challenges that was originally developed in 1946 by Soviet inventor and scientist Genrich Altshuller and his colleagues, and has been continuously developed and refined since then.

TRIZ is based on the idea that the laws of creative problem solving are universally applicable and that they can be identified by analyzing a large number of patents for inventions. the purpose of TRIZ is to help problem solvers anticipate the direction of technological systems and to find innovative solutions that break through traditional ways of thinking and technological barriers.

The TRIZ methodology includes a variety of tools and concepts, including:

① Problem analysis tools:

l Functional analysis: identifying all the components in a system and the relationships between them.

l Problem Formalization: Converting a real problem into a standard problem.

② Problem solving principles and models:

l Principles of Invention: 40 universal principles used to generate innovative ideas for solving problems.

l Contradiction Matrix: Used to resolve technical contradictions in invention problems by converting problem descriptions into standard parameters and using pre-defined solutions.

l MATTER-FIELD ANALYSIS: the use of matter and field concepts to improve systems or solve problems.

③ Innovation process:

l ARIZ (Algorithm for Inventive Problem Solving): a structured problem solving process designed to systematically guide the user from problem description to solution creation.

④ Prediction tools:

l Laws of Technological System Development: describes the general laws and trends that systems follow as they evolve over time.

l S-curve analysis: Evaluate the maturity and potential development space of the technical system.

TRIZ is widely applied in fields such as product design, engineering, and problem-solving. It encourages innovators to go beyond the boundaries of existing knowledge and solve problems through novel approaches. A core concept of TRIZ is that innovation often involves resolving contradictions within a system, known as technical contradictions and physical contradictions. Technical contradictions refer to situations where improving certain aspects of a system may harm other parts of the system. Physical contradictions refer to situations where the same component or feature needs to have different states under different conditions.

The strength of the TRIZ methodology lies in its provision of a systematic innovation process. This process, through the analysis and application of methods previously used to solve similar problems, helps accelerate and guide innovative activities. While initially developed to address engineering and technical problems, the principles and tools of TRIZ have been applied to other fields such as business, management, and social sciences.

3.2 DIKWP-TRIZ Method

The DIKWP-TRIZ method is a comprehensive problem-solving approach that combines the DIKWP model with the classical TRIZ method. This method integrates the transformation process of data, information, knowledge, wisdom, and purpose from the DIKWP model, as well as the innovation principles and tools from classical TRIZ, providing comprehensive and innovative guidance for problem solving.

The DIKWP model describes the transformation process of information and knowledge, emphasizing purpose as the driving force behind interaction and transformation. The DIKWP model consists of five levels: data, information, knowledge, wisdom, and purpose. Data represents the tangible representation of our cognition, information expresses different semantics, knowledge involves understanding and interpretation of the world, wisdom encompasses aspects such as ethics, social morality, and human nature, while purpose represents our understanding of phenomena or problems and the goals we aim to achieve by addressing them.

The classical TRIZ method is a problem-solving methodology that focuses on identifying and resolving contradictions and conflicts. It provides a series of innovation principles and thinking tools to help individuals discover non-traditional solutions. The TRIZ method encourages people to go beyond conventional thinking patterns, identify the root causes of problems at a systemic level, and address contradictions and conflicts by introducing new concepts and solutions.

Specific Steps and Methods of the DIKWP-TRIZ Method:

Problem Definition and Understanding: Firstly, determine the scope and objectives of the problem. Analyze and understand the problem using the levels of the DIKWP model, identifying the data, information, knowledge, wisdom, and purpose involved in the problem. This helps ensure a comprehensive understanding of the problem and provides guidance for subsequent solutions.

Problem Modeling and Analysis: Transform the problem into a model or diagram to better understand the relationships and contradictions within it. Use the levels of the DIKWP model to decompose and analyze the problem, identifying the contradictions and conflicts involved. This helps analyze the problem from different levels and dimensions, and identify the information and knowledge required to solve the problem.

Application of Innovation Principles: Based on problem analysis, apply the innovation principles and thinking tools of classical TRIZ to seek non-traditional solutions. These innovation principles include contradiction resolution principles, resource utilization principles, process transformation principles, and more. They help us think from different perspectives and solve problems.

Generation and Evaluation of Solutions: Based on the application of innovation principles and thinking tools, generate multiple potential solutions. Then, evaluate and select the most feasible and effective solution. This can be done by assessing the cost, risks, benefits, and other aspects of the solutions.

Implementation and Feedback: Transform the selected solution into practical actions and implement it. During the implementation process, collect relevant data and information, and evaluate and adjust the solution. Feedback information helps improve and optimize the solution for better results and efficiency.

Advantages and Benefits of the DIKWP-TRIZ Method:

Comprehensiveness and Systematic Approach: The DIKWP-TRIZ method combines the strengths of the DIKWP model and the classical TRIZ method to understand and solve problems from different levels and dimensions. It considers factors from multiple levels, such as data, information, knowledge, wisdom, and purpose, making problem-solving more comprehensive and systematic.

Innovativeness and Breakthrough Thinking: The innovation principles and thinking tools of the TRIZ method help break conventional thinking patterns and find non-traditional solutions. By integrating the TRIZ method with the DIKWP model, the DIKWP-TRIZ method provides more innovative ideas and possibilities for problem-solving, promoting the development of innovation.

Goal-Driven and Results-Oriented: The DIKWP-TRIZ method emphasizes problem-solving goals and outcomes. It treats purpose as the driving force behind interaction and transformation, helping us clarify the goals of problem-solving and select and evaluate solutions based on those goals. This helps ensure alignment between solutions and goals, and improves the effectiveness and efficiency of solutions.

Integration and Flexibility: The DIKWP-TRIZ method integrates various aspects of the DIKWP model and the TRIZ method, providing strong integration and flexibility. It can select and apply different tools and principles from the DIKWP model and TRIZ method based on the specific characteristics and requirements of the problem, making it adaptable to different types and complexities of problems.

Sustainability and Learning Capability: The DIKWP-TRIZ method emphasizes the process of feedback and learning. By collecting and analyzing data and information during implementation, it allows continuous improvement and optimization of solutions, enhancing problem-solving capabilities and effectiveness. This helps establish a sustainable problem-solving process and promotes individual and organizational learning and innovation capabilities.

In summary, the DIKWP-TRIZ method is a comprehensive and innovative problem-solving approach that combines the DIKWP model with the classical TRIZ method. It emphasizes comprehensiveness and goal-driven problem-solving, helping us understand and solve problems from different levels and dimensions, and promoting innovation and improving the effectiveness and efficiency of solutions. By applying the DIKWP-TRIZ method, individuals can better address complex and challenging problems, driving the development of science, technology, and society.

3.3 Architecture of DIKWP-TRIZ

The architecture of DIKWP-TRIZ is based on the integration of the DIKWP model and the classical TRIZ method, aiming to provide a comprehensive framework for problem-solving. This framework includes several key steps, such as problem modeling, knowledge acquisition, innovative thinking, solution generation, solution evaluation, and optimization. These steps interact and complement each other, forming a complete problem-solving process.

Problem Modeling:

Problem modeling is the starting point of the DIKWP-TRIZ method. In this step, the problem is clearly defined and delimited. Using the hierarchy of the DIKWP model, the problem is decomposed into various aspects of data, information, knowledge, wisdom, and purpose. The data level involves specific facts and observational results of the problem, the information level involves explanations and expressions of the problem, the knowledge level involves understanding and background knowledge of the problem, the wisdom level involves ethical, social, moral, and human-related information, while the purpose level represents the goals and purposes for problem-solving. Through problem modeling, a better understanding of the overall picture and characteristics of the problem can be obtained, providing guidance for subsequent steps.

Knowledge Acquisition:

Knowledge acquisition is one of the crucial steps in the DIKWP-TRIZ method. In this step, relevant knowledge related to the problem is acquired from various sources. These sources can include literature, expert opinions, experimental data, case studies, etc. By acquiring knowledge, the understanding of the problem can be broadened, and the necessary information and concepts for problem-solving can be obtained. Knowledge acquisition can also involve the accumulation and dissemination of lessons learned through learning and research processes.

Innovative Thinking:

Innovative thinking is one of the core steps in the DIKWP-TRIZ method. In this step, the innovation principles and thinking tools of classical TRIZ are applied to analyze and solve the problem. The TRIZ method emphasizes the discovery and resolution of contradictions and conflicts in problem-solving and achieves innovation by introducing new concepts and solutions. Innovative thinking can be propelled by applying TRIZ's innovation principles, contradiction resolution principles, resource utilization principles, process transformation principles, etc. These methods and tools can help people go beyond traditional thinking patterns and discover unconventional solutions.

Solution Generation:

Building upon innovative thinking, solution generation is an important step in the DIKWP-TRIZ method. In this step, multiple potential solutions are generated by applying the results of innovative thinking. These solutions can be based on the innovation principles and thinking tools of the TRIZ method, as well as incorporating the levels and purpose of the DIKWP model for design. Solution generation can be performed through methods such as brainstorming, creative tools, simulation, and modeling.

Solution Evaluation and Optimization:

Solution evaluation and optimization are crucial steps in the DIKWP-TRIZ method. In this step, the generated solutions are evaluated and selected, choosing the most feasible and effective ones. Evaluation can consider multiple factors such as cost, risk, benefits, etc. Optimization can be achieved through adjustments based on feedback information and the learning process. This process can be iterative, continuously optimizing and improving the generated solutions until the optimal one is found.

The architecture of DIKWP-TRIZ provides a systematic approach to problem-solving and fostering innovation. It combines the strengths of the DIKWP model and the TRIZ method, making full use of the capabilities of multi-level problem modeling and innovative thinking. By clearly defining problems, acquiring knowledge, engaging in innovative thinking, generating solutions, and evaluating and optimizing those solutions, the DIKWP-TRIZ method guides individuals in seeking innovative solutions and offers a structured approach for tackling complex problems.

In practical applications, the architecture of DIKWP-TRIZ is applicable to various domains and industries. Whether in engineering for product design and process improvement or in business for marketing and business process optimization, the DIKWP-TRIZ method can provide a robust framework for problem-solving and driving innovation. By integrating the DIKWP-TRIZ method into an organization's problem-solving process and fostering an innovative culture, teams can cultivate their innovation capabilities and improve the efficiency and quality of problem-solving.

In conclusion, the architecture of DIKWP-TRIZ, based on the integration of the DIKWP model and the classical TRIZ method, offers a comprehensive problem-solving framework. Through key steps such as problem modeling, knowledge acquisition, innovative thinking, solution generation, solution evaluation, and optimization, the DIKWP-TRIZ method assists individuals in identifying problems, expanding their thinking, generating innovative solutions, and optimizing these solutions for optimal results. Its application promotes innovation and improvement, providing guidance and support for problem-solving in various domains.

3.4 Key Steps of DIKWP-TRIZ

DIKWP-TRIZ is a problem-solving framework that integrates the DIKWP model and the classical TRIZ method. It provides a series of key steps to help individuals systematically analyze problems, acquire knowledge, engage in innovative thinking, generate solutions, and ultimately evaluate and optimize those solutions. The following will further expand on the key steps of DIKWP-TRIZ, as well as the importance and specific implementation methods of each step.

Problem Modeling:

Problem modeling is the starting point of the DIKWP-TRIZ method. In this step, the problem is analyzed and modeled to transform it from ambiguous descriptions into meaningful representations in the DIKWP format. The purpose of problem modeling is to gain a deep understanding of the essence, characteristics, and influencing factors of the problem, as well as to discover patterns and trends within it. This can be achieved through the following methods:

l Problem Analysis: Conduct a thorough analysis of the problem to understand its background, scope, and relevant aspects. Through problem analysis, key elements and challenges of the problem can be identified.

l Trend Discovery: Observe and identify trends and changes relevant to the problem. This can include trends in technology, society, economy, environment, among other aspects. Trend discovery aids in predicting the future development direction of the problem.

l Pattern Recognition: Identify repeated patterns and structures within the problem. By recognizing patterns, potential regularities and rules of the problem can be discovered.

The result of problem modeling is to transform the problem into the form of DIKWP, making it more structured and actionable. This provides a clear problem definition and guidance for subsequent steps.

Knowledge Acquisition:

Knowledge acquisition is one of the key steps in the DIKWP-TRIZ method. In this step, relevant knowledge in the field is collected and mastered to expand thinking and problem-solving capabilities. Knowledge acquisition includes the following aspects:

l Existing Solutions: Research and understand the existing solutions, including best practices and successful cases in the relevant field. This can provide inspiration, insights, and avoid redundant efforts.

l Domain Knowledge: Learn and study domain knowledge related to the problem, including theories, methods, techniques, and tools. Accumulating domain knowledge can help gain a deeper understanding of the problem and provide support for innovative thinking.

l Expert Opinions: Seek the opinions and advice of experts, particularly those with experience and expertise in the relevant field. The experience and insights of experts can provide valuable guidance for problem-solving.

Knowledge acquisition is an ongoing process that involves continuous learning and updating of knowledge to maintain an understanding of the problem domain. By transforming knowledge into problem-solving capabilities, one can better address complex problems.

Innovative Thinking:

Innovative thinking is one of the core steps in the DIKWP-TRIZ method. In this step, TRIZ's innovative principles and tools are applied, leveraging the acquired knowledge for innovative thinking. The purpose of innovative thinking is to go beyond traditional thinking patterns and discover unconventional solutions to problems. The following are some methods and techniques for innovative thinking:

Ideation Techniques: Utilize various ideation techniques, such as brainstorming, mind mapping, and concept combination, to generate a wide range of ideas and possibilities.

Contradiction Analysis: Identify existing contradictions within the problem and explore ways to resolve them. This involves examining conflicting factors and finding innovative solutions that reconcile these contradictions.

Functional Analysis: Analyze the functions and purposes of different elements within the problem to uncover opportunities for improvement and innovation. This helps in identifying alternative approaches and novel solutions.

Analogical Thinking: Draw inspiration from diverse fields, unrelated to the problem domain, to find analogies and metaphors that stimulate creative thinking and generate innovative ideas.

By applying these methods and techniques, innovative thinking can be fostered, leading to novel and effective solutions for complex problems.

l Contradiction Resolution Principles: Identify contradictions within the problem and seek methods to resolve them. TRIZ provides a series of contradiction resolution principles, such as separation principle, unity principle, and reverse principle, which can help individuals find innovative approaches to address contradictions.

l Resource Utilization Principles: Explore how to better utilize existing resources to solve problems. This includes material resources, energy resources, human resources, among others. By fully leveraging existing resources, more economical and efficient solutions can be achieved.

l Abstraction and Idealization: Abstract the problem from specific contexts and envision ideal solutions. By abstracting the problem, its essence and key elements can be discovered, and idealized thinking can drive innovation.

l Reflection and Reverse Thinking: Employ reverse thinking, which involves considering the problem from a reverse perspective. Through reflection and reverse thinking, conventional thinking patterns can be challenged, leading to unconventional solutions.

l Innovative thinking requires a flexible and open mindset that encourages the exploration of new ideas and approaches. By combining TRIZ's innovative principles and tools, creativity can be stimulated, and potential solutions to problems can be unearthed.

Solution Generation:

Based on innovative thinking, solution generation is conducted. This step involves utilizing innovative thinking and TRIZ tools to generate new solutions. The generated solutions can be entirely new and creative or improvements and optimizations to existing solutions. The following are some methods and techniques for solution generation:

Borrowing and Transfer: Seek inspiration and solutions from other domains or similar problems. Borrowing and transfer thinking can help individuals break free from the constraints of the problem and find new perspectives and innovative points.

Combination and Adaptation: Combine and adapt different solution elements to create new solutions. This can be achieved by integrating different technologies, methods, or concepts.

Gradual Evolution: Generate solutions through incremental iteration and evolution. This can be achieved by continuously experimenting, receiving feedback, and making improvements, gradually optimizing the effectiveness and feasibility of the solutions.

Solution generation is a creative and exploratory process that encourages diversity and a variety of thinking approaches. By applying innovative principles and methods, the diversity and creativity of solutions can be fostered.

Solution Evaluation and Optimization:

After generating solutions, it is necessary to evaluate and optimize them. The purpose of solution evaluation is to ensure their feasibility and effectiveness, considering various factors such as technical feasibility, economic viability, and practicality. The following are some methods and techniques for solution evaluation and optimization:

l Comprehensive Evaluation: Consider various factors that influence the solutions and conduct comprehensive evaluation and trade-offs. This can be achieved by establishing evaluation criteria and models.

l Feedback and Improvement: Improve and optimize the solutions based on evaluation results and feedback information. This can be achieved through iterative and cyclical processes, continually enhancing the quality and effectiveness of the solutions.

By combining the principles, methods, framework, and key steps of DIKWP-TRIZ, we can harness the strengths of the DIKWP model and classical TRIZ methods to achieve comprehensive, innovative, and effective problem-solving. This integrated approach provides a comprehensive mindset and toolkit for promoting innovation and addressing complex problems.

3.5 Application of DIKWP-TRIZ

DIKWP-TRIZ (Data-Information-Knowledge-Wisdom-Purpose + Theory of Inventive Problem Solving) is a comprehensive model and method that combines the DIKWP model and TRIZ theory. It provides a comprehensive and systematic framework for problem modeling, problem-solving, user profiling, and interaction design. The application of DIKWP-TRIZ will be described in detail below.

The problem modeling process of the DIKWP-TRIZ model is as follows:

Data: As the starting point of the DIKWP-TRIZ model, data represents the facts and background information of the problem. When modeling the problem, we collect and organize data relevant to the problem, including problem descriptions, constraints, existing solutions, and knowledge from related domains.

Information: Through the interpretation and understanding of data, we transform it into meaningful information. In the DIKWP-TRIZ model, information includes problem analysis, trend discovery, and pattern recognition. By extracting and organizing information, we can gain deeper insights and understanding of the problem.

Knowledge: Through further processing and analysis of information, we transform it into knowledge. In the DIKWP-TRIZ model, knowledge includes understanding the root causes and influencing factors of the problem, as well as mastery of existing solutions and knowledge from related domains. Knowledge serves as the foundation for problem modeling and problem solving.

Wisdom: With the knowledge we have acquired, we can make wise decisions and judgments. In the DIKWP-TRIZ model, wisdom involves comprehensive evaluation of the problem and innovative thinking. By applying TRIZ's innovation principles and tools, we can generate new solutions and innovative ideas.

Purpose: Purpose is the driving force of the DIKWP-TRIZ model, representing the goals and purposes of the problem modeling and problem-solving process. In the DIKWP-TRIZ model, we guide the interaction and transformation between data, information, knowledge, and wisdom by clarifying the purpose and goals of the problem. This is done to achieve innovation and the purpose of problem-solving.

The problem-solving process of the DIKWP-TRIZ model is as follows:

Problem Modeling: By collecting and analyzing data related to the problem, the problem is transformed into meaningful information. This includes problem analysis, trend discovery, pattern recognition, and understanding the root causes and influencing factors of the problem.

Knowledge Acquisition: Transforming information into knowledge, including mastery of existing solutions and knowledge from related domains. By learning and studying knowledge from relevant domains, we can expand our thinking and problem-solving capabilities.

Innovative Thinking: Utilizing TRIZ's innovation principles and tools, we apply the knowledge we have acquired for innovative thinking. By introducing innovative principles and methods, we can surpass traditional thinking patterns and find better solutions.

Solution Generation: Based on innovative thinking and TRIZ tools, new solutions are generated. These solutions can be completely new and creative, or improvements and optimizations to existing solutions.

Solution Evaluation and Optimization: Evaluate and optimize the generated solutions to ensure their feasibility and effectiveness. This includes considering various factors such as technical feasibility, economic viability, and feasibility.

Through the integration of the DIKWP model and TRIZ theory, the DIKWP-TRIZ model forms a comprehensive problem-solving framework. The DIKWP model helps us understand and transform the information and knowledge of the problem, while TRIZ theory provides innovative thinking and methods to help us generate innovative solutions.DIKWP-TRIZ模型在用户画像和The applications in interaction modeling are as follows:

User Profiling: By collecting and analyzing user data, it is transformed into meaningful user information. This includes data on user behavior, preferences, needs, and goals. Through the analysis and organization of user data, we can create user profiles and gain in-depth understanding of user characteristics and needs.

User purpose Recognition: By analyzing user behavior and needs, the purpose and goals of users are identified. This includes understanding the purpose and expectations of users, as well as predicting possible user behaviors and needs.

Interaction Design: Based on user profiling and purpose recognition, the interaction process and interface between the user and the system are designed. Through well-designed interactions, we can provide personalized and user-friendly experiences that meet user needs and expectations.

Interaction Optimization: By collecting user feedback and behavioral data, the interaction process is evaluated and optimized. This includes analyzing user feedback, tracking user behavior, and evaluating user satisfaction to improve and optimize the interaction process.

Through the application of the DIKWP-TRIZ model, we can better understand user needs and purposes, design personalized and user-friendly interaction experiences, and solve problems through innovative approaches.

Problem modeling is one of the key steps in the problem-solving process of the DIKWP-TRIZ model. By collecting and analyzing the DIKWP (Data-Information-Knowledge-Wisdom-Purpose) relevant to the problem, we can transform the problem into meaningful DIKWP content and gain a deep understanding of the essence and influencing factors of the problem. The following provides a detailed description of each stage in the problem modeling process of the DIKWP-TRIZ model:

Collection and analysis of Data: Firstly, we need to collect data relevant to the problem. Data can be obtained from multiple sources, such as user feedback, market research, experimental data, etc. These data contain facts and background information about the problem, which can help us gain a comprehensive understanding of the problem's characteristics and influencing factors. Through data analysis, we can discover patterns, trends, and correlations in the data, thereby gaining a deeper understanding of the problem's essence.

Extraction and organization of Information: In the problem modeling process, we transform data into meaningful information. By interpreting and understanding the data, we extract key information about the problem and organize it in a structured form. This information includes problem descriptions, existing solutions, constraints, user requirements, etc. By organizing the information, we can establish a complete cognitive understanding of the problem, providing a foundation for subsequent analysis and synthesis.

Construction and analysis of Knowledge: Knowledge is acquired through further processing and analysis of information. In the problem modeling process, we transform information into knowledge by conducting in-depth research and learning about relevant domains, understanding the background and related concepts of the problem. The construction of knowledge includes understanding the fundamental causes and influencing factors of the problem, evaluating and analyzing existing solutions, as well as mastering leading practices and theories in the problem domain.

Application and integration of Wisdom: In the problem modeling process, we apply existing knowledge and experience to reach the level of wisdom. By employing the innovation principles and tools of TRIZ, we can go beyond conventional thinking patterns, discover the root causes of the problem, and propose innovative solutions. The application of wisdom involves comprehensive evaluation and judgment of the problem, as well as the selection and optimization of potential solutions.

Clarification and driving of Purpose: Purpose is the driving force of problem modeling in the DIKWP-TRIZ model, representing the goals and purposes of problem modeling. In the problem modeling process, we clarify the purpose and goals of the problem, guiding the interaction and transformation among data, information, knowledge, and wisdom. By clarifying the purpose of the problem, we can more effectively collect and analyze DIKWP in a targeted manner, and drive the direction and progress of problem-solving.

Through the problem modeling process of the DIKWP-TRIZ model, we can gain a comprehensive understanding of the essence and influencing factors of the problem. By collecting and analyzing DIKWP, we can identify patterns and trends, understand the root causes of the problem, and extract valuable knowledge. This provides a solid foundation for subsequent problem-solving and innovation.

When applying the DIKWP-TRIZ model for problem modeling, here is a specific example:

Let's assume we are facing the following problem: How to improve the existing transportation system to reduce traffic congestion and environmental pollution?

Collection and analysis of Data: Collect data related to the transportation system, including traffic flow, vehicle speed, emissions, etc. By analyzing this data, we can understand the extent of traffic congestion and environmental pollution, as well as their correlations.

Extraction and organization of Information: Extract key information, such as the causes of traffic congestion (narrow roads, congested intersections, etc.) and sources of environmental pollution (vehicle emissions, idling queues, etc.). Organize this information to form a problem description and relevant constraints.

Construction and analysis of Knowledge: Study knowledge in related domains, such as urban transportation planning, traffic engineering, environmental policies, etc. Understand existing solutions, such as public transportation systems, intelligent traffic management systems, promotion of green commuting, etc. Analyze this knowledge to evaluate their applicability and effectiveness.

Application and integration of Wisdom: Apply TRIZ innovation principles, such as reverse thinking, resource utilization, separation principles, etc. Through innovative thinking, propose novel solutions to address traffic congestion and environmental pollution. For example, promote shared mobility models, optimize traffic signal control systems, introduce electric vehicles, etc.

Clarification and driving of Purpose: Clarify the purpose and goals of the problem, such as improving traffic flow, reducing emissions, enhancing residents' quality of life, etc. These purposes guide the interaction and transformation among data, information, knowledge, and wisdom, ensuring that the solutions align with the objectives of the problem.

Through the aforementioned problem modeling process, we collected and analyzed the DIKWP related to traffic congestion and environmental pollution. This enables us to gain an in-depth understanding of the characteristics and influencing factors of the problem and transform them into meaningful problem descriptions and constraints. By further applying the innovation principles and wisdom of TRIZ, we can propose innovative solutions to improve the transportation system and achieve the goal of reducing traffic congestion and environmental pollution.

In the problem-solving process of the DIKWP-TRIZ model, knowledge acquisition is a crucial step. It involves transforming DIKWP content into knowledge and expanding thinking and problem-solving abilities through learning and studying knowledge in relevant domains. Here is a detailed elaboration:

Collection and organization of Data: First, collect data relevant to the problem. For example, for the problem of improving the transportation system, we can collect data on traffic flow, accident statistics, road design and planning, etc. These data provide the foundational information for the problem.

Then, organize and clean the collected data. This includes categorizing the data, removing erroneous or redundant data, and formatting and standardizing the data. By organizing the data, we can better understand and analyze the background and current state of the problem.

Extraction and understanding of Information: Based on the collected data, we extract meaningful information and analyze it. This involves interpreting and understanding the data, discovering patterns, trends, and correlations.

Through analyzing the information, we can understand the problems, bottlenecks, and challenges in the transportation system, as well as possible directions for solutions. This information provides guidance and a basis for further knowledge acquisition.

Learning and studying of Knowledge: In the knowledge acquisition stage, we need to learn and study knowledge in relevant domains. This includes existing solutions, best practices, and relevant theories.

Through literature research, expert interviews, academic papers, etc., we can acquire advanced knowledge about improving the transportation system. For example, we can learn about domain knowledge in areas such as transportation planning, traffic engineering, intelligent transportation systems, and sustainable transportation.

Additionally, we can also learn about domains related to the problem, such as urban planning, environmental science, socioeconomics, etc. This helps us to have a more comprehensive understanding of the problem and think about solutions from multiple perspectives.

Integration and application of Wisdom: Based on the acquired knowledge, we need to integrate various knowledge and apply them to the problem-solving process. This involves the integration and cross-application of knowledge from different domains to generate innovative and effective solutions.

We can compare different solutions and approaches and analyze their advantages and disadvantages. Through reflection and evaluation of various options, we can select the most suitable solution or propose novel innovative solutions.

Guidance and driving of Purpose: The knowledge acquisition process needs to be guided by the purpose and goals of the problem. We need to clarify the purpose and objectives of the problem, such as improving traffic flow, reducing environmental pollution, etc.

These purposes and goals guide our knowledge acquisition process, enabling us to select and apply knowledge relevant to the problem and transform it into solutions.

Through the aforementioned knowledge acquisition process, we can transform the DIKWP content into substantial knowledge and expand our thinking and problem-solving abilities. By learning and studying knowledge in relevant domains, we can understand existing solutions and best practices, providing a basis and inspiration for problem-solving. The knowledge acquisition process needs to be driven by the purpose of the problem to ensure that the knowledge we acquire aligns with the objectives of the problem.

When it comes to the process of knowledge acquisition, here is an example that demonstrates how to transform DIKWP content into knowledge and expand problem-solving capabilities:

Problem Scenario: Let's assume we are facing the issue of urban traffic congestion and aim to improve traffic flow and reduce congestion by optimizing the traffic signal system.

Collection and Organization of Data: Collect data on traffic flow, intersection traffic signal states, road congestion, etc. These data provide information on the operation and current state of the traffic system.

Organize and clean the collected data, categorizing and standardizing it based on parameters such as time, location, and traffic volume for subsequent analysis and understanding.

Extraction and Understanding of Information: By analyzing the data, we can extract meaningful information. For example, analyzing traffic flow data and road congestion data can help identify congested areas and peak hours.

Analyzing the traffic signal state data enables us to understand the timing schemes of the signals and their relationship with traffic flow.

Learning and Studying Knowledge: Learn about domain knowledge related to traffic signal optimization. Study existing solutions and best practices, such as flow-based signal optimization algorithms and principles for coordinating signal systems.

Further study knowledge in relevant domains, such as traffic flow theory, traffic planning, and traffic engineering. Understand theories and methods for improving traffic flow and congestion mitigation.

Integration and Application of Wisdom: Integrate the acquired knowledge and apply it to the problem-solving process. Compare different signal optimization approaches, such as flow-based algorithms and coordinated signal system designs.

Analyze the advantages and disadvantages of various approaches, considering factors such as traffic volume, congestion, pedestrian needs, and environmental impact. Select the most suitable solution based on the purpose and objectives of the problem.

Guidance and Driving of Purpose: Clarify the purpose and objectives of the problem, such as improving traffic flow and reducing congestion. These purposes guide the process of knowledge acquisition, ensuring the selection and application of knowledge relevant to the problem.

Through the example above, we can observe how the process of knowledge acquisition transforms DIKWP content into substantial knowledge and expands problem-solving capabilities. By collecting data, extracting information, learning and studying knowledge, we can acquire domain knowledge related to traffic optimization and apply it to the problem-solving process. The purpose and objectives of the problem guide the selection and application of relevant knowledge, ensuring our solutions align with the problem's goals.

In the DIKWP-TRIZ model, innovative thinking is a crucial step in the problem-solving process. The following is a detailed example illustrating how to employ DIKWP and TRIZ principles and tools for innovative thinking:

Analysis and Collection of Data: Gather data relevant to the problem, such as market demands, competition, and technological trends. These data provide fundamental information about the problem's context and current state.

Analyze the data to identify patterns, trends, and correlations. For instance, analyze market demand data to discover user requirements for specific features or characteristics.

Extraction and Organization of Information: Extract meaningful information through interpretation and understanding of the data. For example, extract the user demand for energy-efficient technologies from the market demand data.

Organize and summarize the information into problem statements suitable for innovative thinking. For example, a problem statement could be: "How to develop a product with highly efficient energy-saving features to meet user demands for energy efficiency?"

Application and Expansion of Knowledge: Apply existing knowledge and domain expertise to understand relevant technologies, markets, and user demands. For example, study the latest advancements in energy-saving technologies, materials science, and product design.

Explore TRIZ (Theory of Inventive Problem Solving) principles and tools such as the contradiction matrix, innovation principles, and trend prediction. These tools provide methods and inspiration for solving technical problems and fostering innovation.

Application and Judgment of Wisdom: Apply acquired knowledge and TRIZ innovation principles for innovative thinking. Based on the problem statement and objectives, combine knowledge and principles to generate multiple potential solutions.

Evaluate and compare different solutions, considering their feasibility, effectiveness, and sustainability. Through the integration of knowledge and innovation principles, select the most innovative and valuable solution.

Guidance and Driving of Purpose: Clarify the purpose and objectives of problem-solving, such as improving product energy efficiency. These purposes guide the process of innovative thinking, ensuring that our innovative solutions align with the problem's objectives.

Maintain interaction and communication with relevant stakeholders (e.g., users, partners) throughout the process of innovative thinking. This helps understand their needs and expectations, further guiding the direction of innovative thinking.

Through the provided example, we can see how innovative thinking leverages DIKWP and TRIZ principles and tools to transcend traditional thinking patterns and seek better solutions. By collecting and analyzing data, extracting meaningful information, applying existing knowledge, and utilizing TRIZ innovation principles, innovative thinking is facilitated. The purpose and objectives of the problem drive the process of innovative thinking, ensuring alignment between the innovative solutions and the problem's objectives. Meanwhile, interaction and communication with stakeholders contribute to obtaining a broader perspective and insights.

When applying the DIKWP-TRIZ model to innovative thinking, the following detailed example highlights the process of content transformation within the DIKWP framework:

Analysis and Collection of Data: Let's assume our problem is to improve the charging efficiency of electric vehicles. We gather data related to the charging process of electric vehicles, including charging time, charging speed, battery capacity, etc. Additionally, we analyze consumer feedback and market demand data to understand the level of user concern regarding charging efficiency.

Extraction and Organization of Information: Through analysis of the collected data, we extract meaningful information. For example, we discover that long charging times are a common concern for users, and we also identify the impact of fast charging on battery lifespan as an important aspect.

We organize this information into a problem statement: How can we improve the charging efficiency of electric vehicles, reduce charging time, and simultaneously consider battery lifespan?

Application and Expansion of Knowledge: We apply existing knowledge to understand the latest advancements in electric vehicle charging technology and relevant research in the field. We study aspects such as battery technology, current transmission, charging equipment, etc.

When applying TRIZ innovation principles, we learn that principles such as analogy and resource utilization may be relevant to the issue of charging efficiency. We further expand our knowledge to explore solutions and innovative thinking in similar domains, such as renewable energy utilization and improving transmission efficiency.

Application and Judgment of Wisdom: Based on the acquired knowledge and TRIZ innovation principles, we begin the process of innovative thinking. We generate multiple potential solutions, considering:

We evaluate and compare each solution, taking into account their feasibility, effectiveness, and sustainability. We consider all factors comprehensively and select the most innovative and promising solution.

l Utilize high-power charging equipment to increase charging speed, while considering the impact on battery lifespan.

l Implement a distributed charging network to deploy charging equipment in more convenient locations, reducing charging time.

l Apply battery management techniques to optimize current transmission during the charging process and enhance charging efficiency.

Driven and guided by Purpose: We specify the purpose and goal of the problem solution, which is to improve EV charging efficiency and reduce charging time while considering battery life. These purposes guide our innovation thinking and ensure that our innovation solutions are aligned with the goals of the problem.

During our innovation thinking, we interact and communicate with our stakeholders. We collaborate and provide feedback to electric vehicle manufacturers, charging equipment suppliers and users to understand their needs and expectations. This helps to gain broader perspectives and insights that further guide the direction of innovation thinking.

With the above example, we can see the application of the DIKWP-TRIZ model in the innovation thinking process. We collect and analyse data to extract meaningful information and translate it into a concrete problem statement. Using prior knowledge and TRIZ principles of innovation, innovative thinking is conducted and multiple solutions are evaluated and compared. The purpose and goals of the problem drive the innovation thinking process, ensuring that the innovation solution is aligned with the goals of the problem. Interaction and communication with stakeholders helps to gain broader perspectives and insights that guide the direction of innovation thinking. This process highlights the importance of DIKWP content translation, transforming data, information and knowledge into concrete innovation thinking and solutions.

Solution generation is a key step in the problem solving process of the DIKWP-TRIZ model. The following detailed examples highlight the process of modelling, analysing and synthesising DIKWP content, as well as solution generation:

Data (Data) collection and analysis: Suppose our problem is to improve urban traffic congestion. We collected data related to urban traffic, including traffic flow, road conditions, and traffic accident statistics. We also analysed public feedback and demand data to understand their main concerns and pain points about traffic congestion.

Information extraction and collation: By analysing the collected data, we extracted meaningful information. For example, we found that excessive traffic flow during peak hours was the main cause of congestion, while road bottlenecks and parking problems were also found to be important factors affecting traffic flow.

We collated this information into a problem statement: How can we reduce urban traffic congestion, improve traffic flow, and solve road bottlenecks and parking problems?

Application and Extension of Knowledge: We used our existing knowledge to learn about the latest developments and relevant research findings in the fields of urban transport planning, traffic management and traffic engineering. We learnt about traffic flow optimisation, traffic signal control and intelligent transport systems.

In applying the tools of TRIZ, we learnt that the principles of 'backward thinking' and 'resource utilisation' may be relevant to the problem of traffic congestion. We further extend our knowledge to solutions and innovative thinking in similar areas, such as traffic diversion strategies and intelligent parking management.

Innovative Thinking and Solution Generation: Based on the knowledge gained and the tools of TRIZ, we started innovative thinking and solution generation. Below are some possible solutions:

We evaluate and compare each solution, considering its feasibility, benefits and sustainability. We consider all factors and select the most innovative and promising solution.

l Introduce intelligent transport systems to adjust traffic flow and reduce congestion through real-time traffic monitoring and optimised signal control.

l Develop transport sharing modes to encourage the public to use public transport, car sharing and bicycles to reduce the number of private cars and reduce traffic congestion.

l Adopt intelligent parking systems to optimise the parking process through real-time navigation and parking space indication to reduce the time spent looking for parking spaces and congestion.

Driven and guided by Purpose: We are clear about the purpose and goals of the problem solution, which are to reduce urban traffic congestion, improve traffic flow, and address road bottlenecks and parking issues. These purposes guide our creative thinking and solution generation process, ensuring that our solutions are aligned with the goals of the problem.

During the solution generation process, we interact and communicate with city planners, transport authorities and the public. We work with relevant stakeholders to understand their needs and expectations and get feedback. This helps to gain broader perspectives and insights that further guide the solution generation.

With the above example, we can see the application of the DIKWP-TRIZ model in the solution generation process. By collecting and analysing data, meaningful information is extracted and transformed into a concrete problem statement. Innovative thinking and solution generation is carried out using existing knowledge and TRIZ tools. The purpose and purpose of the problem drives the solution generation process, ensuring that the solution is aligned with the goals of the problem. Interaction and communication with stakeholders helps to gain broader perspectives and insights that guide solution generation. This process highlights the importance of DIKWP content transformation, turning data, information, and knowledge into concrete innovative thinking and solutions.

When applying the DIKWP-TRIZ model for solution generation, the following is a detailed example that highlights the process of transforming, compensating and calibrating DIKWP content:

Data collection and analysis: Suppose our problem is to improve mobile phone battery life. We started by collecting data on mobile phone battery usage, including charging time, usage time, battery capacity, etc. We also analysed user feedback and requirements. We also analysed user feedback and demand data to understand their main concerns and pain points about mobile phone battery life.

Information extraction and organisation: By analysing the collected data, we extracted meaningful information. For example, we found that reduced battery capacity and excessive power consumption were the main factors affecting mobile phone battery life.

We collated this information into a problem statement: How can we extend the life of mobile phone batteries and reduce battery capacity loss and power consumption?

Application and Extension of Knowledge: We used our existing knowledge to learn about the latest advances and related research results in the areas of battery technology, power optimisation and battery management. We learnt about battery optimisation algorithms, low power mode design and intelligent battery management systems.

In applying the tools of TRIZ, we learnt that the principles of 'resource utilisation' and 'reduction of system complexity' may be relevant to the issue of battery life. We further extend our knowledge to solutions and innovative thinking in similar areas, such as energy saving models, intelligent power management, etc.

Innovative thinking and solution generation: Based on the knowledge gained and the tools of TRIZ, we started to think innovatively and generate solutions. Below are some possible solutions:

In the process of solution generation, we performed the transformation and compensation of DIKWP content. We transformed the collected data into meaningful information and identified key influencing factors. In applying the tools of knowledge and TRIZ, we transformed them into concrete innovative thinking and solutions.

l Introduces an intelligent battery management system that reduces power consumption and extends battery life by monitoring battery usage by applications and optimising battery charging and discharging strategies.

l Optimises system settings, including adjusting screen brightness, closing applications running in the background and limiting network connections to reduce system power consumption.

l Adopt new battery materials and technologies to increase battery capacity and cycle life and reduce battery capacity loss.

Purpose Driven and Validated: We specify the purpose and goal of the problem solution, i.e., to extend the battery life of a mobile phone and to reduce battery capacity loss and power consumption. These purposes guided our creative thinking and solution generation process to ensure that the solution was aligned with the goals of the problem.

During the solution generation process, we performed DIKWP content calibration. We assess the feasibility, effectiveness and sustainability of each solution. We consider all factors and select the most innovative and promising solution.

With the above example, we see the application of the DIKWP-TRIZ model in the solution generation process. By collecting and analysing data, meaningful information is extracted and transformed into a concrete problem statement. Innovative thinking and solution generation is carried out using existing knowledge and TRIZ tools. The purpose and purpose of the problem drive the process of solution generation, ensuring that the solution is aligned with the goals of the problem. We also emphasise the importance of transformation and compensation of DIKWP content, with calibration in transforming data into information and knowledge into innovative thinking and solutions.

When applying the DIKWP-TRIZ model to the problem of semantic communication on the Web, we can consider the following case context:

Suppose our problem is to improve the semantic understanding and interaction capabilities of an intelligent voice assistant. Intelligent voice assistants play an increasingly important role in daily life, but still face the challenge of understanding user's purpose and providing accurate responses.

Data collection and analysis: We first collect usage data of intelligent voice assistants, including users' voice commands, dialogue history, and feedback. We also analysed data from user experience surveys and user requirements surveys to understand the main needs and pain points of users in terms of semantic understanding and interaction.

Information extraction and organisation: By analysing the collected data, we extracted meaningful information. For example, we found that intelligent voice assistants have difficulties in understanding complex commands and context switching, resulting in a degraded user experience.

We collated this information into a problem statement: How can we improve the semantic understanding and interaction capabilities of intelligent voice assistants to enhance user experience and accuracy?

Application and Extension of Knowledge: We used our existing knowledge to learn about the latest advances and research results in related fields such as natural language processing, semantic analysis and dialogue systems. We learnt about speech recognition techniques, semantic parsing algorithms and contextual understanding methods.

In applying the tools of TRIZ, we learnt that the principles of "introducing mediation" and "reducing complexity" may be relevant to semantic understanding and interaction problems. We further extend our knowledge to solutions and innovative thinking in similar areas such as semantic relationship modelling, semantic networks and dialogue management.

Innovative Thinking and Solution Generation: Based on the knowledge gained and the tools of TRIZ, we started innovative thinking and solution generation. Here are some possible solutions:

l Introducing semantic relationship modelling and knowledge graph technology to improve the accuracy of semantic understanding and context-awareness.

l Designs intelligent dialogue management systems to provide a more coherent and accurate interaction experience by handling complex dialogue scenarios and context switching.

l Combine multimodal inputs and outputs such as speech, images and text to provide richer and more accurate semantic understanding and responses.

In the process of generating solutions, we performed DIKWP content transformation and compensation. We transformed the collected data into meaningful information, identifying key influencing factors. When applying knowledge and TRIZ tools, we translate them into concrete innovative thinking and solutions.

purpose (Purpose) driven and calibrated: We specify the purpose and goal of the problem solution, i.e., to improve the semantic understanding and interaction capabilities of intelligent voice assistants, and to improve the user experience and accuracy. These purposes guide our creative thinking and solution generation process to ensure that the solution is aligned with the goals of the problem.

With this case study, we demonstrate the application of the DIKWP-TRIZ model to the problem of semantic communication on the Web. By collecting and analysing data, meaningful information is extracted and transformed into a concrete problem statement. Pre-existing knowledge and TRIZ tools are applied for creative thinking and solution generation. The purpose and purpose of the problem drive the process of solution generation, ensuring that the solution is aligned with the goals of the problem. We emphasise the importance of transformation and compensation of DIKWP content, and calibration is performed in transforming data into information and knowledge into innovative thinking and solutions.

Solution assessment and optimisation is a key step in the problem solving process of the DIKWP-TRIZ model that ensures that the generated solution is feasible, effective and sustainable in all aspects. The process of solution evaluation and optimisation is unfolded in detail below:

DIKWP Modelling and Representation: Firstly, we perform DIKWP modelling and representation of the generated solution into meaningful data, information, knowledge and wisdom. This includes collating and summarising the technical parameters, performance indicators, economic costs and other information in the solution for better analysis and evaluation.

Solution Analysis and Synthesis: Next, we analyse and synthesise the solution in detail. This involves evaluating the various elements and components of the solution, including considerations of technical feasibility, economics, and viability. We analyse the strengths, limitations and potential risks of the solution and assess the extent to which it addresses the problem.

Factor Comparisons and Tradeoffs: In the solution evaluation process, we perform factor comparisons and tradeoffs. This includes comparing the strengths, weaknesses and applicability of different solutions, and considering the importance and impact of each factor. We consider technical feasibility, economic costs, resource inputs, time requirements, and other factors and weigh their relative importance.

Interaction and Feedback: We engage in interaction and feedback during the solution evaluation process. This includes communicating and discussing with relevant stakeholders, professionals and users to gather their opinions and suggestions. By interacting with them, we gain additional insights and feedback to optimise the solution and meet the needs of all parties.

Optimisation of solutions: Based on evaluations and feedback, we optimise our solutions. This may involve adjusting technical parameters, improving performance indicators, optimising resource utilisation, reducing costs and other improvements. We may also consider introducing new innovative principles and tools to further enhance the effectiveness and efficiency of the solution.

Compensation and calibration of the solution: In the process of optimising the solution, we perform compensation and calibration of the DIKWP content. We ensure that the solution does not lose key information and functionality when transforming data into information, knowledge into innovative thinking and solutions. At the same time, we calibrate the feasibility, effectiveness and sustainability of the solution to ensure that it can successfully address the problem and fulfil the need.

Through this process, we are able to fully evaluate and optimise the generated solutions. We transform the solution into meaningful content through DIKWP modelling and representation for detailed analysis and synthesis. We perform factor comparisons and trade-offs, synthesising the importance and level of influence of each factor. We interact and feedback with relevant stakeholders, professionals and users to gain additional insights and feedback. Finally, we optimise and compensate the solution and calibrate its feasibility and effectiveness. This process ensures that our solutions are viable, effective, and able to successfully solve problems and fulfil needs.

Suppose we are solving a problem of battery technology improvement, the specific case is as follows:

DIKWP Modelling and Representation: We collect and analyse data (Data) related to battery technology improvement, including information about the capacity, charging rate, cycle life, etc. of the battery. By interpreting and understanding this data, we transform it into meaningful Information, such as battery performance characteristics, constraints, and so on. By further organising and processing this information, we turn it into Knowledge, e.g. knowledge of battery materials, structural design, etc.

Solution Analysis and Synthesis: We analyse and synthesise existing battery technologies, considering their strengths, limitations and potential room for improvement. We compare different types of battery technologies, such as lithium-ion batteries, sodium-ion batteries, etc., and weigh their performance in terms of capacity, charging speed, and cycle life. Through analysis and synthesis, we identify key elements for improvement, such as increasing battery capacity and reducing charging time.

Innovative Thinking and Application of TRIZ: We use the innovative principles and tools of TRIZ to think innovatively. For example, we can apply the principle of "backwards thinking" and consider using a different combination of materials than traditional batteries, such as sodium oxides, sulphides, etc., to increase battery capacity. We can also apply the "separation principle" to improve the structure of the battery by separating the anode and cathode to increase the charging speed of the battery.

Generation of solutions: Based on innovative thinking and the application of TRIZ, we generated new solutions. For example, we proposed a new structural design of sodium-ion battery using sodium oxide and sulphide as electrode materials to increase the battery capacity. We also designed a new charging system that utilises fast charging technology to reduce charging time. These solutions are the result of knowledge and innovative thinking based on DIKWP's transformation.

Solution evaluation and optimisation: We evaluate and optimise the generated solutions to ensure their feasibility and effectiveness. We consider technical feasibility, such as material availability and processing difficulty; economics, such as production costs and market competitiveness; and viability, such as safety and environmental friendliness. By weighing and comparing these factors, we optimise the solution, adjusting parameters in terms of battery materials, structural design, etc.

Compensation and calibration of the solution: In the process of optimising the solution, we perform compensation and calibration of the DIKWP content. We ensure that new battery technologies do not lose key information and functionality when transforming data into information, knowledge into innovative ideas and solutions. At the same time, we verify the feasibility, effectiveness and sustainability of the solutions, e.g. by experimentally verifying improvements in battery capacity and charging speed.

Through the detailed development of the problem solving process of the DIKWP-TRIZ model for the battery technology improvement case, we can see that in the solution evaluation and optimisation phase, we evaluate the solution comprehensively, taking into account the trade-offs and comparisons of various factors. We transformed the solution into meaningful content through DIKWP modelling and representation, and used the tools of innovative thinking and TRIZ to innovate and optimise. In solution compensation and calibration, we ensure that the solution does not lose key information and functionality when transforming DIKWP content and calibrate it for feasibility and validity. This process ensures that our solutions are feasible, effective and able to successfully address battery technology improvements.

The DIKWP model can be mapped, modelled and analysed with classical TRIZ to help understand and apply TRIZ principles and methods. Below is an example of mapping, modelling and analysis of the DIKWP model with classical TRIZ:

Data: In the DIKWP model, data represents raw, unprocessed facts and figures. In TRIZ, data can be information about the description of the problem, properties and limitations of the system, etc. These data are used in problem modelling and analysis to better understand the nature of the problem.

Information: In the DIKWP model, information is transformed through the interpretation and understanding of data, which provides meaning and insight about the world. In TRIZ, information can be derived from analysing problem data and trend spotting to understand the root causes and influences of a problem.

Knowledge: In the DIKWP model, knowledge is the further organisation and processing of information into a comprehensive understanding of things and phenomena. In TRIZ, knowledge is theories and concepts about TRIZ principles and tools, as well as methods and techniques for solving problems. By transforming information into knowledge, the principles and methods of TRIZ can be better applied to solve problems.

Wisdom: In the DIKWP model, wisdom is the ability to make informed decisions and judgements based on acquired knowledge. In TRIZ, wisdom is the ability to understand a problem, think creatively, and apply TRIZ principles and methods to generate new solutions. By applying TRIZ wisdom, more optimal and innovative solutions can be provided.

Purpose: In the DIKWP model, purpose is the goal or purpose that drives the interaction and transformation between data, information, knowledge and wisdom. In TRIZ, purpose is the goal and purpose of solving problems and achieving innovation.The goal of TRIZ is to improve and optimise systems by applying principles and methods to solve technical conflicts.

By mapping, modelling and analysing classical TRIZ through the DIKWP model, we can understand and apply the principles and methods of TRIZ in a clearer way.The DIKWP model helps us to translate the theory and methodology of TRIZ into meaningful content, from the level of data and information to that of knowledge and wisdom. This helps to better understand and apply TRIZ, solve problems and achieve innovation.

DIKWP Model	Classical TRIZ
Data	Data on the description of the problem, characteristics and limitations of the system, etc.
Information	Gain meaning and insight into issues through interpretation and understanding of data
Knowledge	Theories and concepts about TRIZ principles and tools, as well as problem-solving methods and techniques
Wisdom	Ability to make informed decisions and judgements based on acquired knowledge
Purpose	Solving problems and realising the purpose and purpose of the innovation

The correspondence between the DIKWP model and classical TRIZ can be understood more clearly through the comparative presentation of the above table.

In the DIKWP model, data corresponds to information such as the description of the problem, the characteristics and limitations of the system, which also needs to be collected and analysed to understand the nature of the problem in classical TRIZ.

Information is obtained in the DIKWP model through the interpretation and understanding of the data, which provides meaning and insight into the problem. Similarly, in classical TRIZ, information about the problem is obtained by analysing the problem data and trend spotting.

Knowledge in the DIKWP model is the further organisation and processing of information into a comprehensive understanding of things and phenomena. In classical TRIZ, knowledge is theories and concepts about TRIZ principles and tools, as well as methods and techniques for solving problems.

Wisdom in the DIKWP model is the ability to make wise decisions and judgements by virtue of the knowledge gained. In classical TRIZ, wisdom is reflected in deep understanding and creative thinking about problems and the application of TRIZ principles and methods to generate new solutions.

purpose in the DIKWP model is the purpose or purpose that drives the interaction and transformation between data, information, knowledge and wisdom. In classical TRIZ, purpose is the goal and purpose of solving problems and achieving innovation.The goal of TRIZ is to achieve system improvement and optimisation by applying principles and methods to solve technical conflicts.

Through the comparative display, the correspondence between DIKWP model and classical TRIZ can be seen more clearly, which helps us to understand and apply the principles and methods of TRIZ to solve problems and achieve innovation.

The following is a detailed comparative analysis between DIKWP-TRIZ and classical TRIZ:

Model Structure:

Classical TRIZ: Classical TRIZ is based on a system of innovation principles and tools, including 39 innovation principles, a technical contradiction resolution model, and a contradiction matrix. It provides a structured set of methods and tools for solving technical problems and promoting innovation.

DIKWP-TRIZ: DIKWP-TRIZ is a TRIZ model that extends the DIKWP model. It introduces Purpose from DIKWP as a core driver, combining data, information, knowledge, wisdom, and purpose to more comprehensively solve problems and promote innovation.

Problem Modelling:

Classical TRIZ: Classical TRIZ transforms problems into technical contradictions and contradiction parameters through problem analysis and contradiction identification, and uses innovation principles and tools to resolve the contradictions.

DIKWP-TRIZ: DIKWP-TRIZ takes more factors into account in problem modelling, including data, information, knowledge and wisdom. Through the transformation process of DIKWP modelling, problems are transformed into meaningful DIKWP content and combined with the innovative thinking and tools of TRIZ to solve problems.

Knowledge Acquisition:

Classical TRIZ: Classical TRIZ focuses on the learning and mastery of TRIZ principles and tools to acquire knowledge for problem solving and innovation.

DIKWP-TRIZ: DIKWP-TRIZ not only includes the knowledge acquisition of TRIZ, but also considers the mastery of related domain knowledge. Through the DIKWP model, DIKWP content is transformed into knowledge and combined with TRIZ methods and tools for innovative thinking and problem solving.

Innovative Thinking:

Classical TRIZ: Classical TRIZ engages in systematic innovative thinking through the application of innovation principles and tools. It emphasises going beyond traditional modes of thinking to find new solutions.

DIKWP-TRIZ: DIKWP-TRIZ places innovative thinking in the wisdom hierarchy of the DIKWP model. Through the analysis and synthesis of DIKWP content, combined with the innovative principles and tools of TRIZ, creative thinking and problem solving are carried out.

Solution Generation:

Classical TRIZ: Classical TRIZ generates new solutions by applying innovative principles and tools. These solutions can be new and creative, or they can be improvements and optimisations of existing solutions.

DIKWP-TRIZ: DIKWP-TRIZ integrates DIKWP content in solution generation. By analysing and synthesising the DIKWP content, combined with the innovative thinking and tools of TRIZ, new solutions are generated or existing solutions are improved and optimised.

Solution evaluation and optimisation:

Classical TRIZ: Classical TRIZ emphasises on solution evaluation and optimisation to ensure its feasibility and effectiveness. This includes consideration of various factors such as technical feasibility, economics, viability, etc.

DIKWP-TRIZ: DIKWP-TRIZ considers more factors in solution evaluation and optimisation such as user needs, social impacts, etc. Through the synthesis and comparison of DIKWP models, combined with the tools and methods of TRIZ, the comprehensive evaluation and optimisation of solutions is carried out.

Through the application of the DIKWP-TRIZ model, we are able to achieve more comprehensive and effective problem solving and innovation results by integrating the various levels of DIKWP and combining TRIZ innovation principles and tools in the process of problem solving and promoting innovation.

The following is a detailed comparative analysis of the relative strengths and weaknesses of DIKWP-TRIZ and classical TRIZ:

Advantages of DIKWP-TRIZ:

Comprehensive framework: DIKWP-TRIZ provides a comprehensive framework that combines the DIKWP model with the TRIZ methodology to synthesise the layers of data, information, knowledge, wisdom and purpose to help solve problems and promote innovation.

Emphasis on problem context: DIKWP-TRIZ focuses on understanding and analysing the overall context of the problem, including factors such as the purpose of the problem, user needs, and social impacts. This helps to generate solutions that are more in line with actual needs and social context.

Knowledge Transformation: DIKWP-TRIZ transforms problems and related content into meaningful knowledge through the transformation process of the DIKWP model. This helps to better understand the problem, organise the information, build the knowledge base and apply it in the generation and optimisation of solutions.

Visualisation and interactivity: DIKWP-TRIZ promotes the integration of DIKWP and TRIZ tools with visualisation and interactivity. This makes the process of problem modelling, solution generation and evaluation more intuitive and easier to understand, facilitating collaboration and communication between teams.

Advantages of Classical TRIZ:

Innovation Principles and Tools: Classical TRIZ provides a rich set of innovation principles and tools to help solve technical problems and promote innovation. These principles and tools have been validated by long-term practice and have a certain degree of practicality and feasibility.

Structured approach: Classical TRIZ adopts structured approaches, such as the technical contradiction resolution model and contradiction matrix, which help to analyse and solve problems in a systematic way. This makes the problem solving process more structured and actionable.

Technical Problem Solving Focus: The main focus of classical TRIZ is to solve technical problems, especially to achieve innovation by resolving contradictions. For problems in technical fields, classical TRIZ provides a systematic approach.

Existence of abundant cases: There are already abundant cases of classical TRIZ application in different fields and industries. These cases provide experience and guidance that can be helpful in learning from and applying solutions to similar problems.

Disadvantages of DIKWP-TRIZ:

Relative Complexity: The comprehensive framework and the number of layers considered in DIKWP-TRIZ may add a certain level of complexity and learning cost. For beginners, understanding and applying the model may require some time and training.

Difficulty in knowledge acquisition and transformation: DIKWP-TRIZ requires a certain level of understanding and mastery of both the DIKWP model and the TRIZ method. Particularly for knowledge acquisition and translation, extensive domain knowledge and the ability to organise information may be required.

Disadvantages of Classical TRIZ:

Limitations: classical TRIZ focuses primarily on technical problem solving and may have limited application to problems in non-technical areas. A combination of other methods and tools may be required for solving complex multidisciplinary problems.

Innovation limitations: classical TRIZ provides a fixed set of innovation principles and tools, and more flexible thinking and approaches may be required for some specific problems. Innovative methods of applying TRIZ may be needed when facing non-traditional innovation challenges.

In summary, DIKWP-TRIZ is more integrated, focused on problem context and knowledge transformation, and has stronger visualisation and interactivity compared to classical TRIZ. However, the complexity and difficulty of knowledge acquisition in DIKWP-TRIZ may require additional learning and training. Classical TRIZ, on the other hand, has a rich set of innovation principles and tools, structured methods and a wealth of case studies, but may have limitations in non-technical areas and innovation challenges. Therefore, choosing the right model and methodology for the specific problem and need is key.

Below is a chart comparing the relative strengths and weaknesses of DIKWP-TRIZ and classical TRIZ:

	DIKWP-TRIZ	Classical TRIZ
advantages	Integrated framework, combining the DIKWP model and the TRIZ approach	Provides a wealth of innovative principles and tools
	Emphasis on the context of the issue	Adoption of a structured approach
	Knowledge transformation, turning DIKWP content into knowledge	Rich application cases exist
	Visualisation and interactivity	Focus on technical problem-solving
disadvantages	relatively complex	Limited to technical problem solving
	Difficulty in knowledge acquisition and translation	Limitations of innovation

This diagram illustrates the relative advantages and disadvantages of DIKWP-TRIZ and classical TRIZ.The advantages of DIKWP-TRIZ include its comprehensive framework, emphasis on problem context, knowledge translation and visual interactivity. However, the relative complexity and difficulty of knowledge acquisition and transformation are disadvantages of DIKWP-TRIZ. The advantages of classical TRIZ include its provision of a rich set of innovation principles and tools, the use of a structured approach, and the existence of a wealth of application examples. However, the disadvantages of classical TRIZ are its limitation to technical problem solving and innovation limitations. Choosing the appropriate methodology based on the specific problem and needs is key.

perspective	Advantages of DIKWP-TRIZ	Advantages of Classical TRIZ
Framework and methodology	Integration of the DIKWP model and the TRIZ methodology to provide a comprehensive framework	Provide innovative principles and tools with a structured problem-solving approach
contextual understanding	Focus on the context of the problem, including the purpose of the problem, user needs, social impact, etc.	Focus on solving technical problems, especially innovation through conflict resolution
Knowledge transformation	Transforming DIKWP content into meaningful knowledge	Provide a wealth of case studies to help learn from and apply solutions to similar problems
Visualisation and interactivity	Emphasis on visualisation and interactivity to make the problem solving process more intuitive and easy to understand	No particular emphasis on visualisation and interactivity, more structured approach
complexity	Relatively complex, requiring understanding and application of the DIKWP model and TRIZ methodology	Relatively simplified and easy to learn and apply
Knowledge acquisition and transformation	Emphasis on the importance of knowledge acquisition and translation, requiring extensive domain knowledge and information collation skills	Focus on technical problem solving, with limited application to non-technical areas of the problem
Limitations of innovation	More flexible, able to think outside the box and respond to non-traditional innovation challenges	Innovative principles and tools are relatively fixed, and specific problems may require more flexible thinking and approaches
Problem modelling	Emphasis on translating issues into meaningful DIKWP content, including issue analysis, trend spotting and pattern recognition	Modelling and analysing problems through the theory of contradiction and the principle of innovation
Innovative thinking	Think innovatively beyond conventional thinking in conjunction with the DIKWP model and principles of innovation	Provide innovative principles and tools to help discover new ideas for solutions
Solution Generation	Generation of new solutions, including creative solutions and improvements, using the DIKWP model and TRIZ tools	Generating new solutions using TRIZ tools and principles
Solution evaluation and optimisation	Emphasis on evaluating and optimising solutions, taking into account a variety of factors such as technical feasibility, economics, viability, etc.	Emphasis on evaluation and optimisation of solutions through conflict resolution and technical analysis
user interaction	Focus on user participation and interaction, provide personalised solutions through user profiling and needs analysis	Less emphasis on user interaction and more on technical and problem-solving approaches
Areas of application	Applicable to multiple domains, with special emphasis on areas such as semantic communication and intelligent decision-making	Mainly applied to technical problem solving, especially in engineering, manufacturing and other fields with a wide range of applications
Learning and application difficulty	Relatively high, requiring understanding and application of the DIKWP model and TRIZ methodology	Relatively low, easy to learn and apply

The above comparative analysis demonstrates the relative advantages and disadvantages of DIKWP-TRIZ and Classical TRIZ.DIKWP-TRIZ is relatively prominent in the perspectives of problem modelling, innovative thinking, and user interaction, with an emphasis on integrating the DIKWP model and the TRIZ methodology to provide comprehensive solutions. Classical TRIZ, on the other hand, has advantages in traditional problem solving in innovation principles, problem modelling and application domains, with an emphasis on a structured problem solving approach.

It is important to note that the specific assessment of advantages and disadvantages may vary depending on the actual problem and application context. Choosing the appropriate methods and tools for the actual situation is the key to problem solving.

perspective	Disadvantages of DIKWP-TRIZ	Disadvantages of classical TRIZ
Access to knowledge	Relatively high level of learning and application difficulty, requiring understanding and mastery of the DIKWP model and TRIZ methodology	Focus on technical areas and may not be sufficiently applicable to problem solving in non-technical areas
Solution Generation	May require more innovative thinking and creativity, making it difficult to find practical and innovative solutions	Possible lack of flexibility and diversity in the perspective of solution generation in specific areas
Solution evaluation and optimisation	Evaluating and optimising the solution may require additional factors to be considered, increasing complexity	There may be a lack of a holistic approach to assessment and optimisation, and some factors may not be adequately considered.
Areas of application	Suitable for a wide range of fields, but requires customised applications to suit specific field characteristics	May not have a clear advantage in some specific areas of problem solving
Learning and application difficulty	Relatively high level of learning and application difficulty, requiring understanding and application of the DIKWP model and TRIZ methodology	Relatively low learning and application difficulty, easy to understand and get started

The table above lists the disadvantages of DIKWP-TRIZ and Classical TRIZ.DIKWP-TRIZ may have some challenges in the perspective of knowledge acquisition, solution generation and evaluation and optimisation, which require a high level of learning and application difficulty. Classical TRIZ may have some limitations in the perspective of problem solving in non-technical domains, flexibility and diversity of innovative solutions, and comprehensive evaluation and optimisation methods.

It should be emphasised that although there are some disadvantages, this does not mean that DIKWP-TRIZ and classical TRIZ cannot be applied or have serious limitations in all cases. According to the specific problem and application environment, the selection of suitable methods and tools in the context of the actual situation is the key to solving the problem.

Fusion of DIKWP and classical TRIZ methods can fully utilise their advantages in the problem solving process and provide a more comprehensive and systematic approach. Here is one possible way of fusion:

Identify the problem: First, the problem is modelled and analysed using the DIKWP model. By collecting and analysing relevant data, information and knowledge, the problem is transformed into meaningful DIKWP content. This helps to understand the nature of the problem and the factors behind it.

Applying Classical TRIZ: Depending on the nature and needs of the problem, appropriate Classical TRIZ tools and principles are selected. Classical TRIZ provides a wealth of innovative principles and tools that can help to identify and resolve conflicts in a problem and provide innovative solutions.

Knowledge acquisition and transformation: the DIKWP model can be used to acquire and transform knowledge. In the fusion process, knowledge and principles from the classical TRIZ approach are transformed into the form of DIKWP. This helps to integrate classical TRIZ methods and concepts with the wider body of knowledge, facilitating a more comprehensive and in-depth understanding.

Innovative Thinking and Solution Generation: The innovative thinking and goal-driven nature of the DIKWP model is applied to facilitate the generation of innovative solutions in combination with the innovation principles of classical TRIZ. By introducing purpose and goal-driven innovative thinking, it is possible to go beyond traditional thinking patterns and discover better solutions.

Solution Evaluation and Optimisation: The generated solutions are evaluated and optimised using the DIKWP model. Factors such as technical feasibility, economics and viability are considered and assessment and optimisation methods from classical TRIZ are used to ensure the feasibility and effectiveness of the solution.

By fusing the DIKWP model with classical TRIZ methods, their advantages can be combined to provide a more comprehensive, systematic and innovative approach to problem solving. This fusion can facilitate the processes of knowledge acquisition and translation, innovative thinking, solution generation, and solution evaluation and optimisation, thereby increasing the effectiveness and efficiency of problem solving.

The fusion of the DIKWP model and the classical TRIZ method is an integrated problem-solving approach that combines the transformative processes of data, information, knowledge, wisdom and purpose of the DIKWP model with the innovative principles and tools of the classical TRIZ method. By fusing these two approaches together, more comprehensive, systematic and innovative problem solutions can be provided. The DIKWP-TRIZ fusion process is described in detail below.

Defining the problem: The first step in fusing the DIKWP model and the classical TRIZ approach is to clarify the definition and scope of the problem. Using the DIKWP model, data, information and knowledge related to the problem are collected and analysed. This helps to understand the context, causes and influences of the problem. Through the transformation process of the DIKWP model, the problem is transformed into meaningful DIKWP content, which lays the foundation for subsequent solution generation.

Applying Classical TRIZ: Depending on the nature and needs of the problem, the applicable Classical TRIZ tools and principles are selected. The classical TRIZ approach provides a wealth of innovative principles and tools for solving problems and challenges. For example, use the principle of "backwards thinking" to reverse the perspective of a problem, or the principle of "resource utilisation" to find alternative resources. The application of classical TRIZ methods helps to expand thinking and find non-traditional solutions.

Knowledge acquisition and transfer: A key perspective of the DIKWP model is knowledge acquisition and transfer. In the DIKWP-TRIZ integration process, knowledge and principles from classical TRIZ methods can be transformed into DIKWP form. This helps to integrate classical TRIZ methods and concepts with the wider body of knowledge and promotes a more comprehensive and in-depth understanding. By transforming the knowledge of classical TRIZ into the form of DIKWP, this knowledge can be better integrated and applied.

Innovative Thinking and Solution Generation: The DIKWP model emphasises innovative thinking and goal-driven characteristics. In the DIKWP-TRIZ integration process, this innovative thinking is combined with the innovation principles of classical TRIZ to help generate new solutions. By introducing purpose and goal-driven innovative thinking, it is possible to go beyond traditional thinking patterns and discover superior solutions. The innovation principles of classical TRIZ provide inspiration and guidance to help resolve conflicts in problems and facilitate the generation of creative solutions.

Solution Evaluation and Optimisation: The final step in integrating the DIKWP model and the classical TRIZ approach is to evaluate and optimise the generated solutions. This involves a combination of factors such as technical feasibility, economics, and viability, and the use of evaluation and optimisation methods from classical TRIZ. By assessing the feasibility and effectiveness of the solution, its advantages and room for improvement can be identified. Based on the assessment results, corresponding optimisation and adjustments are made to ensure successful implementation of the solution and achievement of results.

The DIKWP-TRIZ fusion provides a more comprehensive, systematic and innovative approach to problem solving by combining the transformational process of data, information, knowledge, wisdom and purpose of the DIKWP model with the innovative principles and tools of the classical TRIZ method. This fusion helps to approach problem solving from multiple perspectives and dimensions, fostering innovation and increasing the effectiveness and efficiency of problem solving. By integrating DIKWP and Classical TRIZ with each other, we can better understand problems, expand our thinking, generate innovative solutions, and ultimately achieve problem solving success.

3.6 Summary of the chapter

In this chapter of the exchange, we explored in detail the application and integration of the DIKWP-TRIZ model and its key steps and advantages in the problem solving process. The following is a summary of the exchanges in this chapter:

Application of the DIKWP model: we gained insights into the core concepts of the DIKWP model, including data, information, knowledge, wisdom, and purpose, and presented its application in problem modelling, knowledge acquisition, innovative thinking, and solution evaluation.The DIKWP model provides a comprehensive framework that helps us understand and translate the different levels and dimensions of a problem to provide more holistic and in-depth Solutions.

Characteristics of the Classical TRIZ Method: We focus on the core principles and tools of the Classical TRIZ Method and its application to innovation and problem solving. The classical TRIZ method emphasises paradoxes and conflicts in problem solving and provides a range of innovative principles and thinking tools to help us discover non-traditional solutions.

DIKWP-TRIZ fusion: we explore in detail the process of fusion of the DIKWP model and the classical TRIZ approach, emphasising their complementary and enhancing effects. By combining the DIKWP model's process of transforming data, information, knowledge, wisdom and purpose with the innovation principles and tools of classical TRIZ, we can achieve more comprehensive, systematic and innovative solutions to problems.

Comparison of Relative Advantages and Disadvantages: We have conducted a comparative analysis of the relative advantages and disadvantages of the DIKWP-TRIZ and Classical TRIZ approaches.DIKWP-TRIZ has advantages in the perspectives of problem modelling, knowledge acquisition, innovative thinking and solution evaluation, focusing on comprehensiveness and goal-drivenness, and providing a more integrated and in-depth problem solving process. The classical TRIZ approach, on the other hand, emphasises on contradiction and conflict resolution, providing a rich set of innovative principles and tools.

In summary, the integration of the DIKWP-TRIZ model provides a comprehensive and innovative approach to problem solving. By integrating the advantages of the DIKWP model and the classical TRIZ approach, we can understand and solve problems at different levels and dimensions, promote innovation and improve the effectiveness and efficiency of solutions. This fusion approach is important for advancing the practice of innovation and problem solving and provides us with a powerful tool and framework.

4. DIKWP-TRIZ methodology analysis of the forty principles of TRIZ

4.1 Mapping and analysing innovation ideas based on the DIKWP-TRIZ methodology: a reinterpretation of TRIZ's forty universal principles

Abstracts

The forty universal principles of TRIZ are fundamental tools for resolving technological conflicts and stimulating innovative ideas.The DIKWP model, as a framework for information and knowledge management, provides a new dimension of understanding and application of TRIZ. This technical presentation will remap and analyse the forty principles of TRIZ through the DIKWP-TRIZ methodology and explore how this methodology can be applied to generate innovative ideas for problem solving.

Keywords: TRIZ, DIKWP, universal principles, innovative ideas, technical report

1. Introduction

The forty universal principles of TRIZ are tools commonly used in engineering and technological innovation to solve complex problems and generate new solutions.The DIKWP model provides a comprehensive by integrating the concepts of Data, Information, Knowledge, Wisdom, and Purpose. framework for understanding and processing information. Combined with the DIKWP model, we can give deeper semantics to the traditional tools of TRIZ and enhance their application in the innovation process.

2. Overview of the Forty Universal Principles of TRIZ

The forty universal principles of TRIZ are based on the analysis of a large number of patents and involve innovative modes of thinking such as decomposition, extraction and essence. These principles guide engineers and designers to find solutions when faced with technical conflicts. A detailed overview of these forty universal principles is given below.

(1) Segmentation principle: Segmentation of an object or system into different parts for better analysis and processing. Segmentation can be physical or functional.

(2) Extraction principle: Extracting useful parts or characteristics from an object or system to improve overall performance or solve problems. Extraction can be physical or functional.

(3) Local quality principle: Different materials, processes or designs are used in different parts of an object or system to meet different requirements and improve overall performance.

(4) Unification principle: Unifying different parts or functions to simplify the system structure or reduce complexity. This can be achieved through integration, merging or sharing.

(5) Reversal Principle: Changing the direction, order or sequence of objects or systems to resolve conflicts or achieve better results. Reverse thinking can break conventional thinking patterns and find unconventional solutions.

(6) Principle of Multifunctionality: Introducing multiple functions into an object or system to provide more value and flexibility. Multifunctional design can satisfy different needs and reduce waste of resources.

(7) Oscillation Principle: Introduce oscillations or periodic changes to improve the performance of an object or system. Oscillations can be achieved by periodic movements, variations, or alternations.

(8) Feedback principle: Introduces feedback mechanisms to monitor and regulate the behaviour and performance of an object or system. Feedback can be used to control, stabilise or optimise the operation of a system.

(9) Stripping Principle: Stripping unnecessary parts or functions from an object or system to reduce complexity or improve performance. Stripping can be physical or functional.

(10) Preprocessing Principle: Introducing a preprocessing step in front of an object or system to prepare, improve or optimise the execution of subsequent steps. Preprocessing can solve problems ahead of time or reduce the complexity of subsequent processing.

(11) Spatial principle: Changing the location, layout or relative position of objects or systems in physical space to achieve better results or resolve conflicts. Optimisation of spatial layout can improve efficiency and functionality.

(12) Principle of Matter Conversion: Converting matter in an object or system into other forms to achieve a goal or improve performance. Substance conversion can involve the transformation and transfer of energy, matter, or information.

(13) Principle of Reverse Influence: Influencing one part of an object or system by changing it to affect other parts in order to achieve some effect or resolve a conflict. Reverse influence can change the behaviour or performance of a system.

(14) Principle of Substitution: Replacing an original part or function with another material, process, or design to improve performance, reduce cost, or solve a problem. Substitution can take place at different levels, including material, energy and information.

(15) Principle of Dynamic Equilibrium: Introducing balance, symmetry, or stability into an object or system to improve performance, reduce fluctuations, or resolve conflicts. Dynamic equilibrium can be achieved by adjusting force, speed, mass, or other parameters.

(16) Partial or Global Reversal Principle: Improvement or resolution of contradictions is achieved by changing the reverse characteristics of some or all of the properties of an object or system. Reverse features can include size, shape, orientation, colour, etc.

(17) Transformation Process Principle: Introducing a transformation process or intermediate step to change the state, composition, or performance of an object or system. Transformation processes can be used to control, improve or optimise the behaviour of a system.

(18) Physicochemical Effects Principle: The use of physical or chemical effects to change the nature or behaviour of an object or system to achieve a goal or solve a problem. Physicochemical effects can involve temperature, pressure, electromagnetic fields, chemical reactions, etc.

(19) Principle of Continuous or Periodic Action: Introducing continuous or periodic action or movement to achieve improvement or resolve conflicts. Continuous or periodic actions can improve efficiency, reduce energy consumption or optimise resource use.

(20) Connection Principle: Connecting, uniting or integrating different objects or systems to achieve better results or resolve conflicts. Connections can be made physically or functionally.

(21) Principle of staticisation: the transformation of a dynamic object or system into a static one to achieve better results or resolve conflicts. Staticisation can reduce moving parts, lighten loads or improve stability.

(22) Reverse Feedback Principle: By introducing reverse feedback, the object or system is made more stable, controllable or predictable. Reverse feedback can be used to regulate, modify or optimise the behaviour of a system.

(23) Guidance principle: by guiding, directing or adjusting the behaviour of an object or system to achieve better results or resolve conflicts. Guidance can involve directing, controlling, influencing, or motivating.

(24) Physical Field Principle: The use of physical fields, such as gravity, magnetic fields, electric fields, etc., to change the behaviour or performance of an object or system in order to achieve a goal or solve a problem.

(25) Introducing Scaling Principle: Changing the scale, size, or proportions of an object or system by introducing a scaling effect to achieve an improvement or resolve a conflict.

(26) Stacking: Stacking the components of an object or system by stacking them together to achieve greater efficiency, compactness, or integration. Stacking can be done vertically to save space or increase efficiency.

(27) Principle of indirection: by introducing intermediate steps, processes, or objects to achieve improvements or resolve conflicts. Indirection can be used to reduce risk, increase efficiency, or create new opportunities.

(28) Alternative Media Principle: Changing the nature, behaviour or performance of an object or system by introducing an alternative medium or media to achieve a goal or solve a problem. Alternative media may include gases, liquids, solids, forms of energy, etc.

(29) Principle of Backscattering: Improvement or resolution of conflicts is achieved by scattering energy, matter, or information from the output of a system back to the input. Backscattering can change the behaviour, stability or efficiency of a system.

(30) Principle of Flexibility: the introduction of flexibility or variability to adapt to different conditions, requirements or environments. Flexibility can involve structure, materials, design, or mode of operation.

(31) Reverse Accommodation Principle: By changing the way an object or system is accommodated or structured in order to achieve better results or resolve conflicts. Reverse Accommodation can change the form, structure or function of an object.

(32) Principle of Asymmetry: Introducing asymmetry to change the behaviour, performance or effect of an object or system. Asymmetry can be manifested in shape, distribution, composition, or operation.

(33) Mirroring Principle: Introducing mirroring or symmetry in order to change the behaviour, performance or effect of an object or system. Mirroring can be manifested in shape, structure, layout, or operation.

(34) Dramatic Conditions Principle: By introducing extreme or drastic conditions, such as high temperature, high pressure, high speed, etc., in order to change the nature, behaviour or performance of an object or system to achieve a goal or solve a problem.

(35) Principle of Parameter Change: By changing the parameters of an object or system, such as size, shape, speed, temperature, etc., in order to achieve improvements or resolve conflicts. Parameter changes can adjust performance, optimise resource use or improve efficiency.

(36) Hiding principle: By hiding or isolating a part of an object or system in order to achieve improvements or resolve contradictions. Hiding can improve security, reduce interference, or protect important parts.

(37) Process Interruption Principle: Improvement or conflict resolution is achieved by introducing interruptions, pauses, or stops in the process of an object or system. Process interruptions can provide opportunities for adjustment, inspection, or repair.

(38) Principle of Parallel Worlds: By introducing parallel worlds or parallel operations in order to achieve improvements or resolve contradictions. Parallel worlds can be used for testing, comparison, simulation, or backup.

(39) Thermal Expansion Principle: By using the thermal expansion properties of materials to change the shape, structure, or performance of an object or system in order to achieve a goal or solve a problem.

(40) Dynamic Isolation Principle: By introducing adjustable or variable isolation mechanisms to achieve improvements or resolve conflicts. Dynamic isolation allows the degree or timing of isolation to be adjusted as needed.

3. Concepts and processes of the DIKWP model

Data can be understood as a figurative representation of what we perceive as "the same" semantics. Data usually represents a concrete fact or observation with a specific semantic meaning behind it. When working with data, we often look for and extract the same semantics and unify them into a single concept. For example, if we see a flock of sheep, although the size, colour, gender, etc. of each sheep may be different, we will group them into the concept of "sheep" because they share our semantic understanding of the concept of "sheep".

Information is the semantic representation of the cognitive 'difference'. Information usually refers to knowledge or data about the environment or an object that we acquire through our senses and observations. When processing information, we identify and categorise intrinsic differences in the input data. For example, in a car park, although all cars can be classified under the concept of 'car', each car has its own specific characteristics, such as make, model, colour, etc., which are all information.

Wisdom corresponds to the perspective of ethics, social morality, human nature, etc. It is a high level of understanding, synthesis and application of knowledge and information. When dealing with wisdom, we integrate this information and use it to guide decisions. For example, when faced with a decision-making problem, we take into account various perspectives such as ethics, morality, and feasibility, not just technology or efficiency.

Purpose can be understood as a dichotomy (input, output) where both input and output are DIKWP content. Purpose represents our understanding of a phenomenon or problem (input) and the goal we wish to achieve by processing and solving that phenomenon or problem (output). When processing purpose, the AI system processes the input DIKWP content according to its predefined goal (output), and by learning and adapting, makes its output converge to the predefined goal.

4. Mapping of DIKWP-TRIZ methodology to the forty universal principles

4.1 Linking Data to Principles

The first step in the DIKWP-TRIZ methodology is to collect data in relevant fields as a starting point for the mapping process. This data can include patent cases, failure databases, etc. By analysing this data, we can identify situations that are relevant to the forty universal principles of TRIZ.

For example, when we encounter a problem that needs to be improved or solved, we can consult the fault database to find cases of similar situations. If we find a case where the problem was solved by partitioning the object or system, we can relate it to the TRIZ principle of "partitioning".

4.2 Extraction of information and interpretation of principles

In the DIKWP model, information is the organisation and classification of data, while in TRIZ, the forty universal principles provide ways of thinking about problem solving and innovation. Therefore, combining the two can help us extract and interpret information relevant to a problem and relate it to the appropriate principles.

Categorising the forty universal principles and analysing the information about their application in different contexts can help us to understand the logic behind each principle and its practical application. For example, the principle 'colour change' can be applied to the optimisation of visual effects in product design knowledge. By correlating this principle with research and application cases in visual perception, we can extract key information to guide innovative design.

4.3 Construction of Knowledge and Application of Principles

By extracting and interpreting information, we can construct a body of knowledge that combines relevant knowledge with the forty universal principles of TRIZ. This construction of knowledge involves an in-depth understanding of the problem domain and the ability to apply the principles.

For example, when we are faced with a product design problem that needs to be improved, we can use the knowledge construction stage in the DIKWP-TRIZ methodology to correlate product design knowledge with the forty universal principles. By analysing the product's function, material, structure and other perspectives, we can identify the principles that are applicable to the problem, such as the "Separation Principle" and the "Parameter Change Principle".

In the process of applying the principles, we need to choose the appropriate principle according to the specific situation. This involves the practice of wisdom, i.e., choosing the most appropriate principle according to the specific conditions and requirements of the problem. By considering multiple perspectives of the problem, such as technical feasibility, economics and sustainability, we can develop more rational and practical solutions.

4.4 The Practice of Wisdom and the Choice of Principles

In the DIKWP-TRIZ methodology, the fulfilment of the purpose is to ensure that the selected principles are in line with the ultimate innovation goal. This stage involves matching and bridging the selected principles with the innovation goal.

For example, suppose our innovation goal is to increase the adaptability and durability of a product. In this case, the principle 'dynamism' might be combined with this objective, as dynamism allows the product to adapt to changing needs and environments and to extend its lifetime.

By introducing the Purpose Hierarchy into the DIKWP-TRIZ methodology, we can ensure that the principles we choose are aligned with the ultimate innovation goal. This helps to avoid deviation from the goal in solving a problem or carrying out an innovation, and improves the effectiveness and feasibility of the solution.

4.5 Purpose fulfilment and innovation of principles

The integrated application of the DIKWP-TRIZ methodology helps us to approach innovation and problem solving in a more systematic and comprehensive way. It provides a powerful toolbox by combining the data, information and knowledge building processes of the DIKWP model with the forty universal principles of TRIZ.

Firstly, the DIKWP-TRIZ methodology provides the basis for problem solving and innovation through the collection and organisation of data, extracting key information from existing knowledge and experience. Secondly, by interpreting and applying the principles, we can link problems to appropriate principles, providing guidance for innovative designs and solutions. Finally, the DIKWP-TRIZ methodology emphasises purposeful realisation and innovation to ensure that our solutions are in line with the end goal and are practically feasible.

Integrating multiple perspectives of a problem, such as technology, economics, and the environment, the DIKWP-TRIZ methodology can help us make more informed decisions when faced with complex problems and challenges. It provides a systematic approach that promotes the development of innovative thinking and problem solving skills.

5. Detailed Analysis of Forty Universal Principles with DIKWP Mapping

This section explores in turn how each principle is applied and enhanced at each level of the DIKWP model. Example:

Principle 1 (Segmentation):

Data level: identifying a database of cases where segmentation has been applied.

Information level: analysing the characteristics of applications of the segmentation principles in different industries.

Knowledge level: Formulate strategies on when and where to use the segmentation principle.

Wisdom level: Apply segmentation in product design to improve functionality or maintainability.

purpose Level: Ensure that the application of the Segmentation Principle is aligned with the organisation's innovation goals.

Principle 2: Extraction (Extraction):

Data Level: Collect a database of cases and identify cases of application of the Extraction Principle.

Information level: analyse the characteristics and effectiveness of the application of the extraction principle in different industries.

Knowledge level: Build a body of knowledge about when and where to use the extraction principle.

Wisdom level: Apply the extraction principle rationally in the innovation process to improve efficiency and reduce waste.

purpose Level: Ensure that the application of extraction principles is consistent with the organisation's innovation goals, such as improving product quality or reducing costs.

Principle 3: Inversion (Reverse Thinking):

Data level: collect a database of cases of Reverse Thinking to understand its application and effectiveness.

Information level: Analyse the characteristics and innovations of the application of Reverse Thinking in different industries.

Knowledge level: Construct a body of knowledge on when and where to use Reverse Thinking, e.g. adopting Reverse Thinking in product improvement and problem solving.

Wisdom level: Apply Reverse Thinking appropriately in the innovation and problem solving process in order to break the conventional thinking pattern and discover new solutions.

purpose Level: Ensure that the application of Reverse Thinking is aligned with the organisation's innovation goals, e.g. to drive innovation and push traditional boundaries.

6. Methodology for generating innovative ideas through DIKWP-TRIZ

In today's competitive business environment, innovation has become a key factor for sustained growth and success. To help organisations gain a broader and deeper mindset in the innovation and problem solving process, the DIKWP-TRIZ methodology combines the DIKWP model with the forty universal principles of TRIZ. This methodology provides an integrated framework to stimulate innovative ideas and optimise solutions through cross-level analysis, from data to purpose. This paper will explore in detail how the different stages of the DIKWP model can be combined to stimulate new ideas using TRIZ principles, and illustrate how cross-level analysis can facilitate broader and deeper innovation.

DIKWP Model Overview:

The DIKWP model is a five-level knowledge management model that includes Data, Information, Knowledge, Wisdom, and Purpose. Each level has different characteristics and functions and constitutes a knowledge transformation process from Data to Decision. In innovation and problem solving, the DIKWP model provides a structured approach to managing and applying knowledge.

The forty universal principles of TRIZ:

TRIZ is a set of innovation methods and tools developed by Soviet engineer Artur Gainerich Altupov in the 1940s.At the heart of TRIZ are forty universal principles, generalised from analyses of technological developments, which can be used to solve a wide range of technological problems and promote innovative thinking. Each principle involves a different way of thinking and technical principles that can inspire innovators to think outside the box and find new solutions.

DIKWP-TRIZ methodology: thinking leaps between levels

Data Level:

In the data level, by collecting and analysing relevant data, a database of cases can be created to document the application and effectiveness of TRIZ principles in different fields. This data can provide reference and inspiration to help innovators understand the potential and applicability of TRIZ principles.

Information hierarchy:

In the information level, TRIZ principles are combined with cases from specific industries or fields to analyse their application characteristics and combination effects. Through in-depth study of the cases, correlations and complementarities between different principles can be identified, further expanding innovation ideas.

Knowledge Level:

In the knowledge level, the knowledge system about when and where to use TRIZ principles is constructed. Through systematic study and research, innovators can understand the characteristics and scope of application of each principle and learn to apply them to practical problem solving.

Wisdom Level:

In the Wisdom Level, innovators apply TRIZ principles rationally in the process of innovation and problem solving in order to break the conventional thinking mode and discover new solutions. By applying different principles, innovators can gain more possibilities in design, improvement and optimisation.

purpose Level:

In the purpose level, it is ensured that the application of TRIZ principles is aligned with the organisation's innovation goals. Innovators should be clear about their innovation purpose and map the TRIZ principles to the strategic goals of the organisation. This ensures that the direction and goals of innovation activities are aligned to better drive the organisation's innovation capability and competitive advantage.

Analysing across levels: stimulating broader and deeper innovative thinking

Data-to-information level analysis:

By combining data-level case databases with information-level analyses, it is possible to identify the characteristics and effects of the application of TRIZ principles in different industries and fields. This cross-level analysis can help innovators understand which principles have more potential and value in a given context, thus stimulating a broader range of innovative ideas.

Information-to-knowledge level analysis:

Based on the information level of analysis, innovators can construct a body of knowledge about TRIZ principles. This body of knowledge can include information about how each principle works, its scope of application, relevant cases and best practices. Through in-depth learning and understanding of this knowledge, innovators can better apply TRIZ principles and integrate them into their innovation practices.

Knowledge to Wisdom Level of Analysis:

In the knowledge level, innovators can use what they have learnt about TRIZ to solve practical problems and apply different principles flexibly in their innovation process. By combining and applying different principles, innovators can step out of the traditional thinking mode and discover more innovative solutions. This cross-level analysis and application can facilitate deeper and more diverse innovative thinking.

Wisdom to purpose level of analysis:

In the wisdom level, innovators should match the application of TRIZ principles to the organisation's innovation purpose and strategic goals. By clarifying the innovation's purpose and goals, innovators can select and apply TRIZ principles in a more targeted manner, leading to more valuable innovation outcomes.

7. Conclusion

The DIKWP-TRIZ methodology provides an integrated approach and tool for innovation through cross-level analysis and application. By combining the different stages of the DIKWP model with TRIZ principles, innovators can stimulate broader and deeper innovative ideas and optimise solutions. This methodology can help organisations to better apply TRIZ principles in the innovation and problem-solving process, driving innovation capability and solution optimisation. In practice, innovators should use this methodology flexibly, adapting and optimising it to specific contexts and needs, in order to achieve the maximum value and effect of innovation.

4.2 Integrity analysis and purpose-driven optimisation based on the DIKWP-TRIZ methodology: an in-depth exploration of the forty universal principles of TRIZ

Abstracts

The aim of this report is to apply the DIKWP-TRIZ methodology to provide a completeness analysis of the forty universal principles of TRIZ and optimise them in an purpose-driven manner to facilitate the generation of innovative ideas in problem solving.The DIKWP framework combines the five dimensions of Data, Information, Knowledge, Wisdom, and Purpose with the principles of TRIZ to provide researchers and engineers faced with technological paradoxes with a systematic solution conceptualisation path.

Keywords: TRIZ, DIKWP, Principles of Innovation, Integrity Analysis, purpose Driven, Optimization

1. Introduction

TRIZ theory is widely applied in technological innovation with its structured and systematic problem-solving methods. The DIKWP model, as an advanced knowledge management framework, can enhance the application effectiveness of TRIZ, especially in purpose driven innovation processes. By combining the two, we aim to improve the completeness and practicality of TRIZ principles while optimizing for specific innovative purposes.

2. A Review of Forty General Principles of TRIZ

TRIZ provides a series of standardized principles for systematically solving engineering problems. Forty universal principles are universally applicable solutions summarized from a large number of patents.

(1) Segmentation principle: Segmentation of an object or system into different parts for better analysis and processing. Segmentation can be physical or functional.

(2) Extraction principle: Extracting useful parts or characteristics from an object or system to improve overall performance or solve problems. Extraction can be physical or functional.

(3) Local quality principle: Different materials, processes or designs are used in different parts of an object or system to meet different requirements and improve overall performance.

(4) Unification principle: Unifying different parts or functions to simplify the system structure or reduce complexity. This can be achieved through integration, merging or sharing.

(9) Stripping Principle: Stripping unnecessary parts or functions from an object or system to reduce complexity or improve performance. Stripping can be physical or functional.

(20) Connection Principle: Connecting, uniting or integrating different objects or systems to achieve better results or resolve conflicts. Connections can be made physically or functionally.

(25) Introducing Scaling Principle: Changing the scale, size, or proportions of an object or system by introducing a scaling effect to achieve an improvement or resolve a conflict.

3. Overview of DIKWP Model

Data can be understood as a concrete representation of the "same" semantics that we perceive. Data usually represents a concrete fact or observation, which contains a specific semantic meaning behind it. When processing data, we often search for and extract the same semantics, treating them as a unified concept. For example, when we see a group of sheep, although each sheep may have different body shapes, colors, genders, etc., we classify them as the concept of "sheep" because they share our semantic understanding of the concept of "sheep".

Information is the expression of "different" semantics in corresponding cognition. Information usually refers to knowledge or data about the environment or an object obtained through our senses and observations. When processing information, we will identify the inherent differences based on the input data and classify them. For example, in a parking lot, although all cars can be classified as' cars', each car has its own uniqueness, such as brand, model, color, etc. These are all information.

Knowledge corresponds to the semantic meaning of "completeness" in cognition. Knowledge is our understanding and explanation of the world obtained through information. When dealing with knowledge, we abstract complete concepts or patterns through observation and learning. For example, by observing, we know that all swans are white, which is a complete understanding of the concept of "swan" that we have obtained through collecting a large amount of information.

Wisdom corresponds to information on ethics, social morality, human nature, and other aspects, and is a high level of understanding, synthesis, and application of knowledge and information. When dealing with wisdom, we integrate this information and use it to guide decision-making. For example, when facing a decision-making problem, we consider various factors such as ethics, morality, feasibility, etc., rather than just technology or efficiency.

Purpose can be understood as a binary (input, output), where both input and output are DIKWP content. purpose represents our understanding (input) of a phenomenon or problem and the goal (output) we hope to achieve by addressing and solving the phenomenon or problem. When processing purposes, the artificial wisdom system will process the input DIKWP content based on its preset goal (output), and through learning and adaptation, make its output approach the preset goal.

4. DIKWP-TRIZ Methodology

The DIKWP-TRIZ methodology provides a comprehensive and innovative approach and tool through cross level analysis and application. By combining the different stages of the DIKWP model with the TRIZ principle, innovators can stimulate broader and deeper innovative ideas and optimize solutions. This methodology can help organizations better apply TRIZ principles in innovation and problem-solving processes, promoting the improvement of innovation capabilities and the optimization of solutions. In practice, innovators should flexibly apply this methodology, adjust and optimize according to specific situations and needs, in order to achieve the maximum value and effectiveness of innovation.

5. Integrity Analysis of Forty Universal Principles

5.1 Analysis of data hierarchy

In the analysis of data hierarchy, we will focus on the performance and application frequency of various principles in the database, as well as identifying the comprehensiveness and limitations of data collection.

Firstly, we need to understand the performance and application frequency of each principle in the database. This includes the data model, structure, and storage method of the principle in the database. For example, the relational database model is one of the most common data models, which uses tables to organize data and establish relationships. Other common data models include hierarchical models, network models, and object-oriented models. By analyzing the performance of principles in databases, we can understand their specific manifestation and frequency of use in practical applications.

Secondly, we need to evaluate the comprehensiveness and limitations of data collection. The comprehensiveness of data refers to the breadth and depth of data collection, and whether it covers all aspects of the relevant field. We need to consider the methods and sources of data collection, as well as the timeliness and accuracy of the data. At the same time, we also need to identify limitations in data collection, such as issues with data reliability, availability, and privacy protection. By analyzing the comprehensiveness and limitations of data collection, we can evaluate the data support of the principle in practical applications.

In data level analysis, we can conduct case studies based on specific principles. We can study the application cases of each principle under different conditions and how they solve specific problem scenarios. For example, if we consider the principle of data deduplication, we can analyze the application cases of this principle in data backup and storage, and how it solves the problems of storage space waste and data redundancy.

By analyzing the data hierarchy, we can better understand the characteristics and applications of the forty universal principles in data processing, providing a foundation for subsequent information hierarchy analysis.

5.2 Analysis of information hierarchy

In the analysis of information hierarchy, we will focus on the process of converting data into information and provide case studies of the application of various principles under different conditions, as well as scenarios of how they solve specific problems.

Transforming data into information is a process of processing and interpretation. The data itself may be isolated and unorganized, but through processing and interpretation, we can extract meaningful information from it. In the analysis of information hierarchy, we need to explore how to convert data into information and understand the application of different principles in this process.

For each principle, we can provide application case studies. Through case analysis, we can demonstrate the specific application methods of the principle under different conditions. For example, if we consider Data Classification in the principle, we can analyze its application cases in the field of information security and how it helps organizations classify, protect, and manage sensitive data.

In addition, we can also explore the application of principles in solving specific problem situations. For example, the application of data mining principles in big data analysis can help us discover patterns and trends hidden in a large amount of data, thus making more accurate predictions and decisions.

Through information hierarchy analysis, we can gain a deeper understanding of the role of each principle in data processing and interpretation, as well as their ability to solve problems in specific contexts.

5.3 Analysis of knowledge hierarchy

In the analysis of knowledge hierarchy, we will construct a knowledge system about each principle through in-depth analysis of information hierarchy. This includes the scope, effectiveness, and potential cross application of the principle.

For each principle, we can further investigate its applicability. This means that we need to consider in which fields and contexts principles can play a role. For example, the principle of Data Encryption is widely used in the field of information security, which can protect the confidentiality and integrity of data.

In addition, we also need to evaluate the effectiveness of each principle. This includes the effectiveness and effectiveness of the principle in practical applications. We can study existing case studies and empirical studies to evaluate the actual effectiveness of principles in solving specific problems.

In the analysis of knowledge hierarchy, we can also explore the cross application of principles. There may be interrelationships and interactions between different principles. For example, the principles of Data Fusion and Data Cleaning can support each other to jointly improve data quality and availability.

By analyzing the hierarchy of knowledge, we can establish a knowledge system about each principle, gain a deeper understanding of their applicability, effectiveness, and potential cross applications, and provide a foundation for subsequent intelligent hierarchy analysis.

5.4 Analysis of the level of wisdom

In intelligent level analysis, we will apply knowledge to determine when to use which principle is most appropriate, and how to combine multiple principles to build stronger solutions.

Wisdom is the ability to apply knowledge and experience in practice. In the analysis of the level of wisdom, we will explore how to apply knowledge to practice and use wisdom to make decisions.

Firstly, we need to determine which principle is most appropriate to use in a specific context. This requires considering the nature, characteristics, and objectives of the problem. For example, for problems that require data processing and analysis, the principle of data cleaning may be a suitable choice to ensure that data quality meets analysis requirements.

Secondly, we can explore how to combine multiple principles to build more powerful solutions. There may be complementary and synergistic relationships between different principles. By combining multiple principles, we can comprehensively utilize their advantages to solve more complex problems. For example, combining the principles of Data Mining and Machine Learning can achieve intelligent analysis and prediction of large-scale data.

In intelligent level analysis, we will provide guidance for decision-making and solutions by applying knowledge and judgment. This will help us better apply the forty universal principles in practice and achieve effective problem-solving.

5.5 Analysis of purpose hierarchy

purpose is our ultimate goal in pursuing problem-solving and innovation. In the analysis of purpose level, we will focus on linking principles with innovation objectives to ensure that their application is consistent with expected goals.

Firstly, we need to clarify the application purpose of each principle. Different principles may have different application objectives. For example, the application purpose of the principle of Data Visualization may be to transform data into a form that is easy to understand and communicate through visualization to support decision-making and communication.

Secondly, we need to link the application of principles with innovative purposes. The purpose of innovation may be to solve specific problems, improve business processes, enhance user experience, etc. By combining the application of principles with innovative purposes, we can ensure that each principle's application is meaningful, driving innovation and achieving the expected goals.

In the analysis of purpose level, we can also consider the influence and results of principles. We can evaluate the impact of each principle's application on the business, users, and society, as well as the results generated after achieving the goals. This helps us determine whether the application of principles has a positive impact on innovation objectives and provides an optimization direction based on purpose.

In summary, in the analysis of purpose level, we will focus on linking principles with innovation objectives to ensure that the application of each principle is consistent with the expected goals. This will help us achieve purpose driven optimization, applying universal principles to practice, in order to achieve the ultimate goal of innovation and problem-solving.

6. purpose driven TRIZ principle optimization

Definition of purpose: In the optimization of TRIZ principle driven by purpose, it is first necessary to clarify the goals and purposes of the innovation project. This can be achieved through discussions and definitions with relevant stakeholders. A clear purpose will guide the subsequent selection and application of TRIZ principles.

Mapping principle and purpose: Once the purpose is clearly defined, the next task is to map the TRIZ principle to a specific innovative purpose. This requires a deep understanding of the characteristics and application fields of each TRIZ principle, and corresponding them to the innovation purpose. For example, if the innovation purpose is to improve the reliability and stability of the product, one may choose to apply the principles related to eliminating technical contradictions in TRIZ, such as the "separation principle" or the "multifunctional principle".

Application of optimization principle: once TRIZ principle and innovation purpose are reflected, it is necessary to adjust and refine the application of principle in actual situations to optimize the results. This may involve further analysis and evaluation to determine how best to apply the selected principles to achieve innovation goals. At this stage, it is necessary to comprehensively consider factors such as technical feasibility, resource feasibility, and economic feasibility.

7. Case Study: Practical Application of DIKWP-TRIZ Methodology

Case studies are an important way to demonstrate the application of DIKWP-TRIZ methodology in practical technological innovation problems. The following is a brief description of a case:

Case study: Improving the charging efficiency of electric vehicles

Innovation purpose: To improve the charging efficiency of electric vehicles, reduce charging time, and enhance user experience.

Applied TRIZ principles: contradiction resolution principle, separation principle, and conduction principle.

Operating steps:

Definition purpose: Clarify the innovation goal to improve the charging efficiency of electric vehicles.

Mapping principle and purpose: apply the principle of conflict resolution to identify technical conflicts, separation principle to eliminate conflicts, and conduction principle to improve energy transmission efficiency.

Optimization principle application: By analyzing the design and technology of electric vehicle charging systems, applying the "contradiction conduction" model in the contradiction resolution principle, improving the design of charging equipment, and utilizing the concept of "separation energy transmission and control" in the separation principle, the battery charging and vehicle control are separated to improve charging efficiency.

This case study demonstrates how to use the DIKWP-TRIZ methodology to solve specific technological innovation problems and achieve expected goals through purpose driven optimization.

8. Discuss

In this section, we will analyze the application of the DIKWP-TRIZ methodology in practice, discuss its advantages, challenges, and possibilities for improvement. This includes discussions on the flexibility, applicability, and operability of methodology.

9. Conclusion

This section summarizes the role of the DIKWP-TRIZ methodology in enhancing the completeness and application specificity of the forty universal principles of TRIZ, as well as its value in purpose driven innovation optimization. By combining purpose with TRIZ principles, innovative projects can be better guided and expected innovation goals can be achieved.

5. Comparison between DIKWP-TRIZ and Traditional TRIZ

5.1 A Comparative Analysis of the DIKWP-based TRIZ Methodology and Traditional TRIZ in the Innovation of AI Technologies

Abstract

Value TRIZ (Intelligent TRIZ) is an innovative framework that combines traditional TRIZ methodology with modern intelligent decision-making processes. It aims to enhance the social and environmental value of technological and product innovation. This framework emphasizes the use of intelligent decision-making and forecasting during the innovation process to ensure that solutions not only meet current needs but also bring positive social and environmental benefits for the future.

Keywords: Value TRIZ, Intelligent TRIZ, social value, environmental benefits, DIKWP-TRIZ, intelligent decision-making, technological innovation.

1. Introduction

The goal of technological innovation is no longer solely focused on achieving business success and technological breakthroughs but is increasingly being entrusted with ensuring social sustainability and environmental friendliness. The Value TRIZ (Intelligent TRIZ) framework has emerged to guide innovation activities towards greater emphasis on social responsibility and environmental protection through intelligent decision-making and forecasting. This article explores how the Value TRIZ integrates DIKWP resources to maximize social and environmental value through the systematic and creative thinking of TRIZ.

2. The theoretical foundation of Value TRIZ (Intelligent TRIZ)

Value TRIZ is a new innovation methodology based on traditional TRIZ principles, expanded and integrated with considerations of the intelligent dimension. It emphasizes the following key elements:

(1) The essence of intelligent decision-making: In the innovation process, intelligent decision-making means not only analyzing problems and generating solutions but also considering their long-term impact, social value, and environmental benefits.

(2) The role of systematic forecasting: Systematic forecasting helps innovators understand the trends in technological development, anticipate future needs and environmental changes, enabling proactive decision-making in designing solutions.

(3) Maximizing social and environmental benefits: Innovation should not only pursue economic benefits but also enhance social well-being and protect environmental resources. This is the core objective pursued by Value TRIZ.

3. The operational process of Value TRIZ (Intelligent TRIZ)

(1) Problem identification and definition: In Value TRIZ, problem identification is not only based on technological and market needs but also considers social trends and environmental requirements.

(2) Innovative design of solutions: When designing solutions, TRIZ innovation principles are used to guide thinking, combined with intelligent level forecasting tools such as scenario analysis, Delphi method, etc., to ensure comprehensive solutions.

(3) Value assessment of solutions: During the solution design stage, methods such as value engineering and life cycle assessment are used to evaluate the social and environmental impacts of the solutions.

(4) Implementation and feedback of the final solution: After implementing the solution, continuous tracking of its social and environmental benefits is carried out, and adjustments and optimizations are made based on feedback.

4. The application of DIKWP-TRIZ in promoting value innovation

(1) Application of data and information in Value TRIZ: Data and information serve as the foundation of Value TRIZ, helping to collect relevant environmental and social indicators and providing support for innovative decision-making.

(2) Integration of knowledge and wisdom: Knowledge management and decision support systems at the intelligent level are crucial in Value TRIZ. They enhance the quality of decision-making through analysis of historical cases, expert knowledge, and innovative patterns.

Discussion:

Through in-depth discussions on the theory and practice of Value TRIZ, an analysis is conducted on its applicability in different industries and fields, as well as the challenges and opportunities it faces in a globalized and rapidly changing market environment.

Below is a comparative analysis table that provides a detailed description of the differences between traditional TRIZ and Value TRIZ (Intelligent TRIZ):

Characteristics	Traditional TRIZ	Value TRIZ (Intelligent TRIZ)
Core Concept	Solving technical contradictions and innovation problems	integrating technological innovation with the maximization of social, environmental value
Focus of Attention	Technological innovation, problem-solving	comprehensive value innovation, including social and environmental benefits
Decision Basis	Technical parameters, functional models, physical contradictions	Comprehensive consideration of multi-dimensional factors, including technological, economic, social, and environmental aspects
Innovation Drivers	Inherent contradictions within the technological system	Dynamic balance among societal needs, environmental challenges, and technological progress
Solution Evaluation	Optimal functionality, cost-effectiveness	Comprehensive assessment of the long-term value, lifecycle costs, social responsiveness, and environmental impact of the solution
Predictive Methods	Regularities in the evolution of technological systems	Predicting future societal and environmental trends based on scenario planning, Delphi method, and other approaches
Knowledge Management	Relying on patent information, scientific principles, and technological knowledge bases	Integrating interdisciplinary knowledge, expert wisdom, real-time data, and information analysis
Tools and Methods	Contradiction matrix, physical contradiction diagram, innovation principles	Adding value stream diagrams, lifecycle analysis, system dynamics models, and so on
Scope of Solutions	Technological improvements	Integration of technological innovation with the social-environmental-economic system
User Engagement	User needs as input for problem definition	direct involvement of user needs in the iterative process of solution development.
Sustainability	Indirect consideration (through the improvement of technological efficiency)	Direct consideration (as a core evaluation criterion and design principle)
Addressing Complexity	Addressing complex technical problems	adopting comprehensive strategies to deal with the complexity of technology, society, and the environment
Implementation Process	Emphasizing technical development and design	balancing the process of technological innovation with social responsibility implementation
Case Studies and Empirical Research	Focusing on the analysis of technological innovation cases	Introducing case analyses of social innovation and environmental protection
Training and Education	Technical Innovation Training for Professional Engineers and Technicians	Interdisciplinary Team Collaboration, Including Designers, Engineers, Sociologists, Environmental Scientists, and More
Evaluation Metrics	Technical Performance, Cost, Speed	Sustainability Metrics, Social Impact Assessment, Environmental Impact Assessment
Feedback Mechanism	Primarily Focused on Technical Validation and Testing	Including Market Feedback, Social Impact Assessment, and Environmental Impact Tracking
Value-Oriented	Guided by Market and Technical Value	Guided by Global and Long-term Social and Environmental Value
Paradigm	The Paradigm for Solving Technical Problems	The Paradigm for Integrating Sociotechnical Systems

The conclusion drawn from the detailed comparison provided in the table is that traditional TRIZ and Value TRIZ (Intelligent TRIZ) offer different perspectives and approaches in terms of their core concepts, focus areas, and decision-making foundations. In practical applications, these two methodologies can complement each other and form a more comprehensive toolkit for innovation based on specific circumstances and goals.

Value TRIZ (Intelligent TRIZ) introduces a fresh perspective that helps innovators maximize social and environmental benefits while pursuing technological innovation. Through intelligent decision-making and forecasting, Value TRIZ ensures that solutions can address future challenges and contribute to achieving sustainability goals.

5.2 Comparative Analysis between DIKWP-TRIZ and Traditional TRIZ: A New Perspective for AI Technology Innovation

Abstract

This paper provides an in-depth exploration of the application differences between DIKWP-TRIZ and traditional TRIZ in the context of AI technology innovation. Traditional TRIZ holds a significant position in technological innovation with its systematic problem-solving approach, while the DIKWP-TRIZ methodology offers a more comprehensive perspective through hierarchical analysis, integrating data, information, knowledge, wisdom, and purpose. This paper aims to present a detailed comparative analysis of the application of these two methodologies in the context of AI technology innovation, showcasing the advantages of DIKWP-TRIZ in handling complex systems and providing practical innovation tools for researchers and engineers in the field of AI.

Keywords: TRIZ, DIKWP, AI technology innovation, methodology comparison, systematic innovation.

1. Introduction

As the field of Artificial wisdom (AI) technology continues to evolve, innovative thinking methodologies have become crucial in addressing complex technical issues in the AI domain. TRIZ, as a mature innovation methodology, provides universal principles and strategies for problem-solving. DIKWP-TRIZ, as an emerging methodology, combines the DIKWP model with TRIZ principles to tackle even more complex technological challenges. This paper will conduct an in-depth comparative analysis of the application of these two methodologies in AI technology innovation.

2. Overview of Traditional TRIZ Methodology

TRIZ, which stands for "Teoriya Resheniya Izobreatatelskikh Zadatch" in Russian or "Theory of Inventive Problem Solving" in English, is a methodology developed by Soviet inventor and scientist Genrich Altshuller and his colleagues in 1946. Since then, it has continuously evolved and improved.

TRIZ is based on the idea that there are universal patterns in creative problem-solving, which can be identified through the analysis of a large number of patents. The purpose of TRIZ is to help problem solvers predict the evolution of technological systems and find innovative solutions, breaking through traditional thinking and technical barriers.

The TRIZ methodology includes various tools and concepts, such as:

1、Problem analysis tools:

Function analysis: Identifying all components of a system and their relationships.

Problem formalization: Transforming real-world problems into standard problems.

Principles and patterns for problem-solving:

2、Inventive principles: 40 general principles used to generate innovative ideas for problem-solving.

Contradiction matrix: Used to address technical contradictions in inventive problems by transforming the problem description into standard parameters and utilizing predefined solutions.

Substance-field analysis: Using the concepts of substance and field to improve systems or solve problems.

3、Innovation process:

ARIZ (Algorithm for Inventive Problem Solving): A structured problem-solving process designed to guide users systematically from problem description to creative solution.

4、Prediction tools:

Laws of technological system evolution: Describes general patterns and trends followed by systems over time.

S-curve analysis: Evaluates the maturity and potential development space of technological systems.

TRIZ has been widely applied in various fields, including product design, engineering, and problem-solving. It encourages innovators to go beyond the boundaries of existing knowledge and solve problems through novel approaches. A core concept of TRIZ is that innovation often involves addressing contradictions within systems, including technical contradictions and physical contradictions. Technical contradictions refer to situations where improving one aspect of a system may harm another, while physical contradictions involve the need for different states of the same component or characteristic under different conditions.

The strength of the TRIZ methodology lies in providing a systematic innovation process. By analyzing and applying methods previously used to solve similar problems, it helps accelerate and guide innovative activities. While initially developed to address engineering and technical problems, the principles and tools of TRIZ have been applied in other fields such as business, management, and social sciences.

3. Overview of the DIKWP-TRIZ Methodology

DIKWP-TRIZ methodology, through cross-level analysis and application, provides a comprehensive approach and tools for innovation. By combining the different stages of the DIKWP model with TRIZ principles, innovators can stimulate broader and deeper innovative thinking and optimize solutions. This methodology helps organizations better apply TRIZ principles in the process of innovation and problem-solving, promoting the enhancement of innovation capabilities and solution optimization. In practice, innovators should flexibly apply this methodology, adjusting and optimizing it according to specific contexts and needs to achieve maximum value and effectiveness in innovation.

4. The Characteristics of AI Technology Innovation

Innovation in AI technology often involves extensive data processing, algorithm optimization, and system integration. These characteristics require innovative methodologies to possess high adaptability and foresight.

5. Comparison Between DIKWP-TRIZ and Traditional TRIZ

(1) Comparison of Data ProcessingIn AI projects, data serves as the foundation. Traditional TRIZ does not directly address data processing strategies, while DIKWP-TRIZ, on the other hand, starts with data and emphasizes the impact of data quality on innovation.

(2) Comparison of Information ProcessingIn terms of the information layer, traditional TRIZ focuses on the transformation and utilization of information. However, DIKWP-TRIZ provides more detailed methods for information analysis and processing to support more complex decision-making processes. By incorporating a deeper understanding of information and its analysis, DIKWP-TRIZ enables innovators to make more informed and effective decisions throughout the innovation process.

(3) Comparative Analysis of Knowledge Construction:

Knowledge plays a crucial role in AI innovation. Traditional TRIZ promotes knowledge construction through known principles and historical cases. In contrast, DIKWP-TRIZ goes further by facilitating the transformation from information to knowledge and emphasizes the depth and breadth of knowledge.

(4) Comparative Application of the Wisdom Level:

Traditional TRIZ encourages innovators to apply wisdom in problem-solving, while DIKWP-TRIZ explicitly defines wisdom as the application of knowledge, experience, and strategic choices in problem-solving.

(5) Comparative Analysis of purpose Positioning:

In AI technology innovation, the clarity of purpose is vital for project success. DIKWP-TRIZ places purpose at the core, whereas traditional TRIZ focuses more on processes and methods.

6. Application Cases of DIKWP-TRIZ in AI Technology Innovation

In AI technology innovation, the application of the DIKWP-TRIZ methodology can help solve complex problems and guide the development of innovative paths. Below, we will analyze the practical application of DIKWP-TRIZ in the field of AI through specific case studies, and explore how it leads to different innovation paths and solutions.

Case 1: Optimization of Intelligent Transportation Systems

Let's assume our purpose is to improve urban traffic flow and reduce accidents. We first need to collect a large amount of data, including traffic volume, road conditions, and accident records. By analyzing this data, we can extract various information such as congested areas and accident-prone locations.

Through the DIKWP-TRIZ methodology, we can transform this information into knowledge and identify the root causes of the problems. For example, we may discover that certain road designs are not optimal or that traffic signal settings are not scientifically sound. Next, we can utilize TRIZ principles and tools like the contradiction matrix and the nine windows method to find solutions. Possible innovation paths may include changing road structures, optimizing traffic signal control algorithms, or introducing intelligent traffic management systems.

When selecting the best solution, we need to consider various factors such as cost, feasibility, and social impact, which reflect the application of wisdom. Ultimately, by implementing these solutions, we can optimize the intelligent transportation system, improve traffic efficiency, and reduce accidents.

Case 2: Innovation in Intelligent Medical Diagnosis Systems

Let's assume our purpose is to enhance the accuracy and efficiency of medical diagnoses. We can collect medical records, case data, medical literature, and other data sources. By analyzing and extracting information, such as symptom patterns and disease associations, we can convert this information into knowledge.

Applying the TRIZ methodology, we can address challenges in medical diagnosis. For example, we may identify difficulties in diagnosing certain diseases or cases of misjudgment during the diagnostic process. By applying TRIZ principles and tools, we can search for solutions to improve diagnostic accuracy, such as incorporating new medical testing technologies or optimizing data analysis algorithms.

When selecting solutions, we need to consider the unique aspects of the healthcare industry, such as privacy protection and medical ethics, demonstrating the application of wisdom. Ultimately, by implementing these innovative solutions, we can enhance the accuracy and efficiency of medical diagnoses, providing better healthcare services to patients.

From these case studies, it is evident that the application of the DIKWP-TRIZ methodology in AI technology innovation helps us extract useful information and knowledge from data and convert it into specific problem-solving solutions. This methodology not only guides the development of innovative paths but also considers various factors, including cost, feasibility, and ethics, to achieve more comprehensive and sustainable innovation.

However, it is important to note that the application of the DIKWP-TRIZ methodology is not rigid and fixed. It needs to be adjusted and adapted based on specific problems and contexts. Different problems may require different data collection and analysis methods, and different domains may necessitate different knowledge and tools for resolution. Therefore, when applying the DIKWP-TRIZ methodology, it is crucial to flexibly utilize its principles and tools, adjusting and innovating based on specific circumstances.

7. Conclusion

The application cases of the DIKWP-TRIZ methodology in AI technology innovation demonstrate its role in data processing, information extraction, knowledge transformation, wisdom application, and purpose realization. By transforming data into information, knowledge, and wisdom, the DIKWP-TRIZ methodology can guide the development of innovative paths and provide diverse solutions. However, it is necessary to flexibly apply this methodology based on the specific problems and characteristics of the field to achieve more comprehensive and sustainable innovation. As AI technology continues to advance, the application of the DIKWP-TRIZ methodology in the AI field will become increasingly important, providing strong support for solving complex problems and achieving innovation.

6. Method and Application for Semantic Compensation and Verification Using the DIKWP-TRIZ Model

Abstract:

The rapid development of the information age has brought new challenges in data processing and utilization, particularly in ensuring information quality and semantic integrity. The DIKWP-TRIZ model combines the levels of data, information, knowledge, wisdom, and purpose in processing, and incorporates TRIZ innovation principles to promote efficiency and accuracy in information processing. This paper discusses in detail the mapping and processing methods between the levels of the DIKWP-TRIZ model, with a particular focus on strategies for semantic compensation and verification, and explores their role in enhancing the conversion of data to wisdom.

Keywords: DIKWP model, TRIZ, semantic compensation, semantic verification, data processing

1.Introduction

In the context of massive data, effectively processing and transforming data, extracting valuable information, and further forming actionable knowledge and wisdom are key challenges in the field of information technology. The DIKWP-TRIZ model not only includes the multi-level conversion from data to wisdom but also integrates TRIZ theory to enhance the systematicity and innovativeness of information processing. This study aims to explore how the model can effectively compensate and verify semantics at the semantic level, ensuring the integrity and coherence of information.

2.Overview of the DIKWP-TRIZ Model

The DIKWP-TRIZ methodology provides a comprehensive innovative approach and tools through cross-level analysis and application. By combining different stages of the DIKWP model with TRIZ principles, innovators can stimulate broader and deeper innovative thinking and optimize solutions. This methodology can help organizations better apply TRIZ principles in the innovation and problem-solving process, promoting the improvement of innovation capabilities and optimization of solutions. In practice, innovators should flexibly apply this methodology and adjust and optimize it according to specific contexts and needs to achieve maximum value and effectiveness in innovation.

3.Internal Mapping and Processing of DIKWP

(1) Conversion from data to information

The DIKWP-TRIZ model believes that the conversion of data to information not only requires technical support but also relies on innovative problem-solving methods. This level of conversion can be facilitated by TRIZ's functional analysis and resource analysis to find the most effective mapping between data and information.

(2) Conversion from information to knowledge

In the process of transforming information into knowledge, the DIKWP-TRIZ model advocates the use of TRIZ's conflict resolution principles to creatively extract valuable knowledge by identifying and resolving contradictions in information processing.

(3) Conversion from knowledge to wisdom

At the level of knowledge-to-wisdom transformation, the DIKWP-TRIZ model emphasizes the use of TRIZ's principles of system evolution to guide the application of knowledge and the formation of wisdom, ensuring that this process is both logical and insightful.

(4) Formation of purpose from wisdom

When wisdom is transformed into purpose, the DIKWP-TRIZ model suggests applying TRIZ's ideal final result principle, which means that the application of wisdom should be directed towards a clear and beneficial goal, which is defined by the purpose.

(5) Feedback loop between purpose and data

In the DIKWP-TRIZ model, purpose not only drives data collection and processing but also influences the optimization of the entire information processing flow. TRIZ's feedback principles play a role here, improving the efficiency and effectiveness of the overall system through continuous improvement cycles.

4.Semantic Compensation and Verification

4.1 Necessity of semantic compensation

In the DIKWP-TRIZ model, semantic compensation is an important step to ensure that information is not distorted during the transformation process. It involves maintaining the integrity and coherence of meaning between different levels.

4.2 Methods of semantic compensation

Fusion of multiple data sources combined with TRIZ's resource integration principles allows for complementary information between different data sources.

Utilizing TRIZ's trend analysis, combining contextual information with data to enhance the richness of semantics.

Constructing semantic networks and combining TRIZ's principles of system transformation to deepen the logical connections between information.

Human intervention is not only reflected in the use of expert systems but also includes guiding users to provide feedback using TRIZ's innovation principles.

4.3 Methods of semantic verification

Combining consistency checks with TRIZ's principles of physical contradiction resolution to ensure the logicality of information conversion.

Combining cross-validation with TRIZ's multi-screen analysis techniques to improve the accuracy of verification.

Real-time monitoring combined with TRIZ's principles of dynamism to achieve real-time optimization of the information processing flow.

Application Case

The weather forecasting system plays a crucial role in modern society, helping people make informed decisions and plans. However, the accuracy of weather forecasts has always been a challenge, especially in complex and volatile weather conditions. This paper demonstrates how the DIKWP-TRIZ model can be applied to the weather forecasting system to optimize the accuracy of information and the application of wisdom through semantic compensation and verification. We will explore the entire process

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[26] Duan Y. Research on the Application of DIKWP Model in Automatic Classification of Five Personality Traits. DOI: 10.13140/RG.2.2.15605.35047. https://www.researchgate.net/publication/375597087_DIKWP_moxingzaiwudaxinggetezhizidongfenleizhongdeyingyongyanjiu_duanyucongYucong_Duan. 2023.

[27] Duan Y, Gong S. DIKWP-TRIZ method: an innovative problem-solving method that combines the DIKWP model and classic TRIZ. DOI: 10.13140/RG.2.2.12020.53120. https://www.researchgate.net/publication/375380084_DIKWP-TRIZfangfazongheDIKWPmoxinghejingdianTRIZdechuangxinwentijiejuefangfa. 2023.

[28] Duan Y. The Technological Prospects of Natural Language Programming in Large-scale AI Models: Implementation Based on DIKWP. DOI: 10.13140/RG.2.2.19207.57762. https://www.researchgate.net/publication/374585374_The_Technological_Prospects_of_Natural_Language_Programming_in_Large-scale_AI_Models_Implementation_Based_on_DIKWP_duanyucongYucong_Duan. 2023.

[29] Duan Y. The Technological Prospects of Natural Language Programming in Large-scale AI Models: Implementation Based on DIKWP. DOI: 10.13140/RG.2.2.19207.57762. https://www.researchgate.net/publication/374585374_The_Technological_Prospects_of_Natural_Language_Programming_in_Large-scale_AI_Models_Implementation_Based_on_DIKWP_duanyucongYucong_Duan. 2023.

[30] Duan Y. Exploring GPT-4, Bias, and its Association with the DIKWP Model. DOI: 10.13140/RG.2.2.11687.32161. https://www.researchgate.net/publication/374420003_tantaoGPT-4pianjianjiqiyuDIKWPmoxingdeguanlian_Exploring_GPT-4_Bias_and_its_Association_with_the_DIKWP_Model. 2023.

[31] Duan Y. DIKWP language: a semantic bridge connecting humans and AI. DOI: 10.13140/RG.2.2.16464.89602. https://www.researchgate.net/publication/374385889_DIKWP_yuyanlianjierenleiyu_AI_deyuyiqiaoliang. 2023.

[32] Duan Y. The DIKWP artificial consciousness of the DIKWP automaton method displays the corresponding processing process at the level of word and word granularity. DOI: 10.13140/RG.2.2.13773.00483. https://www.researchgate.net/publication/374267176_DIKWP_rengongyishide_DIKWP_zidongjifangshiyiziciliducengjizhanxianduiyingdechuliguocheng. 2023.

[33] Duan Y. Implementation and Application of Artificial wisdom in DIKWP Model: Exploring a Deep Framework from Data to Decision Making. DOI: 10.13140/RG.2.2.33276.51847. https://www.researchgate.net/publication/374266065_rengongzhinengzai_DIKWP_moxingzhongdeshixianyuyingyongtansuocongshujudaojuecedeshendukuangjia_duanyucongYucong_Duan. 2023.

[34] Duan Y. DIKWP Digital Economics 12 Chain Machine Learning Chain: Data Learning, Information Learning, Knowledge Learning, Intelligent Learning, purposeal Learning. DOI: 10.13140/RG.2.2.26565.63201. https://www.researchgate.net/publication/374266062_DIKWP_shuzijingjixue_12_lianzhijiqixuexilian_shujuxuexi-xinxixuexi-zhishixuexi-zhihuixue_xi-yituxuexi_duanyucongYucong_Duan. 2023

[35] Duan Y. Big Data and Small Data Governance Based on DIKWP Model: Challenges and Opportunities for China. DOI: 10.13140/RG.2.2.21532.46724. https://www.researchgate.net/publication/374266054_jiyuDIKWPmoxingdedashujuyuxiaoshujuzhili_zhongguodetiaozhanyujiyu. 2023.

[36] Duan Y. DIKWP is based on digital governance: from "data governance", "information governance", "knowledge governance" to "wisdom governance". "Analysis of the current situation. DOI: 10.13140/RG.2.2.23210.18883. https://www.researchgate.net/publication/374265977_DIKWPjiyushuzizhilicongshujuzhilixinxizhilizhishizhilidaozhihuihuazhilidexianzhuangfenxi. 2023.

[37] Duan Y. Exploration of the nature of data tenure and rights enforcement issues based on the DIKWP model. DOI: 10.13140/RG.2.2.35793.10080. https://www.researchgate.net/publication/374265942_jiyu_DIKWP_moxingdeshujuquanshuxingzhiyuquequanwentitantao_duanyucongYucong_Duan. 2023.

[38] Duan Y. The DIKWP Model: Bridging Human and Artificial Consciousness. DOI: 10.13140/RG.2.2.23839.33447. https://www.researchgate.net/publication/374265912_DIKWP_moxingrenleiyurengongyishideqiaoliang_duanyucongYucong_Duan. 2023.

[39] Duan Y. An Exploration of Data Assetisation Based on the DIKWP Model: Definitions, Challenges and Prospects. DOI: 10.13140/RG.2.2.24887.91043. https://www.researchgate.net/publication/374265881_jiyu_DIKWP_moxingdeshujuzichanhuatanjiudingyitiaozhanyuqianjing_duanyucongYucong_Duan. 2023.

[40] Duan Y. Purpose-driven DIKWP Resource Transformation Processing: A New Dimension of Digital Governance. DOI: 10.13140/RG.2.2.29921.07529. https://www.researchgate.net/publication/374265796_yituqudongde_DIKWP_ziyuanzhuanhuachulishuzizhilidexinweidu_duanyucongYucong_Duan. 2023.

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Appendix

The theoretical foundation of this book (DIKWP series of technological inventions)：

Incomplete	Data	Information	Knowledge	Wisdom	Purpose
Data	Method of Object Recognition in Image Data Based on a Three-Layer Graph Architecture of Data, Information, and Knowledge	Enhanced Method of Object Recognition in Image Data Based on Data Graph, Information Graph, and Knowledge Graph	Design Method of IoT Monitoring System Based on Data Graph, Information Graph, and Knowledge Graph for Investment Decision-making	Cross-Modal Text Ambiguity Resolution Method for Essence Computing and Reasoning in the DIKW Framework	Essence Computing-Based Differential Content Recommendation Method Across Data, Information, and Knowledge Modalities
Information	User Behavior Content Encoding and Decoding Method Across Data, Information, and Knowledge Modalities	Essence Identification Method and Components Across Data, Information, Knowledge Modalities, and Dimensions	Cross-Modal Recommendation Method and Device for Essence Computing and Integrated Reasoning	Modeling and Optimization Mechanism for Content Transmission Based on Data Graph, Information Graph, and Knowledge Graph	Essence Computing-Based Method and Components for Processing Virtual Community Resources Across DIKW Graphs
Knowledge	Automated Security Situational Awareness, Analysis, and Alert System for Typified Resources	Information Privacy Protection Method for Typified Resources in the IoT Environment	A Graph-Based Target-Driven Learning Point and Learning Path Recommendation Method Oriented towards the 5Ws	Personalized and Convenient Adaptive Multilevel Interaction Zone Optimization Configuration Method	Cross-Modal Randomized Privacy Protection Method and System for Essence Computing and Inference
Wisdom	Multi-dimensional Value-Oriented purpose-Based Object Numerical Calculation Method	Value-Driven Hidden Resource Method for Typified Data and Its Graph Representation	Value-Driven Dynamic Recommender System with Multi-Factor Dimensional Space and Multi-Scale Fusion	Personalized Organization and Optimization Method for Network Personnel and Content	Time-Sensitive Blockchain-like Cryptographic System Based on Social Networks
Purpose	purpose-Driven Multi-Modal DIKW Content Transmission Method	purpose-Driven DIKW Content Processing Method and System	purpose-Driven Interactive Form-Filling Method for DIKW Content	Multi-dimensional Value-Oriented purpose-Based Object Numerical Calculation Method	Value-Driven Purpose-Oriented Fusion Optimization System

Incomplete	Data	Information	Knowledge	Wisdom	Purpose
Data	Fairness-Oriented Mapping and Transmission Method of Emotional Content to DIKW	A cross-modal user healthcare data analysis method oriented towards fundamental computation	Modeling and processing optimization mechanisms for content transmission based on data graphs, information graphs, and knowledge graphs	A design method for a typified healthcare resource processing system oriented towards edge computing	Privacy protection method for Internet of Things (IoT) data with a focus on typified resources
Information	Combination optimization method for spatial representation oriented towards groups	Optimization system for Internet of Things (IoT) resource collection and transmission, oriented towards typified resources	A security-definable resource protection method for investment decisions based on data graphs, information graphs, and knowledge graphs	A user satisfaction modeling and display space adjustment method that integrates fairness, user experience, and pricing	purpose-driven group differential privacy protection method and device for the DIKW system
Knowledge	A technology-based multimodal privacy protection method that integrates fairness, justice, and transparent regulatory measures	A learner competency modeling and learning process optimization management system based on data graphs, information graphs, and knowledge graphs	Investment-driven security protection method for Internet of Things (IoT) resources	A customized system for environmentally friendly interactive cookware, defined with a focus on processing-defined health	Emotion expression mapping, measurement, and optimization transmission system oriented towards DIKW resources
Wisdom	Multimodal DIKW content multi-semantic analysis method oriented towards fundamental computation	An investment-driven efficiency optimization method for resource storage based on data graphs, information graphs, and knowledge graphs	An investment-driven design method for an Internet of Things (IoT) monitoring system based on data graphs, information graphs, and knowledge graphs	Emotion-based personalized region generation and display method	Privacy resource handling method and components for DIKW (Data, Information,Knowledge, Wisdom) oriented towards fundamental computation
Purpose	purpose-driven computation and inference DIKW model construction method and device	purpose-driven content enrichment system for adaptive competition and cooperation purposes	Value-driven method for concealing resources of typified data and their graph representation	Interactive region partitioning and transmission optimization processing mechanism based on data graphs, information graphs, and knowledge graphs	purpose-driven interactive form filling method for DIKW content

Imprecision	Data	Information	Knowledge	Wisdom	Purpose
Data	Image Data Object Recognition Method Based on Three-tier Graph Architecture of Data, Information, and Knowledge	A Semantic Modeling and Abstraction Enhancement Method Based on a Framework of Data Graph, Information Graph, and Knowledge Graph with Association Frequency Calculation	Privacy Protection Method for Cross-DIKW Modalities Focused on Essential Computation using Relative Differential Privacy	Oral Language Learning Correction Method Based on Visualization of Biased Organ Morphology and Behavior	Personality Analysis and Content Recommendation Method for Virtual Community Members Based on DIKW Graphs
Information	Cross-Modal and Dimension Warning Method and Components	Personalized English Letter Presentation Style Transformation Method	Enhanced Image Object Recognition Method based on Data Graph, Information Graph, and Knowledge Graph	Method for Customizable Interaction Area Definition, Presentation, and Recognition	Image Data Object Recognition Method Based on Three-Tier Graph Architecture of Data, Information, and Knowledge
Knowledge	Method and System for Carrying Out Value Exchange Conversion Between Data Portraits and Information Portraits	Essential Content Processing Method and System for Multimodal Resources based on Common Sense Reasoning	Value-Oriented Integrated Optimization System for Typed Resources Storage and Computation	Active Adaptive Algorithm for Spatial Presentation Platform Angle Distance	Search Optimization Method Based on Data Graph, Information Graph, and Knowledge Graph
Wisdom	Multidimensional Value-Oriented Object-Oriented Numerical Calculation Method for purpose	Multidimensional Systematized Interaction Mechanism with Definable Privacy Ambiguity	Emotion-Based Personalized Area Generation and Presentation Method	Cross-Modal Typed Privacy Information Resource Differential Protection Method and System	Privacy Resource Protection Method for Cross-Modal DIKW with a Focus on Essential Computation and Inference
Purpose	DIKW Model Construction Method and Device for purpose-Based Computation and Inference	Content Processing Method and System for DIKW Driven by purpose	Intelligent Reminder Mechanism for Matching Scenes, Events, Characters, and purposes	DIKW Resource Analysis Method and System for purpose-Based Computation and Inference	Interaction Cost-Driven Security Protection Method for Typed Resources

1.
A Semantic Modeling and Abstract Enhancement Method Based on Data Graph, Information Graph, and Knowledge Graph Frameworks for Correlation Frequency Calculation

Application No.:	CN201710394911.0	Full Text Download
Application:	2017-05-30	Public/Gazette：	2019-07-23
Publication number:	CN107038262B

The present invention is a semantic modeling and abstract enhancement method based on data graph, information graph, and knowledge graph frameworks for correlation frequency calculation. It is mainly used to obtain reasonable class and object graphs from initial requirement descriptions and application scenarios, belonging to the cross domain of distributed computing and software engineering technology. Expressing discrete entities, objects, attributes, and operations on a data graph, recording the frequency of each entity, object, attribute, or operation, including structural frequency, temporal frequency, and spatial frequency; Mark the interaction relationships between nodes on the information graph, calculate the interaction frequency, which is the number of interactions. When the interaction frequency exceeds the set threshold, integrate multiple nodes, and generate new nodes as entities to continue labeling structural frequency, temporal frequency, and spatial frequency; Applying relationship abstraction rules to further abstract the relationships between classes on the knowledge graph can supplement the completeness of requirement expression and improve development efficiency.

2. A Positive and Negative Bidirectional Dynamic Balancing Search Strategy for Resource Environment

Application No.:	CN201710434314.6	Full Text Download
Application:	2017-06-09	Public/Gazette：	2017-08-04
Publication number:	CN107016135A

The present invention is a bidirectional dynamic balance search strategy for resource environment, belonging to the cross field of distributed computing and software engineering technology. The present invention is mainly used for conducting a limited number of progressive searches on positive and negative discriminative problems proposed by searchers, introducing a three-layer architecture of data graph, information graph, and knowledge graph, organizing resources on the network, understanding the tendency of searchers to retrieve information through semantic analysis, and searching for the requirements proposed by searchers based on positive and negative tendencies, The reliability of the inclined resource is calculated by the number of entries searched each time and the entropy value of the corresponding resource for each entry. False information and invalid information are eliminated with the number of progressive searches, improving the quality of search resources and avoiding the situation of search falling into a dead cycle when facing the problem of infinite super complexity.

3. A Fault Tolerant Intelligent Semantic Search Method Based on Graph Architecture

Application No.:	CN201710435186.7	Full Text Download
Application:	2017-06-10	Public/Gazette：	2017-08-29
Publication number:	CN107103100A

The present invention is a fault-tolerant intelligent semantic search method based on graph architecture, belonging to the interdisciplinary field of distributed computing and software engineering technology. It is mainly used to solve decision-making problems in the face of uncertain and untrue information during the use of search engines. Introduce a three-layer architecture of data graph, information graph, and knowledge graph, organize resources on the network, establish a user investment model based on the user's pre waiting time and planned payment amount, understand the user's tendency to retrieve information through semantic analysis, and allocate user investment according to the proportion of each tendency based on the number of searches for different tendencies. Calculate the reliability of the inclined resource based on the number of entries searched each time and the entropy value of the corresponding resource for each entry. False and invalid information will be excluded with the number of progressive searches. After returning the resource to the user, obtain user feedback. If the user is not satisfied, prompt the user to increase their investment and continue the progressive search.

4. Automatic Security Situation Awareness, Analysis, and Alarm System for Typed Resources

Application No.:	CN201710745700.7	Full Text Download
Application:	2017-08-26	Public/Gazette：	2019-07-16
Publication number:	CN107343010B

The present invention is a development method for an automatic security situational awareness, analysis, and alarm system for typed resources, and provides explanations of resource forms including data, information, and knowledge, as well as conceptual representations of data graphs, information graphs, and knowledge graphs. It belongs to the cross domain of distributed computing and software engineering technology. The present invention proposes to map network security situation and automatic alarm rules into a collection of resource instances of types such as data, information, and knowledge, establish a resource optimization objective function, adjust resource storage and matching schemes through storage and calculation collaboration, optimize the spatial cost of resource storage and the time efficiency of situation awareness, monitor network security situation changes in real-time, and update the automatic alarm rule set, It is conducive to timely response to safety situations.

5. Optimization System for Collection and Transmission of IoT Resources for Typed Resources

Application No.:	CN201710746795.4	Full Text Download
Application:	2017-08-27	Public/Gazette：	2019-07-12
Publication number:	CN107332721B

The present invention is a development method for optimizing the collection and transmission of IoT resources for typed resources. The resources collected by sensor groups in the form of data, information, and knowledge are transmitted to resource processing nodes using IoT transmission networks to optimize resource allocation, belonging to the cross domain of distributed computing and software engineering technology. The present invention proposes to change the scale of resources through type conversion between resources such as data, information, and knowledge, solving the problem of low efficiency in resource transmission and low utilization of network resources due to limited network bandwidth resources.

6. Image Data Target Recognition Enhancement Method Based on Data Graph, Information Graph, and Knowledge Graph

Application No.:	CN201810023920.3	Full Text Download
Application:	2018-01-10	Public/Gazette：	2018-05-18
Publication number:	CN108052680A

The present invention is an image data target recognition enhancement method based on data graph, information graph, and knowledge graph architecture. Mainly used to solve the problem of existing image recognition methods being unable to recognize unlabeled categories in the training set, it belongs to the cross domain of distributed computing and software engineering technology. The key is to start from the existing image type recognition results based on deep learning methods, construct a three-layer graph based on existing image resources, perform feature matching on the data graph for unrecognized image categories to obtain initial matching results, perform relationship matching on the information graph spectrum for identified image categories to obtain intermediate matching results, and finally perform indirect interaction relationship matching on the knowledge graph, Calculate the credibility of the intermediate matching results and sort them, and recommend the matching image category with the highest credibility to the user.

7. Image Information Target Recognition Enhancement Method Based on Data Graph, Information Graph, and Knowledge Graph

Application No.:	CN201810037199.3	Full Text Download
Application:	2018-01-15	Public/Gazette：	2019-07-23
Publication number:	CN108228868B

The present invention is an image information target recognition enhancement method based on data graphs, information graphs, and knowledge graphs, belonging to the cross field of distributed computing and software engineering technology. The purpose of this method is to enable machines to automatically and intelligently perform image recognition, finding information in the image that cannot be directly observed. The present invention establishes a data graph, information graph, and knowledge graph architecture. Firstly, the observable images in the recognition image are combined with the data graph to achieve image classification. Then, the interaction relationships displayed in the image are combined with the information graph to find parallel relationships. Finally, hidden information is found through knowledge reasoning on the paths in the knowledge graph to achieve information recognition.

8. Investment driven IoT resource security protection methods

Application No.:	CN201810192478.7	Full Text Download
Application:	2018-03-09	Public/Gazette：	2018-08-21
Publication number:	CN108429748A

An investment driven method for protecting the security of IoT resources belongs to the cross domain of distributed computing and software engineering technology. Its characteristic is to transform the types of resources existing in the form of data, information, and knowledge in the Internet of Things to increase the difficulty of obtaining resources by unauthorized users, and store the converted resources on data graphs, information graphs, and knowledge graphs, calculating the conversion and storage costs, while considering the transmission of resources in the Internet of Things and calculating the transmission costs, Its characteristic is to calculate the search cost for attackers to search for original form resources by traversing a three-layer graph. Its characteristic is to provide cost-effective resource security protection based on the calculated user investment and resource security level, while allowing service providers to obtain profits from the provided services by integrating user investment and security level values.

9. A Data Privacy Protection Method for Typed Resources in the Internet of Things

Application No.:	CN201810248695.3	Full Text Download
Application:	2018-03-24	Public/Gazette：	2018-08-31
Publication number:	CN108471414A

A type oriented resource oriented IoT data privacy protection method, characterized in that it can protect data privacy in different forms, directly search for data privacy on data graphs and information graphs, and obtain data privacy by combining data, information, and related data on information graphs; Its feature is to distinguish between user information in the form of links and aggregated information, and provide a fusion solution for protecting the privacy of these two forms of information related to data privacy; Its characteristic is to use privacy to evaluate the degree of user data privacy exposure and provide cost-effective privacy protection services, hoping to obtain the strongest level of privacy protection with minimal user investment.

10. A time sensitive blockchain like cryptosystem based on social networks

Application No.:	CN201811091678.X	Full Text Download
Application:	2018-09-19	Public/Gazette：	2018-12-25
Publication number:	CN109086629A

The present invention is a time sensitive blockchain like cryptosystem based on social networks. Social networks are composed of a series of subnets, consisting of nodes that store member information and protocol information between members and users in a specific social network; Divide these nodes into overlapping nodes and non overlapping nodes, calculate the contribution value of the nodes and the time cost spent searching for the nodes, stop the search after the total time cost exceeds the user's expected time, store the searched nodes in the dataset, and perform blockchain processing on the nodes in the dataset; The present invention belongs to the interdisciplinary field of information technology and software engineering.

11. Method of providing customizable and adaptive multi-functional interaction areas for portable mobile terminal users

Application No.:	CN201810938052.1	Full Text Download
Application:	2018-08-17	Public/Gazette：	2018-12-25
Publication number:	CN109085993A

The present invention provides portable mobile terminal users with customizable and adaptive multi-functional interaction areas. The interaction areas are divided into two categories: single-sided and double-sided screens. Users can freely draw the appearance, including size, color, position, and shape. Afterwards, users can customize the corresponding instructions and triggering methods for each interaction area. After the user completes the customization, The present invention provides a method for dividing shapes into complete and incomplete recognition region ranges, and encoding both within and outside the range; At the same time, the system will provide intelligent recommendations for users based on their information and customization, including their gender, age, mood, habits, etc; The present invention belongs to the interdisciplinary field of computer accessory technology and software engineering.

12. Value driven typed data and its graph representation for resource hiding methods

Application No.:	CN201811169042.2	Full Text Download
Application:	2018-10-08	Public/Gazette：	2018-12-21
Publication number:	CN109063214A

The present invention proposes a value driven resource hiding method for typed data and its graph representation based on a data graph in a three-layer knowledge graph that can be automatically abstracted; The characteristic is to measure the various components of security resources and their topological structures through systematic and fully typed dimensionally defined data. Security resources and their topological structures are defined based on data elements of different dimensions, thereby weakening the data resources of security resources through the newly defined different security resources and their topological structures, and achieving the purpose of data resource hiding.

13. A Semantic Modeling Method for Dynamically Abstract Processing Architecture Resources Based on Data Graph, Information Graph, and Knowledge Graph

Application No.:	CN201710394177.8	Full Text Download
Application:	2017-05-28	Public/Gazette：	2017-08-11
Publication number:	CN107038261A

In response to the challenges of existing value oriented or value driven data, information, and knowledge lifecycle assessment methods, the present invention proposes a semantic modeling method based on data graph, information graph, and knowledge graph processing architecture resources that can be dynamically abstracted, belonging to the cross domain of distributed computing and software engineering technology. Based on the data graph, the data observation and collection in software development activity requirement analysis were analyzed, and the structural frequency, temporal frequency, and spatial frequency of the data were labeled. Using information graphs to record the frequency of interaction between entities, abstracting and integrating frequently interacting nodes based on the calculated cohesion between entities, and marking the structural, temporal, and spatial frequencies of the integrated new entities. In the requirement modeling of detailed design activities, the present invention elaborates on the advantages of knowledge graph in requirement expression compared to UML, including the completeness and coverage of semantic expression of requirements.

14. A Defined Resource Security Protection Method Based on Data Graph, Information Graph, and Knowledge Graph for Investment Determined Security

Application No.:	CN201710506336.9	Full Text Download
Application:	2017-06-28	Public/Gazette：	2017-10-03
Publication number:	CN107229878A

The present invention is a resource security protection method based on data graph, information graph, and knowledge graph, which can define the security of investment decisions. It provides an explanation of resource forms including data, information, and knowledge, as well as a conceptual representation of data graph, information graph, and knowledge graph. It belongs to the cross field of distributed computing and software engineering technology. To maintain the security of resources, it is not possible to determine which layer of graph the resources should be stored on solely based on the resource type. The present invention allocates the search and storage types of different types of resources reasonably through the calculation of the transfer cost of resource search object type, resource storage space resource type transfer cost, and resource search cost. The resource storage scheme is determined through the investment of the resource owner, providing resource protection services.

15. A Search Optimization Method Based on Data Graph, Information Graph, and Knowledge Graph

Application No.:	CN201710488750.1	Full Text Download
Application:	2017-06-23	Public/Gazette：	2017-08-11
Publication number:	CN107038263A

The present invention is a search optimization method based on data graphs, information graphs, and knowledge graphs, and provides conceptual representations of data graphs, information graphs, and knowledge graphs, belonging to the cross domain of distributed computing and software engineering technology. Mainly used to search for resources stored in the form of data, information, and knowledge through data graphs, information graphs, and knowledge graphs, calculating the efficiency and cost of searching for resources on different levels of graphs. By parameterizing the search process, the efficiency and cost of searching for resources on data graphs, information graphs, and knowledge graphs are measured, enabling users to obtain relatively effective and accurate resources at the lowest cost and improving search efficiency.

16. A Method for Correcting Oral Learning Based on Visualization of Deviated Organ Morphology and Behavior

Application No.:	CN201810624822.5	Full Text Download
Application:	2018-06-17	Public/Gazette：	2018-11-03
Publication number:	CN108922563A

The present invention is a verbal learning correction method based on visualization of deviant organ morphology and behavior. By comparing the phonemes, stress, word pauses, and intonation of learners' pronunciation with the standard pronunciation, the accuracy of learners' pronunciation and the deviation between the behavior of their pronunciation organs and the standard behavior are calculated, and visualized for learners. The main step is S1. Collect pronunciation information of learners and standard tones, preprocess the collected signals, and extract features; S2. Construct a standard pronunciation organ morphology behavior library for sentences, mapping the pronunciation features of standard pronunciation to the organ morphology behavior library; S3. Calculate the similarity between the phonemes, stress, pauses, and intonation of learners' pronunciation and the standard pronunciation, calculate the deviation value of organ behavior, and visually display it to learners; S4. Comprehensive evaluation of learners' pronunciation based on four indicators and feedback to improve learning efficiency.

17. Value-oriented integrated storage and computing optimization system for typed resources

application number：	CN201710870573.3	Full Text Download
application date：	2017-09-23	Public/Announcement Date：	2018-02-23
Public/Announcement No.：	CN107734000A

The present invention is a value-oriented storage and processing integration optimization method for typed resources, which classifies resource instances collected by the Internet of Things (IoT) into three types of data, information, and knowledge at the conceptual level, and in order to solve the contradiction between the limited bandwidth of the IoT network and the demand for transmission of massive resources, determines a processing scheme for the resources by comparing the cost of directly processing the resources and converting the resource types and then processing them, and belongs to the intersection of distributed computing and software engineering technologies. The invention models the collection, transmission, storage, processing, conversion, creation, display, protection and use of resources as the corresponding activities of the relevant resources, realizes dynamic resource allocation under limited network bandwidth resources, improves the efficiency of resource use and maintains a balanced resource load, optimizes the computational and spatial costs required for resource processing and storage, and maintains the system in a relatively stable state.

18. Target recognition method for image data based on the three-layer mapping architecture of data, information and knowledge

application number：	CN201810074539.X	Full Text Download
application date：	2018-01-25	Public/Announcement Date：	2018-06-29
Public/Announcement No.：	CN108229578A

The present invention is an image data target recognition method based on a three-layer graph architecture of data, information, and knowledge, and belongs to the intersection of distributed computing and software engineering technologies. The present invention mainly introduces the data graph, information graph, and knowledge graph architecture to carry out knowledge reasoning on unidentified images, so as to realize automatic and intelligent recognition of entity targets in pictures or in images captured by cameras. The specific realization step is to find a path that can connect with the unidentified target in the data graph, information graph, or knowledge graph by analyzing the identified target, traversing the entities on the path while performing feature matching, and finally finding the identification result that has the highest degree of match with the unidentified target.

19. Dynamic simulation and display system for off-line suitability of vessel liquids

application number：	CN201910012275.X	Full Text Download
application date：	2019-01-07	Public/Announcement Date：	2019-04-05
Public/Announcement No.：	CN109584027A

The present invention is a container liquid offline suitability dynamic simulation and display system, the present invention for different environments, vessels, solution attributes to establish the influencing factors model, but also through the acquisition of user needs to provide to meet the user's personalized use of equipment. Users can predict the use of any vessel for different environments under the use of various indicators, but also in the use of personal needs through the temperature patch and APP in a very convenient access to the temperature information of the liquid in the vessel in line with the desired temperature of the solution in the use of the vessel, so as to get more convenient. The present invention provides the user with the patch and APP applicable to any vessel greatly reduces the user cost, more environmentally friendly and convenient. The present invention belongs to the temperature detection and water cup technology cross field.

20. An Emotion-Based Approach to Personalized Region Generation and Presentation

application number：	CN201810969038.8	Full Text Download
application date：	2018-08-23	Public/Announcement Date：	2019-01-25
Public/Announcement No.：	CN109271508A

The present invention is a method for generating and displaying personalized regions based on emotions, collecting users' emotional factors, analyzing users' emotions, analyzing individual users' emotions and corresponding emotions that this emotion tends to be based on emotional learning; displaying the emotional concentration of a group of users in a region in a visual way, and later recommending for an individual user a group of users on a region that corresponds to the emotional concentration that meets the user's emotional tendency region, the present invention belongs to the cross field of graphic image and software engineering.

21. Optimization method for group-oriented spatial display combinations

application number：	CN201811538696.8	Full Text Download
application date：	2018-12-17	Public/Announcement Date：	2019-04-09
Public/Announcement No.：	CN109598254A

The present application relates to a group-oriented spatial display combination optimization method and belongs to the intersection of service computing and software engineering. It is characterized in that the present invention uses spatially movable intelligent projection materials to display images such as animations, and meets the fairness, experience and satisfaction of users by means of a reasonable pricing method, adjusting the projection content, adjusting the spatial position of the display system, adjusting the shape of the display system, and employing a plurality of projection screens. The methods of the present invention can be loaded onto a variety of projection devices. The present invention adopts the idea of divide-and-conquer algorithm design to categorize the different shapes constituted by the positions between multiple users into three categories: linear shapes, circular shapes, and irregular shapes illuminated by factors such as terrain. For these three situations, the present invention gives three solutions to solve the problems of unfairness, poor experience and low satisfaction that may exist among users.

22. Active adaptation algorithms for angular distance of space display platforms

application number：	CN201811515487.1	Full Text Download
application date：	2018-12-12	Public/Announcement Date：	2019-04-09
Public/Announcement No.：	CN 109597488A

The present invention discloses a spatial display platform angle distance active adaptation algorithm, which mainly solves the problem that the spatial display platform cannot actively adapt to changes in the user's position and visual angle. The realization process is as follows: (1) Based on the data derived from facial recognition technology, positioning technology, and sensing technology, the orientation can be quickly determined, and the optimal distance of the display platform from the user can be determined by combining the most comfortable viewing distance in the health model. (2) Based on the data analyzing the overall morphology of the user, calculate the vector direction of the user's line of sight (3) Find the tilting direction of the display platform as well as the height based on the direction of the line of sight, and combine with the previous data to obtain the specific position of the display platform (4) Calculate the moving distance and direction of the display platform based on the effects of time, user comfort, and eye fatigue, and other factors. In the field of research adaptive, the present invention can not only meet the user in different positions can maximize the comfort of viewing the display platform, but also has a high efficiency and accuracy.

23. Methods for organizing and optimizing personalized web presence and content

application number：	CN201810911490.9	Full Text Download
application date：	2018-08-12	Public/Announcement Date：	2019-01-04
Public/Announcement No.：	CN 109144494A

The present invention is a method for organizing and optimizing personalized network personnel and content, and the method organizes and optimizes the user's social network resources into two parts, namely, personnel and content; the importance of personnel is sorted while recommending implied real social relationships to the user, and finally the two parts are displayed to the user according to personalized choices to form the user's virtual social network; the user's social relationships are combined with the user's social relationships, and the content is sorted according to the user's personalized choice to rank the importance of the content, and display the content according to the criteria that is most comfortable for the user to view; the present invention belongs to the cross field of social and software engineering.

24. Methods for defining, displaying and identifying areas for customized interaction areas

application number：	CN201810945928.5	Full Text Download
application date：	2018-08-20	Public/Announcement Date：	2019-01-04
Public/Announcement No.：	CN 109144645A

The present invention is a method for defining, displaying and identifying regions of customized interactive regions, the region definition includes appearance definition and instruction definition, the appearance definition means that the user draws the appearance freely, including size, color, position and shape, after the user defines the appearance, the user can customize the corresponding instruction and trigger mode of each interactive region; after the end of the region definition, the present invention gives a display method of the customized region and a recognition method of the shape of an individual and a group, after identifying the region, the region is coded inside and outside the recognized region; the present invention belongs to the cross field of computer accessories technology and software engineering. After the definition of the region, the present invention gives a method for displaying the customized region and a method for recognizing the shape of an individual and a group, and after recognizing the region, the region is coded inside and outside the recognized region; the present invention belongs to the intersection of computer accessory technology and software engineering.

25. A typed medical resource processing system design approach for edge computing

application number：	CN201711316801.9	Full Text Download
application date：	2017-12-12	Public/Announcement Date：	2018-04-20
Public/Announcement No.：	CN 107945880A

The present invention is a method for designing a typed medical resource processing system for edge computing, which solves the problem of transmission optimization of massive typed medical resources under limited bandwidth conditions in an edge computing environment from the perspectives of optimizing the resource-optimized storage, processing, and transmission of edge devices for the Internet of Things (IoT). It belongs to the intersection of IoT and software engineering. The key lies in transforming the types of medical resources based on data mapping, information mapping and knowledge mapping resource processing architectures, and at the same time establishing a bandwidth resource utilization limitation model to balance the network load. The present invention provides medical diagnostic, storage, and transmission services at local nodes and allows user inputs and associated benefit ratios to determine the resource approach for system optimization, keeping the system relatively stable. The system decentralizes computational tasks to edge devices to alleviate the pressure of highly centralized transaction processing in cloud computing environments that triggers the inability to transmit important medical resources in real time.

26. Simulation and display system for measuring and changing the temperature of liquids in containers

application number：	CN201910010040.7	Full Text Download
application date：	2019-01-06	Public/Announcement Date：	2019-05-10
Public/Announcement No.：	CN 109738089A

The present invention is a container of liquid temperature metrics, changes in simulation and display system, the present invention for different environments, vessels, solution attributes to establish a model of the influencing factors, and through the acquisition of user needs to provide to meet the user's personalized use of the device. Users can predict the use of any vessel for different environments under the use of various indicators, but also in the use of personal needs through the temperature patch and APP in a very convenient access to the temperature information of the liquid in the vessel in line with the desired temperature of the solution in the use of the vessel, so as to get more convenient. The present invention provides the user with the patch and APP applicable to any vessel greatly reduces the user cost, more environmentally friendly and convenient. The present invention belongs to the temperature detection and water cup technology cross field.

27. Eco-interactive cookware customization system for process-defined health

application number：	CN201910051556.6	Full Text Download
application date：	2019-01-21	Public/Announcement Date：	2019-06-28
Public/Announcement No.：	CN 109948177A

The present invention is an environmentally friendly interactive cookware customization system for processing-defined health, which mainly provides a system for selecting the optimal container curve among a plurality of peripheral curves and outputting it according to the needs of different users' tastes for food as well as their own personal conditions, using an analysis of the cooking items as well as a variety of models established. The process can also be carried out in real time to remind the user in a personalized way, which provides great convenience to the user. On the other hand, the physical patch is provided, which contains a timer, temperature sensor, etc., so that the user can set the time range of the cooking item as well as input his/her own personal needs through the cell phone APP, and monitor the temperature and the changes in the nature of the cooking item and the temperature, and the curves of the change of the nature of the food over time during the firing process can be obtained, which can then be fed back to the user through the APP.

28. Interactive cost-driven security protection methods for typed resources

application number：	CN201811111385.3	Full Text Download
application date：	2018-09-23	Public/Announcement Date：	2019-02-15
Public/Announcement No.：	CN 109344649A

The present invention is an interaction cost-driven security protection method for typed resources; the present invention is based on a knowledge graph architecture that can be automatically abstracted and adjusted in three layers, namely, data graph, information graph and knowledge graph, and divides security resources into data security resources, information security resources and knowledge security resources, and calculates the resource protection cost, the protector protection cost and the attacker attack cost, which are classified into three different interaction scenarios and are targeted at the three different dynamic interaction scenarios. These three different dynamic interaction situations, in the explicit and implicit security resource situation so that the static resources are not added, deleted, modified or checked, and the dynamic resources are not destroyed, the present invention belongs to the intersection of distributed computing and software engineering.

29. User satisfaction modeling and display space adjustment methodology incorporating fairness, experience and price

application number：	CN201811538692.X	Full Text Download
application date：	2018-12-17	Public/Announcement Date：	2019-04-12
Public/Announcement No.：	CN 109615433A

The present application relates to a method for modeling user satisfaction and adjusting a display space that incorporates fairness, experience and price, and belongs to the intersection of service computing and software engineering. It is characterized in that the present invention uses fairness, experience and price to measure user satisfaction, and maximizes the satisfaction of all users by adjusting the spatial position, angle, shape of the display space and the price that the user has to pay.

30. Multi-dimensional systematic interaction mechanisms with definable privacy ambiguities

application number：	CN201911124039.3	Full Text Download
application date：	2019-11-15	Public/Announcement Date：	2020-02-21
Public/Announcement No.：	CN 110825888A

The present application relates to a multi-dimensional systematic interaction mechanism with definable privacy ambiguity, characterized in that the decision-making system provides the user with decision-making operations by considering the user's social network and the confidence level calculated based on the multi-dimensionality of the data graph, the information graph, and the knowledge graph, and the purpose of the decision-making system is to provide the user with online social networking convenience while maintaining the user's right to know to maximize user satisfaction; the content of the decision-making system involved includes the user's personal label data, the collection of user's liking and disliking label data, the collection of labels in a specific social network and the confidence level calculated based on the multi-dimensionality of data mapping, information mapping, and knowledge mapping; the content of the interactions involved includes the input of the user's personal label, the input of the user's liking and disliking labels, and the output of the decision-making system's judgement; the content of the privacy fuzzification processing involved elements of the mechanism include multi-dimensional data mixing, system data invisible to the user.

31. Multidimensional value-oriented object-oriented numerical computation methods for purpose

application number：	CN201911251907.4	Full Text Download
application date：	2019-12-09	Public/Announcement Date：	2020-04-17
Public/Announcement No.：	CN 111026879A

The present invention provides a multi-dimensional value-oriented object-oriented numerical computation method for purpose, which is characterized in that based on the computation cost, a plurality of imprecise results are processed by adopting a plurality of imprecise rules to satisfy the user's needs, and finally the final result is measured by a multi-dimensional comprehensive value; the specific steps are S1: obtaining the target object and its attributes to be computed; S2: traversing the knowledge graph to determine the operators of the target object and its attributes; S3: calculate multiple numerical results through the attributes and operators of the target object and store them in an array; S4: traverse the knowledge graph to determine the existence rules of the target object and its attributes; S5: validate the obtained numerical results through multiple rules, delete all the numerical results that do not conform to the rules, get the final result, and measure the final result with the multidimensional comprehensive value. Metrics.

32. Interaction area classification and transmission optimization processing mechanism based on data mapping, information mapping and knowledge mapping

application number：	CN201910742770.6	Full Text Download
application date：	2019-08-13	Public/Announcement Date：	2019-11-12
Public/Announcement No.：	CN 110442734A

The present invention provides an interaction area division and transmission optimization processing mechanism based on data mapping, information mapping, and knowledge mapping, and constructs a content library based on data mapping, information mapping, and knowledge mapping by taking into account the accuracy and efficiency of content transmission, realizing the optimization of the transmission scheme and the reconstruction of the transmitted content, solving the problem of the discrepancy in the understanding of the content between different users, and realizing personalized expression and optimization and integration of transmitted content based on the key semantic meanings of the original content, and improving the accuracy and efficiency of content transmission. The reconstructed content, on the basis of retaining the key semantics of the original content, realizes personalized expression and optimizes and integrates the delivered content to improve the accuracy and efficiency of content delivery.

33. Purpose-driven content-population systems to accommodate competition and cooperative purpose

application number：	CN201910956787.1	Full Text Download
application date：	2019-10-10	Public/Announcement Date：	2020-01-21
Public/Announcement No.：	CN 110717318A

The present invention provides an purpose-driven content filling system adapted to competitive and collaborative purpose, wherein a data migration process occurring during the filling process is converted into an purpose-driven competitive process by converting the filled content into a bunch of rules modeling process, wherein each option in a table is modeled according to purpose based on an purpose determining module, a competitive and collaborative filling module, a common-sense reasoning module, an interaction module, and a value-driven module The table is then populated based on the purposeions. Minimizing the time, effort and privacy data invested by the form filler based on the resulting purpose, while solving the challenge of existing form filling systems not working properly when the data is incomplete and the options filled are uncertain.

34. Intelligent alert mechanism for matching scene, event, character and purpose

application number：	CN201911277319.8	Full Text Download
application date：	2019-12-13	Public/Announcement Date：	2020-04-07
Public/Announcement No.：	CN 110969420A

The present invention provides an intelligent reminder mechanism for matching scene, event, character and purpose. In order to achieve automatic adjustment and personalization of the reminder, it is necessary to analyze the influencing factors of the reminder from multiple dimensions; different characters correspond to different reminders, the same character corresponds to different reminders in different scenes and events, and different reminders correspond to different purpose. Combining the initial settings and user data for learning, while taking into account the privacy protection of user data; designing the mechanism of reminder adjustment corresponding to the reminder mechanism, and providing a variety of needs to meet the needs of the setting mode of operation.

35. Optimization mechanisms for content delivery modelling and processing based on data mapping, information mapping and knowledge mapping

application number：	CN201910736935.9	Full Text Download
application date：	2019-08-10	Public/Announcement Date：	2019-11-15
Public/Announcement No.：	CN 110457488A

The present invention provides a content delivery modeling and processing optimization mechanism based on data mapping, information mapping, and knowledge mapping, which achieves reconstruction of the delivered content by constructing a content library based on data mapping, information mapping, and knowledge mapping in consideration of the accuracy and efficiency of the content delivery, solves the problem of the discrepancy in the understanding of the content between different users, and the reconstructed content, on the basis of retaining the key semantics of the original content The reconstructed content, on the basis of retaining the key semantics of the original content, realizes personalized expression and optimized integration of the delivered content, which reduces communication barriers and improves communication efficiency.

36. Value-driven purpose-oriented convergence optimization systems

application number：	CN202010029053.1	Full Text Download
application date：	2020-01-12	Public/Announcement Date：	2020-05-12
Public/Announcement No.：	CN 111143345A

The present invention provides a value-driven purpose fusion-oriented optimization system, in which the data acquisition and processing module, the statistical learning module, the purpose analysis logic judgment module, and the setup and operation module of the subsystems of different controlled objects are controlled holistically through an integrated mode to perform user-purpose fusion modeling, and the value-driven combined with the principle of healthy and favorable, and finally the specific setup is carried out based on the results of the calculations, and the subsystems of different controlled objects are Unified management by the control system, minimizing energy consumption, maximizing comfort experience, and solving the problems of low comfort and waste of energy caused by the controlled objects not being able to switch the controlled objects in time.

37. Value-driven dynamic recommender systems for multifactor dimensional spatial multimedium scale fusion

application number：	CN202010032685.3	Full Text Download
application date：	2020-01-13	Public/Announcement Date：	2020-05-19
Public/Announcement No.：	CN 111177571A

38. Personalized and convenient adaptive multilevel interaction area optimization methodology

application number：	CN201811047171.4	Full Text Download
application date：	2018-09-09	Public/Announcement Date：	2019-01-18
Public/Announcement No.：	CN 109240787A

The present invention is a personalized, convenient and adaptive multi-layer interaction area optimization configuration method, which combines four aspects of personalization, convenience, adaptation and multi-layer modeling, determines the location of the multi-layer interaction area for the user according to the user's hand habit, the range of hand movement, and the length of clicking time, and then provides an adaptation method for the size and location of the interaction area according to the user's age and usage habits, and finally prioritizes the interaction area within and between layers; the invention belongs to the cross-field of graphic image and software engineering. Finally, the interaction area is prioritized within and between layers, so that the user can use it conveniently and quickly; the invention belongs to the intersection field of graphic image and software engineering.

39. Virtual community resource processing method and components for intrinsic computing across DIKW graph

application number：	CN202010728065.3	Full Text Download
application date：	2020-07-23	Public/Announcement Date：	2021-02-02
Public/Announcement No.：	CN 112309521A

The present invention discloses a method, apparatus, device, and readable storage medium for processing resources of a virtual community across a DIKW graph for intrinsic computing, the method comprising: establishing a DIKW graph by utilizing network-type resources of a virtual community; including a data graph, an information graph, and a knowledge graph in the DIKW graph; converting the data, information, and knowledge in the DIKW graph into each other, and using the conversion results to update the DIKW graph until the DIKW graph reaches dynamic equilibrium; extract the interaction relationship tuple of the target client from the DIKW graph, and use the interaction relationship tuple to determine the intimate communication relationship client of the target client; combine the interaction relationship tuple and the intimate communication relationship client to determine the feature label of the target client; determine the target resource corresponding to the feature label from the virtual community from the virtual community; and assigning the target resource to the target client. The present method can realize personalized allocation of resources and can meet the personalized needs of users.

40. An intrinsic computing-oriented approach to cross-modal user medical data analysis

CN202011199039.2	Full Text Download
2020-10-31	Public/Announcement Date：	2019-01-18
CN 109240787A

The present invention provides a cross-modal user medical data analysis method oriented to intrinsic computing, comprising the steps of S1, obtaining an identification type resource of a target user, querying a database and obtaining a private medical data of the target user based on the identification type resource of the user, said database including a local database and an external database; S2, modeling the private medical data of the target user based on a DIKW graph , obtaining an initial type resource; S3, obtaining a target type resource by performing a homo-modal or cross-modal fusion operation on the initial type resource; S4, analyzing and evaluating the health condition of the target user based on the target type resource. The present invention can, on the one hand, perform health assessment based on the patient's existing medical data so as to provide support for the doctor's diagnosis and treatment and avoid unnecessary repeated examinations for the patient, and on the other hand, it can play a role in protecting the privacy of the patient's medical data.

41. Intelligent carrier scheduling methodology oriented towards the exchange of data and information rights for value

application number：	CN202011329165.5	Full Text Download
application date：	2020-11-24	Public/Announcement Date：	2021-03-09
Public/Announcement No.：	CN 112456257A

The present invention provides an intelligent transportation device scheduling method for the exchange of data, information rights and values, comprising: S1, the floor controller calculates the user value of the user belonging to the floor in the waiting queue, and generates an array of user information by arranging the user value in a descending order; S2, the floor controller sends the array of user information to the elevator scheduling system, which generates a scheduling scheme based on the array of user information and sends it to the floor controller; S3, the floor controller sends a notification message to the corresponding user based on the scheduling scheme; S4, the floor controller modifies the user value based on the actual elevator ride result, and the user who has already ridden the elevator is removed from the waiting queue. The elevator scheduling system generates a scheduling plan based on the user information array and sends it to the floor controller; S3, the floor controller sends a notification message to the corresponding user based on the scheduling plan; S4, the floor controller modifies the user value based on the actual result of the elevator ride, and removes the user who has already taken the elevator ride from the waiting queue; S5, the floor controller adds a new user to the waiting queue, and prepares for the next scheduling. The present invention can provide more flexibility for elevator scheduling, provide a fast channel for users with urgent needs, and has high practical value.

42. A Cross-DIKW Modal Privacy Resource Preservation Approach for Intrinsic Computing and Reasoning

application number：	CN202011104613.1	Full Text Download
application date：	2020-10-15	Public/Announcement Date：	2021-01-19
Public/Announcement No.：	CN 112241552A

The present application discloses a cross-DIKW modal privacy resource protection method for intrinsic computation and reasoning, said method comprising: deleting a privacy resource corresponding to said resource protection instruction after receiving said resource protection instruction; querying a derivation path of said privacy resource and determining a derivation cost and a modal transformation cost for each said derivation path; determining, based on said derivation cost and said modal transformation cost, all transformation priorities of said derivation paths and selecting a target derivation path from all said derivation paths based on said transformation priorities; transforming a resource corresponding to said target derivation path from the original modality to another modality. The present application is able to improve the complexity of deducing privacy resources and ensure the security of privacy resources. The present application also discloses a cross-DIKW modality privacy resource protection system for intrinsic computation and reasoning, an electronic device, and a storage device with the above beneficial effects.

43. Recommendation methods and devices for cross-modal fusion of intrinsic computation and reasoning

application number：	CN202010856960.3	Full Text Download
application date：	2020-08-24	Public/Announcement Date：	2020-11-27
Public/Announcement No.：	CN 112000854A

Embodiments of the present invention disclose a cross-modal recommendation method and device for fusion of intrinsic computation and reasoning, and a medium, in which a data graph, an information spectrum graph, and a knowledge graph are constructed for an acquired target task by utilizing a pre-stored library of resource information; and discrepant resources are identified by matching the acquired user resource information with the data graph, the information spectrum graph, and the knowledge graph. According to the pre-established resource fusion cost library, calculate the conversion generation value of the difference resources for resource point transformation. Calculate the access generation value of each transformed resource group according to the corresponding access generation value and access times of each resource type; take the sum of the corresponding access generation value and transformation generation value of each transformed resource group as the final generation value of each transformed resource group, and select the transformed resource group with the smallest generation value as the recommended resource. By dynamically adjusting the difference resources and combining the user's access generation value of the resources, the effect of resource recommendation is effectively improved.

44. A multimodal approach to privacy protection that incorporates the technicalization of the Fair, Equitable and Transparent Statute.

application number：	CN202011098222.3	Full Text Download
application date：	2020-10-14	Public/Announcement Date：	2021-01-15
Public/Announcement No.：	CN 112231750A

The present invention provides a multimodal privacy protection method incorporating the technologization of fairness, justice and transparency regulations, comprising: S1, extracting privacy resources based on behavioral data generated by a user in a network, and constructing a DIKW graph corresponding to the user based on the privacy resources; S2, monitoring whether or not to generate a decision about the circulation of the privacy resources and obtaining information about a participant when the decision is generated, said participant comprising a generator, a communicator and acquirer; S3, analyzing the authority of the participant in the process of circulation of privacy resources based on the DIKW graph, and judging the legality of the decision about the circulation of privacy resources based on the analysis results. The present invention achieves effective protection of user privacy resources based on the intrinsic computation of multimodal and cross-modal content, and optimizes the integrated processing efficiency of storage, transmission, computation, and privacy data protection based on DIKW graph technology.

45. A cross-DIKW modal textual disambiguation approach for intrinsic computation and reasoning

application number：	CN202011103480.6	Full Text Download
application date：	2020-10-15	Public/Announcement Date：	2021-01-15
Public/Announcement No.：	CN 112232085A

The present application discloses a cross-DIKW modal text disambiguation processing method oriented to intrinsic computation and reasoning, said method comprising: obtaining a target text and determining a target data resource and a target information resource in the target text; querying a related resource of the target text based on the target data resource and/or the target information resource, and determining a textual meaning of the target text based on the related resource; if the textual meaning of the target text has a number is greater than 1, then obtaining supplementary resources of the target text and generating a conditional restriction text of the target text based on the supplementary resources; taking the text meaning that conforms to the conditional restriction text as the actual text meaning of the target text, and modifying the target text based on the actual text meaning. The present application is capable of accurately recognizing and eliminating ambiguities existing in the text. The present application also discloses a cross-DIKW modal text ambiguity processing system for essential computing and reasoning, an electronic device, and a storage medium with the above beneficial effects.

46. Essence recognition methods and components across data, information, knowledge modalities and scales

application number：	CN202010692408.5	Full Text Download
application date：	2020-07-17	Public/Announcement Date：	2020-10-27
Public/Announcement No.：	CN 111832727A

The present invention discloses an intrinsic recognition method, apparatus, device, and readable storage medium across data, information, and knowledge modalities and quantities, the method comprising: receiving and parsing a recognition request, determining a target object to be recognized from a plurality of objects to be recognized, and recognition reference data of the target object to be recognized; obtaining recognition reference information and recognition reference knowledge of the target object to be recognized; inputting the recognition reference resource into a comprehensive identification model; the identification reference resources include identification reference data, identification reference information, and identification reference knowledge; the reference resources are identified using a processing module in the comprehensive identification model that matches the reference resources, and identification results are obtained; and the identification results are output. It can be seen that compared with the current machine learning, deep learning and other recognition schemes, in this method, there is no need to collect a large number of samples to train the model to achieve effective recognition.

47. Device-sharing methodology for data and information fusion for purpose-based computing and reasoning

application number：	CN202011468887.9	Full Text Download
application date：	2020-12-25	Public/Announcement Date：	2021-04-02
Public/Announcement No.：	CN 112591568A

The present invention provides a device sharing method for data and information fusion oriented to purpose computing and reasoning, the method comprising: S1, the elevator scheduling system initiates a scheduling bid to a user who initiates a ride demand, responds to a first bid request of the user, generates a scheduling planning scheme and outputs the scheme to the user, and said ride demand comprises event information, demand information, and additional value information; S2, the elevator scheduling system initiates a floor bidding to a filtered user to initiate floor bidding, responding to the second bidding request of the user, generating a list of rides and an elevator scheduling plan based on the floor bidding results; S3, the elevator scheduling system controlling the elevator to execute the elevator scheduling plan. The present invention realizes a value-driven elevator scheduling method based on value, which can ensure that users can realize normal passage through the elevator and also prioritize the emergency needs of the users, thus providing more flexibility for elevator scheduling, and the said method is also applicable to other shared intelligent transportation devices, with a wide scope of application.

48. Feature mining methods and components across data, information and knowledge multimodalities

application number：	CN202011084392.6	Full Text Download
application date：	2020-10-12	Public/Announcement Date：	2021-01-12
Public/Announcement No.：	CN 112214531A

The present invention discloses a feature mining method, apparatus, device, and readable storage medium across data, information, and knowledge multimodalities, the method comprising: obtaining a data resource to be mined; classifying the data resource to obtain at least one type of data among scalar data, vector data, range data, temporal data, and categorical data; performing an association fusion process on the type of data to obtain an association fusion result; and determining the the association fusion result is determined as a feature of the data resource so that the data resource can be processed using the feature. In the method, the data resources are mined and processed to obtain a larger amount of more reliable features, so that the value of the data resources can be better mined.

49. Intrinsic computation-oriented differential content recommendation methods across data-information knowledge modalities

application number：	CN202010693137.5	Full Text Download
application date：	2020-7-17	Public/Announcement Date：	2020-10-27
Public/Announcement No.：	CN 111833022A

The present invention discloses a task processing method, apparatus, device, and readable storage medium across data, information, and knowledge modalities and magnitudes, the invention comprising: obtaining a target task to be executed, and a task realization resource of the target task; wherein the task realization resource comprises at least one of the task realization data, the task realization information, and the task realization knowledge; utilizing a comprehensive assessment model to perform a multidimensional, cross-modal, and cross-measure integrated planning processing to obtain the task planning resources; and executing the target task in accordance with the task planning resources. In this method, when obtaining a target task to be executed, it is only necessary to input its corresponding task realization resources into the comprehensive task evaluation model to obtain the task planning resources, and then execute the target task in accordance with the task planning resources, which can make the execution of the task more in line with the requirements.

50. Task processing methods and components across data, information, knowledge modalities, and dimensions

application number：	CN202011198393.3	Full Text Download
application date：	2020-10-31	Public/Announcement Date：	2021-02-02
Public/Announcement No.：	CN 112307028A

The present invention provides a differential content recommendation method for essential computing that spans data information knowledge modalities. The method comprises the following steps: S1, obtaining basic information of the target user, and connecting a database storing the target user's privacy information; S2. Construct a query function based on the publicly available statistical content of the database to query the target user's personal information data table, and obtain data and information resources; S3. Analyze data and information resources to obtain new information resources, match and push content based on the new information resources of the target user, and push to the target user. The present invention can achieve differential content push in the case of incomplete public data in the privacy database, improving the accuracy of the pushed content.

51. Differential Protection Method and System for Privacy Information Resources Typed Across DIKW Modal

Application No.:	CN202110075080.7	Full Text Download
Application:	2021-01-20	Public/Gazette：	2021-05-18
Publication number:	CN 112818386A

The present invention provides a differential protection method and system for privacy information resources typed across DIKW modalities, which includes: determining the privacy information resources to be protected when receiving a resource query request; Select each candidate resource path in the information trajectory corresponding to the private information resource; Determine the support of each information resource in each candidate resource path to obtain the path support in each candidate resource path; Determine the target resource path in the candidate resource path based on the path support degree in each candidate resource path; Obtain target information resources corresponding to resource query requests based on each information resource in the target resource path; Send target information resources to resource requesting users to achieve protection of private information resources. The application of the differential protection method for cross DIKW modal typed private information resources provided by the present invention can effectively protect private information resources and improve the security of resources.

52. Early warning methods and components across data, information, knowledge modalities, and dimensions

Application No.:	CN202010692385.8	Full Text Download
Application:	2020-07-17	Public/Gazette：	2020-10-30
Publication number:	CN 111860997A

The present invention discloses a warning method, device, device, and readable storage medium that cross data, information, knowledge modalities, and dimensions. The method includes: obtaining perception information and knowledge of a target warning scene, and utilizing sensors to collect real-time perception data of the target warning scene; Identify perceptual information, knowledge, and data as perceptual resources; Input perceptual resources into a multi-dimensional, cross modal, and cross dimensional comprehensive prediction model for data, information, and knowledge fusion for security prediction, and obtain security prediction results; Determine whether the safety prediction results match the preset alarm conditions; If so, use warning equipment for early warning. It can be seen that in this method, there is no need for manual analysis, and real-time and long-term warning can be achieved. Moreover, the comprehensive prediction model can integrate data, information, and knowledge, and can make multi-dimensional, cross modal, and cross dimensional safety predictions, ensuring the accuracy and reliability of safety prediction results.

53. Group Differential Privacy Protection Method and Device for purpose Driven DIKW System

Application No.:	CN202110381129.1	Full Text Download
Application:	2020-04-09	Public/Gazette：	2021-06-25
Publication number:	CN 113032832A

This application discloses a group differential privacy protection method and device for the purpose driven DIKW system, and constructs a DIKW system using the dataset to be privacy protected. Build the first differential privacy mechanism for the DIKW system. Using resource items as the first particle, gather all resource items in the DIKW system to construct a first particle swarm. Using particle swarm optimization algorithm, optimize the first particle swarm and obtain a new DIKW system. Use the new DIKW system as a group data for external release. Compared to the original group data, the privacy data in the new DIKW system has undergone differential privacy, achieving effective protection of privacy data. Moreover, the DIKW system adopts differential privacy mechanism to protect privacy data, which will not have any impact on the application of group data. It can be seen that using the technical solution described in this application can reasonably protect the privacy of personal data in group data while ensuring the effective use of group data.

54. A Multimodal DIKW Content Multi Semantic Analysis Method for Essential Computing

Application No.:	CN202011099503.0	Full Text Download
Application:	2020-10-14	Public/Gazette：	2021-01-15
Publication number:	CN 112232082A

The present invention provides a multimodal DIKW content multi semantic analysis method for essential computing, which includes the following steps: S1. obtaining type resources for semantic recognition, and determining the existence of multi semantics based on the recognition results. The type resources include data resources DDIKW, information resources IDIKW, and knowledge resources KDIKW; S2. When there are multiple meanings, analyze the reasons for the formation of multiple meanings; S3. Based on the reasons for the formation of multiple semantics, corresponding strategies are adopted to convert the original type resources into new type resources, and the final semantic recognition results are obtained. The present invention can help artificial wisdom systems improve the efficiency of language and text recognition and improve the accuracy of recognition results.

55. A Method for Encoding and Decoding User Behavior Content Across Data Information Knowledge Modalities

Application No.:	CN202011196953.1	Full Text Download
Application:	2020-10-31	Public/Gazette：	2021-02-02
Publication number:	CN 112307974A

56. A Cross modal Randomized Privacy Protection Method and System for Essential Computing and Reasoning

Application No.:	CN202110043010.3	Full Text Download
Application:	2021-01-13	Public/Gazette：	2021-01518
Publication number:	CN 112818381A

This application provides a cross modal randomized privacy protection method and system for essential computation and inference, which includes: obtaining content resources of multiple individual objects from multiple data sources; Mapping the content resources of the multiple individual objects into data resources, information resources, and knowledge resources; Data resources include numerical data resources and logical data resources; For each individual object, if the individual object only contains content resources of one data type, perform essential calculations corresponding to the data type for the content resources contained in the individual object; If the individual object includes content resources of multiple data types, then perform essential operations corresponding to the data type for each type of content resource, and perform cross modal operations for each two different data types of content resources; Randomize the calculated content resources. By combining essential operations and randomization processing, data security is effectively ensured.

57. Processing Methods and Components of DIKW Privacy Resources for Essential Computing

Application No.:	CN202110043702.8	Full Text Download
Application:	2021-01-13	Public/Gazette：	2021-05-18
Publication number:	CN 112818382A

This application provides a processing method and component for DIKW privacy resources oriented towards essential computing, which includes obtaining a target individual object, setting the total conversion cost to zero, and establishing a set of pending resources using the privacy resources contained in the target individual object; Extract a privacy resource from the set of pending resources as a pending privacy resource; Based on the conversion cost and the essential calculation cost of the target individual object, the privacy resources to be processed are converted into numerical data resources, logical data resources, and information resources in any mode. The transformed privacy resources are stored in the processed resource set, and the above steps are repeated until the pending resource set is empty. Finally, the privacy resources contained in the target individual object are saved, Replace with the privacy resources included in the processed resource set.

58. Resource Identification Method, Related Devices, and Readable Media Based on DIKW Graph

Application No.:	CN202110431356.0	Full Text Download
Application:	2020-04-21	Public/Gazette：	2021-06-08
Publication number:	CN 112925921A

This application proposes a resource identification method, related devices, and readable medium based on DIKW graph, which obtains the original resources of the target object and the user's purpose; Using the user's purpose and the original resources of the target object, traverse the resource graph in the DIKW system, and derive multiple derivation paths associated with the target object from the resource graph; Among them, resource graphs include: data graphs, consciousness graphs, information graphs, and knowledge graphs; The derivation path includes multiple interconnected resources in the resource graph, and each derivation path includes the original resources of the target object and new resources associated with the target object; The new resources associated with the target object are generated by traversing the resource graph derivation; Due to the ability to derive hidden information in the target object through the DIKW system in this application, the resource identification results of users analyzed from each derivation path will be more comprehensive and accurate.

59. A Relative Differential Privacy Protection Method for Essential Computing Across DIKW Modes

Application No.:	CN202011580150.6	Full Text Download
Application:	2020-12-28	Public/Gazette：	2021-04-20
Publication number:	CN 112685772A

The present invention provides a relative differential privacy protection method for essential computing across DIKW modes, comprising the following steps: S1. obtaining a target individual object to be published, and modeling the target individual object based on DIKW graph; S2. Analyze whether the target individual object contains type resources that can be used for essential or differential calculations to obtain the privacy resources of the target individual object. If not, publish the target individual object. If yes, proceed to the next step. The type resources include one or more of data resources, information resources, and knowledge resources; S3. Implement privacy protection policies for the types of resources contained in the target individual object; S4. Publish target individual objects that have been subjected to privacy protection policies. The present invention can increase the computational cost and difficulty that attackers need to pay to obtain privacy resources based on type resources, thereby effectively protecting user privacy.

60. Intrinsic computing-based cross-modal feature mining methods and components

Application No.:	CN202011387490.7	Full Text Download
Application：	2020-12-01	Public/Gazette：	2021-02-26
Publication number:	CN 112418428A

The present invention discloses a cross-modal feature mining method, apparatus, device, and readable storage medium based on intrinsic computing, the method comprising: obtaining at least two typed resources; the typed resources comprising a data resource, an information resource, and a knowledge resource; carrying out an association fusion process on the at least two typed resources to obtain a fusion result; and determining the fusion result as a cross-modal feature corresponding to the at least two typed resources, the so as to process the at least two typed resources using the cross-mode features. In the method, the cross-modal feature is obtained by performing cross-modal association processing on the at least two typed resources, and the cross-modal feature is obtained by performing association processing on different typed resources, and the cross-modal feature is able to significantly increase the amount of features for resource mining and increase the reliability of the resource mining compared to data mining only on the same modal resources, and the cross-modal feature is more conducive to further processing of the typed resources. The cross-modal feature is more conducive to further processing of typed resources.

61. Delivery methods and systems for the conversion of data portraits into information portraits for value exchange

Application No.:	CN202110043701.3	Full Text Download
Application：	2021-01-13	Public/Gazette：	2021-05-14
Publication number:	CN 112794172A

The present invention provides a delivery method and a system for the exchange and conversion of value between data portraits and information portraits, obtaining information on lift events, lift demand information, bidding information and lift intervals submitted by each user in a target building, and obtaining user credit information; for each floor, calculating the total value of the floors of the floors by using the lift event information, the lift demand information, the bidding information and the user credit information of the users of the floors Determine the floor with the highest total floor value as the target floor; determine the user whose starting point or end point of the elevator ride is the target floor as the target user; determine the final elevator user from all the target users by using the elevator event information, elevator demand information and bidding information of the target user; generate the lift scheduling plan in accordance with the elevator intervals, elevator demand information and bidding information of the final user; and dispatch the lifts according to the lift scheduling plan. According to the lift scheduling plan, the lift is dispatched to meet the user's personalised needs and improve the flexibility of lift scheduling and user experience.

62. Common sense reasoning-based intrinsic content processing method and system for multimodal resources

Application No.:	CN202110074301.9	Full Text Download
Application：	2021-01-20	Public/Gazette：	2021-05-18
Publication number:	CN 112818385A

The present invention provides an intrinsic content processing method and system for multimodal resources based on common sense reasoning. By obtaining a resource and mapping the resource into a typed resource based on a wisdom graph architecture, the resource comes from a plurality of sources, and the typed resource includes at least three modalities of a data resource, an information resource, and a knowledge resource; by performing a homomodal association fusion and/or a cross-modal association fusion based on the obtained typed resource, a new resource is obtained and a modality is determined; and by adopting a randomisation, the new resource is privacy-protected. In this scheme, by complementing each other through the same-modality association fusion and/or the cross-modality association fusion, forming a new resource, and adopting randomisation to protect the privacy of the fused new resource, the purpose of obtaining a complete and determined resource and comprehensively protecting the privacy of the complete and determined resource after processing the resource is achieved.

63. DIKW model construction method and device for purposeful computation and reasoning

Application No.:	CN202110430285.2	Full Text Download
Application：	2021-04-21	Public/Gazette：	2021-06-11
Publication number:	CN 112949321A

The present application discloses a method and apparatus for constructing a DIKW model for purpose-oriented computation and reasoning, wherein said method comprises: acquiring typed resources; wherein said typed resources include data resources, information resources, knowledge resources, and purpose resources; constructing a graph corresponding to each type of said typed resources; constructing a model corresponding to each type of said typed resources based on the graph corresponding to each type of said typed resources, each said parent-child inclusion relationship and/or logical relationship between said typed resources, respectively, to construct a model corresponding to said typed resources of each type; wherein said parent-child inclusion relationship refers to a semantic inclusion relationship between two of said typed resources; said logical relationship refers to a relationship between the semantics expressed between two of said typed resources of the same tier and the semantics expressed by said typed resources of the same upper tier of both; said parent-child relationship as well as said logical relationship between said typed resources is represented in the model by means of different connecting lines between corresponding nodes.

64. Differential user privacy protection methods across data, information and knowledge modalities

Application No.:	CN202011377647.8	Full Text Download
Application：	2020-11-30	Public/Gazette：	2021-02-05
Publication number:	CN 112329069A

The present invention provides a method for privacy protection of user differences across data, information, and knowledge modalities, comprising the steps of S1, obtaining a target data resource, and determining whether the target data resource is associated with a specific user; S2, identifying all the data resources associated with the target data resource as connotative data resources or ephemeral data resources when the target data resource is associated with a specific user; S3, based on the connotative data resource and the attributes and corresponding attribute values of the epitaxial data resources to determine whether they belong to the privacy data resources; S4, performing a privacy protection operation on the privacy data resources when the data requesting party requests access to the privacy data resources. The present invention can achieve the protection of implicit data resources and reduce the risk of user privacy leakage.

65. Purpose-driven multimodal DIKW content delivery methodology

Application No.:	CN202110867169.7	Full Text Download
Application：	2021-07-29	Public/Gazette：	2021-11-12
Publication number:	CN 113645284A

The present invention provides a purpose-driven multimodal DIKW content transmission method applied to a transmission system based on a DIKW mapping transmission protocol, said DIKW mapping transmission protocol comprising an Purpose identification layer, a content review layer, a transmission plan formulation layer and a resource processing layer, said method being based on understanding and analysing the purpose of the three participating subjects, namely, the transmitter, the receiver and the transmission system, by promoting a balance between the purposes of the three participating subjects, and maximising self-interest while satisfying the purposes of the transmitter and receiver as much as possible, and by applying DIKW technology to analyse and model user-generated content and the purposes of the participating subjects in the transmission process, it is possible to obtain a transmission result that better meets the needs of the user and is more desirable at a lower transmission cost relative to existing computer communication protocols.

66. Affective communication methodology based on DIKW content objects

Application No.:	CN202111034260.7	Full Text Download
Application：	2021-09-03	Public/Gazette：	2021-12-17
Publication number:	CN 113810480A

The present invention provides an emotional communication method based on a DIKW content object, comprising the steps of: s101, obtaining transmission content of a sender, converting the transmission content of the sender into a type resource, said type resource comprising a data type resource, an information type resource, and a knowledge type resource; s102, determining content and determining a transmission range of the type resource of the transmission content of the sender by a content determination algorithm; S103, obtaining a DIKW map of the receiving party and converting the transmitted content according to the DIKW map of the receiving party; S104, sending the processed transmitted content to the receiving party. The present invention enables the purpose of the sender to be expressed more aptly and reduces the understanding error between the sender and the receiver, realising higher accuracy and efficient emotional communication.

67. Personality analysis and content pushing method for virtual community people based on DIKW mapping

Application No.:	CN202110788578.8	Full Text Download
Application：	2021-07-13	Public/Gazette：	2021-10-22
Publication number:	CN 113538090A

The present invention provides a virtual community personnel personality analysis and content pushing method based on DIKW mapping, determining whether a user is a new user or an old user, adopting tagging and hot list methods for new users to push content, collecting typed data of old users and constructing a DIKW mapping model from it, and the DIKW mapping model contains a user data mapping, a user information mapping, and a user knowledge mapping. The DIKW mapping model is processed according to the typed resources to obtain the user's historical behaviour records, the user's purchase conversion rate, the percentage of products purchased, and the collection of high-frequency interactive products, and thus obtains the user's preferred products, and then pushes them out, which not only solves the problem of the cold start of the user's new user, but also pushes out the corresponding products according to the old user's historical data on the shopping website to ensure that the products are in line with the user's personality, interests and interests, and the products are in line with the users' interests and interests. This ensures that the products are in line with the user's personality, interests and habits, and are compatible with all major shopping sites.

68. Fairness-orientated DIKW-enabled mapping and delivery method for emotional content

Application No.:	CN202111532716.2	Full Text Download
Application：	2021-12-15	Public/Gazette：	2022-04-19
Publication number:	CN 114374664A

The present invention provides a method of mapping and transmitting DIKWised emotional content based on fairness orientation, comprising: s101, initiating an interaction request by the sender via a first server, confirming the interaction request by the receiver via a second server, and establishing a communication link between the first server and the second server; s102, establishing a personalised DIKW model of the sender and the receiver by the first server and the second server, respectively, and making real-time adjustments to the personalised DIKW model;

S103: The sender initiates an application for the target content of the emotional communication, the first server establishes a DIKW model based on the target content of the emotional communication, identifies the emotional state of the sender, monitors the emotional state of the sender, and sends the target content of the emotional communication to the second server; S104: The second server adjusts, monitors, and sends the target content of the emotional communication to the second server based on the personalised DIKW model of the receiver. adjusting, controlling, and deciding the emotional communication target content, and realising the personalized emotional content display. The present invention can achieve fairness-oriented emotional content transmission and avoid unfair communication problems caused by understanding bias.

69. Blockchain consensus methodology based on the DIKWP model

Application No.:	CN202111658319.X	Full Text Download
Application：	2021-12-30	Public/Gazette：	2022-04-15
Publication number:	CN 114363354A

The present invention provides a blockchain consensus method based on a DIKWP model, comprising the steps of: obtaining typed resources of all blocks in the blockchain, constructing a blockchain DIKWP model based on the typed resources; a user initiating request information, obtaining typed resources based on the request information, constructing a user DIKWP model based on the typed resources; the user DIKWP model performing traversal lookup and purpose comparison of the blockchain DIKWP model, and obtaining a blockchain to be added; the user DIKWP model packing the user initiated request information into a new block to be connected to the end of the blockchain to be added, and performing traversal lookup and purpose comparison of the blockchain to be added. The user DIKWP model traverses the blockchain DIKWP model to find and compare purposes, and obtains the blockchain to be added; the user DIKWP model packs the user-initiated request information into a new block connected to the end of the blockchain to be added, constructs the DIKWP model on the blockchain and the user's request information, and aggregates the blockchain and the user's own initiated content, and the DIKWP model can mutually convert and combine the contents within it. The DIKWP model can convert and combine the internal contents with each other, so that the blockchain to be added can be obtained quickly after traversal search and purpose comparison, and the efficiency of consensus uplinking can be improved.

70. Purpose-driven computationally oriented trans-DIKW modal transport and optimisation system

Application No.:	CN202111006628.9	Full Text Download
Application：	2021-08-30	Public/Gazette：	2022-02-11
Publication number:	CN 114039865A

The present invention discloses a method and apparatus for an purpose computation oriented trans-DIKW modal transmission and optimisation system, which can identify the sender's purpose by analysing the transmission content and invoking an existing DIKW map; determine whether said transmission content is transmittable or not based on the receiver's purpose and the system's purpose; abort the transmission process when said system determines that said transmission content is non-transmittable; when said system determines that said When said system determines that said transmission content is conditional transmission, said system formulates a transmission solution for said condition; when said system determines that said transmission content is direct transmission, said system transmits said transmission content directly to the receiver. The present invention can solve the problem that the traditional content-oriented transmission system is unable to reach a consensus balance between the participating parties during the transmission process, and can formulate a time-saving and economically efficient transmission plan based on the purposes of the participating parties, reduce the bandwidth consumption of network content transmission, minimise the release of personal information, improve the efficiency of network transmission, and realise the maximisation of the interests of the transmitting parties.

71. Personalised alphabet display style change method

Application No.:	CN202111658319.X	Full Text Download
Application：	2018-07-20	Public/Gazette：	2019-01-18
Publication number:	CN 109241750A

The present invention relates to a personalised English letter display style transformation method, belonging to the intersection of software engineering and artificial wisdom, and is characterised by obtaining a user's sentence or document of the style of the letters to be changed, marking the style features of each letter using a five-dimensional feature set F, i.e., (endpoints relative position features, endpoints morphological features, thickness features, colour features, curvature features), and having the user perform a target style transformation of the individual letters to visualise the target style transformation, also labelled with a five-dimensional feature set FU, taking F over FU as input, and combining the classification attributes of the written letters proposed in the present invention, learning the user's change features (SF) for the style change of the letters based on the change feature extraction model (LFEM) between the target style and the original style of a letter, and thereafter, according to the feature parameter SF, the user's style change of the letters that constitute the rest of the letters to perform the style change.

72. DIKW resource analysis methodology and system for purposeful computing and reasoning

Application No.:	CN 202110907780.8	Full Text Download
Application：	2021-08-09	Public/Gazette：	2021-11-09
Publication number:	CN 113628753A

73. DIKW Resource Interpopulation System for Purpose-Based Computing and Reasoning

Application No.:	CN 202111004843.5	Full Text Download
Application：	2021-08-30	Public/Gazette：	2021-11-16
Publication number:	CN 113657083A

The present invention discloses a method and device of a DIKW resource interaction filling system oriented towards purpose calculation and reasoning, which can judge the difference between the purposes of two parties by comparing the purpose of a form or a table item with the purposes of a form filler; grade the value of the filling based on the purpose of the form filler by speculating whether or not the filling of the form or the table item generates a benefit; change the uncertainty in the process of the content filling on demand; change the DIKW probability deviation factor of said content filling on demand; and perform content verification on the data or information filled in by the form analyst and the form filler DIKW probability deviation factor of the content filling is changed on demand; and the data or information filled in by the form filler is content verified, so as to achieve a balance of purpose between the form analyst and the form filler, and to achieve intelligent and reasonable content filling. The present invention can solve the content filling process by filling the form based on the data, purpose, information, and knowledge system of the form filler, standing in the perspective of the form filler, fully considering the purpose of the form filler, and speculating the purpose of the form, and solving the content filling process driven by the purpose, and solving various problems such as the purpose balance and the purpose confrontation between the form filler and the form analyser in the content filling process.

74. Emotion expression mapping, metrics and optimised delivery system for DIKW resources

Application No.:	CN 202111006620.2	Full Text Download
Application：	2021-08-30	Public/Gazette：	2021-11-30
Publication number:	CN 113722505A

The present invention discloses a method and apparatus for a DIKW resource oriented emotion expression mapping, metrics and optimisation delivery system, which can be used to conceptualise and abstract the content of the emotion expression of a sender; transform said conceptualised and abstracted content of the emotion expression into a data-type resource DDIK, an information-type resource IDIK, and a knowledge-type resource KDIK; analyse said content of the emotion expression of a sender analysed to obtain the sender's purpose P; mapping said data type resource DDIK, information type resource IDIK and knowledge type resource KDIK with the sender's purpose P categorised into a DIKW mapping model; analysing said sender's DIKW model to obtain the real content of the transmission and traversing the receiver's DIKW model to reshape the transmitted content into a topology that matches the receiver's cognitive ability; presenting said reshaped transmitted content to the receiver according to the receiver's expectations. The present invention can solve the problem of improper expression due to personal cognitive differences among the participating parties in emotional communication, reduce the understanding bias among the communicating parties, and achieve the accuracy and efficiency of emotional communication.

75. Cross-DIKW modal textual disambiguation for intrinsic computation and reasoning (Canada)

Application No.:	CN202011103480.6	Full Text Download
Application：	2020-10-15	Public/Gazette：	2021-01-15
Publication number:	CN 112232085A

A cross-DIKW-mode ambiguity processing method oriented for essential computing and reasoning, a system thereof, an electronic device and a storage medium are provided. The method includes: obtaining a target text, and determining a target data resource and a target information resource in the target text; retrieving a correlated resource of target text according to the target data resource and/or target information resource, and determining the meaning of the target text according to the correlated resource; if target text has more than one meaning, obtaining a supplementary resource for the target text, and generating a condition-restricted text for the target text according to the supplementary resource; taking a meaning in conformity with the condition limitation as the actual meaning of the target text, and modifying the target text according to the actual meaning. Ambiguity existing in the text can be accurately recognized and eliminated.

76. DIKW-based vehicle path planning methodology

Application No.:	CN 202111663376.7	Full Text Download
Application：	2021-12-30	Public/Gazette：	2022-04-08
Publication number:	CN 114295144A

The present invention provides a vehicle path planning method based on DIKW, the specific steps of which include: obtaining a typed resource of a driver, constructing a DIKW model of the driver based on the typed resource; carrying out path planning based on a starting point and an end point to obtain a plurality of initial paths; obtaining road information of the initial paths, constructing a DIKW model of the initial paths; screening the initial paths by the initial paths in accordance with the purpose of the DIKW model of the driver, and obtaining an optimal path by collecting the typed resource of the driver and extracting the purpose contained therein. The initial path DIKW model filters the initial paths according to the driver's purpose to obtain the optimal path. By collecting the driver's typed resources and extracting the purpose contained therein, the initial paths are filtered according to the driver's purpose to obtain the optimal path during path planning, and the optimal paths obtained can satisfy the driver's needs, which is more efficient compared to the traditional path planning that only considers factors such as the number of traffic lights, the degree of congestion and the mileage, and the number of traffic lights, Compared with the traditional path planning that only considers the number of traffic lights, congestion level, mileage and other factors, it is more humane and can meet the multi-level driving needs of drivers.

77. Purpose-driven DIKW-based content processing method and system

Application No.:	CN 202110909286.5	Full Text Download
Application：	2021-08-09	Public/Gazette：	2021-11-05
Publication number:	CN 113609827A

The present application discloses a content processing method and system based on purpose-driven DIKW, obtaining a resource between a user's purpose and a table item in a form to be populated, constructing a map corresponding to the resource, associating a data map, an purpose map, and an information map through the association relationship between the user's purpose and the respective map, obtaining an association result, and transforming the map to obtain a target map through the association result, and filling the target map with The corresponding information of the target atlas is filled into the table entries in the table to be filled. By means of the above-described scheme, the correlation result containing the correlation relationship among the data map, the purpose map and the information map is obtained, the individual maps are transformed, so that data with correlation is integrated to obtain a stable, complete and directional target map, and the filling result is obtained by filling the table entries to be filled in with the corresponding information of the target map, so as to improve the determinacy and completeness of the filling result. Completeness.

78. DIKW-based area sensing and access cueing approach

Application No.:	CN 202111366568.1	Full Text Download
Application：	2021-11-18	Public/Gazette：	2022-03-01
Publication number:	CN 114120620A

The present invention provides a DIKW-based area sensing and passage prompting method, establishing an area DIKW atlas by obtaining the contents of each area in a map in real time, and obtaining the contents related to a moving target in real time in order to establish a DIKW atlas of the moving target, generating a multi-type electronic fence on the basis of the real-time generated area DIKW atlas and DIKW atlas of the moving target, determining a positional relationship between the moving target and the multi-type electronic fence on the basis of the positional relationship between them, and providing a passage prompt to the moving target through a prompting device based on the positional relationship between them. mapping of the moving target, determining a positional relationship between the moving target and the multi-type electronic fence, and providing a passage prompt to the moving target through a prompting device based on the positional relationship between the two. The present invention can realise dynamic perception of the area based on the real-time evolution of the area and the DIKW mapping of the mobile target, and generate different types of electronic fences based on this, and on this basis provide passage prompts to the mobile target for approaching or entering the area of the electronic fence, so as to assist the mobile target in planning a more secure mobile route.

79. Feature mining methods and components across data, information, knowledge multimodalities (Australia)

Application No.:	2021258057	Full Text Download
Application：	2021-09-14	Public/Gazette：	2023-07-31
Publication number:	202011084392.6

A feature mining method, a feature mining device, a feature mining apparatus for multiple modes including data, information and knowledge, and a readable storage medium are provided. The method includes: obtaining a data resource to be mined; classifying the data resource, to obtain at least one type data of scalar data, vector data, range data, time data and classified data; performing an association and fusion process on the type data, to obtain an association and fusion result; and determining the association and fusion result as a feature of the data resource, to process the data resource by utilizing the feature. With the method, the data resource is mined to obtained more features, and the reliability of the obtained features are higher. In this way, the value of the data resource can be better mined.

80. Purpose-driven interactive form-filling methodology for DIKW content

Application No.:	202111022025.8	Full Text Download
Application：	2021-09-01	Public/Gazette：	2021-12-17
Publication number:	113807063A

The present invention provides an purpose-driven interactive form filling method for DIKW content, comprising the following steps: constructing a first DIKP model based on the form filler information, constructing a second DIKP model based on the form information; comparing the purposes and judging the values based on the first DIKP model and the second DIKP model, and obtaining the filling value level of the form; adopting a reduced According to the difference of filling value level, adopt the method of reducing certainty, fuzzy transfer and defence filling to fill in the form information; and carry out feedback verification for the completed form information. Combining the DIKP system into the automatic form filling system to fill in the information automatically can save the process of filling in information manually by the filler, realizing the intelligent filling of the form, and the filler's purpose is taken into account in the filling process, so that the information filled in will not violate the filler's purpose and protect the privacy of the filler. In the filling process, the purpose of the filler is fully considered so that the filled information will not be contrary to the purpose of the filler and the privacy of the filler is protected.

81. DIKW mapping-based method for confirmation of vaccination concentration

Application No.:	202110830241.9	Full Text Download
Application：	2021-07-22	Public/Gazette：	2021-11-12
Publication number:	113643785A

The present invention provides a method for confirming vaccination concentration based on a DIKW map, said method establishes a DIKW map of a target user to be vaccinated, extracts one or more of data resources, information resources and knowledge resources from the DIKW map for analysis and calculation, so as to obtain information resources containing the user's physical condition, and determines a recommended vaccination concentration for the target user by combining information resources about the physical condition of the target user and appropriate vaccination concentration for people with different physical conditions. By combining the information resources about the physical condition of the target user and the appropriate vaccination concentration for people with different physical conditions, the present invention comprehensively analyses the physical condition of the target user through the establishment of the DIKW mapping, and obtains the corresponding recommended vaccination concentration based on the information resources about the physical condition of the user, so as to be able to recommend the vaccination concentration adapted to the physical condition of different individual users, and achieve customized vaccination, which helps to improve the physical condition of the target user and improve the health of the user. This enables customised vaccination and helps to improve the vaccination rate.

82. Methods, apparatus, storage media and electronic devices for updating DIKW atlases

Application No.:	202110908781.4	Full Text Download
Application：	2021-08-09	Public/Gazette：	2021-11-05
Publication number:	113609307A

The present invention discloses methods, apparatuses, storage media and electronic devices for updating a DIKW atlas, which can be performed by obtaining a first data resource (a first data node name and a first data semantic interpretation) and a first purpose resource (a first purpose node name and a first purpose semantic interpretation); formally associating the first data node name and the first purpose node name to obtain a first information node name; semantically fusing the first data semantic interpretation and the the first purpose semantic interpretation are semantically fused to obtain the first information semantic interpretation; the first information node name and the first information semantic interpretation are added as a first information resource to an information resource type mapping; and in the DIKW mapping, the first data resource and the first purpose resource are concatenated, and a branch on the concatenation is drawn to point to the first information resource, thereby updating the DIKW mapping. The present invention can continuously fuse and generate new resources to enrich the DIKW atlas, thereby improving the breadth and depth of the search with high accuracy.

Duan Yucong, male, currently serves as a member of the Academic Committee of the School of Computer Science and Technology at Hainan University. He is a professor and doctoral supervisor and is one of the first batch of talents selected into the South China Sea Masters Program of Hainan Province and the leading talents in Hainan Province. He graduated from the Software Research Institute of the Chinese Academy of Sciences in 2006, and has successively worked and visited Tsinghua University, Capital Medical University, POSCO University of Technology in South Korea, National Academy of Sciences of France, Charles University in Prague, Czech Republic, Milan Bicka University in Italy, Missouri State University in the United States, etc. He is currently a member of the Academic Committee of the School of Computer Science and Technology at Hainan University and he is the leader of the DIKWP (Data, Information, Knowledge, Wisdom, Purpose) Innovation Team at Hainan University, Distinguished Researcher at Chongqing Police College, Leader of Hainan Provincial Committee's "Double Hundred Talent" Team, Vice President of Hainan Invention Association, Vice President of Hainan Intellectual Property Association, Vice President of Hainan Low Carbon Economy Development Promotion Association, Vice President of Hainan Agricultural Products Processing Enterprises Association, Visiting Fellow, Central Michigan University, Member of the Doctoral Steering Committee of the University of Modena. Since being introduced to Hainan University as a D-class talent in 2012, He has published over 260 papers, included more than 120 SCI citations, and 11 ESI citations, with a citation count of over 4300. He has designed 241 serialized Chinese national and international invention patents (including 15 PCT invention patents) for multiple industries and fields and has been granted 85 Chinese national and international invention patents as the first inventor. Received the third prize for Wu Wenjun's artificial wisdom technology invention in 2020; In 2021, as the Chairman of the Program Committee, independently initiated the first International Conference on Data, Information, Knowledge and Wisdom - IEEE DIKW 2021; Served as the Chairman of the IEEE DIKW 2022 Conference Steering Committee in 2022; Served as the Chairman of the IEEE DIKW 2023 Conference in 2023. He was named the most beautiful technology worker in Hainan Province in 2022 (and was promoted nationwide); In 2022 and 2023, he was consecutively selected for the "Lifetime Scientific Influence Ranking" of the top 2% of global scientists released by Stanford University in the United States. Participated in the development of 2 international standards for IEEE financial knowledge graph and 4 industry knowledge graph standards. Initiated and co hosted the first International Congress on Artificial Consciousness (AC2023) in 2023.

Prof Yucong Duan

DIKWP Research of Artificial Consciousness

AGI-AIGC-GPT Evaluation Research

DIKWP Group，Hainan University

duanyucong@hotmail.com

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Introduction

1.1 DIKWP

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1.3 Purpose-driven approach to fusion of data, information, knowledge, and wisdom for invention and creation:DIKWP-TRIZ

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DIKWP-TRIZ：purpose driven integration of DIKWP

Introduction

1.1 DIKWP

1.2 TRIZ

1.3 Purpose-driven approach to fusion of data, information, knowledge, and wisdom for invention and creation:DIKWP-TRIZ

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