博文

This paper deeply discusses the application and innovation of DIKWP model in blockchain technology. This paper focuses on how the DIKWP model (data, information, knowledge, wisdom and purpose) can promote the realization of adaptive consensus strategy in the blockchain environment, optimize the balance between processing efficiency and security, and strengthen the management of multimodal digital assets through the design of intelligent contracts and the construction of value-oriented consensus mechanism. This paper also explores the potential of DIKWP in practical application scenarios and looks forward to its important role in promoting the technological innovation and development of blockchain.

1 Introduction

With the rapid development of blockchain technology and the expansion of its application scope, it faces new challenges in multimodal digital asset management and adaptive consensus strategy. DIKWP model provides a new perspective and method to strengthen the function and efficiency of blockchain technology. The purpose of this paper is to explore how the DIKWP model can be applied innovatively in blockchain technology, especially in adaptive consensus strategy and multimodal digital asset management.

2 Adaptive consensus strategy and implementation mechanism DIKWP in blockchain environment

In the development of blockchain technology, it is very important to construct an effective adaptive consensus strategy in the face of multi-source heterogeneous resources and multimodal digital assets. This report focuses on the application of DIKWP (Data, Information, Knowledge, Wisdom, purpose) model in the blockchain network environment, and discusses how to realize the determination of content objects, the balance between processing efficiency and security, usage modeling, intelligent contract design, and the construction of value-oriented consensus mechanism at all stages of digital assets.

2.1 DIKWP content object determination method

Data layer (D): Before winding, identify and verify the original data in digital assets, such as transaction records and user activity logs.

Information layer (I): extract key information from the data, such as the time, place and participants of the transaction.

Knowledge layer (K): Extract knowledge from information, such as trading patterns and user behavior trends.

Wisdom layer (W): based on knowledge, predict and insight into future trends and provide support for decision-making.

purpose layer (P): Define the goals and expectations of digital asset management and application.

2.2 Building a balance between processing efficiency and security

Efficiency optimization: through algorithm optimization and data structure adjustment, the efficiency of blockchain in dealing with multimodal digital assets is improved.

Security guarantee: implement encryption technology and access control to ensure that the security of digital assets is not destroyed.

2.3 Blockchain usage modeling and intelligent contract design of multimodal digital assets

Modal transformation: design a mechanism to make digital assets flow between different DIKWP levels and realize multi-dimensional application.

Life cycle management: Manage the whole life cycle of digital assets from creation, circulation to extinction through smart contracts.

2.4 Value-oriented consensus mechanism construction strategy

Consensus algorithm: develop a new consensus algorithm to ensure that consensus in the network is reached on the basis of value orientation.

System development: build a blockchain system that adapts to multi-modal digital assets and supports complex trading and application scenarios.

2.5 Demonstration application research

Application scenario: Explore the potential of DIKWP-AC model in real-world applications, such as smart cities and supply chain management.

Demonstration project: develop and deploy specific demonstration projects to demonstrate the practicability and effectiveness of the value-oriented multi-modal digital asset blockchain system.

The application of DIKWP model in blockchain environment, especially in the processing and management of multimodal digital assets, shows great potential. By implementing adaptive consensus strategy, balance between efficiency and security, intelligent contract design and value-oriented consensus mechanism, DIKWP-AC system can not only improve the application efficiency and security of blockchain technology, but also promote the healthy development of digital assets. In the future, this model is expected to be widely used in many fields and promote the innovation and development of blockchain technology.

3 transformation of consensus cognition based on DIKWP in blockchain environment

With the rapid development of blockchain technology, it is particularly critical to transform the consensus mechanism from conceptual space to semantic space. The application of DIKWP (Data, Information, Knowledge, Wisdom and purpose) model in the blockchain environment not only promotes this transformation, but also provides a deep cognitive foundation, thus enhancing the effectiveness and adaptability of the consensus mechanism.

3.1 Transformation from Conceptual Space to Semantic Space

Conceptual space: The consensus mechanism of traditional blockchain systems is mostly based on static and superficial conceptual understanding, such as transaction verification and data integrity.

Semantic space: Through the DIKWP model, the blockchain system is upgraded from simple data and information processing to in-depth understanding of the semantics of the content, involving user purposes, market trends and ethics.

3.2 The application of dikwp in blockchain consensus mechanism

Data layer (d) application:

Collect and verify raw data, such as transaction records and user behavior data.

Convert it into semantic information and provide basic facts for the consensus mechanism.

Information layer (I) application:

Extract key information from the data, such as trading motives and user preferences.

Provide a deeper situational understanding for the consensus mechanism.

Knowledge layer (k) application:

Turn information into specific knowledge, such as market trends and regulatory requirements.

Provide professional and background knowledge for consensus decision.

Wisdom layer (w) application:

Combined with the output of knowledge layer, innovative solutions and strategies are put forward.

In-depth consideration of promoting consensus mechanism at the moral and ethical levels.

purpose layer (p) application:

Clarify the objectives and expected effects of the consensus mechanism.

Based on the system objectives and user needs, formulate consensus strategies.

3.3 Transformation and realization of consensus mechanism

Enhancement of semantic understanding: DIKWP model makes consensus mechanism not only pay attention to factual data, but also dig deep into the purpose and purpose behind the data.

Dynamic adaptability: As the situation changes, the consensus mechanism can be adjusted in real time according to new data and information.

Individualization and precision: Based on the DIKWP model, the consensus mechanism can more accurately reflect the needs and expectations of all parties.

The application of DIKWP model in blockchain environment greatly improves the efficiency and accuracy of consensus mechanism. Through the transformation from conceptual space to semantic space, the consensus mechanism not only becomes more flexible and adaptable, but also can better meet the needs of different participants. This transformation has laid a solid foundation for the further development and application of blockchain technology, which indicates a more efficient, transparent and just digital future.

4 Transformation from conceptual space to semantic space: the application of DIKWP model in blockchain consensus mechanism

4.1 Background: Traditional blockchain consensus mechanism

Definition of concept space: In the traditional blockchain system, the consensus mechanism usually operates based on static and superficial concepts. For example, transaction verification mainly depends on data integrity and immutability of transactions.

Limitations: Although this method ensures the security and consistency of the system, it often ignores the deeper social, economic and personal motives behind the transaction.

4.2 Turn to semantic space: the introduction of DIKWP model

Definition of semantic space: DIKWP model advocates a method to upgrade from simple data and information processing to in-depth understanding of content semantics. This method not only deals with the data itself, but also discusses the purpose, situation and deep meaning behind the data.

User's purpose: By analyzing the behavior and historical data of transaction participants, DIKWP model can reveal the real motives and expectations of users.

Market trends: DIKWP model can analyze market data and trends and provide broader business insight for blockchain applications.

Ethical and moral considerations: At the intellectual level, the DIKWP model considers ethical and moral factors to ensure that blockchain decision-making conforms to social and cultural values.

4.3 Implementation method

Application of data layer: collecting and verifying transaction data, while considering the source and authenticity of the data.

Mining information layer: extracting key information from data, understanding and explaining the background and environment of transactions.

Application of knowledge layer: combine information with legal, market and technical knowledge to form a deep understanding.

Innovation in the intellectual layer: In the intellectual layer, we use knowledge and information to formulate long-term strategies and solutions, while considering social responsibility and ethical influence.

Clarity of purpose layer: Clarify the goal of blockchain application and transaction, and ensure that decisions and actions are in line with long-term strategy.

Through the application of DIKWP model, the blockchain consensus mechanism has changed from conceptual space to semantic space, providing a more comprehensive and in-depth understanding of transactions. This transformation makes the blockchain system not only safe and reliable in technology, but also more adaptable and forward-looking in social and ethical aspects. This deep semantic understanding and dynamic adaptability will open up a new road for the future application of blockchain and realize a more intelligent and humanized digital solution.

5 Detailed explanation of the application DIKWP in blockchain consensus mechanism

In blockchain technology, consensus mechanism is the core of maintaining network security and consistency. The introduction of DIKWP (data, information, knowledge, wisdom and purpose) model not only improves the efficiency of consensus mechanism, but also shows excellent adaptability in dealing with multi-source heterogeneous resources.

5.1 Application of Data Layer (D)

Data collection: In the blockchain environment, collecting original data such as transaction records, user behavior data, network logs, etc. forms the basis of consensus decision.

Data verification: ensure the authenticity and integrity of data, and eliminate forged or misleading information.

Semantic transformation: the original data is transformed into meaningful semantic information, which provides the initial factual basis for the consensus mechanism.

5.2 Application of Information Layer (I)

Key information extraction: extract key information from huge data, such as trading motives, user behavior patterns and their backgrounds.

Situational understanding: The information layer analyzes the data background to provide a deep situational understanding for transactions and user behavior.

Consensus support: Based on this information, consensus mechanism can make more accurate and well-founded network decisions.

5.3 Application of Knowledge Layer (K)

Knowledge construction: the data in the information layer is further transformed into specific knowledge, such as market dynamics, user needs and regulatory requirements.

Professional support: provide necessary professional knowledge to make the consensus decision-making process more professional and accurate.

Background fusion: combining knowledge from different fields to provide a comprehensive perspective for consensus decision-making.

5.4 Application of Wisdom Layer (W)

Innovative solutions: Based on the output of the knowledge layer, the intelligent layer puts forward innovative solutions and strategies for specific situations.

Moral and ethical considerations: when formulating consensus strategies, consider moral and ethical factors to ensure the comprehensiveness and rationality of decision-making.

Strategy formulation: combine market, technology and social factors to form a long-term strategy and action plan.

5.5 Application of purpose Layer (P)

Clear objectives: Clear the objectives and expected effects of the consensus mechanism, and ensure that decisions and actions are consistent with the system objectives and user needs.

Consensus strategy: Based on the objectives of the system, formulate a consensus strategy that adapts to user needs and network conditions.

Strategic guidance: as the guiding layer of decision-making, the purpose layer provides direction and motivation for the whole consensus mechanism.

The application of DIKWP model in the blockchain consensus mechanism has greatly improved the depth and breadth of decision-making. It can not only process a large amount of data and information, but also transform these data and information into useful knowledge, wisdom and goal-oriented strategies. Through this multi-dimensional processing, the blockchain consensus mechanism is not only more advanced in technology, but also better in adaptability, ethics and strategy, which opens up new possibilities for the future application of blockchain technology.

Conclusion

Through the research of this paper, we show the application potential and practical effect of DIKWP model in blockchain technology. The introduction of DIKWP model not only improves the efficiency and security of blockchain system, but also promotes deeper semantic understanding and decision-making process. The application of this methodology provides a new direction and possibility for the future development of blockchain technology, especially in the management of multimodal digital assets and the construction of adaptive consensus strategy.

摘要

本文深入探讨了DIKWP模型在区块链技术中的应用与创新。文章聚焦于DIKWP模型（数据、信息、知识、智慧、意图）如何在区块链环境中促进自适应共识策略的实现，优化处理效率与安全性的平衡，并通过智能合约设计及价值导向共识机制的构建，加强多模态数字资产的管理。本文还探索了DIKWP在实际应用场景中的潜力，展望了它在推动区块链技术创新和发展中的重要角色。

1 引言

随着区块链技术的迅速发展和应用范围的扩大，其在多模态数字资产管理和自适应共识策略方面面临新的挑战。DIKWP模型提供了一种新的视角和方法，用于强化区块链技术的功能和效率。本文旨在探讨DIKWP模型如何在区块链技术中实现创新应用，特别是在自适应共识策略和多模态数字资产管理方面。

2 DIKWP在区块链环境中的自适应共识策略与实现机制

在区块链技术的发展过程中，面对多源异构资源和多模态数字资产，构建一个有效的自适应共识策略变得至关重要。本报告聚焦于DIKWP（数据、信息、知识、智慧、意图）模型在区块链网络环境中的应用，探讨如何在数字资产的各个阶段实现内容对象的确定、构建处理效率与安全性的平衡、使用建模、智能合约设计，以及价值导向共识机制的构建。

2.1 DIKWP内容对象确定方法

数据层（D）：在上链前，识别和验证数字资产中的原始数据，例如交易记录、用户活动日志等。

信息层（I）：提取数据中的关键信息，如交易的时间、地点、参与方等。

知识层（K）：从信息中提炼知识，如交易模式、用户行为趋势等。

智慧层（W）：基于知识对未来趋势进行预测和洞察，为决策提供支持。

意图层（P）：明确数字资产管理和应用的目标和预期。

2.2 构建处理效率与安全性的平衡

效率优化：通过算法优化和数据结构调整，提高区块链在处理多模态数字资产时的效率。

安全性保障：实施加密技术和访问控制，确保数字资产的安全性不被破坏。

2.3 多模态数字资产的区块链使用建模与智能合约设计

模态转换：设计机制，使数字资产在不同DIKWP层次间流转，实现多维度的应用。

生命周期管理：通过智能合约，管理数字资产从创建、流通到消亡的整个生命周期。

2.4 价值导向共识机制构建策略

共识算法：开发新型共识算法，确保在价值导向的基础上达成网络中的共识。

系统开发：构建适应多模态数字资产的区块链系统，支持复杂的交易和应用场景。

2.5 示范应用研究

应用场景：探索DIKWP-AC模型在现实世界应用中的潜力，如智能城市、供应链管理等。

示范项目：开发和部署具体的示范项目，展示价值导向的多模态数字资产区块链系统的实用性和有效性。

DIKWP模型在区块链环境中的应用，尤其是在多模态数字资产的处理和管理方面，展现出巨大的潜力。通过实现自适应共识策略、效率与安全性的平衡、智能合约设计，以及价值导向的共识机制，DIKWP-AC系统不仅能提高区块链技术的应用效率和安全性，还能促进数字资产的健康发展。未来，这一模型有望在多个领域实现更广泛的应用，推动区块链技术的创新和发展。

3 基于DIKWP的共识认知在区块链环境中的转化

随着区块链技术的快速发展，将共识机制从概念空间转化到语义空间显得尤为关键。DIKWP（数据、信息、知识、智慧、意图）模型的应用在区块链环境中不仅促进了这一转化，还提供了深层次的认知基础，从而增强了共识机制的有效性和适应性。

3.1 从概念空间到语义空间的转化

概念空间：传统区块链系统的共识机制多基于静态和表面的概念理解，例如交易验证和数据完整性。

语义空间：通过DIKWP模型，区块链系统从单纯的数据和信息处理升级到深入理解内容的语义，涉及用户意图、市场趋势和伦理道德等方面。

3.2 DIKWP在区块链共识机制中的应用

数据层（D）应用：

收集和验证原始数据，例如交易记录和用户行为数据。

转化为语义信息，为共识机制提供基础事实。

信息层（I）应用：

从数据中提取关键信息，例如交易动机和用户偏好。

为共识机制提供更深层次的情境理解。

知识层（K）应用：

将信息转化为具体的知识，比如市场动态和法规要求。

为共识决策提供专业和背景知识。

智慧层（W）应用：

结合知识层的输出，提出创新解决方案和策略。

促进共识机制在道德和伦理层面的深度考虑。

意图层（P）应用：

明确共识机制的目标和预期效果。

基于系统目标和用户需求，制定共识策略。

3.3 共识机制的转化实现

语义理解的增强：DIKWP模型使共识机制不仅关注事实数据，还深入挖掘数据背后的意图和目的。

动态适应性：随着情况的变化，共识机制能够根据新的数据和信息进行实时调整。

个性化和精准化：基于DIKWP模型，共识机制可以更准确地反映各方的需求和预期。

DIKWP模型在区块链环境中的应用极大地提升了共识机制的效率和准确性。通过从概念空间向语义空间的转化，共识机制不仅变得更加灵活和适应性强，而且能够更好地满足不同参与者的需求。这一转变为区块链技术的进一步发展和应用奠定了坚实的基础，预示着一个更为高效、透明和公正的数字化未来。

4 从概念空间到语义空间的转化：DIKWP模型在区块链共识机制中的应用

4.1 背景：传统区块链共识机制

概念空间定义：在传统区块链系统中，共识机制通常基于静态和表面的概念进行操作。例如，交易验证主要依赖于数据完整性和事务的不可更改性。

局限性：这种方法虽然保证了系统的安全性和一致性，但往往忽视了交易背后更深层次的社会、经济和个人动机。

4.2 转向语义空间：DIKWP模型的引入

语义空间的定义：DIKWP模型提倡一种从单纯的数据和信息处理升级到深入理解内容语义的方法。这种方法不仅处理数据本身，还探讨数据背后的意图、情境和深层含义。

用户意图：通过分析交易参与者的行为和历史数据，DIKWP模型可以揭示用户的真实动机和预期。

市场趋势：DIKWP模型能够分析市场数据和趋势，为区块链应用提供更广阔的业务洞察。

伦理道德考量：在智慧层，DIKWP模型考虑伦理和道德因素，确保区块链决策符合社会和文化价值。

4.3 实现方法

数据层的应用：收集和验证交易数据，同时考虑数据的来源和真实性。

信息层的挖掘：从数据中提炼出关键信息，理解和解释交易的背景和环境。

知识层的应用：将信息与法律、市场和技术知识结合，形成深入的理解。

智慧层的创新：在智慧层，利用知识和信息制定长期策略和解决方案，同时考虑社会责任和伦理影响。

意图层的明确：明确区块链应用和交易的目标，确保决策和行动符合长期战略。

通过DIKWP模型的应用，区块链共识机制从概念空间向语义空间转变，为交易提供了更全面、深入的理解。这种转变使得区块链系统不仅在技术上安全可靠，而且在社会和伦理层面上更具适应性和前瞻性。这种深层次的语义理解和动态适应能力将为区块链的未来应用开辟新的道路，实现更加智能化和人性化的数字解决方案。

5 DIKWP在区块链共识机制中的应用详解

在区块链技术中，共识机制是维护网络安全性和一致性的核心。DIKWP（数据、信息、知识、智慧、意图）模型的引入不仅提高了共识机制的效率，而且在处理多源异构资源内容时展现了卓越的适应性。

5.1 数据层（D）的应用

数据收集：在区块链环境中，收集原始数据如交易记录、用户行为数据、网络日志等，构成共识决策的基础。

数据验证：确保数据的真实性和完整性，排除伪造或误导性信息。

语义转化：原始数据被转化为有意义的语义信息，为共识机制提供初始事实基础。

5.2 信息层（I）的应用

关键信息提取：从庞大的数据中提取关键信息，如交易动机、用户行为模式及其背景。

情境理解：信息层分析数据背景，为交易和用户行为提供深层次的情境理解。

共识支持：基于这些信息，共识机制能够作出更加精准和有根据的网络决策。

5.3 知识层（K）的应用

知识构建：信息层的数据被进一步转化为具体知识，例如市场动态、用户需求和法规要求。

专业支撑：提供必要的专业知识，使共识决策过程更具专业性和准确性。

背景融合：结合来自不同领域的知识，为共识决策提供全面的视角。

5.4 智慧层（W）的应用

创新解决方案：智慧层依托于知识层的输出，提出针对特定情境的创新解决方案和策略。

道德和伦理考虑：在制定共识策略时，考虑道德和伦理因素，确保决策的全面性和合理性。

策略制定：结合市场、技术和社会因素，形成长期的策略和行动计划。

5.5 意图层（P）的应用

目标明确化：明确共识机制的目标和预期效果，确保决策和行动与系统目标和用户需求一致。

共识策略：基于系统的目标，制定适应用户需求和网络条件的共识策略。

战略指导：意图层作为决策的指导层，为整个共识机制提供方向和动力。

DIKWP模型在区块链共识机制中的应用极大地提升了决策的深度和广度。它不仅能够处理大量数据和信息，还能将这些数据和信息转化为有用的知识、智慧和目标导向的策略。通过这种多维度的处理，区块链共识机制不仅在技术上更加先进，而且在适应性、伦理性和策略性上也更为出色，为区块链技术的未来应用开拓了新的可能性。

总结

通过本文的研究，我们展示了DIKWP模型在区块链技术中的应用潜力和实际效果。DIKWP模型的引入不仅提升了区块链系统的效率和安全性，而且促进了更深层次的语义理解和决策过程。这种方法论的应用，为区块链技术的未来发展提供了新的方向和可能性，特别是在多模态数字资产的管理和自适应共识策略的构建方面。

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[2] Duan Y. DIKWP Processing Report on Five Personality Traits. DOI: 10.13140/RG.2.2.35738.00965. https://www.researchgate.net/publication/375597092_wudaxinggetezhide_DIKWP_chulibaogao_duanyucongYucong_Duan. 2023.

[3] 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.

[4] 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.

[5] 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.

[6] 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.

[7] 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.

[8] 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.

[9] 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.

[10] 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.

Data can be regarded as a concrete manifestation of the same semantics in our cognition. Often, Data represents the semantic confirmation of the existence of a specific fact or observation, and is recognised as the same object or concept by corresponding to some of the same semantic correspondences contained in the existential nature of the cognitive subject's pre-existing cognitive objects. When dealing with data, we often seek and extract the particular identical semantics that labels that data, and then unify them as an identical concept based on the corresponding identical semantics. For example, when we see a flock of sheep, although each sheep may be slightly different in terms of size, colour, gender, etc., we will classify them into the concept of "sheep" because they share our semantic understanding of the concept of "sheep". The same semantics can be specific, for example, when identifying an arm, we can confirm that a silicone arm is an arm based on the same semantics as a human arm, such as the same number of fingers, the same colour, the same arm shape, etc., or we can determine that the silicone arm is not an arm because it doesn't have the same semantics as a real arm, which is defined by the definition of "can be rotated". It is also possible to determine that the silicone arm is not an arm because it does not have the same semantics as a real arm, such as "rotatable".

Information, on the other hand, corresponds to the expression of different semantics in cognition. Typically, Information refers to the creation of new semantic associations by linking cognitive DIKWP objects with data, information, knowledge, wisdom, or purposes already cognised by the cognising subject through a specific purpose. When processing information, we identify the differences in the DIKWP objects they are cognised with, corresponding to different semantics, and classify the information according to the input data, information, knowledge, wisdom or purpose. For example, in a car park, although all cars can be classified under the notion of 'car', each car's parking location, time of parking, wear and tear, owner, functionality, payment history and experience all represent different semantics in the information. The different semantics of the information are often present in the cognition of the cognitive subject and are often not explicitly expressed. For example, a depressed person may use the term "depressed" to express the decline of his current mood relative to his previous mood, but this "depressed" is not the same as the corresponding information because its contrasting state is not the same as the corresponding information. However, the corresponding information cannot be objectively perceived by the listener because the contrasting state is not known to the listener, and thus becomes the patient's own subjective cognitive information.

Knowledge corresponds to the complete semantics in cognition. Knowledge is the understanding and explanation of the world acquired through observation and learning. In processing knowledge, we abstract at least one concept or schema that corresponds to a complete semantics through observation and learning. For example, we learn that all swans are white through observation, which is a complete knowledge of the concept "all swans are white" that we have gathered through a large amount of information.

Wisdom corresponds to information in the perspective of ethics, social morality, human nature, etc., a kind of extreme values from the culture, human social groups relative to the current era fixed or individual cognitive values. When dealing with Wisdom, we integrate this data, information, knowledge, and wisdom and use them to guide decision-making. For example, when faced with a decision-making problem, we integrate various perspectives such as ethics, morality, and feasibility, not just technology or efficiency.

Purpose can be viewed as a dichotomy (input, output), where both input and output are elements of data, information, knowledge, wisdom, or purpose. 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 purposes, the AI system processes the inputs according to its predefined goals (outputs), and gradually brings the outputs closer to the predefined goals by learning and adapting.

Yucong Duan, 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 intelligence 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-AC Artificial Consciousness Laboratory

AGI-AIGC-GPT Evaluation DIKWP (Global) Laboratory

DIKWP research group, Hainan University

duanyucong@hotmail.com

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