|
DIKWP Core Components Explained
(初学者版)
Yucong Duan
International Standardization Committee of Networked DIKWP for Artificial Intelligence Evaluation(DIKWP-SC)
World Artificial Consciousness CIC(WAC)
World Conference on Artificial Consciousness(WCAC)
(Email: duanyucong@hotmail.com)
You said:hello
ChatGPT said:
ChatGPT
Hello! How can I assist you today?
4o
You said:do you know DIKWP model
ChatGPT said:
ChatGPT
Yes, I am familiar with the DIKWP model. It consists of five elements: Data (D), Information (I), Knowledge (K), Wisdom (W), and Purpose (P). The model is used for processing, analyzing, and understanding natural language, as well as artificial consciousness systems. It was introduced by Professor Duan Yucong and applies in areas like semantic reasoning, artificial intelligence, and computational processes.
Would you like to explore a specific aspect of the DIKWP model?
4o
You said:yes, I am curious in exploring DIKWP model with you
ChatGPT said:
ChatGPT
That's great! The DIKWP model is a powerful framework that offers a structured way to approach the flow of information from raw data to purposeful action, and it has many applications in areas like artificial intelligence, natural language processing, and semantic reasoning.
To guide our exploration, we can break down the model's components and their relationships:
Data (D): The raw facts or concrete manifestations that represent the 'sameness' of elements in the system. Data in DIKWP refers to the most basic elements, which don't hold much meaning until they are processed.
Information (I): Information arises when data is structured or organized, allowing for recognition of 'differences.' It introduces context to the data, providing a comparison or relationship between the facts.
Knowledge (K): Knowledge represents a deeper understanding and synthesis of information. It connects multiple pieces of information into something 'complete,' revealing insights or patterns across broader contexts.
Wisdom (W): Wisdom is a more refined form of knowledge, decided by human interpretation and context. It often incorporates judgment and foresight, allowing for more abstract decision-making or reasoning.
Purpose (P): Purpose is the goal or intention that drives the entire DIKWP process. It gives direction to the flow of Data, Information, Knowledge, and Wisdom, providing a reason for processing and interaction between these elements.
Understanding Specific Transitions: We can focus on the transformations between each component, such as how Data becomes Information or how Knowledge evolves into Wisdom.
Applications of DIKWP: We can look into practical applications of DIKWP in fields like AI, where this model can be used to simulate artificial consciousness or improve natural language processing.
DIKWP*DIKWP Interaction: This is a unique concept in the model, involving cross-category interactions that create more complex cognitive processes. We can dive into how this concept operates in generating new meanings and outputs.
DIKWP Cognitive Profiles: If you're interested, we can explore how DIKWP is used to build cognitive profiles for communication, especially in resolving cognitive differences between parties.
Which part of the DIKWP model would you like to focus on, or do you have a specific area you’re curious about?
4o
You said:do you know the original cognition of DIKWP semantics: "sameness"? "difference"?"complete"?
ChatGPT said:
ChatGPT
Yes, the original cognition of DIKWP semantics, especially focusing on the terms "sameness," "difference," and "complete," plays a foundational role in understanding how the model operates. These terms are essential to how data, information, knowledge, and wisdom are generated and evolve within the DIKWP framework. Here’s a breakdown of each:
1. Sameness (Related to Data):Meaning: In the context of DIKWP, "sameness" refers to elements that share identical or similar characteristics, forming the core foundation of Data. When we talk about "sameness," we're identifying concrete, raw manifestations that don't yet contain relational or contextual meaning.
Cognitive Role: "Sameness" in cognition allows us to group things based on their identical or common attributes. It's how we recognize and categorize raw facts or inputs, serving as the starting point for any processing. Without identifying "sameness," raw data remains scattered and unstructured.
Example: Consider a dataset of temperatures recorded over a week. The concept of "sameness" allows us to see that all these temperatures represent a similar type of data—numerical values measuring heat—but without additional context.
2. Difference (Related to Information):Meaning: "Difference" refers to the recognition of distinctions between elements of Information. Once raw data has been organized or structured, the process of identifying differences allows it to take on meaning. This is the stage where the relationships and contrasts between elements are highlighted.
Cognitive Role: Cognitively, "difference" is where information becomes useful. By understanding the differences, one can infer relationships, meaning, and context. This allows the system or individual to make comparisons and understand variability in the data.
Example: In the temperature dataset, "difference" helps us notice changes in temperature over time, such as which days were hotter or colder. These differences create meaningful information from the raw data.
3. Complete (Related to Knowledge):Meaning: "Complete" represents a state where sufficient Information has been gathered to form Knowledge. This means that a collection of differences and relationships has reached a point where they can be synthesized into a fuller understanding or a holistic view. "Complete" is not necessarily about absolute fullness but rather enough information to generate actionable knowledge.
Cognitive Role: "Complete" enables the transformation of fragmented information into an organized system of knowledge, allowing us to apply this understanding in a broader context. It also implies a level of certainty or sufficiency in the cognitive process where various informational elements fit together to explain a phenomenon.
Example: Continuing with the temperature dataset, knowledge would be the recognition that certain patterns (like higher temperatures at specific times of the day) are complete enough to predict future weather behavior. "Complete" doesn't require every single data point but a sufficient amount to form conclusions.
Interrelation and Evolution:The interplay between these three concepts drives the transition from raw data to purposeful action:
Sameness leads to the collection of data by identifying things that belong to the same category or set.
Difference gives context and meaning by distinguishing between data points, thus generating information.
Complete consolidates these differences into a coherent body of knowledge, creating a comprehensive understanding that allows for informed decision-making.
In an artificial consciousness system based on DIKWP, these concepts would be used to:
Recognize raw inputs (data) through identifying "sameness" (e.g., temperature sensors detecting the same type of data).
Organize and process this input into meaningful differences, turning data into information (e.g., changes in temperature indicating trends).
Synthesize these differences into a knowledge system (complete), which could then be used for decision-making or prediction.
This process not only reflects how human cognition operates but also how an artificial system could mimic understanding and evolve from basic data processing to higher-level reasoning.
Would you like to explore any specific part of this further, or see how these concepts apply in a real-world scenario?
4o
You said:DIKWP model is not a hierarchical model like DIKW but a networked interactive model with transformations across the DIKWP components.
ChatGPT said:
ChatGPT
Yes, you're absolutely right! The DIKWP model differs fundamentally from the traditional DIKW hierarchy. While DIKW (Data, Information, Knowledge, Wisdom) is often presented as a linear or hierarchical model, the DIKWP model emphasizes networked interactions and transformations across its components—Data (D), Information (I), Knowledge (K), Wisdom (W), and Purpose (P). Here's a deeper dive into how this distinction works and why it matters:
1. Non-Hierarchical Nature of DIKWP:In a traditional DIKW model, there is a clear linear progression: Data leads to Information, which leads to Knowledge, and finally to Wisdom, each building on the previous stage. This hierarchical approach implies a one-way flow.
The DIKWP model rejects this hierarchical structure in favor of dynamic interactions. Each component of DIKWP interacts with and transforms into the others. It's not a one-way process but a networked system where transformations happen in multiple directions. Data can become Information, but Information can also modify Data, Knowledge can affect how Information is understood, and Wisdom can reshape how Knowledge is structured or interpreted, all driven by a central Purpose.
Transformation Across Components: In the DIKWP model, every component can influence or transform into the others based on context or specific requirements, not simply in a linear fashion. For example:
Data (D) can lead to Information (I), but new insights from Knowledge (K) can recontextualize or reformat Data itself.
Information (I) can evolve into Knowledge (K), but additional Purpose (P) can refine that Information, even sending it back to a more basic form of Data for reinterpretation.
Purpose (P) is the key driver, reshaping how Data, Information, Knowledge, and Wisdom interact dynamically, which is crucial for goal-oriented problem-solving.
This cross-transformation is what makes the DIKWP model so versatile. It simulates real-world cognitive processes, where information is rarely processed in a strictly linear fashion. Instead, understanding evolves as new connections and insights emerge across different stages of cognition.
3. Purpose (P) as a Central Element:Purpose (P) in DIKWP is not a static endpoint, but rather an active, central element that drives the networked interactions. It shapes the relationships between Data, Information, Knowledge, and Wisdom, ensuring that every transformation aligns with an intended goal or purpose.
For example, depending on the Purpose, Data may be transformed directly into Knowledge without needing intermediate steps. Or, if the Purpose shifts, it may necessitate revisiting Information or reinterpreting Wisdom. This flexibility is vital for adapting to new situations or requirements.
One of the most distinctive aspects of the DIKWP model is its cross-category interactions, referred to as DIKWP*DIKWP interactions. This refers to how different components across various DIKWP models interact and transform. For example, the Knowledge from one system can inform or transform the Information in another, creating a cross-model synergy.
This networked transformation allows the model to solve complex, multi-domain problems, often referred to as cross-category associations. For instance, Information from one domain may directly impact the Wisdom of another domain, creating new insights that couldn’t emerge in isolation.
Traditional models often assume a Closed World Assumption (CWA), where all necessary information is known, and the system processes data in a well-defined manner. The DIKWP model, however, shifts towards an Open World Assumption (OWA). Here, not all information is available or certain, and components interact dynamically to fill gaps, solve uncertainties, and evolve with the changing environment.
Once the system enters an OWA, the interaction between DIKWP components allows the model to refine and close the open world to find a concrete solution. This transformation from open to closed and back allows for a highly adaptive approach to problem-solving.
The DIKWP model is uniquely capable of managing uncertainty. For instance, incomplete Data can be compensated by Knowledge, or inconsistent Information can be resolved by using Wisdom. The components work together in a mutual transformation, which allows the model to handle incomplete, imprecise, or inconsistent information effectively.
This mutual transformation approach allows DIKWP to “close the gap” where uncertainty exists, leveraging interactions between its components to achieve results that would be impossible in a strictly hierarchical model.
Let’s look at an artificial consciousness system applying the DIKWP model:
Data (D): The system receives sensory input (raw data) from its environment.
Information (I): It organizes the data to detect patterns, perhaps recognizing different objects or environmental conditions.
Knowledge (K): The system compares this information with prior knowledge (e.g., learned from previous experiences or external databases) to make predictions or generate deeper insights.
Wisdom (W): Based on the system’s purpose, it applies higher-level reasoning to make a decision, choosing the most appropriate action based on the combined data, information, and knowledge.
Purpose (P): The overarching goal (e.g., maintaining safe operation or achieving a specific task) drives the interaction between these components, continuously influencing how they transform each other in real-time.
In this scenario, Data doesn’t flow linearly to Wisdom but is constantly re-evaluated, reshaped, and modified based on feedback loops across the network. New incoming Data could change the Knowledge or Purpose, and Wisdom might prompt a reinterpretation of raw Data.
Summary of the DIKWP Model:Not Hierarchical: Unlike DIKW, the DIKWP model doesn’t follow a strict hierarchy but relies on dynamic, networked interactions and transformations across its components.
Interactive and Flexible: Every component (Data, Information, Knowledge, Wisdom, Purpose) can interact and transform each other, depending on the situation or the Purpose.
Central Role of Purpose (P): Purpose drives the transformations, giving direction to the entire process.
Cross-Category Interaction (DIKWP*DIKWP): Different DIKWP systems can interact, leading to cross-domain insights and solutions.
Handling Uncertainty: The model’s interactive nature allows it to deal with incomplete, inconsistent, or imprecise data, transforming them across components to close gaps.
Would you like to explore how these transformations can be applied to specific scenarios, such as artificial intelligence or knowledge management?
4o
You said:Now, please re-anwer the previous question: do you know the original cognition of DIKWP semantics: "sameness"? "difference"?"complete"?
ChatGPT said:
ChatGPT
Certainly! Let me now answer with a deeper understanding of the DIKWP model's interactive and networked nature, focusing on the original cognition of the terms "sameness," "difference," and "complete" in the context of DIKWP semantics.
1. Sameness (D – Data):Original Cognition: In the DIKWP model, sameness refers to the recognition of elements or instances that share identical or similar characteristics. This notion of "sameness" is tied to the most fundamental level of cognitive perception—Data. It involves perceiving elements that fit within the same category or share a similar form without necessarily implying any deeper relationship or meaning.
Function in DIKWP: "Sameness" is essential to identifying what is uniform or unchanging across instances, forming the basis for collecting raw data. Data manifests "sameness" because, at this stage, no transformation into information or knowledge has occurred yet—it is purely the recognition of identical elements.
Example: In a set of recorded temperatures, "sameness" refers to the fact that all values are measurements of temperature, regardless of what the specific values are. They share the same unit and type of data, forming a coherent dataset.
Original Cognition: Difference comes into play when elements of data are compared and contrasted, producing Information. It is through recognizing distinctions, variations, and relationships that difference emerges. Cognitively, "difference" allows the system (or human mind) to move beyond recognizing "sameness" and understand the contrasts between data points, enabling the formation of information.
Function in DIKWP: Information is generated when data is transformed by identifying how elements differ or relate. "Difference" is critical in providing context, meaning, and relationships to otherwise raw and unstructured data. The recognition of differences provides the foundation for turning raw data into meaningful patterns or insights.
Example: In the temperature dataset, "difference" helps identify that some days are warmer than others, creating information about temperature variations. The data points now carry meaning due to their differences in values.
Original Cognition: Complete in DIKWP semantics refers to the point at which sufficient information has been gathered to form Knowledge. "Complete" is not about having all possible information but rather achieving a state where enough connections and relationships have been identified to construct a meaningful and usable understanding of a situation or concept.
Function in DIKWP: Knowledge arises when various pieces of information (derived from differences) coalesce into a comprehensive understanding. "Complete" means that enough information has been synthesized to create knowledge that can be applied in decision-making or action. In DIKWP, the completeness of knowledge does not imply perfection but sufficient contextual understanding for the current purpose.
Example: In the temperature dataset, "completeness" might be achieved when patterns such as "warmer in the afternoons and cooler in the mornings" are identified. These patterns allow for the development of knowledge, such as anticipating temperature changes over the course of the day.
Sameness is tied to the recognition of identical or similar data elements, forming the foundation of Data (D).
Difference arises from comparisons and relationships between data points, transforming raw data into Information (I).
Complete occurs when enough differences are synthesized into a coherent body of understanding, forming Knowledge (K).
In the DIKWP model, these concepts are not isolated but interact in a networked manner:
Sameness is not just a one-time observation of data but can be revisited and recontextualized as new information or knowledge emerges.
Difference doesn't simply transform data into information but can also redefine how the data is perceived as knowledge or wisdom evolves.
Complete is a dynamic state—knowledge can grow or change as new data and information enter the system, constantly updating what is considered complete.
Would you like to explore further how these concepts evolve interactively within the DIKWP framework, or how they apply to a specific scenario?
1/1 | 闂傚倸鍊搁崐鎼佸磹閹间礁纾归柟闂寸绾惧綊鏌熼梻瀵割槮缁炬儳缍婇弻鐔兼⒒鐎靛壊妲紒鐐劤濠€閬嶅焵椤掑倹鍤€閻庢凹鍙冨畷宕囧鐎c劋姹楅梺鍦劋閸ㄥ綊宕愰悙宸富闁靛牆妫楃粭鎺撱亜閿斿灝宓嗙€殿喗鐓¢、鏃堝醇閻斿弶瀚奸梻浣告啞缁诲倻鈧凹鍣i崺銏″緞閹邦厾鍘卞┑鈽嗗灠閻忔繃绂嶉崷顓犵<妞ゆ棁鍋愭晶锔锯偓瑙勬礀閵堟悂骞冮姀銏㈢煓闁割煈鍠曠槐鐔封攽閻樻剚鍟忛柛鐘愁殜閵嗗啴宕ㄩ鍥ㄧ☉铻栭柛娑卞幘椤︻噣姊洪幐搴㈢闁稿﹤缍婇幃锟犲Ψ閿斿墽鐦堥梻鍌氱墛缁嬫帡鏁嶅鍡曠箚闁圭粯甯楅幉鍝ョ磼鏉堛劌娴柟顔规櫊閹粌螣閻撳孩閿繝鐢靛剳缁茶棄煤閵堝鏅濇い蹇撴噸缁诲棝鏌涢锝嗙婵$偘绮欓弻娑㈠箛閵婏附鐝曢梺鍝勬閸楀啿顫忕紒妯诲闁告稑锕ラ崕鎾绘⒑瑜版帗鏁遍柛銊ユ贡濡叉劙鎮欑€涙ê顎撻梺鍛婃尭瀵墎绱炴惔銊︹拺闁诡垎鍛啈濡炪値鍋勯ˇ顖炴偩闁垮绶為柟閭﹀幘閸橆亝绻濋悽闈涗粶闁诲繑绻堝畷婵嗏堪閸喓鍘藉┑鐘绘涧鐎氼剟鎮橀崣澶嬪弿濠电姴鍟妵婵嬫煙椤旀儳鍘寸€殿喖鐖奸獮鎰償椤斿吋鏆忛梻鍌氬€烽懗鍫曞箠閹捐鍚归柡宥庡幖缁狀垶鏌ㄩ悤鍌涘:1 | 濠电姷鏁告慨鐑藉极閸涘﹥鍙忛柣鎴f閺嬩線鏌涘☉姗堟敾闁告瑥绻橀弻锝夊箣濠垫劖缍楅梺閫炲苯澧柛濠傛健楠炴劖绻濋崘顏嗗骄闂佸啿鎼鍥╃矓椤旈敮鍋撶憴鍕8闁告梹鍨甸锝夊醇閺囩偟顓洪梺缁樼懃閹虫劙鐛姀銈嗏拻闁稿本鐟︾粊鐗堛亜椤愩埄妲搁柣锝呭槻铻i悶娑掑墲閻忓啫鈹戦悙鏉戠仸缁炬澘绉归、鏇熺鐎n偆鍘梺鍓插亝缁诲啴宕幒妤佺厸闁告劑鍔庢晶娑㈡煛閸涱喚鍙€闁哄本绋戦埥澶愬础閻愯尙顔戞繝鐢靛仜閻楀﹪鎮¢垾鎰佹綎闁惧繐婀遍惌娆愮箾閸℃ê鍔ら柛鎾存緲椤啴濡堕崱妤冧淮濡炪倧绠撳ḿ褔顢氶敐鍡欑瘈婵﹩鍘藉▍婊堟⒑閸涘﹦鈽夐柛濠傤煼瀹曠増鎯旈妸銉у幒闁瑰吋鐣崝宀€绮诲杈ㄥ枑閹兼惌鐓堥弫濠囨煕閺囥劌鐏¢柣鎾寸☉椤法鎹勯悜姗嗘!濠电偛鎳庡Λ娑氭閹烘梹瀚氶柤纰卞墮椤e搫顪冮妶搴′簻缂佺粯锕㈤獮鏍捶椤撶喎鏋傞梺鍛婃处閸嬪棝鏁嶈箛娑欌拻濞撴埃鍋撴繛浣冲嫮浠氶梻浣呵圭€涒晠鎮¢敓鐘茬畺闁汇垻枪椤懘鏌曢崼婵囶棏闁归攱妞藉娲嚒閵堝懏鐎惧┑鐘灪閿氶柍缁樻崌閸╋繝宕ㄩ鎯у箥闂備礁鎲¢崹顖炲磹閺嶎偀鍋撳鐐 | 濠电姷鏁告慨鐑藉极閸涘﹥鍙忛柣鎴f閺嬩線鏌涘☉姗堟敾闁告瑥绻橀弻锝夊箣閿濆棭妫勯梺鍝勵儎缁舵岸寮婚悢鍏尖拻閻庨潧澹婂Σ顔剧磼閻愵剙鍔ゆい顓犲厴瀵鏁愭径濠勭杸濡炪倖甯婇悞锕傚磿閹剧粯鈷戦柟鑲╁仜婵″ジ鏌涙繝鍌涘仴鐎殿喛顕ч埥澶愬閳哄倹娅囬梻浣瑰缁诲倸螞濞戔懞鍥Ψ瑜忕壕钘壝归敐鍛儓鐏忓繘姊洪崨濠庢畷濠电偛锕ら锝嗙節濮橆厼浜滈梺绋跨箰閻ㄧ兘骞忔繝姘厽閹艰揪绲鹃弳鈺呭几椤忓嫧鏀介柍銉ㄥ皺閻瑦鎱ㄦ繝鍐┿仢鐎规洦鍋婂畷鐔碱敆閳ь剙鈻嶉妶鍥╃=濞达絿鐡旈崵娆撴煟濡や焦灏い鏇稻缁绘繂顫濋鈹炬櫊閺屾洘寰勯崼婵堜痪闂佸搫鍊甸崑鎾绘⒒閸屾瑨鍏岀痪顓炵埣瀹曟粌鈹戠€n偅娅旂紓鍌氬€烽悞锕傚礉閺嶎厽鍎庢い鏍ㄥ嚬濞兼牗绻涘顔荤盎鐎瑰憡绻傞埞鎴︽偐閹绘帩鍔夐梺浼欑悼閸忔﹢骞冨Δ鍛濠㈣泛锕f竟鏇㈡⒒娴e摜绉烘俊顐ユ硶缁牊鎷呴搹閫涚瑝闂佸搫绉查崝瀣崲閸℃稒鐓忛柛顐g箓閳ь剙鎲$粋宥嗐偅閸愨斁鎷洪柣搴℃贡婵敻藟婢跺浜滈柨鏃囶嚙閻忥箓鏌涢埞鍨仼妞ゆ挸銈稿畷鍗炍熼懖鈹倝姊绘笟鈧ḿ褑鍣归梺鍛婁緱閸ㄦ壆鏁幒鏃傜=闁稿本鑹鹃埀顒勵棑缁牊绗熼埀顒€鐣烽幇鏉夸紶闁靛/鍛帬闂備礁婀遍搹搴ㄥ窗閹捐纾婚柟瀛樼贩瑜版帒绀傞柛蹇氬亹缁嬪洭姊绘担绋胯埞婵炲樊鍙冨濠氭晲婢跺﹥顥濋梺鍦圭€涒晠宕曢幘缁樺€垫繛鍫濈仢閺嬬喎鈹戦悙璇у伐妞ゎ偄绻掔槐鎺懳熺拠宸偓鎾绘⒑閹呯闁硅櫕鎸剧划顓㈠灳閺傘儲鏂€闂佺粯鍔栬ぐ鍐棯瑜旈弻锝呂旈崘銊愩垽鏌i敐鍥у幋妤犵偛娲鍫曞箰鎼达紕銈跺┑锛勫亼閸婃牠骞愰懡銈囩煓闁瑰鍋熼々鏌ユ煟閹伴潧澧柛娆忕箲娣囧﹪顢涘⿰鍐ㄤ粯婵炲瓨绮撶粻鏍箖濡も偓椤繈鎮欓鈧锟� | 濠电姷鏁告慨鐑藉极閸涘﹥鍙忛柣鎴f閺嬩線鏌涘☉姗堟敾闁告瑥绻橀弻锝夊箣閿濆棭妫勯梺鍝勵儎缁舵岸寮婚悢鍏尖拻閻庨潧澹婂Σ顔剧磼閻愵剙鍔ゆい顓犲厴瀵鏁愭径濠勭杸濡炪倖甯婇悞锕傚磿閹剧粯鈷戦柟鑲╁仜婵″ジ鏌涙繝鍌涘仴鐎殿喛顕ч埥澶愬閳哄倹娅囬梻浣瑰缁诲倸螞濞戔懞鍥Ψ瑜忕壕钘壝归敐鍛儓鐏忓繘姊洪崨濠庢畷濠电偛锕ら锝嗙節濮橆厼浜滈梺绋跨箰閻ㄧ兘骞忔繝姘厽閹艰揪绱曟禒娑㈡煟閳╁啯绀堢紒顕嗙到铻栭柛娑卞枓閹锋椽鏌℃径灞戒沪濠㈢懓妫濊棟闁挎洖鍊归崐鍨叏濮楀棗浜滅€规挸妫涢埀顒侇問閸犳盯顢氳閸┿儲寰勯幇顒夋綂闂佺粯蓱瑜板啰绮婇鈧缁樼瑹閳ь剙岣胯閻忔瑩姊虹粙鎸庡攭濞存粠鍓涢崚鎺旂磼濡偐鐦堝┑顔斤供閸樻悂骞忓ú顏呪拺闁告稑锕﹂埥澶嬵殰椤忓懏缍戦柍缁樻崌瀵挳鎮╅顫闂佺ǹ绻愰ˇ顖涚妤e啯鈷戠憸鐗堝笚閿涚喓绱掗埀顒佹媴閾忛€涚瑝闂佸搫绉查崝瀣崲閸℃稒鐓忛柛顐g箓閳ь剙鎲$粋宥嗐偅閸愨斁鎷洪柣搴℃贡婵敻藟婢跺浜滈柨鏃囶嚙閻忥箓鏌涢埞鍨仼妞ゆ挸銈稿畷鍗炍熼懖鈹倝姊绘笟鈧ḿ褑鍣归梺鍛婁緱閸ㄦ壆鏁幒鏃傜=闁稿本鑹鹃埀顒勵棑缁牊绗熼埀顒€鐣烽幇鏉夸紶闁靛/鍛帬闂備礁婀遍搹搴ㄥ窗閹捐纾婚柟瀛樼贩瑜版帒绀傞柛蹇氬亹缁嬪洭姊绘担绋胯埞婵炲樊鍙冨濠氭晲婢跺﹥顥濋梺鍦圭€涒晠宕曢幘缁樺€垫繛鍫濈仢閺嬬喎鈹戦悙璇у伐妞ゎ偄绻掔槐鎺懳熺拠宸偓鎾绘⒑閹呯闁硅櫕鎸剧划顓㈠灳閺傘儲鏂€闂佺粯鍔栬ぐ鍐棯瑜旈弻锝呂旈崘銊愩垽鏌i敐鍥у幋妤犵偛娲鍫曞箰鎼达紕銈跺┑锛勫亼閸婃牠骞愰懡銈囩煓闁瑰鍋熼々鏌ユ煟閹伴潧澧柛娆忕箲娣囧﹪顢涘⿰鍐ㄤ粯婵炲瓨绮撶粻鏍箖濡も偓椤繈鎮欓鈧锟� | 闂傚倸鍊搁崐鎼佸磹閹间礁纾归柟闂寸绾惧綊鏌熼梻瀵割槮缁炬儳缍婇弻鐔兼⒒鐎靛壊妲紒鎯у⒔閹虫捇鈥旈崘顏佸亾閿濆簼绨奸柟鐧哥秮閺岋綁顢橀悙鎼闂侀潧妫欑敮鎺楋綖濠靛鏅查柛娑卞墮椤ユ艾鈹戞幊閸婃鎱ㄩ悜钘夌;闁绘劗鍎ら崑瀣煟濡崵婀介柍褜鍏涚欢姘嚕閹绢喖顫呴柍鈺佸暞閻濇洟姊绘担钘壭撻柨姘亜閿旇鏋ょ紒杈ㄦ瀵挳鎮㈤搹鍦闂備焦鐪归崹钘夅缚瑜嶉悺顓㈡⒒娴g懓顕滄繛鎻掔箻瀹曟劕鈹戠€n亞鐣洪悷婊勬煥閻g兘宕¢悙鈺傜€婚棅顐㈡祫缁查箖藟閿熺姵鈷戦柤濮愬€曞瓭濠电偠顕滄俊鍥╁垝婵犲洤绠绘い鏃囧亹閸濇绻涚€电ǹ孝妞ゆ垵妫濋幃锟犳偄闂€鎰畾濡炪倖鐗楃喊宥夊箚閸垻纾奸柣娆愮懃濞诧箓鎮″▎鎾寸厱闊洦娲栫敮璺衡攽椤旇偐澧涚紒缁樼洴閹虫繈鏌囬敃鈧▓顓㈡⒑鐎圭媭娼愰柛銊ョ秺閸┾偓妞ゆ帒锕︾粔鐢告煕鐎Q冧壕闂備礁缍婇弨鍗烆渻娴犲钃熼柨婵嗩槹閸嬫劙鏌涜箛鎾村殌闁糕晛鎳樺娲川婵犲啰鍙嗛梺鍏兼た閸ㄥ爼宕洪姀鐙€鍚嬪璺猴功閻も偓闂備胶绮〃鍛存偋韫囨稑鍌ㄩ柣銏犳啞閳锋垿鏌涘☉姗堝伐闁诲繒鍠栭弻娑欑節閸愵亜鈷堥梺閫炲苯澧柣鏃戝墴楠炲繘鏁撻敓锟� | 闂傚倸鍊搁崐鎼佸磹閹间礁纾归柟闂寸绾惧綊鏌熼梻瀵割槮缁炬儳婀遍埀顒傛嚀鐎氼參宕崇壕瀣ㄤ汗闁圭儤鍨归崐鐐烘偡濠婂啰绠荤€殿喗濞婇弫鍐磼濞戞艾骞堟俊鐐€ら崢浠嬪垂閸偆顩叉繝闈涱儐閻撴洘绻涢崱妤冪缂佺姴顭烽弻鈥崇暆閳ь剟宕伴幘鑸殿潟闁圭儤顨呴~鍛存煟濡櫣锛嶅ù婊庝簽缁辨捇宕掑▎鎺戝帯婵犳鍣g粻鏍晲閻愭潙绶為柟閭﹀劦閿曞倹鐓曢柡鍥ュ妼閻忕姷绱掗悩宕囨创闁哄本鐩、鏇㈡晲閸℃瑯妲版俊鐐€曟鍝ョ矓閻熼偊娼栭柧蹇撴贡閻瑦绻涢崱妯哄姢闁告挾鍋撶换娑氣偓娑欋缚閻倕霉濠婂簼绨绘い鏇悼閹风姴霉鐎n偒娼旈梻渚€娼х换鎺撴叏閸儱惟闁挎棁妗ㄧ花濠氭⒑閸濆嫬鈧悂鎮樺┑鍫㈢闁哄秲鍔嶉崣蹇涙偡濞嗗繐顏存繛鍫熺矒閺岀喖顢欓悡搴⑿╁Δ妤婁簷閸楀啿鐣烽妸鈺婃晣鐟滃骸袙婢舵劖鈷戞慨鐟版搐閻掓椽鏌涢妸鈺€鎲鹃柕鍡楀暞缁绘繈宕掗妶鍛吙闂備礁鎼悮顐﹀磿鏉堚晝涓嶉柣鐔稿櫞瑜版帗鏅查柛娑卞枦绾偓闂備礁鎲¢悷銉ノ涘┑鍡╂綎闁惧繐婀辩壕鍏间繆椤栨繂鍚规い锔哄劦濮婅櫣绮欓崠鈥充紣濠电姭鍋撻梺顒€绉撮悞鍨亜閹哄秷鍏岄柛鐔哥叀閺岀喖宕欓妶鍡楊伓 |
Archiver|手机版|科学网 ( 京ICP备07017567号-12 )
GMT+8, 2025-1-20 08:20
Powered by ScienceNet.cn
Copyright © 2007-2025 中国科学报社