Structural Comparison and Extension Research of Original

Structural Comparison and Extension Research of Original TRIZ Theory and DIKWP-TRIZ Method

Yucong Duan

International Standardization Committee of Networked DIKWPfor Artificial Intelligence Evaluation(DIKWP-SC)

World Academy for Artificial Consciousness(WAAC)

World Artificial Consciousness CIC(WAC)

World Conference on Artificial Consciousness(WCAC)

(Email: duanyucong@hotmail.com)

Introduction

TRIZ (Theory of Inventive Problem Solving) was founded by the former Soviet scientist G. Altshuller in 1946. It is a methodological system designed to systematically solve innovation problems. Classical TRIZ includes 39 engineering parameters and 40 inventive principles, deriving general solutions for creative problems—such as segmentation, extraction, and inversion—by analyzing a vast number of patent inventions. The core idea of TRIZ is solving through contradictions: when two engineering parameters conflict, a breakthrough solution can be found by applying inventive principles. However, with the advent of the digital and artificial intelligence era, traditional TRIZ has exposed limitations in dealing with cognitive uncertainty, semantic conflicts, and high-level purposes. To address this, Chinese scholar Professor Yucong Duan creatively proposed the DIKWP-TRIZ method, deeply integrating TRIZ with his original DIKWP cognitive model to form a new paradigm for a future-oriented intelligent invention system.

The DIKWP model adds a "Purpose" layer to the traditional DIKW pyramid (Data-Information-Knowledge-Wisdom) and expands the linear relationships between layers into a bidirectional networked interaction relationship. This extension allows the five elements—Data, Information, Knowledge, Wisdom, and Purpose—to interact and provide feedback, thereby more comprehensively representing the incompleteness, inconsistency, and imprecision (the "3-No" problems) in the cognitive process. DIKWP-TRIZ utilizes the multi-layer semantic network provided by the DIKWP model to embed TRIZ inventive principles into the cognitive process to solve complex innovation problems. This method emphasizes Purpose-driven and semantic transparency, ensuring that every step of reasoning has a clear semantic basis when solving specific problems, and ultimately making the solution consistent with the initial goal.

This paper aims to comparatively analyze the structural similarities and differences between the original TRIZ theory and the DIKWP-TRIZ method, and boldly extend and refine DIKWP-TRIZ based on the DIKWPDIKWP semantic module network. First, it introduces the engineering parameters and inventive principles of TRIZ, as well as the DIKWP model and the DIKWP-TRIZ framework. Subsequently, it details the mapping and transformation paths of TRIZ's 39 parameters and 40 principles within the DIKWP 25 semantic modules (5×5 bidirectional transformation matrix). Then, it analyzes the module coverage completeness, path efficiency, and expression redundancy of the DIKWP-TRIZ network. Following this, it discusses the consistency and potential conflicts between TRIZ and DIKWP in semantic space, as well as the differences in subject-object mapping strategies between the two. Finally, supported by original theories such as Semantic Sovereignty, Reflexive Causal Paths, and Multi-layer Knowledge Evolution, the paper proposes a conception for an extended DIKWP-TRIZ model, including new modules, path optimization, and cross-layer mapping mechanisms, in order to provide theoretical references and engineering adaptation suggestions for the construction of future intelligent invention systems. This report strives for systematic originality, academic rigor, and structural clarity, providing a valuable reference for relevant theoretical dissemination and academic citation.

Overview of TRIZ Theory and DIKWP-TRIZ Method

Brief Description of Original TRIZ Theory

TRIZ (Теория решения изобретательских задач, Theory of Inventive Problem Solving) is a methodology oriented towards technological invention and innovative design. TRIZ is based on the concept that the solution to creative problems follows extractable universal laws, and these laws can be obtained by analyzing a large number of patent inventions. TRIZ contains various tools and models, with core elements including:

39 Engineering Parameters: Used to formally describe contradictory elements in a technical system (such as weight, strength, power, precision, etc.). When two parameters in a design trade-off against each other and cannot be optimized simultaneously, a technical contradiction is formed.

40 Inventive Principles: 40 general innovation strategies distilled to break contradictions between engineering parameters and provide creative solutions. For example, Principle 1 "Segmentation" advocates breaking a system into independent parts to overcome overall contradictions; Principle 13 "Inversion" encourages thinking about the problem from the opposite direction; Principle 35 "Parameter Changes" emphasizes changing the physical state or dimensions of an object to achieve a breakthrough, etc.

Contradiction Matrix: Also known as the Inventive Principle Matrix, it is a hallmark tool of classical TRIZ. The rows and columns of the matrix correspond to the parameter to be improved and the parameter that should not deteriorate. When a pair of parameters conflict, a corresponding set of inventive principles can be found in the matrix as solution ideas.

The general process of using the TRIZ method to solve a problem includes: first, analyzing the problem and abstracting it into a standard contradiction, identifying the involved engineering parameters; then, using the contradiction matrix to find relevant inventive principles; and finally, applying the inventive principles to generate specific solutions. For instance, in the problem where password unlocking is easily stolen, if "Security" is viewed as the parameter to be improved and "Ease of Operation" as the parameter not to deteriorate, a matrix query might yield hints like Principle 3 "Local Quality," Principle 10 "Preliminary Action," Principle 13 "Inversion," and Principle 25 "Self-service." These hints guide the designer to consider solutions such as locally enhancing security components, adding pre-verification steps, reverse verification mechanisms, or self-service security measures. However, the traditional TRIZ framework mainly focuses on physical and technical contradictions, assuming problem parameters are clear and complete. It is often helpless in situations of insufficient information or cognitive uncertainty. Furthermore, TRIZ lacks consideration of design Purpose and ethical factors, nor does it involve semantic consistency verification of solutions. These limitations pose challenges for applying TRIZ to artificial intelligence and complex cognitive systems.

DIKWP Model and Semantic Module Network

The DIKWP model is a cognitive hierarchy model proposed by Professor Yucong Duan, extending the traditional Data-Information-Knowledge-Wisdom (DIKW) framework by adding a Purpose element at the top layer. Unlike the linear DIKW pyramid, DIKWP connects the five levels with a networked bidirectional interaction structure, thereby closely mimicking the cognitive process of a real intelligent agent. The meanings of the DIKWP levels are as follows:

Data: Corresponds to the concrete representation of "sameness" semantics in cognition, i.e., the recording of observed concrete facts. The Data layer focuses on forming concepts by inducing common features, such as extracting the common concept of "sheep" from seeing many different sheep.

Information: Corresponds to the expression of "difference" semantics in cognition, emphasizing the characterization of specific object difference features. The Information layer focuses on extracting differentiated details from data for classification and description, such as distinguishing different car brands, colors, models, etc.

Knowledge: Corresponds to "completeness" semantics in cognition, i.e., forming a comprehensive understanding of things by accumulating a large amount of information. The Knowledge layer refines universal patterns and laws, such as learning through repeated observation that "swans are white" and treating it as complete knowledge.

Wisdom: Involves highly synthesized knowledge and value judgments, including ethics, morals, and humanistic factors. The Wisdom layer weighs the social impact and moral standards of technical solutions during decision-making, focusing not only on feasibility but also on whether it aligns with goodwill and long-term interests.

Purpose: Defined as a tuple (Input, Output), representing the intelligent agent's understanding of a problem (Input) and the expected goal to be achieved (Output). The Purpose layer drives the entire cognitive process, guiding the processing and transformation of information at each layer so that the output gradually approximates the preset goal.

In the DIKWP model, the relationship between the five levels is not simple one-way accumulation, but involves bidirectional semantic mapping and transformation. This constitutes the DIKWPDIKWP semantic module network, i.e., the transformation relationships between all five elements in pairs, totaling 5×5=25 possible transformations (including intra-layer and cross-layer bidirectional transformations). This network can be imagined as a matrix, where the rows and columns represent the "Start Level of Transformation (From)" and the "End Level of Transformation (To)," respectively. Each unit (module) of the matrix corresponds to a specific semantic transformation process. For example, "Data→Information" represents how to extract or generate information from the data layer; "Wisdom→Data" represents using high-level wisdom to guide the collection of low-level data; and even "Purpose→Purpose" represents the alignment or refinement between different purposes. Figure 1 visually illustrates the structure of the DIKWPDIKWP semantic network, where each line represents a type of level transformation.

(Due to platform limitations, the embedded description of Figure 1 is omitted: DIKWPDIKWP semantic module network topology diagram, please refer to the matrix or network diagrams in relevant literature.)*

The DIKWPDIKWP semantic network aims to comprehensively cover the semantic gaps and hierarchical divides that may appear in an intelligent agent's cognitive process. Traditional systems often suffer from insufficient data, fragmented information, contradictory knowledge, lack of wisdom, or unclear purposes. The DIKWP network provides a systematic method to map problems to various layers and compensate for incompleteness and eliminate inconsistency through inter-layer transformation. For example, if information is insufficient, the "Wisdom→Data" module can guide the collection of new data from a higher-level decision perspective to enrich information; or if there is a contradiction in the knowledge layer, the "Data→Knowledge" module can introduce new data or information to break the deadlock. This networked thinking considers both vertical semantic evolution (from data to wisdom to purpose) and supports horizontal cross-layer supplementation and feedback, thus laying a solid foundation for cognitive modeling and solving complex problems.

DIKWP-TRIZ Innovation Method Framework

The DIKWP-TRIZ methodology is an emerging innovative problem-solving framework proposed by Professor Yucong Duan's team based on the integration of the aforementioned DIKWP model and classical TRIZ theory. Its original intention is: to embed TRIZ innovation tools into the DIKWP cognitive architecture, enabling artificial intelligence systems to discover and solve contradictions in a multi-layered cognitive space like humans, thereby achieving autonomous innovation capabilities. DIKWP-TRIZ combines the two theories organically through the following key concepts:

Cognitive Semanticization of TRIZ Principles: DIKWP-TRIZ re-examines the 40 inventive principles of TRIZ, endowing them with semantic meanings at the Data, Information, Knowledge, Wisdom, and Purpose levels, and establishing corresponding relationships between TRIZ principles and DIKWP level transformations. For example, the "Segmentation" principle manifests in the Data layer as the splitting and reorganization of datasets, in the Knowledge layer as dividing knowledge modules to eliminate conflicts, and in the Wisdom layer as decomposing complex decisions into multiple steps. Through semanticization, each TRIZ principle is no longer an isolated technical trick but becomes a cognitive operation guide running through multiple layers of DIKWP.

DIKWP Modeling of Problem Representation: When solving practical problems, DIKWP-TRIZ first expresses the problem as a DIKWP multi-level model, depicting what raw facts the problem has at the Data layer, what difference features are involved at the Information layer, what rule conflicts are involved at the Knowledge layer, what value criteria are considered at the Wisdom layer, and what final goal requirements are specified at the Purpose layer. This representation ensures that all aspects of the problem (from technical details to purpose ethics) are characterized, which is more comprehensive than traditional TRIZ describing only technical contradictions.

DIKWP Inter-layer Transformation for Solving Contradictions: When a problem is located at a certain level (or across layers), DIKWP-TRIZ attempts to find a solution within the same level or across levels. Same-level solving refers to applying the corresponding TRIZ principle set directly at the layer where the problem resides (e.g., data layer defects solved by data layer related principles); cross-layer solving calls upon the 25 pre-defined DIKWP transformation strategies to transform the problem to other layers for resolution. These transformation processes are supported by TRIZ principles, such as "Add Data" corresponding to resource utilization principles, "Filter Data" corresponding to abstraction (extraction) principles, and "Constrain Solution with Higher Wisdom" embodying principles of introducing macro considerations. With the interaction of the DIKWP network, the system can resolve contradictions by detouring or "separating first, then combining" between different levels.

Semantic Mathematics and Purpose Alignment: To make TRIZ principles automatically reasonable in AI systems, DIKWP-TRIZ introduces Semantic Mathematics formalization tools to axiomatically define the content and transformation rules of each DIKWP layer. The "Three Axioms" of existence, uniqueness, and transitivity in Semantic Mathematics ensure that the semantic mapping process is complete, consistent, and closed. This means that when the system generates a solution idea based on a certain DIKWP transformation (associated with several TRIZ principles), it can verify its semantic rationality and Purpose matching degree through Semantic Mathematics before materializing it into a specific plan. The entire process forms a semantic-concept closed loop, making the solution generation both innovative and explainable.

In summary, the DIKWP-TRIZ method process can be summarized as: "Problem DIKWP Representation → Intra-layer/Cross-layer Contradiction Identification → TRIZ Principle Mapping → General Solution Generation → Semantic Verification and Concrete Plan Implementation". Its prominent advantage lies in: every step of reasoning has a cognitive basis (Is data sufficient? Is information consistent? Is knowledge complete? Is wisdom ethical? Is Purpose feasible?), making the process transparent and traceable; at the same time, due to the consideration of incomplete data and semantic uncertainty, the resulting solution is more robust and adaptive, promising for application in the autonomous innovation of complex AI systems. DIKWP-TRIZ is hailed as a "revolutionary extension" of traditional TRIZ, marking the leap of innovation methodology from the engineering field to the intelligent system field. Currently, the research team has developed a DIKWP-TRIZ prototype system for intelligently generating patent solutions and plans to incorporate it into the artificial consciousness testing platform to evaluate AI's innovation capabilities.

Mapping of TRIZ Principles to DIKWP*DIKWP Semantic Network

Correspondence between 25 DIKWP Semantic Modules and TRIZ Principles

As mentioned earlier, the DIKWPDIKWP semantic module network contains 25 basic transformation types. Researchers have systematically mapped the 40 inventive principles of TRIZ to these 25 types of transformations, forming a one-to-one correspondence relationship set. In other words, each type of DIKWP element transformation (From→To) is associated with a set of appropriate TRIZ principles to guide inventive thinking during that transformation process. Table 1 lists the principle numbers in each unit of this correspondence matrix based on the latest public data:

Table 1: Mapping Relationship of TRIZ Inventive Principles in DIKWP Five-Element Transformation Matrix

Transformation (From→To)	Corresponding TRIZ Principle Numbers (Partial)
Data→Data (Data layer self-transformation)	1, 2, 5, 10, 12, 18, 26, 30
Data→Information (Data generates Information)	3, 5, 9, 17, 28, 35
Data→Knowledge (Data sublimates to Knowledge)	6, 24
Data→Wisdom (Data inspires Wisdom)	40
Data→Purpose (Data supports Purpose)	4, 11, 15, 29
Information→Data (Information feeds back to Data)	10, 22
Information→Information	13, 17
Information→Knowledge	15, 24
Information→Wisdom	23, 32
Information→Purpose	16, 32
Knowledge→Data	8, 9, 25, 27
Knowledge→Information	3, 13
Knowledge→Knowledge	22, 34
Knowledge→Wisdom	15, 40
Knowledge→Purpose	25, 31, 35
Wisdom→Data	6, 24, 25, 35
Wisdom→Information	16, 22
Wisdom→Knowledge	3, 23
Wisdom→Wisdom	15, 34
Wisdom→Purpose	10, 15, 20, 33, 39
Purpose→Data	10, 19, 21, 23
Purpose→Information	6
Purpose→Knowledge	2, 15
Purpose→Wisdom	36, 40
Purpose→Purpose	1, 14, 37, 38

(Note: Numbers correspond to TRIZ inventive principle numbers. e.g., 1=Segmentation, 2=Extraction, ... 40=Composite Materials, etc. The table above is derived from appendix data in Reference [21], translated and organized by the author.)

From Table 1, it can be seen that each DIKWP transformation type corresponds to at least 1 (and up to 5) TRIZ principles. These principles provide creative inspiration for the transformation process. For example:

Data→Data (Data Layer Internal Improvement): Corresponds to 8 TRIZ principles, such as Segmentation (1), Extraction (2), Preliminary Action (10), Parameter Changes (35), etc. They guide how to directly transform and optimize data itself, such as decomposing and refining data, extracting key parts, processing data in advance, or changing data representation forms to make up for deficiencies in the data layer. This transformation is particularly important when dealing with insufficient data or poor quality.

Data→Information (Extracting Information from Data): Corresponds to principles like Local Quality (3), Merging (5 - contextually adjusted), Another Dimension (17 - contextually adjusted), etc., implying that extracting meaningful information from raw data requires attention to local detail quality and taking early processing strategies. This transformation helps solve situations of cluttered data lacking useful information, uncovering patterns and differences in raw data.

Information→Knowledge (Condensing Information into Knowledge): Corresponds to principles Dynamics (15) and Intermediary/Mediator (24 - contextually adjusted), suggesting flexible adjustment of information structures and accumulating sufficient information to trigger cognitive transitions to form complete knowledge. When information fragments are scattered and difficult to form a system, these principles can be used to integrate information and dynamically update to obtain complete knowledge.

Wisdom→Data (Wisdom Feedback to Data): Corresponds to principles Universality (6), Intermediary (24), Self-service (25), Parameter Changes (35). This indicates that the Wisdom layer can identify the need for more data or different data perspectives through generalization laws, redundancy checks, etc., and actively acquire them, thereby guiding low-level data collection with high-level decisions. For example, to meet ethical or safety requirements (Wisdom layer concerns), it may be necessary to increase monitoring data or constrain data ranges at the Data layer (reflecting the role of principles 24 and 35).

Purpose→Wisdom (Purpose Guiding Wisdom): Corresponds to as many as 5 principles including Preliminary Action (10), Dynamics (15), Continuity of Useful Action (20), Feedback (33 - contextually adjusted), Inert Atmosphere (39 - contextually adjusted). These principles inspire how to adjust the decision-making framework of the Wisdom layer starting from the final purpose to keep it consistent with the goal. For example, utilizing feedback mechanisms (33) to continuously calibrate decisions, or introducing new assessment standards (39 Phase Transition) to ensure decisions fit the Purpose. This transformation embodies the constraint and shaping of Wisdom by Purpose, which is crucial in artificial intelligence decision-making.

It is worth noting that some TRIZ principles appear in multiple transformation units, i.e., "one-to-many" mapping. For example, Principle 15 "Dynamics" appears in Data→Purpose, Information→Knowledge, Knowledge→Wisdom, Wisdom→Wisdom, Purpose→Knowledge, and other transformations (see Table 1), indicating that the strategy of "dynamic adjustment" is universally applicable in transformations at different levels. Similarly, Principle 10 "Preliminary Action" appears repeatedly in Data→Data, Information→Data, Purpose→Data, Wisdom→Purpose, etc., indicating that the idea of taking measures in advance and preventing problems before they happen is valuable in multiple links. This principle overlap reflects the cross-context applicability of some general innovation strategies, but may also introduce issues of expression redundancy and application inconsistency, which will be analyzed specifically later.

Overall, through the mapping in Table 1, the 40 principles of TRIZ are completely (or nearly completely) covered across the 25 semantic modules of DIKWP, achieving the fusion of traditional engineering innovation experience with cognitive hierarchy problem solving. This is equivalent to constructing a reference manual of "Cognitive Contradiction → TRIZ Principle → Innovative Solution". When facing complex problems, different aspects of the problem (data gaps, information contradictions, knowledge conflicts, etc.) can be quickly located to the corresponding DIKWP transformation module, and the recommended TRIZ principle set can be looked up accordingly to inspire solution ideas. As Wu & Duan et al. pointed out in their research, this mapping provides structured guidance for applying TRIZ in cognitive space, helping to identify potential overlaps and redundancies, thereby optimizing the application of TRIZ principles in complex scenarios.

Transformation Paths and Problem Solving Process Example

To intuitively understand the application of TRIZ principles mapped in the DIKWP network, we use a simple case to illustrate the DIKWP-TRIZ problem-solving path:

Case: Traditional digital password unlocking methods are easily leaked via peeping or keyboard logging, requiring an improved unlocking method to enhance security.

DIKWP-TRIZ Solution:

DIKWP Problem Representation: Represent the problem across the five DIKWP layers:

Data Layer: Involves the data of user password input, and stealing method data such as peeping and keyboard logging (e.g., observing video streams, keyboard input logs).

Information Layer: Different stealing methods (peeping vs. keyboard logging) are differentiated information of data; they share the "same" semantics of "stealing passwords" but in different ways.

Knowledge Layer: Existing knowledge indicates that simple password input methods have security vulnerabilities, requiring a more complete authentication mechanism to eliminate theft under the same semantics.

Wisdom Layer: Requires the new solution to consider user convenience, privacy protection, and other values while improving security, ensuring it doesn't become counterproductive due to excessive complexity.

Purpose Layer: The final Purpose is explicitly "to prevent passwords from being directly stolen," i.e., guaranteeing the security of the unlocking process.

Contradiction Identification: In the TRIZ framework, this is a conflict between "Security" and "Ease of Use." In the DIKWP framework, a contradiction between the Information Layer and the Purpose Layer can be further identified: to achieve high security (Purpose), current information (information on stealing methods) shows that conventional password input is unsafe; simultaneously, the Knowledge layer has "incompleteness," lacking a complete knowledge solution that balances security and convenience.

Intra-layer Solving Attempt: First attempt solution ideas within each layer:

Data Layer: Directly improve data input forms, such as Adding input data types (applying Principle 1 Segmentation or 26 Copying, e.g., adding a one-time verification code data) or Filtering sensitive data (applying Principle 2 Extraction, e.g., inputting only partial information) to reduce the possibility of theft.

Information Layer: Change information features, such as Introducing interference information (Principle 16 might be slightly redundant or 22 "Blessing in Disguise," turning harmful info into harmless) making it hard for peepers to distinguish real from fake.

Knowledge Layer: Introduce new knowledge modules, such as Two-Factor Authentication Knowledge or Biometric Recognition Knowledge to replace old knowledge (corresponding to Principle 35 Parameter Changes or 40 Composite Materials, applying new technology principles).

However, these intra-layer solutions may have deficiencies: changing data alone might not be safe enough; changing only information or knowledge might sacrifice convenience. At this point, cross-layer transformation needs to be considered.

Cross-layer Transformation Solving: Apply the 25 transformation methods defined by DIKWP-TRIZ:

Purpose→Data: Redesign the data input method starting from the Purpose. The Purpose requires "not being stolen," so consider transforming original password data into high-dimensional complete input (e.g., biometric data), making stealing methods unable to directly acquire this data. This corresponds to the Purpose→Data module, mapping TRIZ principles like 10 "Preliminary Action," 19 "Periodic Action," 21 "Skipping," 23 "Feedback." Specific applications involve configuring biometric modules in the system beforehand (Preliminary Action), guiding users to use biometric features instead of passwords (Skipping key steps), and adding feedback verification steps in the interaction.

Data→Purpose: Conversely, map the new data input to the Purpose to check if it can achieve the expected goal. Here, the new "biometric data" input meets the Purpose of "not being acquired by peeping," because peeping at fingerprints or irises is far harder than peeping at passwords. This step reflects the role of the Data to Purpose module, and applications of principles 4 "Asymmetry," 11 "Beforehand Cushioning," 15 "Dynamics": ensuring Purpose achievement through asymmetric information (unique biometric features) and dynamic changes (e.g., random feature sampling each time).

Data→Knowledge: Transform the introduced new biometric data into system knowledge, i.e., the system must learn and master how to process this data for identity verification. This involves Principle 6 "Universality" (treating different biometric features as universally available verification methods) and 24 "Intermediary" (changing verification medium from memory passwords to body features). By collecting a large amount of fingerprint/face data to train recognition models, the system forms new complete knowledge in the Knowledge layer: "Identity can be reliably verified through biometric features."

Knowledge→Wisdom: Elevate the new solution to the Wisdom layer to evaluate its ethics and feasibility. Consideration of privacy issues (does collecting biometric data violate user privacy?) and backup plans (how to respond if biometric recognition is breached) is needed. This step corresponds to Knowledge→Wisdom transformation, referencing Principle 15 "Dynamics" to ensure strategy flexibility and 40 "Composite Structures" to introduce multiple guarantees. A possible approach is adding user informed consent (ethical requirement) and providing backup verification methods, ensuring the solution is accepted at the Wisdom layer.

Wisdom→Purpose: Finally, compare the adjusted solution with the original Purpose to confirm the solution achieves the purpose of improving security without violating ethics. The Wisdom→Purpose transformation applies principles 33 "Feedback," 39 "Inert Atmosphere" (or homeostasis), verifying solution effects through decision feedback loops to ensure the output aligns with the preset goal.

Solution Generation and Verification: Through the above layer-by-layer transformations, the resulting innovative solution is "using high-dimensional input like biometric recognition to replace traditional password unlocking." This solution simultaneously satisfies security and relative convenience, and passes the ethical acceptability check of the Wisdom layer. Compared to the few inventive principle hints directly given by traditional TRIZ (such as Preliminary Action, Inversion, etc.), the solution generated by DIKWP-TRIZ is more complete and has realistic feasibility. From the perspective of TRIZ's 40 principles, traditional TRIZ found principles 3, 10, 13, 25 in this case; while DIKWP-TRIZ, through multi-layer transformation, comprehensively utilized principles 4, 6, 10, 11, 15, 19, 21, 23, 24, 29, etc. (see Table 2 comparison). This confirms that DIKWP-TRIZ can introduce more diverse principle combinations and guarantee solution consistency with semantic goals.

Table 2: Comparison of Inventive Principles Used by Traditional TRIZ and DIKWP-TRIZ Solutions

TRIZ Solution Principles Used	DIKWP-TRIZ Solution Principles Used
3 (Local Quality), 10 (Preliminary Action), 13 (Inversion), 25 (Self-service)	4 (Asymmetry), 6 (Universality), 10 (Preliminary Action), 11 (Beforehand Cushioning), 15 (Dynamics), 19 (Periodic Action), 21 (Skipping), 23 (Feedback), 24 (Intermediary), 29 (Pneumatics/Hydraulics - interpreted as flexibility)

(The DIKWP-TRIZ solution principle set above comes from the synthesis of principles involved in various transformations in Table 1, used here to illustrate possible principle combinations. In actual application, DIKWP-TRIZ selects the optimal set of principles based on the specific situation to avoid unnecessary redundancy.)

Through this case, we can see that the DIKWP-TRIZ method guides us to analyze layer by layer and innovate across layers, making the final solution technically feasible (solving the problem of easy password theft), semantically closed-loop (solution hits the original Purpose directly), and value-compatible (considering privacy and convenience). Behind this process is the "path-based application" of TRIZ inventive principles in the DIKWP semantic network: problem transformations at different levels correspond to different principle combinations, passing layer by layer, ultimately converging into a systematic solution. This path search can be computationally realized through graph search algorithms—constructing a directed graph with five layer nodes and transformations as edges, starting from the node where the problem lies, searching for a path to the goal (Purpose satisfaction), with edges on the path carrying TRIZ principles as heuristic weights. Research indicates that this kind of search can often find multiple feasible paths; darker colored paths indicate more principle options (i.e., more potential branches, higher uncertainty). For example, Data→Data corresponds to 5 principles, making it the transformation with the most potential branches, shown as dark color on the graph; while Data→Wisdom has only 1 principle (40), appearing light-colored, with a relatively single and clear path. If a selected principle in a path does not work, the algorithm can continue to explore alternative paths along the network, possessing strong robustness. This mechanism reflects the advantage of DIKWP-TRIZ over traditional TRIZ in solving complex problems—being able to utilize network structure to search for optimal solutions in a larger solution space, rather than being limited to the few principles given by the contradiction matrix.

Analysis of Module Coverage Completeness, Path Efficiency, and Expression Redundancy

Module Coverage Completeness

Module coverage completeness focuses on whether the 25 transformation modules of the DIKWP semantic network fully cover the TRIZ knowledge base and all dimensions required for innovative problem solving. From the aforementioned mapping results, almost all of TRIZ's 40 inventive principles have been incorporated into the 25 transformation relationships. This means that the DIKWP-TRIZ framework theoretically achieves comprehensive inheritance of TRIZ experience: for every classic innovation principle, a place can be found in the cognitive network. Simultaneously, every cognitive transformation module is supported by at least one TRIZ principle, with no "blank modules" uncovered. This bidirectional coverage ensures knowledge base completeness and problem space completeness.

It is worth noting a detail in the mapping process—among TRIZ principle numbers, almost all from 1 to 40 appeared, except for Principle 7 ("Nested Doll" principle) which was not listed in the matrix (see Table 1). This may stem from the following reasons: first, the nesting principle emphasizes embedding one object into another, belonging to the category of system structure optimization, which might be viewed as a supplement to other principles (like Segmentation, Merging) in DIKWP semantic level transformations and not listed separately; second, it does not rule out an omission during literature recording. However, apart from this exception, the remaining 39 principles have all been mapped, so we can approximately consider the coverage of TRIZ principles by DIKWP-TRIZ as complete. In other words, every transformation scenario in the DIKWP network can find corresponding innovative inspiration without the "blind spots" found in traditional TRIZ.

More crucially, DIKWP-TRIZ not only covers existing TRIZ knowledge but also expands the dimensions of problem solving, achieving a more comprehensive coverage completeness. Traditional TRIZ mainly deals with technical level contradictions (physical parameter conflicts), while DIKWP-TRIZ extends the coverage range to cognitive level incompleteness/inconsistency. For example, "insufficient data," "imprecise information," "contradictory knowledge," "conflict between wisdom and purpose," etc., are problem types not explicitly covered by traditional TRIZ, but can find corresponding modules and solutions in the DIKWP network. Therefore, the DIKWP-TRIZ module network vertically connects levels from concrete technical parameters to abstract semantic goals, and horizontally encompasses the process from fact acquisition to value judgment, truly achieving full coverage of the innovation problem space. This enhances the adaptability of the innovation methodology to uncertainty and high-level constraints. For instance, in AI applications, data is often incomplete, and knowledge may be inconsistent, while ethical purposes must be met; under these complex requirements, traditional TRIZ cannot give direct guidance, whereas DIKWP-TRIZ provides a theoretical framework covering these factors.

Of course, complete coverage does not mean indiscriminate listing. DIKWP-TRIZ still needs to ensure that the principle set covered in each module truly fits the semantic scenario. For example, the Knowledge→Wisdom module includes principles 15 and 40 (Dynamics and Composite Structures), which are clearly closely related to the scenario of "elevating knowledge to wisdom, incorporating value considerations"; in contrast, forcing the "Nested Doll" principle here might be meaningless. Thus, while pursuing coverage comprehensiveness, DIKWP-TRIZ also emphasizes semantic appropriateness—ensuring that every mapped principle is relevant and useful in that context. This semantic matching has been fully verified by the research team using Semantic Mathematics and expert knowledge, enhancing the credibility of the methodology.

Transformation Path Efficiency

Transformation path efficiency refers to whether the process of finding a solution from the problem in the DIKWP-TRIZ network is efficient and concise, as well as the comparison in solving steps relative to traditional TRIZ. Factors affecting path efficiency include: path length (number of transformation steps), number of options per step (branching degree), and search or reasoning strategies.

Path Length: Ideally, DIKWP-TRIZ hopes to lead from problem to Purpose achievement with as few transformation steps as possible for maximum efficiency. However, some complex problems may involve multi-level contradictions, necessitating multiple cross-layer jumps. For example, a problem originating from data defects might need Data→Information to distill the crux, then Information→Knowledge to form new cognition, and finally Knowledge→Purpose to verify the goal. This appears to have more steps than traditional TRIZ's "one-step" application of a principle. But the benefit of these extra steps is that each step solves sub-problems at different levels, making the final solution more reliable. Therefore, slightly longer paths are the price for comprehensiveness. In practice, efficiency can be improved by optimizing paths: if a transformation step already largely satisfies the Purpose, one can terminate early without traversing all layers. For instance, in the previous case, if introducing biometric recognition (Data→Purpose) already solves the main contradiction, subsequent Knowledge→Wisdom only needs verification, without major changes to the solution.

Branching Degree and Search Strategy: From Table 1, the number of usable principles contained in each transformation module varies significantly. Modules like Data→Data contain up to 5-8 principles, meaning when a problem is in these modules, there are many optional branches, reducing efficiency if tried one by one. Conversely, modules like Data→Wisdom have only 1 principle, making the corresponding solution direction relatively single and clear. To improve efficiency, DIKWP-TRIZ needs to introduce heuristic search or optimization strategies. Literature proposes a Graph Search Algorithm: treating the five DIKWP layers as nodes and transformations as edges for searching. During the search, weights are assigned to each edge (such as number of optional principles or prior success rate), using heuristics (A* or greedy) to prioritize paths with lower weights (high success rate, fewer branches). Thus, the system will prioritize trying transformations expected to be most effective, avoiding getting stuck in combinatorial explosions at high-branching points. For example, if a direct path like Data→Wisdom is found to have the lowest weight, perhaps applying Principle 40 directly solves the problem, avoiding detours. Or comparing the two-step Data→Info→Knowledge with the one-step Data→Knowledge, the system can judge which is more efficient based on historical experience. Researchers found through heat map analysis that the number of optional principles for most transformations is between 2 and 4, with only a few like Data→Data reaching 5. Therefore, except for individual cases, the branching degree of the DIKWP network is not alarming, and with reasonable search strategies, the overall solution can still be completed within polynomial time*.

Intelligent Pruning and Parallelism: DIKWP-TRIZ can also utilize AI technology to improve path efficiency. On one hand, redundant paths can be pruned based on semantic similarity: if two different transformation sequences ultimately have equivalent effects, only one needs to be explored. This relies on the transitive closure property of Semantic Mathematics, normalizing different paths' semantic results for comparison. On the other hand, the DIKWP network essentially exists as multiple possible transformations in parallel, allowing full use of parallel computing or multi-agent collaboration to explore multiple paths simultaneously, then select the best. This is incomparable to traditional TRIZ based on serial human brain thinking. Preliminary practice shows that a DIKWP-TRIZ intelligent system can try multiple groups of principle combinations in parallel, generating a large number of candidate solutions in a short time, then screening out the best solution through semantic consistency. This parallel solving greatly improves the exploration efficiency of complex innovation problems.

Overall, DIKWP-TRIZ sacrifices some "single-step simplicity" (no longer applying principles in one step like TRIZ), but through multi-layer decomposition and intelligent search, it gains systematicity and sufficiency of the solving process. In terms of efficiency, as long as good algorithms and heuristic measures are designed, DIKWP-TRIZ can still provide answers within acceptable time, while the solution quality is higher. This reflects an engineering trade-off: exchanging computational power and algorithms for solution quality. With the development of artificial intelligence technology and computing power, this trade-off is worthwhile and is the direction of evolution for future invention and innovation methods. After all, in more complex cognitive scenarios, getting a thoughtful and reliable solution, even if it takes a little more computing resource, is better than getting an incomplete or even wrong quick answer.

Expression Redundancy and Principle Overlap

Expression redundancy refers to the possibility that different transformation modules or the same module in DIKWP-TRIZ may reference functionally similar or repetitive inventive principles, leading to redundancy in innovation guidance information. As analyzed above, principles 15, 10, 3, etc., appear in multiple semantic modules, indicating that certain TRIZ principles have cross-scenario generalization. However, if these principle hints are listed indiscriminately in every relevant module, it may cause confusion in the application of the methodology: Exactly when should this principle be used, and for which level is it best?

For example, Principle 15 "Dynamics" maps to both Data→Purpose and Knowledge→Wisdom modules. When the system encounters a problem requiring "dynamic adjustment," if these two modules appear sequentially on the same solution path, it might receive similar hints twice. The first time in Data→Purpose might have already considered dynamically adjusting data to meet the Purpose, and the second time in Knowledge→Wisdom suggests dynamically adjusting knowledge rules to consider Wisdom. This requires judging whether the focus of the two dynamic actions is different, otherwise, it is redundancy. Of course, dynamic adjustment at both levels might be necessary, but how to avoid repetitive application or how to coordinate two applications without conflict in such a scenario is a consistency problem that DIKWP-TRIZ needs to solve.

Addressing this issue, researchers like Duan et al. have recently proposed strategies to distinguish the connotation of principles under different transformation contexts and reduce the impact of repetitive redundancy. Specific strategies include:

Principle Semantic Decomposition: Semantically refine TRIZ principles with broad applicability according to DIKWP level requirements. For example, "Dynamics" at a lower level focuses on Input Dynamics (e.g., dynamic data input schemes), while at a higher level, it focuses on Decision Dynamics (e.g., dynamic strategy adjustment). By adding level-related prefix qualifiers, one "Dynamics" principle can be distinguished into "Dynamic Input Principle" and "Dynamic Decision Principle," corresponding to Data→Purpose and Knowledge→Wisdom respectively. This refinement ensures every hint has unique value.

Principle Application Conditions: Set trigger conditions or scope of application for each principle in each module. When a repetitive principle appears, check if the current context meets its application conditions; apply only when truly needed. For example, apply the Dynamics of Knowledge→Wisdom only when knowledge rules are too rigid affecting value judgment; otherwise, skip this hint to avoid repetition with the previous Data→Purpose dynamic adjustment.

Disambiguation and Priority: If multiple similar principles appear at a certain stage (e.g., simultaneous hints of "Segmentation" and "Separate then Combine," which are essentially about dismantling problems), judge their degree of repetition through semantic algorithms and retain the one more pertinent to the problem needs. Or assign priorities to these principles, choosing the one with higher probability based on historical success experience. The rest serve as backups, not presented to the user or AI in the first round.

Result Consistency Verification: Use the uniqueness axiom of Semantic Mathematics to perform consistency checks on solutions after applying similar principles multiple times. If contradictions introduced by repetitive principles are found in the solution (e.g., two dynamic adjustments in opposite directions), backtrack to modify one application or select a backup principle until the internal solution is coordinated. This ensures that although hints may repeat, the final output solution has no redundant conflicts.

Through these measures, DIKWP-TRIZ strives to retain the multi-perspective inspiration brought by principle overlap while avoiding cognitive burden and inconsistency caused by repetition. From another angle, principle overlap is not entirely harmful: it highlights the commonality of some innovation laws across different levels. This is exactly new knowledge discovered by DIKWP-TRIZ. For example, we found that the "Feedback" principle (33) is important not only in Wisdom→Purpose but also in Purpose→Data, Purpose→Wisdom, etc.—indicating that feedback mechanisms are indispensable in links from goal guiding action to decision calibrating goal. This provides clues for improving the methodology: perhaps "Feedback" can be elevated to a cross-layer meta-principle running through everything. This actually leads to higher-level theoretical considerations like Semantic Sovereignty and Reflexive Paths, which we will discuss in subsequent sections.

Overall, the problem of expression redundancy is controllable through good design. Researchers have recognized the potential risk of inconsistency and adopted means of "context distinction and semantic disambiguation" to mitigate it. With the refinement of DIKWP-TRIZ theory, we expect to see a more refined and coordinated principle system, where each innovation principle has a clear positioning in the entire network, not overlapping or conflicting with each other, but forming a complementary and mutually exclusive organic combination. This will greatly enhance the usability and reliability of DIKWP-TRIZ in practical applications.

Consistency and Conflict in Semantic Space

Semantic Consistency: Complementarity of TRIZ and DIKWP

Although TRIZ and DIKWP-TRIZ differ greatly in form and focus, they share commonalities in the core idea of innovation solving and can form complementarity in semantic space:

First, their goals are consistent: both are to solve contradictions and drive innovation. TRIZ focuses on technical contradictions, while DIKWP-TRIZ extends to cognitive semantic contradictions; essentially, both seek breakthrough points for conflicts. Therefore, the spirit of many TRIZ principles can be migrated to the DIKWP framework. For example, the "Thinking Outside the Box" emphasized by TRIZ (like Principle 13 Inversion) manifests in DIKWP as encouraging jumping from one level to another to view the problem (like jumping from Knowledge to Wisdom to re-examine). Similarly, TRIZ promotes "Resource Utilization" (Principle 35 Parameter Changes, etc.), which in DIKWP semantic space requires fully utilizing existing data and knowledge resources to refine solutions. Clearly, the two methods have intrinsic consistency in innovation strategies.

Second, DIKWP-TRIZ performs semantic reinforcement and inclusion of the TRIZ method. DIKWP-TRIZ does not discard the essence of TRIZ but reinterprets it in a broader cognitive scenario. The benefit is: TRIZ principles gain clear context in DIKWP. For instance, the "Isolation" principle is a physical concept in TRIZ, but in the DIKWP Information layer, it might mean isolating noise data, and in the Wisdom layer, isolating moral risks. In this way, the application of TRIZ principles becomes more precise and explainable. Simultaneously, the DIKWP model greatly enriches its own solving toolbox with the TRIZ principle library. Originally, DIKWP only gave abstract requirements for "transformation," not necessarily clear on how to transform; now with TRIZ instance guidance, each transformation corresponds to some operable methods. Thus, it can be said that DIKWP-TRIZ achieves the semantic fusion of TRIZ and cognitive science, combining the advantages of both.

Third, from the system result perspective, TRIZ and DIKWP-TRIZ can mutually verify each other's effects. If DIKWP-TRIZ proposes a solution using a certain TRIZ principle in a step, we can look back with the traditional TRIZ perspective: Did this principle solve the technical contradiction it was supposed to? Conversely, for an innovative solution given by TRIZ, DIKWP-TRIZ can further test its consistency and completeness at the Wisdom and Purpose layers. If inconsistency is found, such as a solution potentially violating ethics or not solving cognitive bias, it indicates the TRIZ solution is imperfect in semantic space and needs DIKWP-TRIZ supplementation and adjustment. Thus, the two frameworks form a synergistic relationship: TRIZ provides specific inspiration, DIKWP-TRIZ performs global semantic calibration, ensuring the final innovation is both creative and semantically self-consistent.

Finally, it is worth emphasizing that the proposal of the DIKWP-TRIZ methodology also confirms and expands some theoretical predictions of TRIZ. In his later years, Altshuller predicted that future innovation methods would need to utilize more interdisciplinary knowledge and information, and need to endow machines with creative thinking. DIKWP-TRIZ takes a step in this direction—it integrates knowledge engineering and semantic technology, allowing AI systems to also apply TRIZ principles for autonomous innovation. It can be considered that DIKWP-TRIZ is the inheritance and development of TRIZ theory in the new era context; the two share the same fundamental pursuit and have no fundamental conflict.

Semantic Conflict: Differences and Potential Contradictions

Despite the aforementioned consistency, TRIZ and DIKWP-TRIZ originated from different backgrounds, and differences in their focus may lead to conflicts at the semantic level, mainly reflected in the following aspects:

Differences in Problem Definition and Focus: TRIZ is accustomed to abstracting problems into concise technical contradictions expressed with limited parameters; DIKWP-TRIZ spreads the problem out, describing its manifestations in Data, Information, Knowledge, Wisdom, etc. This leads to inconsistent problem boundaries. For example, a manufacturing problem in TRIZ is a conflict between two parameters; but in DIKWP-TRIZ representation, it also involves data reliability, operator knowledge level, corporate goals, and other peripheral factors. If defining the problem narrowly with TRIZ, DIKWP-TRIZ would consider the description insufficient; conversely, TRIZ might feel DIKWP-TRIZ considers too many non-essential factors. Thus, there is tension in problem semantic scope between the two frameworks. Solving this conflict requires clarification during application: DIKWP-TRIZ can be compatible with TRIZ's simplified problem description but will automatically supplement its missing cognitive factors; TRIZ must accept expanded problem formulations, otherwise, the solution is prone to missing important semantic constraints.

Inconsistency in Evaluation Standards and Objective Functions: TRIZ's solution evaluation criteria are usually implied as "technical performance improvement with no side effects," rarely involving morals, long-term impacts, etc.; whereas DIKWP-TRIZ explicitly incorporates ethics and values of the Purpose Layer into the goal. Consequently, for the same solution, TRIZ might consider it a feasible innovation (because technical indicators are met), but DIKWP-TRIZ might judge it inappropriate (because it violates ethics or corporate strategy). For example, a solution improving efficiency but sacrificing safety redundancy might be approved by TRIZ based on efficiency metrics, but rejected by DIKWP-TRIZ considering safety hazards from the Wisdom layer. This belongs to Target Semantic Conflict. To resolve this, it is necessary to introduce DIKWP's value judgment when using TRIZ, that is, adding Wisdom and Purpose review links to the traditional TRIZ process. This can be viewed as an improvement to the TRIZ process, aligning it with DIKWP-TRIZ standards.

Differences in Methodological Assumptions: TRIZ assumes sufficient knowledge and data support analysis, i.e., problem parameters are clear and determined. DIKWP-TRIZ focuses on handling incomplete/uncertain information. In practical problems, if data/knowledge is insufficient, TRIZ tools (like the contradiction matrix) might be unable to proceed, while DIKWP-TRIZ will attempt to complete information before solving. This reflects the difference in assumptions between the two. Blindly using TRIZ to handle a 3-No problem (incomplete data, vague knowledge) might lead to wrong conclusions; conversely, using DIKWP-TRIZ to handle a simple contradiction with sufficient and clear information might appear as overkill and lengthy. The solution is to select or fuse methods according to problem characteristics: for typical engineering contradictions with clear information, use TRIZ directly for efficiency, then use DIKWP to check high-level consistency; for problems with incomplete information, apply DIKWP-TRIZ for layered analysis, then embed TRIZ principles for solving. This combination of dynamic and static approaches can maximize strengths and avoid weaknesses.

Differences in Terminology and Thinking Modes: TRIZ comes from engineering, with intuitive concrete terms (weight, temperature, mechanical system, etc.); DIKWP-TRIZ terms lean towards abstract cognition (Data, Semantics, Purpose). If using both methods in an interdisciplinary team, engineers might find it hard to understand "semantic inconsistency," while researchers feel "abstracting a parameter" is too limited. This is a conflict at the Language Semantic Level. To this end, it is necessary to establish a Terminology Mapping Table or Intermediate Ontology, mapping TRIZ technical terms to DIKWP cognitive terms. For example, map "Physical Parameter X Increase" to "Information Layer X Feature Enhancement," "System Efficiency" to "Purpose Layer Efficiency Indicator," etc. Through such subjective-objective terminology alignment, users in both fields can understand the meaning of each other's solutions. This is actually a question of Subject-Object Mapping Strategy, detailed in the next section.

Differences in Subject-Object Mapping Strategies

"Subject-Object Mapping" refers to how the problem proposer (Subject) maps the objective problem to model representation during the problem-solving process, and how the solution maps back to the objective world. TRIZ and DIKWP-TRIZ have different strategies in this regard:

TRIZ Subject-Object Mapping: In TRIZ, the subject (engineer) abstracts the objective problem into a standard engineering contradiction. This mapping is unidirectional and simplified: extracting a few parameters from complex reality and mapping them to the TRIZ model (e.g., Parameter A vs. Parameter B conflict). The subject's role is to provide abstraction, while the object (the problem itself) is basically simplified to parameter symbols after mapping. After solving, the subject translates the abstract solution back into a specific plan applied to the objective problem. TRIZ emphasizes seeking common ground while reserving differences during mapping: different problems are abstracted into the same contradiction model to apply the same principle. This mapping strategy is efficient but requires the subject to have rich experience to correctly abstract and re-concretize the solution.

DIKWP-TRIZ Subject-Object Mapping: In DIKWP-TRIZ, the subject (can be human or AI agent) holographically projects the objective problem in five-layer space, describing it at Data, Information, Knowledge levels, etc. The mapping is multi-directional and rich: it reflects the subjective understanding of the problem and retains details of the objective problem (Data level); during mapping, subject and object interact continuously, e.g., extraction of data and information requires subject knowledge guidance, and wisdom layer evaluation feeds back subject values. It can be said that DIKWP-TRIZ mapping is more like a dialogue: the subject dialogues with the problem context, gradually building a digital twin cognitive model. After solving, the AI or subject directly applies the solution guided by high-level Purpose to the objective world. This mapping strategy emphasizes Subject-Object Synchronization: during solution formation, the subject's cognition (model) and the evolution of the objective problem are synchronized, constantly calibrated through data feedback. Therefore, mapping is not a one-time completion but a dynamic process throughout the solution.

The above differences lead to differences in the role of the user subject: TRIZ relies more on Humans as smart intermediaries to translate back and forth; while DIKWP-TRIZ attempts to let the Machine subject (Artificial Intelligence) also participate in mapping, allowing machines to understand problems and partially solve them automatically through semantic models. Machines can execute massive mapping and reasoning work within the DIKWP framework, which is unimaginable in TRIZ. Therefore, in terms of subject-object mapping strategy, DIKWP-TRIZ fits the needs of the future intelligent era better: it provides a semantic channel for AI to understand and solve problems, enabling AI to become one of the innovation subjects. This extends the extension of "Subject" from merely humans to Human-Machine Hybrid Subjects or even autonomous AI subjects.

However, in the transition period, how human engineers and AI coordinate the use of two mappings remains a challenge. For example, an experienced engineer might feel DIKWP representation is too cumbersome and wish to use the contradiction matrix directly; while AI needs detailed semantic information to reason. This requires a Hybrid Mapping Mode: perhaps humans first use TRIZ to quickly locate key contradictions, then hand the results to the DIKWP-TRIZ system to enrich other level information, then AI performs multi-layer solving, and finally humans review high-level Purpose. This is similar to a Leader-Assistant mapping loop: Human leads in TRIZ abstraction, AI leads in DIKWP refinement, each leveraging their strengths.

In summary, the difference in subject-object mapping strategies between TRIZ and DIKWP-TRIZ reflects the difference between traditional engineering thinking and cognitive computing thinking. TRIZ tends towards Subject-Object Dichotomy, viewing the object problem as an external analyst; DIKWP-TRIZ tends towards Subject-Object Unity, where the subject embeds their understanding of the problem into the model, merging with objective data. This fusion mapping is exactly the viewpoint emphasized in artificial consciousness research: intelligent agents should map the external world and their own goals into a unified internal cognitive model before making decisions. Therefore, DIKWP-TRIZ's mapping strategy is more adaptable to complex, autonomous innovation environments, while TRIZ's mapping excels in clarity and speed. Future methodologies may find a balance point between the two, retaining the concise and intuitive advantages of TRIZ mapping while incorporating the comprehensive and deep benefits of DIKWP mapping.

Extension of DIKWP-TRIZ Method under Core Theoretical Fusion

Understanding the differences and connections between TRIZ and DIKWP-TRIZ, we attempt to introduce original theoretical ideas such as Semantic Sovereignty, Reflexive Causal Paths, and Multi-layer Knowledge Evolution at a higher level to extend and refine the DIKWP-TRIZ method. The goal is to build an upgraded DIKWP-TRIZ model oriented towards a more complex future intelligent invention system, and propose corresponding theoretical extensions and engineering adaptation suggestions.

Innovative Semantic Control Incorporating "Semantic Sovereignty"

The concept of Semantic Sovereignty was proposed by Professor Yucong Duan, initially describing the control power of a country over its language, culture, and values expression in the digital environment. Introducing it into the context of this study, Semantic Sovereignty can be extended as: Ensuring the subject's autonomous control over semantic interpretation and value orientation during the innovation process. Specifically for DIKWP-TRIZ, this means that at every stage of solution generation, the subject (whether individual, organization, or AI agent) should retain sovereignty over semantic understanding and decision preferences, not being swayed by improper biases of tools or algorithms. This idea guides the following extensions:

Enhancing the Leading Role of the Purpose Layer: Although the DIKWP model already has a Purpose layer, the subject's autonomous definition and adjustment rights over Purpose semantics should be further emphasized. For example, in enterprise applications, introduce an "Enterprise Semantic Sovereignty" mechanism: enterprises can customize the Purpose evaluation criteria of the DIKWP-TRIZ system, ensuring output solutions align with corporate culture and strategic goals without deviation. This can be achieved by adding a Semantic Constraint Module in the Purpose layer, which scores and filters candidate solutions based on rules provided by the subject (values, safety red lines, etc.).

Introducing Semantic Audit Module: Before finalizing a solution, add an independent Semantic Sovereignty Audit module. Based on the guidance of the subject (human decision-maker or regulator), it performs a semantic scan on the solution text/design, checking for inappropriate semantic content or implied consequences violating subjective/objective values. This module can rely on current AI adversarial review technologies and value consistency assessments. Through semantic audit, ensure the final solution is transparent and controllable to the subject at the semantic level.

Semantic Game of Multi-Subjects: In large-scale complex innovation (like national major projects), different stakeholders may have their own semantic sovereignty needs. The extended model can introduce Multi-Purpose Layers, each representing a subject's Purpose, then through semantic game (negotiation, voting) obtain a Synthetic Purpose to guide the innovation process. This prevents solution imbalance caused by a single subject's semantic preference, reflecting broader Semantic Democracy.

Semantic Blockchain Evidence: To consolidate semantic sovereignty, consider using blockchain technology to record key semantic decisions during the innovation process (such as Purpose adjustment, ethical trade-offs). This allows tracing who made the semantic choice behind each solution decision and based on what criteria, preventing the AI system from secretly changing semantic goals. This transparency helps subjects trust AI output and enhances system credibility.

Through the above improvements, DIKWP-TRIZ will better reflect the principle of "Human-Centric" or "Subject-Centric" in the innovation process, ensuring technical means serve human semantic Purpose rather than deviating. For AI autonomous innovation systems, this is equivalent to embedding a set of "values" into the system, not losing human Sovereignty control over semantics and values even when generating solutions highly automatically. With the development of artificial consciousness and sovereign AI concepts, perfection in this direction will make future innovation systems both efficient and reliable.

Constructing Closed-loop Feedback of "Reflexive Causal Paths"

Reflexive Causal Paths mean that there are causal links within the system that can self-trigger and self-feedback, i.e., the system's output conversely becomes part of the input, forming a closed-loop causal relationship. In the DIKWP-TRIZ framework, many reflexive mechanisms are actually implied, such as Wisdom layer output guiding Data layer collection, Knowledge precipitation becoming a new starting point for future decisions, etc. These are reflexive paths where high-level results feed back to low levels or current results accumulate into future causes. To improve the adaptability and continuous improvement capability of the innovation process, these reflexive causal loops can be further formalized and strengthened:

Full Process Closed-loop Model: Extend the DIKWP-TRIZ solving flow from linear to a closed loop. That is, when a conclusion is reached or a plan is implemented, it does not terminate, but feeds the implementation results back: new data and information are incorporated into the DIKWP model, updating the knowledge base; wisdom layer evaluation results influence the next round of Purpose, and so on. We can add an arc from practice back to the Purpose layer on the methodology architecture diagram, labeled "Reflexive Update." This gives the innovation process continuous learning characteristics; with every problem solved, the system becomes smarter.

Micro Reflexive Mechanisms: Besides the overall closed loop, local reflexive paths should also be supported. For example, introduce a Hypothesis-Verification Module: simulate the effect of a principle application; if new contradictions are found, immediately correct the input and apply again. This process might iterate multiple times in one solving process, equivalent to honing the solution in a Local Micro-loop. For instance, Data layer tries a data segmentation strategy, finds the Information layer still inconsistent, then reflexively returns to Data layer to adjust the segmentation method, deduce again, until information is consistent before proceeding. This micro-loop improves single solving quality and reduces solution failure risk.

Meta-cognitive Reflexivity: Let the system learn to reflect on its own reasoning path, identifying if it has hit a dead end and self-correcting. For this, a "Path Audit Agent" can be established to monitor the solving progress. If it fails to converge for a long time or circles repeatedly, trigger a strategy change (e.g., switch to another set of principles or try jumping layers). This is essentially introducing self-feedback at the Meta-layer of the DIKWP network, treating the state of the reasoning process itself as data for analysis. Meta-cognitive reflexivity can prevent algorithms from falling into local optima or logical traps, improving solving efficiency and intelligence.

Topological Closure Guarantee: Use Semantic Mathematics to ensure the entire DIKWP network topology is strongly connected and closed, i.e., any state can return to itself through a series of transformations. This way, there are no dead-end paths that cannot return or continue. From a graph theory perspective, this requires transformations to have Transitive Closure properties. In practice, verify that every node has a loop (even if long) back to itself. If a node is found without a loop, consider adding new transformations or new principles (this might be the clue to the missing Principle 7 mentioned earlier). This topological completeness is also a form of reflexivity, globally ensuring the cyclic potential of system behavior.

By strengthening reflexive causal paths, the DIKWP-TRIZ system will possess greater Autonomy and Adaptability. On one hand, every innovation result is internalized as system experience, making the system smarter with use; on the other hand, even if external conditions or goals change, the system can quickly adjust strategies through internal feedback, equivalent to having a "Self-Correction" function. This is an important step towards artificial consciousness and general intelligent innovation—an innovation system capable of self-reflection and evolution. It is foreseeable that future DIKWP-TRIZ systems or products will not just be tools, but more like Symbiotic Innovation Partners, growing together with humans in reflexive cycles.

Dynamic Extension Supporting "Multi-layer Knowledge Evolution"

Multi-layer Knowledge Evolution refers to the evolution and upgrading of knowledge at different abstraction levels over time. In the DIKWP model, knowledge at each layer accumulates and updates constantly: data expands with collection, information refines with pattern discovery, knowledge improves through learning, wisdom advances with experience and lessons, and Purpose may adjust due to environmental changes. If this multi-layer evolution can be incorporated into the DIKWP-TRIZ framework, it will be more competent in Long-term Continuous Innovation. To this end, the following extensions can be considered:

DIKWP^T Model Introducing Time Dimension: Extend the original five-dimensional model to six dimensions, where the sixth dimension is Time or Version. DIKWP^T allows representing DIKWP states at different moments. For example, Knowledge(t0) → Knowledge(t1) represents the evolution of the knowledge layer from t0 to t1. Then treat evolution as a special transformation operation, which can merge into the 25-module network becoming a new dimension (strictly speaking, 5 dimensions plus time isn't representable by a simple 5×5 matrix, but can be considered in tensor form). By monitoring changes in knowledge and wisdom at different time points, the system can predict future conflicts or brew innovation in advance. For example, discovering the knowledge curve is about to hit a bottleneck, actively adjust Purpose to plan new innovation goals ahead of time.

Module Self-Evolution and Addition: During multi-layer evolution, new patterns and needs may emerge, requiring New Modules to handle them. For instance, with technological development, new concepts beyond the existing five layers might appear (some propose adding "Trust" or "Philosophy" layers to DIKWP). The extended DIKWP-TRIZ should possess Openness, allowing the addition of new element layers and corresponding transformation modules in the framework, associating them with original TRIZ principles or new inventive principles. Even if focusing on five layers now, interfaces should be left in theory. A practical approach is modular design: encapsulating each level and its transformations into modules; adding a layer only requires defining the transformation set interacting with other layers to embed into the framework. This makes DIKWP-TRIZ Prospective and Extensible, not solidified in the current version.

Knowledge Base Evolution and Case Learning: DIKWP-TRIZ should be accompanied by a Knowledge Base growing over time. Every solved problem case is converted into knowledge archive (as mentioned earlier, knowledge precipitation). The extended model can introduce a Case Learning Module, periodically analyzing new cases in the knowledge base, comparing with old knowledge to discover the evolution of the knowledge system. For example, if a certain principle combination frequently solves new types of problems successfully, elevate it to a new meta-principle; if certain outdated knowledge is inapplicable in the current scenario, mark it for elimination. This is similar to biological evolution, constantly Selecting and Mutating innovation knowledge, keeping the knowledge base highly adaptive. The transitivity guarantee of Semantic Mathematics can be used to verify consistency between new and old knowledge, preventing self-contradiction in knowledge evolution.

Cross-layer Migration and Analogy: An important way of knowledge evolution is Analogy Migration: migrating an innovation application from one layer to another for reuse. For example, migrating an optimization method from the Information layer to the Knowledge layer to become a new knowledge principle. Without an evolution mechanism, this cross-layer analogy is hard to discover. But under the multi-layer evolution perspective, one can track changes in solution patterns of different layers over time to discover Similar Morphology. Once similarity is determined, a Cross-layer Mapping Mechanism can be introduced to import methods from one layer to another. This essentially adds a type of Cross-layer Mapping Module, countable as a sixth type of module, functioning as "Pattern Analogy." Through this mechanism, originally independent innovation practices of various layers will draw from each other, producing synergistic evolution. For example, data augmentation methods in machine learning (Data layer innovation) might inspire ideas in knowledge reasoning (Knowledge layer) via analogy, and vice versa. This can greatly enrich the library of innovation principles.

In summary, the Multi-layer Knowledge Evolution mechanism makes DIKWP-TRIZ a Times-advancing, Self-renovating system. It not only solves current problems but accumulates evolutionary power during the solution process, preparing for more complex future problems. This is similar to innovation evolution in human society: each generation's innovation accumulation provides foundation and inspiration for the next. If DIKWP-TRIZ successfully simulates this evolutionary characteristic, it will no longer be just fixed 40 principles/25 modules, but will constantly grow the 41st, 42nd principles, or even generate completely new levels of innovation concepts. It can be said that this makes DIKWP-TRIZ truly an Organically Evolving Innovation Ecosystem.

Integration of New Modules, Path Optimization, and Cross-layer Mapping Mechanisms

Based on the above theoretical extensions, we can outline the framework of the Extended DIKWP-TRIZ Model and refine a few suggestions for key improvements:

Adding Semantic Control and Feedback Modules: Add two modules traversing the whole in the original framework: (a) Semantic Sovereignty Control Module, running through problem representation, solution screening, and result review, providing semantic calibration for important decisions; (b) Reflexive Feedback Module, running through solution generation, implementation, and re-learning, achieving closed-loop improvement. These two modules are not divided by layer but by function, yet they need to interact with DIKWP layers. In engineering implementation, they can be integrated as independent components with the existing DIKWP-TRIZ engine interfaces. For example, the Semantic Control Module outputs a dynamic constraint list, and the Reflexive Feedback Module provides a decision loop trigger.

Optimizing Transformation Path Strategy: Beyond algorithmic heuristic search, theoretically introduce a Path Cost Model. Assign a cost metric (like step time cost or uncertainty) to each transformation, and find the path with minimum total cost through mathematical optimization. Especially in the extended version, introduced reflexive loops may produce longer paths, so inefficiency must be prevented. Consider borrowing Reinforcement Learning ideas, letting the system "learn" which path is optimal through multiple solvings. In the long run, perhaps a "Meta-Matrix" can be formed, specifically storing best solving paths for different problem types on the DIKWP network (equivalent to an upgrade of the classic contradiction matrix) for reference by successors.

Cross-layer Mapping and Analogy Mechanism: As stated in the multi-layer evolution section, it is suggested to incorporate Cross-layer Pattern Mapping as a formal mechanism. Add a special type of transformation module, noted as Mapping(X→Y), representing mapping the solution pattern of Layer X to Layer Y as an alternative. This is actually similar to the Super-class of Inventive Principles in TRIZ—transplanting a solution idea from one field to another. Realizing this requires Semantic Graphs or Ontologies to detect if problem descriptions of two layers have structural similarity semantically. If similar, cross-layer analogy principles can be recommended. For example, discovering a software optimization problem is structurally analogous to a mechanical optimization problem semantically, then mapping the mechanical field's TRIZ solution over. This broadens solution sources and reflects the idea of Innovation Analogy. Engineering-wise, the TRIZ principle library can be extended to add some Cross-domain Principle entries, or build a case-driven mapping library.

Theoretical and Standard Extension: With model extension, clear definitions for new elements need to be given theoretically for engineering implementation and academic dissemination. For instance, define "DIKWP-TRIZ Semantic Sovereignty Criteria" as a set of formal rules; propose "Reflexive Principles" listing them as new TRIZ principles or methodological principles, etc. This helps systematize extension results. Even consider pushing for Standardization: such as formulating industry standards for DIKWP-TRIZ application in AI innovation, writing semantic control, feedback, reflexivity requirements into it, promoting consistent interfaces and parameters across different implementations. This is similar to Altshuller establishing standard parameters and principle lists during TRIZ promotion; today we can formulate standard semantic modules and extension mechanism checklists.

Engineering Adaptation and Application: Finally, and importantly, consider how to land the extended model in engineering practice. Suggested directions include:

Developing DIKWP-TRIZ 2.0 Software Tools: Based on existing prototype systems, add the aforementioned new module functions, enhance human-machine interaction interfaces, allowing users to configure semantic sovereignty parameters, observe path feedback loops, etc.

Adaptation for Specific Domains: For example, for Large Language Model (LLM) innovation problems, embed DIKWP-TRIZ into LLM reasoning to guide the model to produce more innovative answers; or for Enterprise R&D, integrate enterprise knowledge bases for custom optimization. These adaptations can test the practical value of extended modules.

Training and Promotion: Create training materials and case libraries for the extended DIKWP-TRIZ, train R&D personnel to master semantic-level innovation methods. This is especially important for talent transitioning from traditional TRIZ. Or hold competitions and evaluations, letting different teams use the extended method to solve difficult problems, refining the model in actual combat.

Continuous Feedback: Collect user and AI agent feedback during application to iterate the model. This itself is applying "reflexive evolution" to model development, forming a production-research closed loop.

Summarizing the above, the extended DIKWP-TRIZ model will present new features: Subject Purpose Controllable, Path Loop Adaptive, Knowledge System Evolutionary Expandable, Multi-domain Experience Integrated. It retains the original DIKWP-TRIZ cognitive network structure while introducing new mechanisms at key nodes to enhance intelligence and generalization capabilities. This model has the potential to become the core architecture of future intelligent invention systems, providing a blueprint for building artificial innovation agents with human-like creativity and self-evolutionary capabilities.

Conclusion

This paper systematically compared the structural similarities and differences between the original TRIZ theory and the DIKWP-TRIZ method originated by Professor Yucong Duan, and based on the DIKWP*DIKWP semantic module network, explored the development and refinement of DIKWP-TRIZ. The analysis shows:

Traditional TRIZ is characterized by linear hierarchical contradiction analysis, emphasizing fixed engineering parameter conflicts and general principle solutions. It is highly effective for Technical Problems but appears inadequate when facing Cognitive Uncertainty and High-level Value Constraints.

DIKWP-TRIZ, relying on the five-element network structure, maps TRIZ's 40 principles to 25 cognitive transformation relationships, achieving comprehensive coverage of innovation principles in the Data-Information-Knowledge-Wisdom-Purpose multi-layer space. Through a combination of Intra-layer Optimization and Cross-layer Complementarity, it can compensate for data insufficiency, resolve knowledge conflicts, balance wisdom ethics, thereby solving more complex "3-No" problems. Compared to traditional TRIZ, DIKWP-TRIZ has advantages in open architecture, multi-dimensional problem focus, feedback-style contradiction handling, dynamic complementary method types, and adaptive innovation processes.

In the detailed mapping comparison, we saw that TRIZ's engineering parameters and inventive principles found new semantic habitats in the DIKWP network. Each DIKWP module carries a set of TRIZ principles, forming a complete correspondence matrix; meanwhile, some principles are reused across modules, hinting at internal connections and redundancies within the principle system. This highlights DIKWP-TRIZ's comprehensive inheritance of TRIZ knowledge while exposing potential principle overlaps and application consistency issues. Through semantic distinction and context qualification, redundancy can be minimized, ensuring the methodology is rich yet rigorous.

TRIZ and DIKWP methods complement each other in semantic space but also have conflict tensions. On one hand, both share the core philosophy of solving contradictions and pursuing innovative breakthroughs; the birth of DIKWP-TRIZ is essentially the extension of TRIZ thought into the cognitive domain. On the other hand, due to differences in problem formulation scope, target evaluation standards, and subject mapping strategies, traditional TRIZ solutions may be inconsistent with DIKWP-TRIZ requirements (e.g., ignoring ethics, assuming perfect information). Such conflicts can be reconciled through Model Fusion and Process Reinforcement, such as introducing DIKWP high-level checks in the TRIZ process, or embedding TRIZ simplified steps in DIKWP solving. In the future, with the popularization of human-machine cooperative innovation modes, we expect to blend the strengths of both methods, establishing unified Subject-Object Mapping and Semantic Docking mechanisms, allowing engineers and AI to seamlessly switch thinking paradigms to calmly cope with innovation challenges of different natures.

More prospectively, leveraging theories like Semantic Sovereignty, Reflexive Causal Paths, and Multi-layer Knowledge Evolution, we proposed extension directions for DIKWP-TRIZ. Semantic Sovereignty ensures human value dominance in innovation, giving AI systems clear value boundaries; Reflexive Closed Loops give systems self-learning and self-correcting capabilities, making every innovation a cornerstone for a higher starting point next time; Knowledge Evolution and Cross-layer Mapping allow the innovation principle library to grow constantly, breaking inherent frames to achieve cross-domain knowledge migration and autonomous evolution. The extended DIKWP-TRIZ fusing these ideas will no longer be a closed static methodology but an Open Evolutionary Innovation Ecosystem. It can self-update with environmental and need changes, and even birth entirely new innovation paradigms and principles, ceaseless like a living organism.

Looking towards the construction of intelligent invention systems in the next 5-10 years, we have reason to expect such a scenario: enterprises or research teams deploy DIKWP-TRIZ Innovation Brains, which aggregate global patent wisdom and local semantic sovereignty principles. Upon receiving a complex challenge, it can instantly build a cognitive model, call upon rich interdisciplinary principles, and find ingenious solutions through reflexive trial and error; throughout the process, human experts collaborate with AI assistants—AI provides divergent multi-level proposals, humans apply high-level Purpose screening, both interacting iteratively to finally output solutions with both creativity and credibility, feeding back new experience to enrich the knowledge base. Such a system will greatly improve innovation efficiency, reduce trial-and-error costs, and empower humanity to break through more technical and social difficulties.

Of course, realizing the above vision still requires deep research into many theoretical and engineering issues. But the discussion in this paper indicates that method innovation based on DIKWP-TRIZ is feasible and full of potential. It provides us with a path to connect Engineering Innovation and Cognitive Evolution, giving a clear direction to the seemingly distant goal of "Artificial Intelligence's Innovation Capability." Henceforth, we will continue to refine the extended DIKWP-TRIZ model, enriching its theoretical connotation and application scenarios through more case verifications and prototype implementations. We believe that in the near future, a New Innovation System integrating semantic intelligence and inventive creativity will gradually take shape, driving humanity into a new era of innovation-driven development. As advocated by Professor Yucong Duan's team, we are witnessing and participating in the expansion and maturity of "Chinese People's Own Original Invention and Creation Methods," which is both a tribute to and transcendence of classic TRIZ, and a beautiful pioneering of future intelligent innovation.

References

Altshuller G. Algorithm of Inventive Problem Solving, published in 1973. (Altshuller's classic work on TRIZ foundations)

Yucong Duan. DIKWP-TRIZ Method: An Innovative Problem Solving Method Integrating DIKWP Model and Classical TRIZ, Preprint, 2023.

Yucong Duan et al. Research on Enterprise Problem Solving Theory Supported by DIKWP-TRIZ Model and Semantic Mathematics, Technical Report, 2025.

Wu, K.; Duan, Y. DIKWP-TRIZ: A Revolution on Traditional TRIZ Towards Invention for Artificial Consciousness. Applied Sciences 14 (23), 10865, 2024.

Yucong Duan et al. Mapping and Analysis of Innovative Ideas Based on DIKWP-TRIZ Methodology, Zhihu Column, 2024.

Yucong Duan. Technological-Institutional Collaborative Path of "Semantic Sovereignty": National Intelligent Strategy Centered on DIKWP Model, Research Report, 2023.

Citation Sources

(PDF) DIKWP-TRIZ Method: An Innovative Problem Solving Method Integrating DIKWP Model and Classical TRIZ, https://www.researchgate.net/publication/375380084_DIKWP-TRIZfangfazongheDIKWPmoxinghejingdianTRIZdechuangxinwentijiejuefangfa

(PDF) Research on Enterprise Problem Solving Theory Supported by DIKWP-TRIZ Model and Semantic Mathematics, https://www.researchgate.net/publication/392665673_DIKWP-TRIZmoxingyuyuyishuxuezhichengdeqiyewentiqiujielilunyanjiu

DIKWP-TRIZ: A Revolution on Traditional TRIZ Towards Invention for Artificial Consciousness, https://www.mdpi.com/2076-3417/14/23/10865

Evolution of thought from religious belief to humanism: DIKWP Semantic Analysis - ScienceNet, https://wap.sciencenet.cn/blog-3429562-1481419.html

ScienceNet—Construction of Semantic Sovereignty System from the Perspective of Sovereign AI - Yucong Duan's Blog, https://blog.sciencenet.cn/blog-3429562-1492393.html

Yucong Duan: Large Models and High-Quality Datasets under Sovereign AI, https://www.sdbdra.cn/newsinfo/8531210.html

Detailed Explanation of Semantic Sovereignty: New Topics in Global Digital Governance - Shandong Big Data Research Association, https://www.sdbdra.cn/newsinfo/8471623.html

(PDF) DIKWP×DIKWP Semantic Mathematics Helping Large Models Break Cognitive Limits Research Report, https://www.researchgate.net/publication/389068734_DIKWPDIKWP_yuyishuxuebangzhudaxingmoxingtuporenzhijixianyanjiubaogao

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