DIKWP level-oriented TRIZ innovation scheme generation method

Abstract

The invention provides a method for generating a TRIZ innovation scheme oriented to a DIKWP level. The method comprises the following steps: performing hierarchical analysis on a problem under a DIKWP model, identifying contradictions existing in each level, selecting a matched TRIZ invention principle combination for each contradiction by utilizing a pre-constructed DIKWP * TRIZ mapping matrix, generating a corresponding solution thought in combination with a semantic context of the problem, and finally synthesizing schemes of each level to form a complete innovation scheme. According to the method, semantic adaptation application of the TRIZ principle in complex cognitive problem solving is achieved, it is ensured that the generated scheme has creativity and is highly consistent with the final purpose, and systematicness, correlation and effectiveness of innovative scheme generation are greatly improved.

Description

Technical Field

[0001] This invention relates to a method for automatically generating innovative solutions, belonging to the fields of computer-aided innovative decision-making and cognitive intelligence. Specifically, this method combines the principles of classical TRIZ (Theory of Inventive Problem Solving) with the DIKWP cognitive model to resolve complex problem conflicts hierarchically and generate innovative solutions. Background Technology

[0002] Traditional TRIZ methods comprise 40 standard inventive principles and were initially applied primarily to solving engineering and technical problems. For example, by establishing a contradiction matrix, conflicts in specific problems are mapped to corresponding inventive principles, and solutions are proposed accordingly. TRIZ has been successfully applied in mechanical design, manufacturing, and project management, providing a structured and efficient approach to innovation. However, classic TRIZ also has limitations: its terminology and methods originate from the engineering field, and direct application in non-engineering scenarios such as software development and service design often fails to meet these requirements, necessitating reinterpretation and modification for effectiveness. Furthermore, TRIZ tools themselves are relatively complex, requiring new users to possess a strong knowledge base and undergo training to master, making direct application difficult for beginners. TRIZ also faces challenges in semantic understanding when dealing with incomplete or cross-domain information: traditional TRIZ lacks effective mechanisms for handling incomplete, inconsistent, or ambiguous input data. Some researchers have attempted to improve TRIZ through methods such as ontology semantics, for example, by linking different knowledge sources through semantic similarity to address information inconsistency. However, such methods require precise semantic computation and a large knowledge base, making implementation in large-scale dynamic applications still quite challenging.

[0003] In contrast to the TRIZ method described above, the DIKWP model is an extension of the classic DIKW (Digital-Knowledge-Wisdom) model, adding a highest level of "Purpose." The DIKWP model can analyze and process problems hierarchically from an intention-driven perspective, mapping problems to cognitive, semantic, and conceptual spaces. Existing research shows that the DIKWP model can meticulously describe and handle complex and ambiguous problems such as incompleteness, inconsistency, and imprecision within these spaces. For example, ambiguity in textual descriptions can be reflected in the conceptual space, and contradictions in the cognitive subject's perception can be manifested in the cognitive space. By linking and integrating these three spaces, the DIKWP model can more comprehensively model complex problems. In summary, DIKWP provides a full-link cognitive framework from data to intention, but currently lacks methods for organically combining it with specific innovative tools (such as TRIZ principles).

[0004] In summary, existing technologies lack a method for generating innovative solutions that can be tailored to different cognitive levels, leveraging the creativity of TRIZ's inventive principles while considering the semantic context and ultimate goal of the problem. To address this technological gap, this invention proposes a TRIZ innovative solution generation method oriented towards the DIKWP level, thereby resolving the aforementioned deficiencies in existing technologies. Summary of the Invention

[0005] The purpose of this invention is to provide a TRIZ innovation solution generation method oriented towards the DIKWP level. By combining the DIKWP cognitive model with the TRIZ invention principle, it realizes the automatic generation of contradiction solutions and innovative solutions for complex problems at various cognitive levels, thereby overcoming the problems of limited application scope and semantic adaptation difficulties of existing technologies.

[0006] To achieve the above objectives, this invention provides an innovative solution generation method. Its core idea is to apply the 40 inventive principles of TRIZ to the DIKWP cognitive hierarchy model, constructing a context-aware automatic innovative solution generation mechanism. Specifically, this invention maps each TRIZ inventive principle to the transformation relationships between different levels in the DIKWP model. The DIKWP model includes five levels: Data (D), Information (I), Knowledge (K), Wisdom (W), and Intent (P), with cognitive transformation and enhancement relationships between each level from low to high. Based on this, this invention constructs a DIKWP×TRIZ mapping matrix (5×5, with 25 level transformation scenarios), mapping each transformation between DIKWP levels (such as Data→Information, Information→Knowledge, Knowledge→Wisdom, Wisdom→Intent, etc.) to a carefully selected combination of TRIZ inventive principles. Through this matrix, different cognitive levels perform their respective functions, employing different inventive principles, thus ensuring the systematic nature and consistency of the solution generation process. For example, the transformation from the data layer to the information layer focuses on resolving contradictions in extracting useful information from raw data. TRIZ principles such as the "separation principle" can be applied to filter noise and extract key information. Similarly, the transformation from the intelligence layer to the intent layer aims to ensure that the solution aligns with strategic goals, which may lead to high-level conflicts. In such cases, TRIZ strategies such as "preemptive action" or "redirection" can be introduced to adjust the direction of the solution, ensuring that the final solution matches the intended intent. Through this system mapping, this invention automatically invokes corresponding combinations of innovative principles when solving problems at different levels, leveraging the creativity of TRIZ principles while avoiding the repeated use of the same principle or the generation of contradictions at different cognitive stages.

[0007] In the process of generating a solution, the method of this invention first performs DIKWP hierarchical analysis on the problem to be solved, identifying contradictions or improvement needs at each level. Then, for each identified contradiction, it invokes a TRIZ inventive principle or combination of principles that matches its cognitive level. It is worth emphasizing that the selection of principles is not a rigid application, but rather an intelligent optimization combined with the semantic context of the contradiction. In other words, the system uses the conceptual space-to-semantic space mapping mechanism provided by the DIKWP model to transform the conceptual description of the problem into a formal semantic representation, thereby understanding the real context and intent behind the problem and matching it with the application scenario of the TRIZ principle. This context-aware mechanism allows the selection of TRIZ principles to be appropriate to the situation: for example, in the contradiction at the data → information level, if semantic analysis finds that the problem involves a large amount of noisy data interfering with the acquisition of effective information, the system prioritizes principles such as "separation" or "filtering" to solve it; as another example, at the wisdom → intent level, if analysis shows that the current solution deviates from the final strategic goal, principles such as "preemptive action" or "alternating course" are introduced to adjust the solution to fit the intent. By combining semantic context for principle matching, this invention ensures that the selected innovative principles not only fit the specific problem scenario but also retain the creative inspiration of the TRIZ principles themselves.

[0008] The aforementioned DIKWP×TRIZ matrix mapping mechanism and semantic matching strategy work together to form an automatic innovation solution generation engine. After identifying contradictions at each level and selecting the corresponding inventive principles, the system generates preliminary solution fragments for each cognitive level. Subsequently, the innovative ideas from different levels are integrated to form a complete solution. Since each sub-solution at each level follows its corresponding TRIZ principle and serves the goal of that level, the integrated overall solution supports each other at each cognitive level without conflict. In other words, the data-level solution ensures information quality, the knowledge-level solution optimizes decision-making rules, and the wisdom / intent-level solution ensures alignment of strategic goals. Through this bottom-up solution construction process, the final output innovation solution includes both concrete and implementable technical improvements and high-level strategic optimizations, achieving logical coherence, clear hierarchy, and consistent objectives.

[0009] In summary, this invention expands the applicability of TRIZ principles in solving complex cognitive problems by proposing a systematic mapping relationship between the DIKWP cognitive hierarchy and the TRIZ inventive principle. Furthermore, it introduces semantic context awareness and intent-driven innovation mechanisms to ensure that innovative measures at each level are coordinated and advanced towards the ultimate goal. While maintaining the high creativity of TRIZ, the method of this invention significantly improves the relevance and effectiveness of the generated solutions to practical application scenarios.

[0010] Compared with the prior art, the present invention has the following beneficial effects:

[0011] Enhancing the comprehensiveness and systematic nature of the solution: This method provides corresponding innovative strategies for different cognitive levels of the problem, generating solutions that encompass improvements from the data layer to the intent layer, exhibiting comprehensiveness across the entire spectrum. Compared to traditional solutions that only focus on the technical level, the solutions produced by this invention not only include innovations at the engineering and technical level (such as the application of new technologies and parameter optimization), but also involve higher-level improvements such as management process optimization, business model innovation, and even strategic adjustments. Therefore, the solution can simultaneously address both the surface symptoms and the underlying causes of the problem, demonstrating a stronger systematic approach.

[0012] To avoid conflicting and repetitive principles and improve the consistency and quality of the solution: A DIKWP×TRIZ matrix hierarchical mapping mechanism is adopted, with different inventive principles used at different levels. This avoids conflicts that may arise from the repeated use or improper combination of the same principle across multiple stages. Each innovative principle functions at its most suitable cognitive level, ensuring that all components within the solution are coordinated and consistent, without contradictions. Compared to traditional TRIZ applications where different teams may inadvertently use the same principle at different stages, leading to conflicting or redundant solution elements, this invention eliminates such inconsistencies through hierarchical guidance, thereby improving the overall quality and feasibility of the solution.

[0013] Accelerating the innovation process and improving R&D efficiency: This invention utilizes a computer system to automatically perform conflict identification and solution generation, significantly reducing the time required for manual brainstorming and repeated trial and error. Traditionally, expert teams may need weeks to brainstorm and find a breakthrough, while the system of this invention can often provide diverse innovative solution options in a shorter time. Through a structured, automated innovation method, companies can obtain a large number of creative solution ideas within a shorter development cycle, greatly improving R&D innovation efficiency. This ability to quickly generate multiple solutions is particularly useful in scenarios requiring significant creative input, such as engineering design and product improvement, helping teams quickly select the best ideas and accelerate project progress.

[0014] Lowering the barrier to innovation and enhancing cross-disciplinary applicability: Because this invention has semantically adapted TRIZ principles according to cognitive levels, users who are not TRIZ experts can receive clear and guiding solutions. Traditional TRIZ methods require engineers with extensive experience to use effectively, and further explanation and adjustment of terminology and principles are needed when applied in non-engineering fields. This invention reduces the burden of manual translation and adaptation by introducing semantic analysis and automatic principle matching. Even in non-engineering fields such as management and business, the system can transform problems into semantic representations at the DIKWP level and invoke appropriate innovative principles. Therefore, this invention expands the applicability of TRIZ methods in interdisciplinary fields, and users from different backgrounds and departments can obtain effective solutions to their problems through this system.

[0015] The solutions are aligned with strategic objectives, enhancing practical value: The "Purpose" layer, a special addition to the DIKWP model, ensures that the solutions generated by this method always revolve around the ultimate intent. Every innovative measure in the solution is traceably linked to the strategic needs or ultimate goals of the problem-solving party, avoiding situations where one aspect is overlooked. The intent-driven mechanism ensures that the solutions are not only novel but also aligned with the organization's long-term interests and strategic direction. For example, applying the "preemptive action" principle in the conflict between the wisdom and intent layers can eliminate the potential for the solution to deviate from the objective in advance, ensuring that the final implemented solution is highly consistent with the expected strategy. Because the solutions are more aligned with strategic objectives, they are more likely to gain the approval of decision-makers, resulting in a higher success rate in actual implementation, and thus their commercial and application value is correspondingly enhanced. Attached Figure Description

[0016] Figure 1 Schematic diagram of DIKWP×TRIZ mapping matrix

[0017] Figure 2 This is a schematic diagram of the system's functional architecture. Detailed Implementation

[0018] The present invention will be further described below with reference to the accompanying drawings and specific embodiments. It should be noted that, without departing from the principles of the present invention, various details and steps of the implementation methods can be adjusted according to different application scenarios.

[0019] like Figure 2 As shown, the system of this invention mainly includes the following modules: a DIKWP contradiction analysis module, a TRIZ principle matching module, and a solution generation module. The description of the problem to be solved (e.g., an innovation requirement or technical challenge proposed by an enterprise) first enters the contradiction analysis module as input. This module performs DIKWP hierarchical division and analysis on the problem, extracting the main contradictions or conflicts existing in each of the data layer, information layer, knowledge layer, wisdom layer, and intent layer. For example, at the data layer, there may be a conflict between "data noise and information extraction"; at the knowledge layer, there may be a conflict between "fixed rules and flexible adaptation"; at the wisdom layer, there may be a trade-off between "decision efficiency and accuracy"; and at the intent layer, there may be a conflict between "short-term gains and long-term strategy". The contradiction analysis module uses the DIKWP model to locate these key issues at the corresponding cognitive levels and outputs the corresponding contradiction descriptions for each level.

[0020] Next, the contradictions at each level obtained from the analysis are passed to the TRIZ principle matching module. This module contains a pre-constructed DIKWP×TRIZ mapping matrix (such as...). Figure 1As shown in the diagram, the system stores a set of TRIZ inventive principles matched to each cognitive level transition scenario. The matching module searches the matrix for the corresponding combination of inventive principles based on the source and target levels of the contradiction. For example, for a contradiction moving from the data level to the information level, the matrix retrieves principle combinations suitable for the "data → information" transition; for a contradiction moving from the wisdom level to the intention level, it retrieves the corresponding "wisdom → intention" transition principle combinations. Simultaneously, the matching module also filters and optimizes the initially selected principle combinations based on the semantic context of the problem. Specifically, the system uses natural language processing and other technologies to perform semantic analysis on the problem description, comparing the key semantic features related to the problem with the application context of each principle in the TRIZ principle library. This further refines the selection of principles, making them more suitable for the manifestation of the contradiction in the specific scenario. For example, if the semantic factor of "a large amount of noisy data" is detected in the data → information level conflict, the "separation" (separating noise) principle from TRIZ or the related "filtering" principle will be prioritized. Similarly, if the risk semantic of "deviating from strategic goals" is identified in the wisdom → intention level conflict, principles such as "preemptive action" or "alternating course" will be emphasized to adjust the direction of the solution. Through the processing of this module, each point of conflict is associated with a combination of innovative principles that fit its context.

[0021] Next, the module generates a solution. This module generates specific solution fragments or proposed measures for each contradiction and its corresponding TRIZ principle. Methods such as rule-based templates, case library retrieval, or generative AI models can be used to generate solution fragments. For example, for a data-layer contradiction using the "separation principle," the module can suggest improvements such as "introducing preprocessing steps to filter noisy data and improve the effective information extraction rate." For a knowledge-layer contradiction using the "bionic principle," the module can generate a solution such as "designing dynamically adjustable business rules based on natural evolution mechanisms to improve the system's adaptability to environmental changes." For a wisdom / intent-layer contradiction applying the "preemptive action" principle, the module proposes measures such as "establishing a deviation warning mechanism before decision implementation to correct solutions that may deviate from strategic goals in advance." Each of these solution fragments is designed for a specific cognitive level of contradiction, possessing local effectiveness and specificity.

[0022] Finally, the solution generation module integrates the solution fragments from each level to construct a complete innovative solution. The integration follows a bottom-up approach: lower-level solutions support higher-level solutions, and higher-level solutions guide lower-level solutions. For example, technical improvements from the data and information layers are embedded into the decision-making process optimization plan of the knowledge layer, and then incorporated into the decision-making framework of the wisdom layer, ultimately ensuring that the entire solution aligns with the organization's strategic goals at the intent level. During the integration process, if a deviation is found between a solution at a certain level and higher-level goals, the system can iteratively adjust the measures at previous levels (this is already reflected in the application of the aforementioned "pre-emptive action" principle). After this series of processes, the system outputs a final innovative solution report or action plan list for user evaluation and implementation.

[0023] To more clearly illustrate the method of this invention, the above process is explained below with an example. Suppose a manufacturing company wants to improve the efficiency and innovation of its production system. This problem involves multiple layers of contradictions:

[0024] Data-to-Information Layer Conflict: Noise is mixed in with the large amount of raw data collected by sensors, affecting the accuracy of extracting effective information. To address this conflict, the system identifies the clash between "data noise" and "information acquisition," locating the data-to-information layer transformation scenario in the DIKWP matrix. Based on this, the matching module selects a combination of TRIZ's "separation principle" and "filtering principle." The solution generation module then proposes a solution: adding a data preprocessing step to filter and clean the sensor data to remove noise and extract key information.

[0025] The contradiction between knowledge and intelligence: Existing production processes operate according to fixed rules, lacking a mechanism for flexible adjustment based on environmental changes, which contradicts the goal of pursuing intelligent decision-making optimization. The system maps this problem as a conflict in the advancement from the knowledge layer to the intelligence layer, namely "fixed rules vs. flexible decision-making." The matching module selects the "bionic principle" and "simplification principle" from TRIZ to address this. Based on these principles, the solution generation module provides corresponding solutions: drawing on biological evolution and adaptive mechanisms, optimizing the production rule system, simplifying overly complex and rigid rules, and introducing an adaptive adjustment module to enable the decision-making process to dynamically optimize based on real-time feedback, thereby improving the level of intelligent decision-making.

[0026] The conflict between intelligence and intent: Short-term decisions aimed at rapidly improving production efficiency may lead to excessive resource consumption, contradicting the company's long-term strategic goals for sustainable development. This can be seen as a conflict between the intelligence layer (decision efficiency) and the intent layer (strategic sustainability). After identifying this high-level conflict, the system applies the principle of "preemptive action" within the matrix for guidance. The solution generation module thus proposes: When implementing efficiency-enhancing solutions, simultaneously develop preventative measures, such as establishing resource consumption early warning and compensation mechanisms, to ensure that improvements in production efficiency do not deviate from the company's long-term green and sustainable development goals.

[0027] By integrating the aforementioned fragmented solutions at various levels, the system of this invention ultimately forms a comprehensive innovative solution: it includes both specific technical improvements such as data processing and rule optimization, and safeguards oriented towards strategic goals. The entire solution is interconnected from bottom-level execution to high-level decision-making, achieving both increased production efficiency and ensuring the attainment of corporate strategic objectives. Therefore, the method of this invention can generate coordinated and consistent innovative measures for complex problems at different cognitive levels, helping enterprises quickly obtain solutions that are both creative and practical.

[0028] It should be noted that the above embodiments are only used to help understand the method flow and advantages of the present invention, and do not constitute a limitation on the scope of protection of the present invention. The method steps and module division of the present invention can be adjusted and expanded according to specific applications. For example, the semantic analysis can be implemented by using a machine learning model to train an enterprise-specific semantic network, and the solution generation module can also be combined with a knowledge graph or a large language model to enhance the generation effect. Any equivalent substitutions and improvements made to the process or components using the ideas of the present invention should be covered within the scope of protection of the present invention.

Claims

1. A TRIZ innovation scheme generation method oriented towards the DIKWP level, characterized in that, Includes the following steps: Step 1: Perform hierarchical analysis of the problem to be solved using the DIKWP model to identify contradictions or conflicts at each cognitive level: data, information, knowledge, wisdom, and intent. Step 2: For each contradiction identified in Step 1, use the pre-constructed DIKWP×TRIZ mapping matrix to retrieve the TRIZ inventive principle or principle combination corresponding to the cognitive level to which the contradiction belongs, and select an appropriate inventive principle based on the semantic context of the problem. Step 3: Based on the selected TRIZ inventive principle, generate corresponding partial solutions or innovative measures suggestions for each contradiction. Step 4: Integrate the partial solutions generated at each cognitive level to obtain a complete innovative solution. The lower-level solutions provide support for the higher-level solutions, while the higher-level solutions constrain and guide the lower-level solutions, ensuring that the integrated innovative solution remains consistent across all cognitive levels and aligns with the predetermined objective.

2. The method according to claim 1, characterized in that: The step of combining semantic context when matching TRIZ inventive principles includes performing semantic analysis on the problem description, matching the semantic representation of the problem with the expected application scenarios of TRIZ inventive principles, and selecting the most suitable combination of inventive principles based on the actual problem context.

3. The method according to claim 1, characterized in that: The DIKWP×TRIZ mapping matrix covers the transformation scenarios between any two levels in the DIKWP model, totaling 5×5=25 scenarios. Each scenario is pre-associated with a corresponding set of TRIZ invention principles to resolve common contradiction types during the transformation process at that level.

4. The method according to claim 1, characterized in that: In the process of integrating the solutions in step 4, when it is found that some solutions at a certain level conflict with the goals at a higher level, the process further includes: adjusting the aforementioned solutions or introducing additional TRIZ principles based on the hierarchical association of the DIKWP model, so as to eliminate the conflict and ensure that the integrated innovative solutions are coordinated with each other at each level and point to the final goal without contradiction. An automatic innovation solution generation system is provided for implementing the TRIZ innovation solution generation method oriented towards the DIKWP level as described in claim 1, characterized in that the system comprises: The contradiction analysis module is used to receive the description information of the problem to be solved and perform hierarchical analysis of the problem based on the DIKWP cognitive model to identify contradictions at each level, including the data layer, information layer, knowledge layer, wisdom layer and intention layer. The principle matching module is used to store the DIKWP×TRIZ mapping matrix and, based on the contradiction points at each level from the contradiction analysis module, to call the TRIZ invention principle or principle combination that matches the corresponding level of the contradiction from the mapping matrix, and select the appropriate innovative principle scheme in combination with the semantics of the problem. The solution generation module is used to generate partial solutions based on the inventive principles corresponding to each contradiction provided by the principle matching module, and to integrate multiple partial solutions at different levels to form a complete innovative solution output.

5. The system according to claim 5, characterized in that: The principle matching module pre-stores a DIKWP×TRIZ mapping matrix, which categorizes 40 TRIZ invention principles into 25 cognitive level conversion scenarios based on their applications. When retrieving invention principles, the principle matching module uses the mapping matrix to ensure that different cognitive levels call different sets of invention principles.

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