Clustering TRIZ Analysis Method for Inventive Principle Prioritization
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Solution Overview
Problem
Designers face challenges in efficiently determining no-worsening features and prioritizing inventive principles when using TRIZ matrices, particularly for less-experienced users, leading to delays and inefficiencies in the design process.
Innovation Solution
A clustering TRIZ analysis method is introduced, which constructs cluster elements based on TRIZ matrix features and principles, calculates display times, and determines discrimination values to establish a priority order for clusters, utilizing models like display time, Bayes probability, fuzzy value, and combined fuzzy value and Bayes probability models.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual determination of no-worsening features and priority among inventive principles is used, then experienced users can make informed decisions, but less-experienced users face difficulties and the process becomes time-consuming
Solution Approach 1:
The patent replaces manual mechanical analysis with an intelligent system that automatically determines no-worsening features and prioritizes inventive principles. The system uses computational algorithms to analyze TRIZ contradiction matrices, calculate display times, and generate priority rankings without requiring manual intervention, thus eliminating the time loss while maintaining accuracy.
Solution Approach 2:
The intelligent system enables self-service by automatically performing tasks that previously required expert human judgment. The system independently identifies no-worsening features, calculates display times for inventive principles, and generates priority orders without external assistance, making the TRIZ analysis accessible to users of all experience levels.
2Ease of operation
If detailed manual analysis of each inventive principle is performed, then accurate prioritization is achieved, but the design process experiences delays
Solution Approach 1:
The patent segments the complex TRIZ analysis process into distinct computational steps: constructing display time models, calculating display times for each inventive principle, determining discrimination values, and generating priority orders. This segmentation allows the system to process information systematically and efficiently, improving productivity while maintaining ease of operation through automated execution of each segment.
Solution Approach 2:
The system creates computational models that replicate the expert decision-making process. By copying the logical structure of manual TRIZ analysis into an automated algorithm, the system achieves both the accuracy of detailed analysis and the speed of automated processing, thereby improving design process efficiency without sacrificing prioritization quality.
3Loss of information
If clustering of TRIZ elements is implemented, then systematic prioritization is achieved, but computational complexity increases
Solution Approach 1:
The patent merges multiple TRIZ elements (contradiction matrices, inventive principles, no-worsening features) into integrated clusters based on their relationships. By combining these elements into coherent groups, the system reduces the apparent complexity while preserving all necessary information, as the clustering organizes data in a manageable structure that maintains analytical completeness.
Solution Approach 2:
The system transforms the representation of TRIZ data by introducing new parameters such as display time and discrimination value. These parameter changes convert complex qualitative relationships into quantitative measures that are easier to process computationally, thereby reducing analytical complexity while maintaining the completeness of the TRIZ analysis through preserved relational information.
Data Source
AI summary
The TRIZ decision process of the clustering method proposed by this invention uses the characteristics and invention rules from the contradiction matrix table resulting from massive quantities of patent inferences to find a similar or approximate character group and invention rule group of the physical meanings, and also applies statistics to calculate the number of display times of the groups to be the basic foundation. Apart from the number of display times, Bayes probability, fuzzy object oriented method and Bayes probability combined with fuzzy object oriented method can be used as the system. The reading value is utilized as a foundation for prioritizing the sequence of consideration for the groups, in which the system reading value constructed by different models gives designers lots of options to perform the reading, so as to acquire the undesired result features of the prioritized consideration.