Text Knowledge Acquisition Method of Collaborative Product Design Based on Genetic Algorithm

Chongyuan Li,Mengmeng Yang,Naeem Jan

doi:10.1155/2022/3661477

Chongyuan Li, Mengmeng Yang + Show 1 more

Open Access

https://doi.org/10.1155/2022/3661477

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Abstract

In order to overcome the problems of poor clustering effect and large error of text knowledge acquisition in traditional text knowledge acquisition methods, a new text knowledge acquisition method of collaborative product design based on genetic algorithm is proposed in this paper. The definition of collaborative product design text knowledge clustering is given. According to the operation process of the genetic algorithm, the chromosomes of clustered text are constructed and encoded and the initial population is obtained. The fitness function of clustering is constructed by the DB index evaluation method; the selection, crossover, and mutation operators in the genetic algorithm are determined; and the objective function of collaborative product design text knowledge clustering is constructed. After the text knowledge clustering is completed, the text knowledge data of collaborative product design are obtained in an all-around way by using the method of rough set and neural network. The experimental results show that compared with the traditional text knowledge acquisition methods, the clustering effect of the proposed method is better and the text knowledge error is reduced up to 0.02.

Highlights

Chromosome Construction and Coding of ClusteringBased on the above clustering results of collaborative product design text knowledge, text knowledge is obtained according to rough set theory
In the process of collaborative product design, multidisciplinary design teams in distributed, remote, and dynamic environments involve many complex interactive tasks [5]. e practice results show that the sharing and reuse of design knowledge can reduce unnecessary repeated labor and shorten the product development cycle
Reference [9] proposes a text knowledge acquisition method based on word meaning disambiguation of knowledge map, uses TFIDF model to obtain the set of text feature words, uses the word meaning sequence relationship expressed by knowledge map to determine the unique semantics of polysemy in a specific semantic environment, completes the vectorial representation of text at the level of word meaning concept, and realizes text knowledge acquisition

Summary

Chromosome Construction and Coding of Clustering

Based on the above clustering results of collaborative product design text knowledge, text knowledge is obtained according to rough set theory. E text knowledge acquisition methods proposed in this study can be divided into the following steps: using self-organizing mapping (SOM) neural network method, based on the above clustering text data, the data are discretized, and the data interval is divided; rough set theory is used to extract rules to determine the input text data. E K-means clustering is optimized by genetic algorithm, the collaborative product design text knowledge is clustered, and the rough set combined with neural network method is used to obtain the collaborative product design text knowledge data in an all-round way [22]

Experimental Verification

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Conclusion