Big Data-Driven Product Innovation Design Modeling and System Construction Method

Abstract

In order to improve the image quality of innovative design of manufacturing products, reduce the dependence on experts, increase the amount of research data, and accurately sort and select the best alternatives, this paper proposes the KENPI method, which integrates perceptual engineering and neural style transfer, normalizes the content map through nm model, realizes style transfer, and generates new product images. Use ORDD perceptual engineering to collect a large number of perceptual word data, establish product semantic space, use TF-EPA to obtain perceptual words, and use word clustering combined with degree adverbs to evaluate the sensibility of products. Under the KE-GRA-TOPSIS method, considering user preferences, accurately sort and select the product design alternatives with multiple criteria, and establish the auxiliary system of product innovative design. The experimental results show that the style transfer effect of nm model is better, the style intensity of the product is enhanced, and the average texture evaluation of sample 3 is increased by 0.30 points. The average absolute value of DOD phrase in BP neural network is 0.0765, which is lower than the MLR method, and the performance of the former is better than the latter. The relative closeness of A6 scheme under KE-GRA-TOPSIS method is 0.57, which is 0.02 higher than the KT method, indicating that the KE-GRA-TOPSIS method is better than the KT method. The research improves the way of obtaining user demand data, enhances the strength of and product style, and improves the competitiveness of products.