结合KE/PSO-SVR的香炉评价与设计方法

Evaluation and Design Method of Incense Burner Combining Kansei Engineering and Particle Swarm Optimization-Support Vector Regression

中文关键词: 设计评价  香炉设计  用户情感需求  感性工程  优化支持向量回归

英文关键词: design evaluation  incense burner design  users' emotional needs  Kansei engineering  optimized support vector regression

中文摘要:

      目的 旨在设计一款融合文化元素的香炉，通过融合机器学习算法提高设计的高效性和准确性。方法 首先，使用里克特量表法设计问卷进行调研。在收集到足够的反馈后，使用粒子群优化-支持向量回归(PSO-SVR)方法对产品特性-用户反馈这一非线性组合进行建模并得出最优模型。以优化后的模型为基础，得出最优的产品特性组合并以此为依据进行产品设计。结论 以该方法进行的香炉设计生成结果用户满意度高，设计过程更为便捷。用户对最优设计的评价结果与系统的预测结果高度一致，验证了该方法的可行性和先进性。本研究更好地平衡了设计师的主观体验和用户的客观信息，提高了产品设计决策的准确性，帮助设计师开发出与用户需求密切相关的创新产品。

英文摘要:

      The work aims to design an incense burner incorporating cultural elements, so as toimprove the design efficiency and accuracy by integrating machine learning algorithm. Firstly, the Likert scale method was used to design the questionnaire and conduct a questionnaire survey. After collection of enough questionnaire feedback, the Particle Swarm Optimization-Support Vector Regression (PSO-SVR) method was used to model the nonlinear combination of product feature-user feedback and obtain the optimal model. Based on the optimized model, the optimal combination of product characteristics was obtained and the product design was carried out. The results show that the user satisfaction of the incense burner design is high and the design process is more convenient. In addition, the user evaluation results of the optimal design closely match the predicted results of the system, which verifies the feasibility and advancement of the proposed method. This study better balances the subjective experience of designers and objective information of users, improves the accuracy of product design decisions, and helps designers develop innovative products that are closely related to user needs.