目的 为确保产品更好地满足用户的情感需求与体验偏好,旨在帮助设计师快速而准确地量化用户情感需求,从而不断迭代优化产品。方法 首先,引入人工智能技术进行情感需求量化与分析,通过市场调研及文献收集,确定新能源汽车样本及相关感性词汇;其次,对新能源汽车进行用户感性意象分析,采用因子分析法对感性词汇进行降维和聚类,以形成感性因子;最后,运用粗糙集属性约简算法提取影响消费者满意度的关键设计特征,并通过反向传播神经网络构建感性因子与关键设计特征之间的映射关系,以预测最符合用户情感需求的产品设计组合方案。结果 借助人工智能生成图像技术设计出一款符合用户感性意象的新能源汽车造型设计,进一步验证了该方法的可行性。结论 使用感性工学结合人工智能技术设计的新能源汽车造型设计能够有效提升用户的满意度与情感体验,帮助设计师准确把握消费者情感需求,可为相关产品设计提供理论支持。
Abstract
To ensure that products better meet users' emotional needs and experience preferences, the work aims to help designers quickly and accurately quantify users' emotional needs so that they can iteratively optimize their products. Firstly, the artificial intelligence technology was introduced to quantify and analyze emotional needs. Through market research and literature collection, the samples of new energy vehicles and related emotional vocabulary were determined. Secondly, the user's emotional imagery of new energy vehicles was analyzed, and the factor analysis was used to downgrade and cluster the emotional vocabulary in order to form emotional factors. Finally, rough set attribute approximation algorithm was used to extract key design features affecting consumer satisfaction, and the mapping relationship between the perceptual factors and key design features was constructed by back propagation neural network to predict the product design combination that best met users' emotional needs. A new energy vehicle styling design that met the user's emotional imagery was designed with the help of artificial intelligence generative image technology, which further validated the feasibility of the method. The new energy vehicle styling design using Kansei engineering combined with AI technology can effectively improve users' satisfaction and emotional experience, help designers to grasp consumers' emotional needs, and provide theoretical support for related product design.
关键词
感性工学 /
粗糙集理论 /
新能源汽车 /
反向传播神经网络
Key words
Kansei engineering /
rough set theory /
new energy vehicle /
back propagation neural network
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