基于在线评论的产品感性设计知识建模方法研究

随着大数据资源的不断丰富及分析处理技术的逐渐成熟，工业设计正在转变为基于大数据的智能设计，如何有效获取并利用大数据资源实现智能设计是工业设计及数据挖掘领域基础理论研究的共性难题。.本项目以产品在线评论大数据为研究对象，从评论挖掘、情感分析、知识工程、感性工学交叉融合研究角度，提出一种基于在线评论挖掘的产品感性设计知识建模方法，重点探索中文在线评论文本数据知识化过程与设计过程知识演化机制的关键科学问题。提出面向产品感性设计的在线评论数据挖掘方法获取感性知识；基于感性工学理论，以产品多维造型特征空间与用户情感意象认知空间的组织方式表达感性知识，并提出知识约简与量化方法；建立产品设计过程知识转化与预测模型并开展实例验证。该研究从多学科交叉融合视角解析产品智能设计过程的知识发现与推理，拓展和推动工业设计基础理论研究，为大数据时代工业设计迈向智能化提供新思路和新方法。

With the continuous enrichment of resources and the gradual maturity of analysis and processing technology of big data, industrial design is transforming into intelligent design based on big data. How to effectively obtain and use big data resources to realize intelligent design is a common problem in the basic theoretical research of industrial design and data mining. .The project takes product online review big data as the research object, from the perspective of review mining, sentiment analysis, knowledge engineering, and Kansei engineering cross-integration research, proposes a product perceptual design knowledge modeling method based on online review mining, focusing on exploring the becoming knowledge process of Chinese online review text data and the knowledge evolution mechanism of design process. An online review data mining method oriented to product perceptual design is proposed to acquire perceptual knowledge. Based on the theory of Kansei engineering, the perceptual knowledge is expressed by the organization of product multi-dimensional modeling feature space and user emotional image cognitive space, and knowledge reduction and quantification methods are proposed. The knowledge transformation and prediction model of product design process are established and verified by an example. This research analyzes the knowledge discovery and reasoning of the product intelligent design process from the perspective of interdisciplinary integration, expands and promotes the basic theoretical research of industrial design, and moves toward intelligence for industrial design in the big data era.