基于数据融合模型的产品意象造型评价方法研究

Product Image Modeling Evaluation Method Based on Data Fusion Model

中文关键词: 产品意象造型  数据融合模型  用户情感需求  支持度函数  蝴蝶效应

英文关键词: product image modeling  data fusion model  emotional needs of the user  support for degree functions  butterfly effect

中文摘要:

      目的 为解决在产品意象评价中由于蝴蝶效应的存在而导致评价结果出现偏差、案例库选取不准确等问题，提出了一种基于数据融合模型的产品意象造型评价方法。方法 首先确定研究样本和感性意象，基于语义差异法设置调查问卷，进行了产品感性意象调查，挖掘用户的感性需求;其次通过模拟退火算法优选改进支持度函数参数，构建用户感性需求数据融合模型;然后以用户感性需求数据平均值和数据融合值作为输入进行对比，展开产品意象造型评价，指导最优方案的选取;最后以鼠标为例对所提方法进行实例验证。结论 基于数据融合模型的产品意象造型评价方法相较于传统平均值数据分析更为准确和客观，可有效把握用户的情感需求，减少产品意象造型评价的误差，指导产品典型案例库的选取。

英文摘要:

      The work aims to address issues in product image evaluation caused by the butterfly effect, such as biases in evaluation results and inaccurate selection of case databases, and propose a method for product image styling evaluation based on a data fusion model. Firstly, the research sample and perceptual image were determined, and a survey questionnaire was designed based on the semantic differential method to conduct a survey on the perceptual image of the product and explore users' perceptual needs. Secondly, the simulated annealing algorithm was used to optimize the parameters of the improved support function, and a data fusion model for users' perceptual needs was constructed. Then, the mean value of users' perceptual needs data and the data fusion value were used as inputs for comparison, and the evaluation of product image styling was conducted to guide the selection of the optimal solution. Finally, the proposed method was validated with a case study using a mouse as an example. The product image styling evaluation method based on the data fusion model is more accurate and objective compared with traditional average value data analysis. It effectively captures users' emotional needs, reduces errors in product image evaluation, and guides the selection of a typical case database.