需求驱动的云平台产品关键设计特征识别方法  

Requirement-driven recognition method for key design features of products in cloud platform

作　　者：苏兆婧 余隋怀[1] 初建杰[1] 于明玖[1] 宫静 黄悦欣 SU Zhaojing;YU Suihuai;CHU Jianjie;YU Mingjiu;GONG Jing;HUANG Yuexin(Key Laboratory of Industrial Design and Ergonomics, Ministry of Industry and Information Technology, Northwest Polytechnic University, Xi'an 710072, China)

机构地区：[1]西北工业大学工业设计与人机工效工信部重点实验室,陕西西安710072

出　　处：《计算机集成制造系统》2021年第12期3604-3613,共10页Computer Integrated Manufacturing Systems

基　　金：国家重点研发计划专项资助项目(2019YFB1405702)。

摘　　要：为完善云服务平台产品设计知识发现系统,同时进一步提升需求与服务的匹配效率,提出一种基于转换器的双向编码表征(BERT)和随机Lasso的产品关键设计特征识别方法。首先,实验采用真实产品用户反馈数据集并对其进行人工标注,以BERT预训练语言模型为基础,建立输出层以训练设计领域命名实体识别模型,实现对显性设计特征的自动识别。实验表明,所提方法可以实现较好的性能,精确率、召回率、F1分数分别为90.55%、97.16%和93.68%。同时,提出一种知识迁移思想,在当前大数据环境下,利用随机Lasso算法挖掘其中蕴含的关键设计特征并加以重用,实现了对隐性设计特征的精确定位。To improve the product key design knowledge discovery system of cloud service platform and further improve the matching efficiency of requirements and services,a product design key features recognition method based on Bidirectional Encoder Representations from Transformers(BERT)and random Lasso was proposed.First,the user feedback of real products was adopted in the experiment and was annotated manually.Based on BERT model,the output layer was built,and the named entity recognition model in the design field was trained to realize the automatic recognition of explicit design features.Experimental results showed that the proposed method could achieve better performance,precision and recall,and F1 scores were 90.55%,97.16%and 93.68%respectively.Simultaneously,a new idea of knowledge transfer was proposed.In the current big data environment,the key design features contained in it could be mined and reused by using random lasso algorithm,so as to realize the accurate positioning of implicit design features.

关 键 词：工业设计 用户需求 基于转换器的双向编码表征 命名实体识别 随机Lasso 产品设计 

分 类 号：TB472[一般工业技术—工业设计] TP181[自动化与计算机技术—控制理论与控制工程]