摘要
针对云制造环境下产品研发知识服务匹配问题,在对知识资源属性详细描述的基础上,文章提出基于属性相似度衡量知识服务资源间的差异性,以达到知识服务匹配的精准性。基于相似度,以改进的K-means聚类算法对知识服务资源聚类,形成属性相近的类簇;基于可拓论完成类簇的初选,在类簇中完成知识服务匹配,以减小知识服务匹配的搜索空间。仿真结果表明,基于属性相似度与改进K-means聚类的知识服务匹配策略可极大提升知识服务匹配的效率,有效解决知识服务匹配问题。
Aiming at the problem of product R & D knowledge service matching in cloud manufacturing, based on the detailed description of knowledge resource attributes, the difference between knowledge service resources is measured based on attribute similarity to achieve the accuracy of knowledge service matching. Based on similarity, the improved K-means clustering algorithm is used to cluster knowledge service resources to form clusters with similar attributes;and based on extension theory, the primary selection of clusters is completed. Finally, knowledge service matching is completed in clusters to reduce the search space of knowledge service matching. Simulation results show that the matching strategy based on attribute similarity and improved K-means clustering can greatly improve the efficiency of knowledge service matching and effectively solve the problem of knowledge service matching.
作者
郭宗祥
GUO Zong-xiang(College of Mechanical Engineering,Shaanxi Institute of Technology,Xi′an 710300,China)
出处
《组合机床与自动化加工技术》
北大核心
2020年第9期171-174,共4页
Modular Machine Tool & Automatic Manufacturing Technique
基金
陕西国防工业职业技术学院专项科研计划项目(Gfy19-27)
陕西省自然科学基础研究计划重点项目(2019JZ-50)。
关键词
云制造
知识服务匹配
K-MEANS聚类
属性相似度
cloud manufacturing
knowledge service matching
K-means clustering
attribute similarity
