基于语义相似度与改进PSO算法的云制造能力需求模型与匹配策略研究*

doi:10.16731/j.cnki.1671-3133.2025.06.004

现代制造工程 ›› 2025, Vol. 537 ›› Issue (6): 30-44.doi: 10.16731/j.cnki.1671-3133.2025.06.004

基于语义相似度与改进PSO算法的云制造能力需求模型与匹配策略研究^*

李晓波, 郭银章

太原科技大学群智计算与云计算实验室,太原 030024

收稿日期:2024-11-11 出版日期:2025-06-18 发布日期:2025-07-16
通讯作者: 郭银章,博士,教授,主要研究方向为群智计算与云计算、云制造与云安全。
作者简介:李晓波,硕士,讲师,主要研究方向为健康管理与维修决策优化、云制造与云安全。E-mail:lixiaobo@tyust.edu.cn
基金资助:
^*国家自然科学基金资助项目(61876123);山西省中央引导地方科技发展资金项目(YDZJSX20231A044)

Research on cloud manufacturing capability demand model and matching strategy based on semantic similarity and improved PSO algorithm

LI Xiaobo, GUO Yinzhang

Crowdsourcing and Cloud Computing Laboratory,Taiyuan University of Science and Technology,Taiyuan 030024,China

Received:2024-11-11 Online:2025-06-18 Published:2025-07-16

摘要/Abstract

摘要： 针对云计算环境下智能制造资源服务化共享中制造能力与任务需求之间的搜索匹配与服务组合问题,提出了一种基于语义相似度与改进粒子群优化(Particle Swarm Optimization,PSO)算法的云制造能力需求模型与匹配策略。首先,在提出云制造能力需求模型的基础上,采用领域本体树的概念提出了概念相似度、句子相似度和数值相似度的计算方法,实现了基于语义相似度的云制造能力需求智能化服务搜索;然后,针对云制造能力的服务组合问题,在分析了制造能力服务质量(Quality of Service,QoS)属性的基础上,采用层次分析法(Analytic Hierarchy Process,AHP)将各个属性进行归一化求和,给出了一种基于改进PSO算法的服务组合方法;最后,通过实验对比发现所提出的方法优于现有方法并实现了云制造能力需求智能匹配原型系统。

关键词: 云制造能力, 任务需求, 搜索匹配, 服务组合, 语义相似度, 改进粒子群优化算法

Abstract: Addressing the search matching and service composition issues between manufacturing capabilities and task requirements in the context of intelligent manufacturing resource service sharing in cloud computing environments,this study presents a cloud manufacturing capability demand model and matching strategy based on semantic similarity and an improved Particle Swarm Optimization (PSO) algorithm is presented. First,building upon the proposed cloud manufacturing capability demand model,the concept of domain ontology tree is introduced to calculate concept similarity,sentence similarity,and numerical similarity,thereby achieving intelligent service search for cloud manufacturing capability demands based on semantic similarity. Subsequently,addressing the issue of service composition for cloud manufacturing capabilities,based on analyzing the Quality of Service (QoS) attributes of manufacturing capabilities, a service composition method based on an improved PSO algorithm is proposed using the Analytic Hierarchy Process (AHP) to normalize and sum various attributes. Finally,experimental comparisons demonstrate the superiority of the proposed approach over existing methods,leading to the realization of an intelligent matching prototype system for cloud manufacturing capability demands.

Key words: cloud manufacturing capabilities, task demands, search matching, service composition, semantic similarity, improved Particle Swarm Optimization (PSO) algorithm

中图分类号:

TP391

李晓波, 郭银章. 基于语义相似度与改进PSO算法的云制造能力需求模型与匹配策略研究^*[J]. 现代制造工程, 2025, 537(6): 30-44.

LI Xiaobo, GUO Yinzhang. Research on cloud manufacturing capability demand model and matching strategy based on semantic similarity and improved PSO algorithm[J]. Modern Manufacturing Engineering, 2025, 537(6): 30-44.

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基于语义相似度与改进PSO算法的云制造能力需求模型与匹配策略研究^*

Research on cloud manufacturing capability demand model and matching strategy based on semantic similarity and improved PSO algorithm

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