Research On Service Resource Adaptation Method For Intelligent Cloud Manufacturing

Cloud Manufacturing 1.0 has promoted the rapid development of manufacturing.With the deep integration of technologies such as artificial intelligence and cloud computing and manufacturing,smart cloud manufacturing(Cloud Manufacturing 2.0)has attracted extensive attention and research from academia and industry.As one of the key links of smart cloud manufacturing,the effective adaptation of manufacturing service resources can effectively virtualize various island-like manufacturing resources and realize the on-demand adaptation of service resources through the smart cloud platform,which is helpful for manufacturers and distributors to customize production flexibly and adapt quickly,so as to meet the personalized needs of customers.This paper focuses on smart cloud manufacturing scenarios,and mainly studies the method of stable adaptation of service resources between manufacturers and dealers,and the rapid adaptation of service resources between dealers and customers.The research contents are as follows:(1)A correlation model of smart cloud manufacturing service resources is established.Island-like service resources in smart cloud manufacturing is integrated through key technologies such as manufacturing knowledge filters and associative representation models.And effectively respond to supply and demand preferences in the service resource adaptation scenario of the three main bodies of manufacturers,distributors and customers in smart cloud manufacturing,finally improve the stability and speed of adaptation.(2)A method for stable adaptation of service resources between manufacturers and distributors is proposed.Aiming at the problem of mutual adaptation between multiple manufacturers and multiple dealers,a manufacturer-to-dealer-based ranking model,a dealer-to-manufacturer-based Qo S ranking model,and a bilateral adaptation between manufacturers and dealers based on sequence values are constructed.The matching model is used to solve the bilateral adaptation problem through the improved Gale-Shapley algorithm.Service resource adaptation is improved on the three indicators of reliability,Qo S and user satisfaction.(3)A quick adaptation method for the service resources of dealers and customers is proposed.In order to quickly respond to customer needs,construct a similarity model of dealer service resources based on multi-dimensional attributes,a customer service resource demand model based on ontology,and a quick adaptation model between customers and dealers based on correlation degree,through improved spectral clustering The algorithm narrows the candidate range of service resources and improves the efficiency of matching supply and demand of service resources.