The Research On Optimal Matching Of Supply And Demand Of Knowledge Resources In Cloud Manufacturing

In the 21 st century,mankind has entered the era of knowledge economy.Knowledge economy is based on knowledge innovation,sharing and application.With the development of knowledge economy,the importance of knowledge resources has been further deepened,and knowledge has gradually become a key economic resource.For the manufacturing industry in our country,moving from a big manufacturer to a powerful manufacturer,and integrating manufacturing process with the knowledge resources.In the process of whole life cycle of product development,to provide a smooth channel for circulation of knowledge resources and enhance the penetration of knowledge resources.It is an inevitable development trend to provide intelligent support for the whole manufacturing process.However,the lack of independent innovation ability and knowledge resources,and the unbalanced allocation and utilization of resources in current domestic manufacturing industry,which seriously restrict the development of China's manufacturing industry.So academician Li bohu put forward the concept of Cloud Manufacturing.It is the start and extension of cloud computing in the manufacturing,it embodies the idea of "intensively using scattered resources" and "dispersedly serving centralized resources",and centrally manages distributed resources,and provides intelligent services for the whole life cycle of manufacturing companies business activities.But,there are still some deficiencies in the research of cloud manufacturing that research just focuses on hard resources but ignores relevant knowledge resources which is the core of cloud manufacturing system operation,and it is lack of targeted knowledge resource optimization and matching technology.In the aspect of optimization and matching of knowledge resource,the influence of the interaction dimension between provider and demander is ignored.Therefore,this paper presents a new method for optimizing and matching knowledge resources under the environment of could manufacturing by combining with the characteristics of knowledge resource and analyzing the theory of could manufacturing,optimization and matching of manufacturing.This method divides optimization and matching of knowledge resource into two stages.The first stage is functional attribute matching.Firstly,it constructs a formal description model of knowledge resource between provider and demander based on ontology,so that both the provider and demander of knowledge resources can be read and understood by other users of the platform.Then starting from the necessary demand of knowledge resources for demander to match the field attribute,basic attribute,input and output attribute,status attribute of provider,and requested attribute of demander in sequence.Eliminating the knowledge resource that don't meet requirements of functional attribute through setting threshold value and to get preliminary candidate resource set.The second stage is performance attribute matching.First of all it puts forward the concept of knowledge service capability,then analyzes the influence factors of knowledge service capability according to three dimensions of enterprise,service,interaction,and establishes the evaluation model of knowledge resource performance attribute based on the knowledge service capability.After that,prioritize knowledge resource performance attributes according to quantitatively solving evaluation model by could manufacturing,and evaluating performance attribute by expectation and superentropy of could manufacturering,the best performance attribute is the best match object.In the end,verified the validity of this method through the simulation analysis of the example,and proved the superiority of this method by comparing with the actual method.