Research On The Effectiveness Of Manufacturing Cloud Services And The Optimization Evaluation Algorithm Of Cloud Services Solution In Cloud Manufacturing Environment

There are a large number of identical or similar manufacturing cloud services in Cloud Pool,and there are also some false manufacturing cloud services.This brings some heavy burden to cloud users to select the good cloud services.In order to protect the interests of cloud users and cloud manufacturers,this paper explores the effectiveness of manufacturing cloud service identification and cloud service optimization solutions.Firstly,the "Tron Arm Mall" cloud platform is used as the source of data acquisition,and a crawler framework based on the characteristics of the target webpage is established to obtain 9 evaluation index information such as cloud service performance,parameters,scores and so on.Index original information is quantified and standardized,and experimental data sets of construction machinery products are established.Secondly,it analyzes the characteristics of false manufacturing cloud services and the influencing factors of cloud service credibility,and establishes a system of distinguishing indicators for the effectiveness of manufacturing cloud services.Combining the Monte Carlo estimation algorithm with the hypervolume algorithm,a hypervolume algorithm based on Monte Carlo simulation estimation is obtained.The algorithm realizes the rejection and cleaning of false manufacturing cloud services in the experimental data set.On the basis of GA-based fuzzy C-means clustering algorithm,this paper proposes a fuzzy C-means clustering algorithm based on optimization objectives to evaluate the trustworthiness of cloud services by improving the processing of objective functions,improving cross-operation and increasing adaptive mutation operations.The algorithm uses the Iris standard data set to verify the stability and accuracy of the proposed algorithm.The proposed algorithm is used to evaluate the credibility of the cleaned manufacturing cloud service and determine the cloud service credibility level.Then,analyze the factors that affect the Qos of manufacturing cloud services,and establish a comprehensive evaluation system of Qos for manufacturing cloud services.Analyze the relationship between cloud service indicators,combine the advantages and disadvantages of comprehensive evaluation,and choose a combination of subjective and objective evaluation method based on analytic hierarchy process and entropy method to calculate the comprehensive evaluation value of cloud services.According to the level of credibility of cloud service and the value of Qos comprehensive evaluation value,an optimization evaluation algorithm for manufacturing cloud services based on the credibility and Qos comprehensive evaluation is established.Finally,the algorithm of this paper is verified and optimized for experimental data sets.The hypervolume algorithm was used to identify the false manufacturing cloud services and check the deleted cloud service information,which proved that the algorithm was highly correct;the cloud service data set after cleaning was classified with fuzzy credibility,and the experiment proved that the GA fuzzy C-means clustering algorithm based on the optimization goal has fast convergence speed,good stability,and high accuracy in the clustering of data sets.The AHP and entropy method weight values are calculated for the cleaned cloud service data,and the consistency check of weights is passed.Get the comprehensive evaluation value of cloud service Qos.In the instance verification,the obtained credibility level and comprehensive evaluation value are applied to the scheme optimization method proposed in this paper to obtain the priority order of the cloud service solution and push the preferred cloud service solution for the cloud user.