Intelligent Manufacturing Production And Quality Analysis System Based On Big Dat

With the continuous development and wide application of Internet technology,industrial data shows an explosive growth trend.For enterprises that have strict control over production requirements,the traditional industrial production model cannot effectively handle the large-scale data currently generated.In order to solve this problem,the intelligent manufacturing analysis system came into being.The intelligent manufacturing analysis system is based on big data technology.Through big data technology combined with relevant analysis algorithms to find information on industrial data,it can effectively optimize the production process,improve production efficiency,save production costs,and provide scientific production solutions.This thesis takes the production data of industrial uranium in a certain factory as the research object,and researches an intelligent manufacturing production and quality analysis system based on big data.The main work and research results carried out are as follows:(1)Mining association rules.The K-means algorithm and the improved Apriori algorithm are combined to mine the influence of various variables of the industrial uranium measurement data on the product quality.According to the characteristics of data distribution,the data is first classified to avoid the discretization of data affecting the discovery of association rules,and to provide effective classification data for the discovery of association rules later.Then,the concept of promotion rate is introduced into the Apriori algorithm to increase the discovery threshold of association rules.After analyzing the mined association rules,it is found that the redundant rules are greatly reduced,and the algorithm has good performance.(2)Production scheduling.Further improve the genetic algorithm with the goal of the shortest production scheduling completion time.By introducing the concept of matrix coding,a mathematical model of production scheduling is built to increase the readability of chromosomes.In the crossover operation,select the row-column crossover method.The pairwise comparison of individuals not only increases the diversity of samples,improves the computational efficiency of the algorithm,but also avoids the loss of outstanding individuals.Finally,the termination principle of the algorithm is designed to avoid computational redundancy in the algorithm.(3)System realization and result visualization.At the end of the thesis,a big data cluster is built,data storage and operation are carried out on the cluster,the production and quality analysis system of industrial uranium intelligent manufacturing is designed and implemented,and the function is verified.At the same time,in order to make the research results more meaningful,the SSM(Spring + Spring MVC + My Batis)framework was used to build a system test platform to visualize the results.