Product Lifecycle Management System Based On Industrial Internet Of Things Technology

In recent years,with the development of economy and technology,the scale of manufacturing industry has increased year by year,and the demand for industrial Internet of Things(IIo T)technology and product lifecycle management(PLM)system has also been increasing.The IIo T provides the possibility to connect all production line equipment or products,and the PLM system provides the possibility to manage the equipment or products,increasing the relevance between production processes.However,the development of the current IIo T technology and PLM system are still not mature,and there is no systematic solution.In order to solve the above problems,this paper proposes a set of product lifecycle management system based on the industrial Internet of Things technology,which integrates the monitoring of production,sales and after-sales.The contents and innovations of this paper are as follows:Firstly,design and implement a set of software and hardware solutions.The hardware system is divided into two parts,namely IIo T production line equipment and Io T product.The software system is divided into three parts: Io T platform,back-end server and front-end server.The back-end server implements the layered design and microservice architecture design based on the Spring Boot framework and the Spring Cloud Alibaba framework,which improves the usability,scalability,and robustness of the back-end server.The front-end server is implemented based on the Vue.js framework,which implements the dynamic sidebar function and improves the user experience.Secondly,design and implement related product recommendation function.In view of the shortcomings of the I?Apriori algorithm,an improved Apriori algorithm is proposed.The algorithm uses an improved key-value pair structure to store the item information in the transaction on each bit of the data,thereby achieving the improvement of algorithm efficiency.The improved Apriori algorithm tested in this paper has better performance than the Apriori and I?Apriori algorithms.Thirdly,design and implement the intelligent scheduling function of job shop tasks based on genetic algorithm.In view of the problems in the existing job shop scheduling algorithm,an improved genetic algorithm is used.While using "multiple chromosome coding",the algorithm uses an adaptive function to dynamically change the crossover probability and mutation probability,thereby improving the convergence speed and avoiding the problem of local optimization.After testing,the improved genetic algorithm has better performance.