Development Of Big Data Platform For Office Chair Production Line And Optimization Of Production Cycle

Driven by the Industry 4.0 era,the continuous improvement of enterprise digitization has triggered the explosive growth of industrial data.The manufacturing industry has entered the era of big data,and mining the value behind industrial data has become a new topic.However,due to the large amount of industrial data,complex data structure,and diversified storage media,mining valuable information from these data is also a difficult problem.Based on the characteristics of the office chair production line,this paper builds an office chair production line data platform to manage the office chair production line data,and then proposes an improved genetic algorithm to optimize the office chair production beat matching degree,and uses simulated annealing to improve The particle swarm algorithm is compared with it,and the results of the algorithm are compared and analyzed.The main research contents of this paper are as follows:First,design the data platform system architecture of the office chair production line.The big data platform architecture for the office chair production line constructed in this paper is divided into four layers: data collection layer,data business layer,data storage layer and data display layer.The data collection layer uses various sensors and gateway devices to collect real-time data on the production line into the big data platform system.The data service layer uses the Spark distributed real-time stream processing computing engine to process real-time stream data,guarantee the security of enterprise data through RDD operators,filter the collected data,and perform data optimization calculations for production rhythms.The massive industrial data collected by the data storage layer storage system provides data support for production cycle optimization and visual display.The data display layer uses Fine Report graphical report software,which displays various data from multiple dimensions through data charts.Secondly,an optimization algorithm is introduced to optimize the production cycle of office seats.For the office chair production process,a data model was built according to the total time of the process,and the maximum matching degree of the production cycle was taken as the optimization goal.An improved genetic algorithm is proposed,which uses a dual strategy selection method to increase the convergence speed of the population,and obtains the production process allocation plan according to the optimal solution of the genetic algorithm to improve the rationality of the production process allocation.In order to verify the rationality of the algorithm,an improved particle swarm optimization algorithm(SA-PSO)based on the simulated annealing algorithm is introduced to calculate the calculation example,and the calculation results of different optimization algorithms are compared to verify the rationality of the algorithm.Finally,based on the previous architecture,a big data processing system platform for the office chair production line was built.Describes the software and hardware configuration of the data platform and the functional modules of the data platform.Combining the office chair production line data for application verification and verifying the feasibility of data processing on the office chair production line by the data platform can provide effective help for the production management of the chair industry.Based on this data platform,production data can be effectively managed and the production line capacity of chair industry enterprises can be improved.