Optimal Scheduling Of Warp Knitting Working Driven By Big Data

Warp knitting is an indispensable and important part of the textile industry.The informatization and intelligence of the warp knitting workshop are of great significance to the entire textile production.The rapid development of big data and the Internet of Things further promotes the digital reform of warp knitting workshops.The scheduling and optimization of warp knitting workshops is the key to improving efficiency,increasing benefits and enhancing corporate competitiveness.(1)This paper studies the system architecture of warp knitting workshop based on5 G technology,investigates the key technologies of 5G,and builds warp knitting workshop based on the end-pipe-side-cloud architecture.Introduce and analyze the key technologies of each part of the architecture,in order to meet the monitoring,transportation,security,collection and other needs of the warp knitting workshop.Provide reference and direction for the textile industry,especially the warp knitting workshop,in the future intelligent production transformation.(2)Build the big data architecture of the warp knitting workshop.First introduce the characteristics of big data and the significance of big data analysis,and build a data analysis service architecture.Secondly,from data collection to data interpretation,part of the process and its key technologies are analyzed,and on this basis,the warp knitting workshop big data architecture from the equipment layer to the collaboration layer is further built.Guide the future digital and intelligent development of warp knitting workshop.(3)Research on the multi-objective static scheduling of warp knitting machines,with the optimization goal of minimizing the maximum completion time(makespan)and the minimum advance/delay penalty cost,an improved multi-objective fuzzy particle swarm optimization(f-MOPSO)is proposed.).According to the actual order situation of the warp knitting workshop,the coding and decoding of particles are specifically designed,and the initialization of the algorithm,the inertial weight of the particle flight,and the diversity of the particle swarm are optimized.Analyzing the measured data from the factory proved its effectiveness in the warp knitting workshop scheduling problem and solved the demand for cost reduction and efficiency increase in actual production.(4)Study the dynamic scheduling of warp knitting machines,analyze and classify the disturbance events that affect its production,and then classify different types of disturbance events,and the settings of different levels are mapped to different scheduling strategies.On the basis of static scheduling objectives,stability and robustness is proposed as an additional optimization objective of optimal scheduling,and algorithmic solution for complete rescheduling is performed.In addition,based on the multi-objective annealing fuzzy algorithm,the neighborhood search rules are set,and the fuzzy rules are designed according to the temperature and step length during annealing.Experiments based on the results of static scheduling have proved the effectiveness of the algorithm,which is closer to the actual production situation,and has instructive significance for the warp knitting workshop to further reduce costs and increase efficiency.