Research On Resource Scheduling Optimization Of Intelligent Workshop Based On Edge Computing

At present,with the integration of the Internet and traditional industries,industrial production is becoming more and more digital and intelligent.Due to the huge expansion of data volume,the application of cloud computing and the Internet of Things is increasing year by year.However,more and more industrial production needs to deal with the problem of resource scheduling in real time and efficiently.Therefore,this thesis studies the resource scheduling of intelligent workshop based on the edge computing technology.The main research contents are as follows:(1)By analyzing the Io T-cloud architecture and the reference architecture of edge computing in the intelligent workshop,the reference architecture of edge computing is selected to be added into the Io T-cloud architecture,so as to design the resource scheduling architecture of the intelligent workshop.(2)For the single-objective and multi-objective problems of computing resources,the network nodes of computing resources are set with the help of network topology theory.For the singleobjective problems,the mathematical model is built with the optimization objective of minimizing the time delay,and the MJ algorithm is used to solve the model,followed by example simulation.For multi-objective problems,a mathematical model is built to minimize the time delay and energy consumption.The Hybrid algorithm of interior point method and branch and bound is used to solve the problem,then the instance simulation to verify the adaptability of the model.(3)In view of the plant resources of single objective and multi-objective problem is studied,for single objective problem,maximum completion time as the optimization goal to build mathematical model of the niche technology is adopted to improve the population initialization,adaptive rotation Angle to dynamic adjustment,and join the quantum mutation strategy improved quantum genetic algorithm model,realize single objective workshop resources scheduling optimization,then the instance simulation;For multi-objective problem to production time(T),green manufacturing assessment coefficient(G),cost(C)as the optimization goal to build mathematical model,using niche co-evolution strategy,repeat the individual control strategy and the entropy weight method selection strategy to improve the NSGA-Ⅱ algorithm model,multi-objective workshop resources scheduling optimization,then the instance simulation to verify the adaptability of the model.This article contains 37 figures,21 tables,and 64 references.