Research On The Evaluation Of Intelligent Manufacturing Capability Based On BP Neural Network

Manufacturing industry is an important pillar of national economic development in a country or region.The level of manufacturing industry development reflects the comprehensive strength of a country or region.After the outbreak of the global financial crisis in 2008,developed countries such as the United States,Germany,the United Kingdom,and Japan have all introduced a national manufacturing development strategy that focuses on the development of intelligent manufacturing.Through intelligent manufacturing,it has reshaped the manufacturing industry and revitalized the real economy.In order to realize the transformation from the manufacturing country to the manufacturing power,the Chinese government also proposed “Made in China 2025”,which focuses on intelligent manufacturing in May 2015 to vigorously promote intelligent manufacturing.In the advance of China's intelligent manufacturing,how to evaluate and improve the intelligent manufacturing capabilities of China's manufacturing industry is a concern for governments and enterprises at all levels.Through the evaluation of intelligent manufacturing capabilities of manufacture,it is beneficial for enterprises to understand the level of intelligent manufacturing development,to clarify the basic conditions,advantages,and deficiencies of enterprise intelligent manufacturing,and to provide the basis for the enterprise to formulate intelligent manufacturing development planning and intelligent technical transformation decision.Based on this,it also provides tools for governments and industry authorities at all levels to provide guidance and evaluate the level of smart manufacturing development in manufacturing companies in the region.Therefore,the evaluation of intelligent manufacturing capability is an unavoidable issue in the promotion of smart manufacturing in China,which is worth further research.This thesis took the smart manufacturing capabilities of 23 major provinces and cities as the research object.Based on the research results of previous generations,it is based on relevant indicators and data released by the National Bureau of Statistics and the Ministry of Industry and Information Technology that reflect the level of China's smart manufacturing development,focus on the manufacturing process to carry out intelligent manufacturing capacity evaluation research.Firstly,through the analysis and comparison of factor analysis,analytic hierarchy process and artificial neural network and other commonly used evaluation methods,BP neural network was selected to evaluate the intelligent manufacturing capabilities of major provinces and cities in China.Secondly,20 secondary indicators such as the number of applied patents,the average corporate Internet of Things coverage,and the average number of graded highway miles were selected through statistical analysis and other technologies from the aspects of product innovation capability,information level,and product circulation capability.An intelligent manufacturing capability evaluation index system has been constructed.By training different learning algorithms of BP neural network,a BP neural network model is established by gradient descent method with adaptive learning rate and momentum factor,and the number of optimal hidden layer neurons is determined.Finally,23 sets of original sample data of 20 evaluation indicators were input into the model for simulation.Through the simulation,the intelligent manufacturing capability evaluation values of the 23 evaluation objects selected in this paper are provided,which provides decision-making reference for the development level of intelligent manufacturing in the region.