The evaluation of enterprise supply chain intelligent manufacturing system for agricultural interconnection data based on machine learning

Abstract

The present work aims to promote the modernization of the real economy and facilitate the manufacturing industry to construct an intelligent manufacturing system. Analysis and evaluation are performed on the intelligent manufacturing system (IMS) of enterprises under the Internet of Things (IoT). First, the connotation and characteristics of machine learning (ML) in the era of IoT are explained, followed by the introduction to neural networks. Second, the optimization process of the neural network is described. Since it has no memory, the traditional deep neural network (DNN) needs to be improved. Then, the Generative Adversarial Network (GAN) is utilized to expand the sample data. Moreover, the confusion matrix is used in the indicator evaluation. The backpropagation neural network (BPNN) is optimized by the genetic algorithm (GA) to improve the network configuration and model performance. In addition, the connotation and development status of IMS are analysed, and the feasibility of applying deep learning to the evaluation of IMS is discussed. Finally, the evaluation model of IMS is designed and built via a neural network, which is ultimately verified in the simulation experiment. The experimental results indicate that the BPNN‐GA algorithm has advantages in predicting fitting accuracy, taking up less memory, and shortening training time; it can achieve generalization and accuracy in the forefront of optimal Pareto value, and the fitting accuracy attains 95.3%. The horizontal evaluation of IMS proves the effectiveness and feasibility of the evaluation model. This shows that the BPNN‐GA algorithm has an excellent evaluation effect on IMS. And the IMS evaluation model is applied to assess the IMS of sample enterprises. The evaluation results demonstrate that there are problems in the current manufacturing industry. For example, the transactions are challenging to be completed smoothly, information technology is updated slowly, and IMS is dependent on collecting resources.