Research and application of network status prediction based on BP neural network for intelligent production line

In the intelligent production line network communication process of the Industrial IoT, communication node congestion will cause the communication quality to decrease, thereby affecting the production efficiency. Therefore, accurately predicting the status of network and making adjustments to the network in real time is of great significance to improving the quality of network communication. Aiming at the urgent problem of the network communication quality of the intelligent production line, this paper proposes a network status prediction algorithm for the intelligent production line. The algorithm uses the ARMA prediction model to predict the network data, and calculates and predicts the entire network operation through the optimized BP neural network. At the same time, an intelligent production line network prediction system is designed based on the algorithm. The system can predict the network operation status in advance, reducing the impact of network status fluctuations on the production efficiency of the intelligent production line. The simulation results show that after a large number of network data prediction experiments, the optimal data prediction period is obtained. Under this period, the accuracy of network status prediction reaches 90%. (C) 2021 The Authors. Published by Elsevier B.V.