Self-testing Algorithm of Metro One-button Switch Station Equipment Based on a Neural Network

Abstract

According to the operation and management requirements of subway stations, equipment managed by the station, such as lighting, escalators, broadcasting, etc., must be operated with one-button switch according to a certain process every day. The first step in one-button switch station operation is to perform routine self-testing on these devices. Traditional self-testing operations mainly rely on manual handling by operators, which greatly affects their work efficiency. To optimize the operational processes, it is necessary to establish intelligent self-testing algorithms. An AC-CNN (Attention Causal Convolutional Neural Network) model to increase the self-testing efficiency in subway switching stations was established in this paper. AC-CNN model is a deep learning prediction network based on attention mechanism, which can capture the causal convolutional layer of the time before and after relationship as the basic network structure, optimize the convergence process through residual connections, and fuse global and local attention representations through matrix multiplication in the attention mechanism module to reduce useful information loss. The algorithm used the one-hot encoding and sliding window feature engineering operations to preprocess the historical self-testing operation data and completed the prediction of the self-testing information of metro one-button switch station equipment through the deep analysis of AC-CNN. The on-site application results indicated that AC-CNN had greater advantages in terms of accuracy and operation time compared to those of existing BP neural networks, GBDT, and LSTM algorithms, which can significantly improve the work efficiency of subway staff on one-button switch station.