Development and implementation of air switch detection system based on docker technology

There are a large number of air switches in substations, computer rooms and other places. It is often necessary to artificially change the opening and closing states of the air switches for specific tasks. Due to the high labor cost and potential safety hazards, it has great potential to implement edge-computing-based deep learning models in these scenarios. However, these scenarios have complex conditions and many electromagnetic interferences, and the long-term stable operation of deep learning models requires high virtual isolation technology involved in edge computing. In this context, this paper uses virtual isolation technology and deep learning model for air switch detection, uses the trained air switch detection model based on YOLOX deep learning framework to make mirror images, and completes isolation enhancement in the generation of mirror images. Besides, on this basis, a safe and efficient control decision-making scheme based on neural network is proposed.