부산대학교 도서관

This article proposes a production command cockpit safety management system based on Deep Neural Network (DNN). This system can monitor real-time production line data and network attack information and display them as a cockpit. When the cockpit graphical interface data is abnormal, management personnel can timely detect safety hazards and take corresponding security measures. This system can handle abstract and complex problems, effectively improving the production command cockpit's stability, safety, and efficiency. The system can accurately and efficiently identify, analyze, and process internal and external risk factors through many production command simulations, with an accuracy rate of over 98%. It has good feasibility and application prospects.