The braking system performance of freight trains is an important factor to ensure the safety of trains. The semi-physical simulation platform of freight train braking system is to realize the research of how many trains are based on the use of fewer trains. In this paper, a scheme for obtaining the braking performance of large-form trains by intelligent control of small-form trains is proposed. In this platform, based on the single-model train, the cascade of the air pressure changes in the process of train braking is adopted. Method converts the train into the next train. In this scheme, the self-designed hardware platform and software platform are used to collect, process and control the air pressure change process during the train braking process. In this paper, the data processing and real-time control strategies are mainly studied. The main control object in this paper is the proportional solenoid valve, which achieves the purpose of controlling the internal pressure change of the train through the control of the proportional solenoid valve. The braking process of the train is a process in which the air pressure changes rapidly, so the control of the solenoid valve has a high real-time requirement. In order to improve the accuracy and real-time control of the solenoid valve, a double closed-loop control strategy based on the train braking model is designed. The accuracy of the control is guaranteed by the double feedback of the system. In order to ensure the real-time control of the solenoid valve, the neural network algorithm is used to predict the data online. The online prediction algorithm is used to predict the running state of the train. The fault detection of the train running state is performed, and the train is corrected with the predicted value. Sexual control, which in turn improves the real-time and accuracy of the control system.
