基于数据-物理模型融合驱动的城轨列车信息在线感知方法研究OA

As a typical application of intelligent transportation in urban rail transit,smart urban rail transit(SURT)enhances system efficiency through data-driven transformation with its technical foundation rooted in the synchronous perception of train position and electrical parameters.Traditional train information perception methods,highly dependent on direct measurements from on-board sensors,face challenges such as high hardware costs,difficulty in retrofitting existing trains,and poor synchronization in communicating independent position and electrical parameter data;thus,this paper proposes a data-physics model fusion-driven online perception method for urban rail train information to address these issues.The method constructs a cascaded random forest-neural network model under a collaborative data-driven and model-driven framework:first,a random forest classifier is leveraged to identify train operation organization in real time using the measurement features of traction substation(e.g.,hourly traffic rate),and then a neural network model maps the electrical parameters and position of the lowest-voltage train from traction substation data(e.g.,voltage,power).Further integrating the spatiotemporal rigid constraints of train operation diagrams,the study designs a confidence interval projection method to correct positioning results by fusing the joint confidence interval of voltage-position with the operation diagram time-axis,enabling online derivation of global train operation states.Finally,the applicability of the proposed method in long-distance power supply sections is validated through a single traction substation fault scenario from a domestic metro line.Simulation results show that under peak-hour operation density(20 train pairs per hour),the real-time global train state perception in the power supply section is effective and accurate,providing a low-cost perception solution for real-time energy management of traction power supply systems compared with traditional direct sensor measurement.