Method and apparatus for train re-scheduling based on diffusion model and reinforcement learning

A method for training a neural network model for train re-scheduling, the neural network model comprising a diffusion model and a reinforcement learning model, and the method comprising:

• obtaining historical time series data indicating states at respective time points during a historical train operation process and a historical train scheduling strategy corresponding to the historical train operation process, the states comprising a train operation state, a resource allocation state and an external environment state, the historical train scheduling strategy comprising an action that was performed by a train scheduling system;
• adding noise to the historical time series data based on a forward diffusion process through the diffusion model to obtain noise-added historical time series data;
• and
• training the reinforcement learning model by using the noise-added historical time series data as sample data and using the historical train scheduling strategy as label data.

A train scheduling system, comprising:

• a computing device comprising a processor and a memory coupled to the processor and storing instructions that, when executed by the processor, cause the processor to:
• obtain time series data indicating states at respective time points during a train operation process, the states comprising a train operation state, a resource allocation state and an external environment state;
• providing the obtained time series data to a trained neural network model to generate a scheduling strategy for train re-scheduling, the scheduling strategy comprising an action to be performed by a train scheduling system,
• wherein the time series data has different noise in a case where an emergency occurs during the train operation process compared with a case where no emergency occurs during the train operation process, and
• wherein the neural network model comprises a diffusion model and a reinforcement learning model, the diffusion model is configured to remove the noise in the time series data based on a reverse diffusion process to obtain denoised time series data, and the reinforcement learning model is configured to generate the scheduling strategy based on the denoised time series data;
• and
• send the generated scheduling strategy to an execution apparatus communicatively coupled to the computing device;
• and
• the execution apparatus configured to perform the scheduling strategy in response to receiving the scheduling strategy from the computing device.