Steel industry multi-energy flow system optimal scheduling method based on maximum entropy reinforcement learning

The invention provides a steel industry multi-energy flow system optimal scheduling method based on maximum entropy reinforcement learning, and belongs to the technical field of information, and the method comprises the steps: firstly collecting field real industrial data, carrying out the denoising and defect increasing of the data, and simplifying a field pipe network; secondly, establishing a day-ahead model by considering the load balance of the multi-energy-flow system, establishing mathematical models of a byproduct gas system, a high-pressure steam system and an electric power system, and establishing an energy conversion equipment operation efficiency model; the method comprises the following steps: establishing a gas-heat-electricity intra-day model by using an energy conversion equipment operation efficiency model, taking a byproduct gas pipe network as an input side and taking a high-pressure steam pipe network and an electric power system as output sides, and converting the gas-heat-electricity in