The invention discloses a
simulation and optimization method for a
natural gas industrial
system in a rough environment, and relates to the technical field of intelligent optimization, and the method comprises the steps: collecting environment data, constructing a physical constraint
diffusion model, and generating an
extreme environment data set through embedding a hydrodynamic equation and a thermodynamic law; in combination with real sensor data of a plurality of gas field nodes, pre-training a federated transfer learning framework, eliminating cross-environment data distribution difference through resistance feature alignment, and constructing a global environment
threat prediction model; based on the
simulation result and the real-time sensor data flow, a multi-target
fitness function is constructed, and control variables are generated through
population evolution and dynamic strategy adjustment; and inputting the
control variable into a pre-trained digital twinborn
verification model, comparing a
simulation result with a residual error of a real-time sensor
data stream, triggering an
incremental learning mechanism, and updating a global environmental
threat prediction model. According to the method, the high-fidelity
extreme environment data set is generated through the physical constraint
diffusion model, and the physical consistency of multi-field
coupling simulation is improved.