The application provides a gas filling whole-process data driving optimization method and
system and a storage medium, which comprises the following steps: constructing constraint conditions with safety pressure boundary and pressure change rate as cores by
monitoring pressure and temperature in real time and calculating
state entropy increase gradient; generating an initial
filling rate curve by using a
generative adversarial network; combining the constraint, efficiency and equipment loss to perform multi-
objective evaluation on a
discriminator of the network, and adjusting a safety weight in real time according to the entropy increase gradient; and
smoothing and optimizing the curve by
wavelet transform and nonlinear Kalman filtering, wherein the
decomposition layer number and the filtering parameter are both associated with the entropy increase gradient, so that a stable and safe execution
rate curve is generated, and intelligent closed-
loop control of the filling process is realized.