The invention relates to the technical field of fluid
automation control, and discloses a quantitative filling oil way control method and
system based on
reinforcement learning, and the method comprises the steps: firstly generating a physical reference opening instruction based on a fluid
mechanics model; calculating a rheological
hysteresis factor to identify the
viscosity characteristic of the fluid by superposing a micro-disturbance
signal and analyzing the downstream pressure
frequency response; and constructing a flow velocity adaptive
state vector by adopting equal-volume interval sampling, and inputting the flow velocity adaptive
state vector into the
reinforcement learning network to output residual correction. Meanwhile, dynamic
transmission time lag is calculated according to the rheological
hysteresis factor and the real-time flow velocity, and time axis causal alignment of flow rewards and historical actions is executed. And finally, synthesizing the reference instruction, the correction amount and the micro-disturbance
signal, driving the regulating valve after water
attack suppression logic constraint, and executing end point braking at the
tail section. The problems of time-
delay matching and sample
distortion under the long-pipeline variable-
viscosity working condition are solved, and the quantitative filling precision and the
system stability are improved.