The invention provides an intelligent regulation and control method for a three-stage
constructed wetland recirculating aquaculture system, and belongs to the technical field of
machine learning. The method comprises the following steps: firstly, continuously collecting
water quality data and operation
control data of each
control unit, and constructing a multi-source heterogeneous
data set under a unified time scale; then, constructing a
pollution evolution
trend prediction model, and capturing a dynamic evolution trend of
water quality along with time and control behavior changes; then, under the guidance of a prediction result, analyzing the similarity of historical states and the sensitivity of regulation and control response, automatically identifying key
control parameters which influence the
water quality change of the
system at present, and reasoning the dynamic adjustable boundary of the key
control parameters; and finally, constructing a
reinforcement learning strategy network fusing
state prediction, a parameter boundary and a control target, realizing multi-target tradeoff among
pollutant removal efficiency, a water
quality standard-reaching rate and operation
energy consumption, and outputting an efficient and steady control strategy through continuous interactive training. According to the invention, efficient, accurate and robust operation of the
wetland system can be realized.