The invention discloses an
electrolyte acid-
zinc ratio concentration regulation and control method based on
deep learning, and the method comprises the following steps: S1, collecting the operation data of an
electrolyte system, and constructing an input
data set; s2, inputting the input
data set into the improved SSDNet model, and calculating a
residual value; s3, identifying a variable type and a
concentration response interval corresponding to the
residual value; s4, constructing a dynamic weight sensitive input vector, and generating a differential weighted
residual value by responding to a dynamic reconstruction function and weighting
processing; s5, generating a state
feature vector based on the state change rate, the
conductivity change trend and the regulation and control frequency; adjusting and responding to parameter configuration of the dynamic reconstruction function, and updating a residual weighting strategy; s6, executing joint training operation; and S7, if the predicted concentration value is not within the set
target range, outputting an acid liquid or
zinc liquid injection instruction, and completing closed-loop regulation and control of the acid-
zinc ratio concentration. According to the invention, high-precision prediction and flexible regulation and control of the acid-zinc specific concentration are realized, and the real-time performance and stability of concentration control are improved.