The invention relates to the field of
sewage treatment, in particular to a dynamic calibration and multi-agent cooperative control method for an intelligent mine water dosing
system, which comprises the following steps: S1, collecting multi-
source data; s2, extracting a characteristic alumen ustum image; s3, standard liquid is injected into the
turbidity meter measuring
pool to obtain the slope for first calibration, and second calibration is carried out through a
back propagation neural network soft measurement model in combination with the feeding flow and the characteristic alumen ustum image; s4, the flow, the concentration, the
turbidity and the image are input into a
Gaussian mixture model to judge working conditions, and the total basic dosing amount is obtained through feedforward of a
time sequence convolutional network-
back propagation neural network mixture model; s5, according to the
turbidity and the target deviation and the ideal floc characteristics and the actual deviation, the total compensation dosage is calculated through a fuzzy proportion-integral-
differential algorithm, and the total basic dosage is adjusted; and S6, constructing a 9-
grid reference ratio look-up table to obtain a reference addition ratio, outputting a correction coefficient according to a hard correction rule (cost priority, extreme
dose protection and oscillation suppression), and finally calculating the addition amount of the main flocculant and the coagulant aid.