The application provides a
pickling unit automatic speed control method and
system based on multi-factor constraints, and relates to the technical field of cold-rolled
strip steel production. In view of the defects that the existing control relies on manual operation or primary
automation and is difficult to cope with multiple constraints and instantaneous working conditions, the method comprises the following steps: constructing a multi-factor constraint model covering process, equipment, logistics, acid liquid and instantaneous working conditions; collecting corresponding data in real time; dynamically calculating a
global optimal speed setting value through multi-layer decision logic, calculating a basic speed first and then superimposing an instantaneous working condition constraint, and directly triggering emergency disposal through high-priority alarm; issuing an instruction for execution and closed-loop feedback, and combining
machine learning for self-learning optimization. The application realizes automatic
intelligent control of the
unit speed, guarantees quality and
equipment safety, improves production efficiency and continuity, and reduces
energy consumption cost.