The application relates to the technical field of industrial
temperature control, and discloses a
temperature control method and device based on multi-step prediction and an instrument
system. The method obtains multi-source operation data of a
temperature control object according to a sampling period, wherein the multi-source operation data comprises multi-temperature-zone temperature sequences, environmental parameters and executor states; the multi-source operation data is input into a CNN-GRU
hybrid prediction model,
spatial correlation features between the multi-temperature-zone temperature data are extracted through a
convolution layer,
time sequence lag features of temperature changes are extracted through a GRU layer, and temperature prediction values of future time steps are output; based on a current
temperature error, an error integral, an error change and future temperature prediction values, a PID parameter optimizer based on PPO is used to generate a PID parameter adjustment amount under an Actor-Critic framework; whether the PID parameter is updated is judged through an adaptive event triggering mechanism, prediction reasoning, parameter update judgment and control output are executed by a temperature control instrument or an edge control node. The instrument
system comprises a temperature control instrument, a
data acquisition module, an executor, a cloud platform and a
mobile APP client. The cloud platform is used for model training, strategy optimization,
user authentication and message pushing and the like non-real-time tasks, and the
mobile APP client is used for displaying temperature states, prediction trends, alarm information and providing control parameter configuration. The scheme can introduce future temperature change trends into a PID parameter adjustment process, is helpful to improve the control response of a multi-temperature-zone temperature control object under coupled, lagged and nonlinear working conditions, and reduces unnecessary parameter updates in a stable running stage.