The present application relates to the technical field of
centrifuge control, and particularly relates to a
centrifuge temperature control parameter optimization method and
system, which comprises the following steps: collecting multiple parameters of the
centrifuge to construct a sample
data set; training an XGBoost
stage classification model based on the sample
data set, and outputting the working condition probability of the centrifuge, presetting the chamber temperature and
refrigeration power of the centrifuge at the next moment, calculating the temperature precision
penalty coefficient, the temperature change rate risk coefficient and the
energy consumption penalty coefficient, dynamically adjusting the weights of the temperature precision
penalty coefficient, the temperature change rate risk coefficient and the
energy consumption penalty coefficient based on the proportion of steady speed, speed up and speed down in the real-time working condition to construct a target function, and calculating the comprehensive
control error; selecting multiple groups of historical data most similar to the current working condition as reference data, selecting the compressor speed corresponding to the reference data that minimizes the comprehensive
control error as the optimal speed by traversing each reference data, and delivering the optimal speed to the compressor for execution, thereby improving the
temperature control precision of the centrifuge.