Temperature Prediction of Grape Cold Chain Transportation Based on Multivariable Grey Model

摘要

The traditional cold chain transportation monitoring system can only monitor the environmental parameters in the refrigerated compartment, but cannot predict. It can only remedy the environmental condition in the refrigerated compartment when it has reached the critical state which affects the quality of fresh grapes, which causes economic losses to a certain extent. In this paper, a forecasting method based on multi-variable grey model is proposed to predict the internal environment of refrigerated compartments. The experimental results show that the root mean square relative error and the average relative error of the multi-variable grey model prediction method are 2.358923% and 0.085023%, respectively. The algorithm can accurately reflect the monitoring data in the cold chain car.