Cold-chain logistics temperature prediction method, and temperature regulation and control method

Abstract

The invention discloses a cold-chain logistics temperature prediction method, and a temperature regulation and control method. The cold-chain logistics temperature prediction method comprises the following steps: performing normalization processing on collected data; inputting the data set into an ELM model, and calculating a model evaluation value; optimizing the input weight w and the hidden layer bias b of the ELM model by using a mayfly algorithm, continuously updating the positions of male mayflies and female mayflies, carrying out crossover operation so as to obtain new w and b, calculating a new model evaluation value based on the new w and b, and updating the minimum model evaluation value so achieve that the error of a prediction result is minimum; judging whether the mayfly algorithm reaches the maximum number of iterations, and if the condition is met, outputting an optimal solution, or otherwise, continuing iterative optimization until a stop condition is met; and outputting optimal parameters of the mayfly algorithm, substituting a result into the extreme learning machine model, outputting a predicted value and evaluating the performance of the model. According to the method, a more accurate temperature prediction result can be obtained, and temperature can be effectively regulated and controlled based on the result.

Description

technical field [0001] The invention relates to an improvement of a cold chain logistics temperature prediction method, in particular to a cold chain logistics temperature prediction method and a temperature control method based on a ephemera-extreme learning machine, and belongs to the technical field of cold chain logistics. Background technique [0002] With the improvement of living standards, people's demand for fresh food such as fruits and vegetables is increasing. Compared with ordinary goods, fresh foods such as fruits and vegetables still have vital signs during transportation, and will consume organic substances through respiration, thereby reducing the nutritional content of fresh foods. In addition to respiration, the internal factors of fresh food (heat, moisture, climate factors) and the transportation environment (vibration, temperature, oxygen, carbon dioxide) will have an impact on the quality. Among these factors, temperature is the most influential. For...

AI Technical Summary

Problems solved by technology

In the limited research, it is found that the current research has the following deficiencies: on the one hand, the factors considered are not comprehensive

Existing studies did not consider the influence of spatial location on temperature, and did not consider the influence of load and other factors on temperature

Second, forecasting needs to be improved

Existing research mainly uses the neural network method for prediction, but the convergence speed of the neural network is slow, and it is prone to overfitting and underfitting

The prediction accuracy of the model needs to be improved

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