Abstract
The cold chain transportation monitoring system is mainly responsible for data monitoring and control in the cold chain transportation environment, ensuring the stability of the cold chain compartment environment, and playing an important role in the transportation and storage of fresh agricultural products. The traditional Internet of Things cold chain control system uses manual input of control parameters to realize the monitoring of the cold chain compartment environment, which lacks intelligence. Therefore, the BP (Back propagation, BP) neural network is used to build a cold chain transportation monitoring model to realize the dynamic monitoring of the cold chain compartment environment. Considering that the BP network is easy to fall into the problem of “local fall”, which leads to the decline of the prediction accuracy of the model, the improved discrete artificial bee colony algorithm is used to optimize the parameters of the BP neural network, and the fish swarm algorithm is used to decode the discrete population to obtain the final result. Decimal value to construct the DABC-BP cold chain transportation control model. The experimental simulation results show that in the \(f_{1} (x)\) function optimization test, the improved DABC-BP (Discrete Artificial Bee Colony-Back Propagation Neural Network, DABC-BP) algorithm has the best optimization accuracy, and the minimum logarithm after 1000 iterations is 10−7. Meanwhile, the DABC algorithm has the best convergence performance, reaching convergence after 821 iterations. Compared with related hybrid algorithms such as mayfly algorithm and extreme learning machine, this research method is suitable for harsher environments and more sensitive to temperature monitoring. The research has important reference value for the transportation and storage of modern agricultural products.
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The research is supported by: Young and middle-aged fund project of **'an Traffic Engineering Institute China, Research on the development path of agricultural products logistics under the background of live commerce, (No., 2022KY-81).
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Miao, Y. Design of agricultural product cold chain transportation monitoring system based on Internet of Things technology. Proc.Indian Natl. Sci. Acad. 89, 235–246 (2023). https://doi.org/10.1007/s43538-023-00156-y
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DOI: https://doi.org/10.1007/s43538-023-00156-y