Design of Cold Chain Storage and Transportation Monitoring System for Fresh Agricultural Products Based on Artificial Intelligence

Based on RFID technology, GPS technology and GPRS technology, a cold chain logistics monitoring system based on radio frequency identification technology, GPS technology and GPRS technology is constructed. This system can realize the monitoring of the logistics status and quality of cold chain produ...

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Bibliographic Details
Published in2024 IEEE 3rd International Conference on Electrical Engineering, Big Data and Algorithms (EEBDA) pp. 1402 - 1406
Main Author Qiu, Weichao
Format Conference Proceeding
LanguageEnglish
Published IEEE 27.02.2024
Subjects
Agricultural products
artificial intelligence
cold chain logistics
cold chain storage and transportation monitoring system
Fresh agricultural products
information system
Monitoring
Particle swarm optimization
Temperature distribution
Temperature measurement
Time measurement
Transportation
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Summary:Based on RFID technology, GPS technology and GPRS technology, a cold chain logistics monitoring system based on radio frequency identification technology, GPS technology and GPRS technology is constructed. This system can realize the monitoring of the logistics status and quality of cold chain products, and release the relevant data such as temperature, cargo status, GPS positioning through the network. If the temperature in the transmission box exceeds the specified range, an alarm is issued so that the staff can take measures in time. This paper studies the logistics distribution time of fresh agricultural products under the condition of e-commerce. It is optimized for the shortest lead time. On this basis, a new solving algorithm is established. On this basis, the particle swarm optimization method is used to optimize the logistics path in order to minimize the logistics time. The experimental results show that the new method proposed in this study can effectively protect the transportation and storage of fresh agricultural products and reduce their losses.
DOI:10.1109/EEBDA60612.2024.10485948