Deep learning-based frozen shrimp transportation intelligent early warning method and system

Through the intelligent early warning method based on deep learning and the multimodal data fusion model, the problem of incomplete quality monitoring in frozen shrimp transportation is solved, early risk warning and environmental monitoring of frozen shrimp transportation is achieved, and data accuracy of the transportation process and the intelligent level of cold chain logistics are improved.

CN120450561APending Publication Date: 2025-08-08JIANGNAN UNIV
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Patent Information

Application Number
CN202510438993.9
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2025-04-09
Publication Date
2025-08-08

AI Technical Summary

Technical Problem

The quality monitoring of existing frozen shrimps is not comprehensive and the early warning is not timely. The traditional monitoring system has a single function and cannot comprehensively consider a variety of influencing factors. There is a lack of in-depth mining and analysis of historical data, resulting in damage to the quality of frozen shrimps and loss of the entire batch of goods during transportation.

Method used

Using intelligent early warning methods based on deep learning, by building mutual information networks and multimodal data fusion models, using deep learning networks to conduct real-time analysis and prediction of environmental data during transportation, triggering intelligent early warning mechanisms, and notifying relevant personnel through various methods or automatically adjusting transportation container equipment to ensure the transportation environment of frozen shrimps.

Benefits of technology

It has achieved early warning of risk during the transportation of frozen shrimp, reduced the risk of quality deterioration caused by temperature control failure or mechanical damage, improved the accuracy and consistency of monitoring data, and improved the intelligence level of cold chain logistics.

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Abstract

The invention discloses an intelligent early warning method and system for frozen shrimp transportation based on deep learning, and belongs to the technical field of cold-chain logistics and intelligent monitoring. According to the method, a multi-dimensional data analysis model is constructed by using a deep learning technology, and intelligent monitoring and anomaly prediction of a transportation environment are realized. The system is composed of a data acquisition module, a deep learning analysis module and an early warning decision module, can automatically identify potential risks (such as temperature abnormity and equipment faults) in the transportation process, and timely notifies related personnel through a multi-level early warning mechanism (such as short messages, mails and system popup windows). Through self-learning and optimization of the deep learning model, the accuracy and the real-time performance of early warning are improved, the loss risk of frozen shrimps in the transportation process is effectively reduced, the product quality is guaranteed, meanwhile, the intelligent level of cold-chain logistics is improved, and remarkable economic benefits and application values are achieved.
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