Traffic safety early warning method and equipment for dangerous cargo transport vehicle

The risk assessment model established through convolutional neural network and data fusion technology can monitor and evaluate the risks of dangerous goods transport vehicles in real time, and automatically generate early warning information, solving the safety problems of dangerous goods transport vehicles under extreme weather or complex terrain, improving the accuracy and early warning effect of risk assessment.

CN120544378AInactive Publication Date: 2025-08-26INNER MONGOLIA OPEN UNIVERSITY (INNER MONGOLIA OPEN UNIVERSITY OF THE ELDERLY)
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Patent Information

Application Number
CN202510615858.7
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2025-05-14
Publication Date
2025-08-26
Estimated Expiration
Not applicable · inactive patent

AI Technical Summary

Technical Problem

In the latest technology, it is difficult to accurately assess the risks of dangerous goods transport vehicles in extreme weather or complex terrain, resulting in frequent accidents such as overturning, rear-end collisions, and leakage, threatening the safety of life and property.

Method used

A convolutional neural network algorithm is used to establish a risk assessment model, combine data fusion technology to monitor vehicle status and environmental data in real time, divide risk levels and formulate early warning strategies, the system automatically generates timely and accurate early warning information, and drivers and managers activate emergency plans based on the early warning information, and optimize early warning strategies through feedback.

Benefits of technology

Improve the accuracy and early warning effect of risk assessment in extreme weather or complex terrain, reduce accidents and ensure vehicle safety.

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Abstract

The invention discloses a traffic safety early warning method and device for a dangerous cargo transport vehicle, and the method comprises the steps: S1, monitoring the operation state of the dangerous cargo transport vehicle in real time, and collecting environment data in real time; and S2, based on the collected data, establishing a risk assessment model by using a convolutional neural network algorithm, analyzing the influence of extreme weather and complex terrains on the safety of the dangerous cargo transport vehicle, developing a risk assessment algorithm, and according to a risk assessment result, the risk assessment model established by using the convolutional neural network algorithm in the invention can be used for evaluating the safety of the dangerous cargo transport vehicle. And specific influences of extreme weather and complex terrains on vehicle safety can be deeply analyzed. The risk assessment model can learn various risk factors in a complex environment and comprehensively assess the risk factors, so that the accuracy and reliability of risk assessment are improved. The system can continuously monitor the change condition of the risk, timely adjust the early warning strategy according to the actual condition, and continuously improve the adaptive capacity and the early warning effect.
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