Traffic safety detection method and system based on big data analysis

Through the traffic safety detection method based on big data analysis, the traffic situation fusion template is generated using the fusion characterization information and historical data sets of the traffic IoT data set, which solves the limitations of the existing traffic congestion detection methods in data fusion and dynamic changes, and achieves more accurate and efficient traffic congestion detection.

CN119723898AInactive Publication Date: 2025-03-28GUIZHOU SHUAN INTELLIGENT TECH CO LTD
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Patent Information

Application Number
CN202510231507.6
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2025-02-28
Publication Date
2025-03-28
Estimated Expiration
Not applicable · inactive patent

AI Technical Summary

Technical Problem

The existing traffic congestion detection methods have limitations. A single data source is difficult to fully reflect the dynamic changes of the road network, the misjudgment rate is high, and the ability to model spatiotemporal correlation and the dynamic evolution of sensor data is lacking when dealing with heterogeneous data fusion, resulting in limited generalization performance in complex traffic situations.

Method used

The traffic safety detection method based on big data analysis is adopted, and the traffic situation fusion characterization information is mined by obtaining the traffic IoT data set to be detected, and the first fusion characterization information is generated. Based on this information index, multiple historical traffic IoT data sets matching the data set to be detected, a traffic situation fusion template is generated to detect traffic congestion levels.

Benefits of technology

It improves the accuracy and efficiency of traffic congestion detection, can more comprehensively reflect dynamic changes in the road network, reduce the misjudgment rate, enhance the generalization ability of complex traffic situations, and can dynamically adjust the detection strategy to improve matching efficiency.

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Abstract

The invention provides a traffic safety detection method and system based on big data analysis, and the method comprises the steps: obtaining a to-be-detected traffic internet-of-things data set, carrying out the fusion representation information mining of the to-be-detected traffic internet-of-things data set, and obtaining first fusion representation information; the first fusion representation information comprises multiple pieces of data dimension representation information corresponding to multiple data dimensions; indexing a plurality of historical traffic internet-of-things data sets matched with the traffic internet-of-things data set to be detected according to the first fusion representation information, and obtaining a traffic congestion level mark corresponding to each historical traffic internet-of-things data set according to second fusion representation information corresponding to each historical traffic internet-of-things data set and the traffic congestion level mark corresponding to each historical traffic internet-of-things data set; generating a traffic situation fusion template according to the first fusion representation information of the traffic internet-of-things data set to be detected; and according to the traffic situation fusion template, detecting a traffic congestion level mark of a traffic internet-of-things data set to be detected. According to the invention, the traffic jam grade marks can be efficiently and accurately obtained.
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