Method, device, equipment and storage medium for judging abnormal traffic scene

Abstract

The present application discloses a method for judging abnormal traffic scenes, including: determining the corresponding traffic information node network according to each traffic information data and the correlation between each traffic information data; using the feature vector of each traffic information data to calculate The network characteristics of the traffic information node network; the network characteristics are input into the pre-trained scene judgment model to determine whether the corresponding traffic scene is abnormal. It can be seen that this method can more comprehensively obtain the characteristic connection between the traffic information data and the abnormal traffic situation, and thus can improve the judgment accuracy of the abnormal traffic scene. The present application also discloses a judging device, equipment and computer-readable storage medium for abnormal traffic scenes, all of which have the above beneficial effects.

Description

technical field [0001] The present invention relates to the field of traffic information, in particular to a judging method, device, equipment and computer-readable storage medium for abnormal traffic scenes. Background technique [0002] With the continuous development and popularization of traffic informatization in the country, the requirements for the comprehensive application ability of traffic informatization are getting higher and higher. In the method of judging abnormal traffic scenes, in order to reduce on-site inspections by law enforcement personnel and improve law enforcement efficiency, the existing technology collects traffic information data, and then inputs the collected traffic information data into a pre-trained scene judgment model, using The model determines whether the traffic scene is normal or not. However, the methods in the prior art are not accurate in judging the abnormal traffic scene, so the situation of false alarm of the abnormal traffic scen...

AI Technical Summary

Problems solved by technology

However, the methods in the prior art are not accurate in judging the abnormal traffic scene, so there may be false positives in the abnormal traffic scene

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