Traffic abnormal scene judgment method and device, equipment and storage medium

Abstract

The invention discloses a traffic abnormal scene judgment method, which comprises the steps of determining a corresponding traffic information node network according to traffic information data and anassociation relationship between the traffic information data; calculating network features of the traffic information node network by utilizing the feature vectors of the traffic information data; and inputting the network features into a pre-trained scene judgment model, and determining whether the corresponding traffic scene is abnormal or not. Visibly, the method can more comprehensively obtain the feature relationship between the traffic information data and the traffic abnormal condition, so that the judgment accuracy of the traffic abnormal scene can be improved. The invention furtherdiscloses a traffic abnormal scene judgment device, equipment and a computer readable storage medium, which all 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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