The application discloses an operation and maintenance video abnormal behavior automatic auditing method based on
artificial intelligence, and specifically comprises the following steps: collecting operation and maintenance field videos in real time through proxy
software, and carrying out pretreatment such as denoising, background removal and
image enhancement to improve the accuracy of target detection and
behavior recognition; the behavior target of operation and maintenance personnel in the video is identified by using a YOLO target detection model to obtain boundary box coordinates, category labels and confidence scores; the detection result is transmitted to a
Kalman filter for continuous tracking of the target to generate motion trajectory data; 3D
convolutional neural network (3D-CNN) is used to analyze space-time features, judge and
label abnormal behaviors; finally, the labeled information is integrated with the video data, video
metadata is embedded through FFmpeg, the quick
jump function of the labeled information is realized, the audit personnel are helped to quickly locate and examine abnormal behaviors, and the auditing efficiency and the intelligent level of operation and
maintenance management are greatly improved.