Power monitoring image anomaly detection method and device, electronic equipment and storage medium

Abstract

The invention provides a power monitoring image anomaly detection method and device, electronic equipment and a storage medium. The power monitoring image anomaly detection method comprises the stepsof classifying RGB features of a power monitoring image by using a pre-trained spatial CNN model; performing fusion classification on the optical flow features and the scene features of the power monitoring image by using a pre-trained fusion classification model; fusing the RGB classification result and the optical flow scene fusion classification result to obtain an anomaly detection result, wherein the optical flow features and the scene features are subjected to fusion classification by using the fusion classification model, so that the scene recognition effect is improved, the misjudgmentfrequency of behavior recognition is reduced, the learning ability of the CNN to the optical flow information is effectively improved, human behaviors can be recognized, and abnormal problem alarmingcan be timely and effectively carried out.

Description

technical field [0001] The invention relates to the technical field of artificial intelligence, in particular to a method and device for detecting anomalies in power monitoring images. Background technique [0002] Due to the long-term high-voltage, high-current working environment, power equipment is prone to failure during operation, resulting in paralysis of the power system. If the power equipment is abnormal and cannot be resolved in time, it may cause huge economic losses and social impact. [0003] With the development of the power grid in the direction of scale and intelligence, the traditional method of manual monitoring of power equipment can no longer meet the needs of the power system. Surveillance, in the traditional sense, is simply live streaming of monitoring screens. When encountering unexpected abnormal conditions (such as abnormal conditions in the operating environment), most monitoring equipment cannot quickly and accurately identify them, and thus can...

AI Technical Summary

Problems solved by technology

However, there are still some deficiencies in these studies at this stage, for example: the presence of people in the operating environment of electrical equipment may affect the operation of electrical IoT equipment, but most traditional video surveillance cannot recognize human behavior, resulting in existing images. Monitoring cannot timely and effectively issue alarms for abnormal problems

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