Intelligent maintenance-free power grid monitoring method and system based on cloud edge collaborative deep learning

Abstract

The invention discloses an intelligent maintenance-free power grid monitoring method and system based on cloud edge collaborative deep learning, and the method comprises the steps that sensing equipment collects a monitoring image, and gives an alarm according to alarm information; the edge computing node identifies the monitoring image based on the monitoring image and a power grid anomaly detection model issued by the cloud computing center, and sends alarm information to the sensing device when determining that there is a power grid anomaly; the identified monitoring image is transmitted tothe cloud computing center; the cloud computing center trains a deep learning model according to the stored training set images to generate a power grid anomaly detection model, trains and updates the power grid anomaly detection model according to the identified monitoring images, and issues the power grid anomaly detection model to the edge computing nodes. The invention further discloses a corresponding power grid monitoring system. By adopting the method and the system disclosed by the invention, the operation burden of the cloud computing center can be reduced.

Description

technical field [0001] The invention belongs to the field of power grid security protection, and in particular relates to an intelligent maintenance-free power grid monitoring method and system based on cloud-edge collaborative deep learning. Background technique [0002] The application of intelligent video surveillance in power systems is particularly important, but traditional video surveillance has deficiencies in timely processing and after-the-fact forensics, and massive videos require a lot of manpower to identify and analyze anomalies in video images. With the development of intelligence and deep learning, intelligent surveillance video has gradually become a development trend. Artificial intelligence algorithms are used to continuously analyze video images. Once an abnormality is found, an early warning message is sent to the staff immediately, which can reduce the workload of staff's equipment maintenance. , Improve the efficiency and effectiveness of the entire mo...

AI Technical Summary

Problems solved by technology

[0004] The images obtained from massive video streams are transmitted to the cloud computing center, which will consume a large amount of network bandwidth, which may cause problems such as service interruption and network delay, so the real-time performance cannot be guaranteed;

[0005] Image processing tasks are concentrated in the cloud computing center, which requires a large amount of computing resources and increases the computing burden of the cloud computing center

Images