Index Terms—edge computing, image processing, convolution attention mechanism network difficult to meet the precise real-time requirements of power grid applications, making it difficult to truly achieve online analysis and wide-area coordination of the power grid. At present, the analysis and application of power visual images mainly use the centralized cloud computing model. The model of centralized cloud computing is to upload all visual data to the cloud computing center through network communication and use the powerful computing power of the cloud computing center for visible data storage and analysis. However, the power deep vision method based on the centralized cloud computing model needs to occupy a large number of computing resources. It cannot meet the requirements of the power Internet of Things for comprehensive perception and efficient collaboration. Edge computing is a new computing model proposed under the rapid development of the Internet of Things, artificial intelligence, big data and cloud computing. A smart platform with computing, storage, and application capabilities can be deployed on the network side near the data source. Provide intelligent services in the edge sense, to get faster network service response and meet the realtime requirements of the industry in business processing [6]. Therefore, researching common key technologies such as edge computing and cloud-side coordination, constructing a power system operation framework based on edge computing, and proposing a wide-area coordinated operation control technology framework for the power system are effective means to address opportunities and challenges in the development of a new generation power grids [7]. In response to the problems mentioned above, this paper uses edge computing and image processing technologies to find hidden safety hazards promptly and alert them. This paper bases on the broad application prospect of the deep vision and edge intelligence of electric power artificial intelligence. It mainly processes the perceived power visual images through edge intelligence and completes the analysis and calculation of visual images closer to the sensing terminal. It is a new type of power system visual image calculation mode. Use the computing resources of the nearby edge server to analyze and process the visual image data perceived by the intelligent terminal in real-time, to quickly detect the equipment faults
and defects in the visual image of the inspection, Alarm the power equipment that has identified the defect in time, and upload the recognition result and some visual images to the cloud computing center.
The paper mainly studies the framework of power artificial intelligence edge computing system and security analysis model. The framework of power artificial intelligence edge computing system mainly includes three parts: security intelligent analysis cloud platform, security management and control edge computing management system, edge computing super box. The contributions of this paper are: 1) The security intelligent analysis cloud platform provides unified management of the edge side in the cloud. We can view all registered edge nodes and their status, and distribute the applications, models, and functions listed on the cloud to the edge side to realize the function of multiple development and deployment at one time. 2) The edge computing management system implements the management of edge-end connected devices and the application of data models and carries out personnel management and control, equipment identification management, workflow management and administration, equipment inspection and other services in the station and the job site. 3) The edge computing super box adopts the NVIDIA GPU + ARM CPU architecture, deploys an edge computing management system, and provides performance and energy efficiency that can increase the running speed of artificial intelligence software and consume less power.
II. Related Work
