Design of an Intelligent Computer Room Equipment Safety Monitoring System Based on Computer Vision

This article used OpenCV (Open Source Computer Vision Library) to preprocess images, and then used positioning methods and horizontal and vertical projection methods to accurately locate the key information to be recognized. Then, convolutional neural networks were utilized to recognize and analyze the segmented images. Based on the characteristics of the layout and dynamic environmental monitoring of the computer room, the computer room equipment safety monitoring system designed in this article includes an environmental monitoring module, a power monitoring module, a security access control monitoring module, and an abnormal alarm module. The average current intensity in the sleep state was 2μA, 10 mA in the wake state, 110 mA in the send state, and 15 mA in the receive state. The computer room equipment safety monitoring system has increased the efficiency and quality of computer room operation and maintenance, improved the intelligence level of power communication room monitoring, and provided a reference for technical solutions for real-time monitoring of other power communication rooms.