Design and Research of an Intelligent Classroom Device Control System based on Visual Monitoring Analysis

Given the current problems in university and primary/secondary school classrooms—low efficiency of manual operation for electrical equipment, significant energy waste, and non-standardised management—this paper proposes an intelligent classroom equipment control system based on visual analysis of surveillance cameras. Using the existing classroom surveillance network, deep learning-based visual algorithms are applied in the system for real-time analysis of surveillance video to accurately detect the presence of people, their numbers, locations, and environmental conditions in the classroom. According to the pre-set intelligent control logic, the system can automatically start or stop the operation of classroom lighting, fans, air conditioning devices, multimedia equipment, etc., and adjust their operating parameters. The architecture of the system and its hardware components are presented in this paper, along with the principles of the visual recognition algorithm, equipment control strategy and software implementation. The stability and energy-saving effects of the system are shown in practice through tests. Based on the test results, the recognition accuracy of the system for personnel exceeds 95%, it can adaptively regulate various electrical devices according to the actual usage conditions of classrooms, effectively reduces unnecessary energy consumption in classrooms, and significantly improves the intelligence and precision of equipment control. Therefore, this system has good practical application value and is likely to be widely used.