Switchgear cabinets are widely used in the power system, featuring overhead incoming and outgoing lines, cable incoming and outgoing lines, busbar interconnection, etc. They are mainly suitable for various different places such as power plants, substations, petrochemical industry, metallurgy rolling steel, light industry textiles, factory mines, residential communities, high-rise buildings, etc. However, switchgear cabinet failures are frequent, such as heating faults, insulation faults, mechanical transmission operating mechanism faults, electrical component faults, etc. The device developed by this technology implements online real-time monitoring of the temperature rise and partial discharge of high-voltage switchgear cabinets. Through ZigBee wireless transmission technology communication, it utilizes information fusion technology and deep learning theory to achieve fault monitoring and pre-diagnosis of switchgear cabinets, promptly identify accident hazards, and ensure the safe and stable operation of the power grid. The product uses infrared and ultraviolet photoelectric sensors to monitor two types of fault characteristics: temperature rise and partial discharge. The fusion detection of these two features results in a high detection accuracy. The accuracy rate of normal state detection reaches 100%. The host size is 110mm*70mm*55mm, which can be built-in without affecting the layout of switchgear cabinets. It has a simple structure, is easy to install and use, with low prototype costs and can be mass produced, resulting in high economic benefits. The related achievements have been publicly patented as 'Method for Electrical Equipment Fault Monitoring Based on the Fusion of Infrared and Ultraviolet Photoelectric Sensing'.