Regarding the safety risks caused by improper wearing of safety helmets

smoking

and illegal entry into hazardous areas during the operation of power system

this paper studies an intelligent warning method for safety risks in operations based on improved YOLO (you only look once) v7 deep learning model. Firstly

the architecture for training and using a deep learning-based operation risk warning model is discussed. Then

the object recognition and detection method based on the YOLO v7 deep learning model is analyzed

and convolutional block attention module (CBAM) and k-means ++ methods are introduced to improve the model. Finally

the improved YOLO v7 model is used to analyze and verify its effectiveness in the aforementioned safety risk warning. Experimental results show that compared to the previous YOLO series detection methods

the improved YOLO v7 model has better performance in detecting operational safety risks in power system

and can meet the real-time detection and risk warning needs of the novel power system for operational risks.