In power monitoring scenarios， real-time monitoring and analysis of personnel behavior are crucial for ensuring the safe and stable operation of power systems. However， directly exposing facial information in monitoring videos without proper processing poses serious privacy risks. Traditional face detection-based blurring methods face challenges such as insufficient robustness and high computational costs in complex power environments， making them hard to meet both accuracy and real-time requirements. To address these issues， a real-time face blurring method based on head skeleton point detection was proposed. Firstly， a lightweight head skeleton point detection framework based on a hierarchical processing strategy was designed to locate personnel regions in compressed videos rapidly and stitch the cropped areas at original resolution to batch-detect head skeleton points of all the people， thus improving detection efficiency and accuracy. Secondly， an adaptive inter-frame optimization strategy was introduced， to use frame differencing to detect changes in the number of personnel quickly and adjust detection frequency dynamically by incorporating a tracking mechanism for personnel detection boxes， thereby reducing redundant computational overhead effectively. Finally， a prototype system for real-time face blurring was constructed on edge nodes， and its performance was validated through experiments. Experimental results indicate that taking the KAPAO-S model as an example， the proposed method improves the face blurring accuracy in monitoring videos by 3.6 percentage points and reduces the processing time per frame by 2.5 ms approximately compared to the original model， thereby ensuring accuracy and real-time performance at the same time.