Abstract: Large construction machinery serves as a crucial tool for power system overhaul and modification operations. The safety distance during its operation near live electrical equipment is strictly regulated, making the development of intelligent measurement methods for safety distance monitoring essential for ensuring operational safety. However, the operation process is characterized by extensive working ranges and complex electrified environments, presenting significant challenges that existing methods fail to adequately address. Therefore, this paper proposes an intelligent measurement method for safety distance of construction machinery based on binocular stereo perception and safety zone segmentation. First, the stereo disparity value of the work scene is obtained using the PSMNet stereo perception model, while the 3D world space coordinate information of the work scene is acquired through a coordinate transformation-based 3D reconstruction method. Then, by using regional element identification and the Canny edge detection model, the positions of construction machinery and the contours of safety area boundaries are precisely identified. Finally, the minimum Euclidean distance changes horizontally and vertically between construction machinery and safety area boundaries are tracked, allowing us to quantify and dynamically alert for large machinery's operational safety distances. The result shows that this method achieves an average error rate of merely 3.3% for the 3D spatial reconstruction of complex construction machinery scenes, and the recognition accuracy for the diversified elements within these scenes reaches 94.5%. Compared with the existing methods, the proposed method has superior detection accuracy and enhanced practical applicability.