Journal of Networking Technology Yiwei Huang, Jingteng Chen, Xianghan Zheng, Weipeng Xie, Huan Wang, Zhibin Xie, Yunliang Li 16 2 2025 https://doi.org/10.6025/jnt/2025/16/2/51-61 https://www.dline.info/jnt/fulltext/v16n2/jntv16n2_2.pdf This paper designs a distribution network external force damage early warning system based on audio classification technology, focusing on the potential damage that large construction machinery might cause to cables. By utilizing audio data, the system can accurately monitor and analyze the real-time status of construction sites to identify potential cable damage activities. Leveraging a cloud-edge collaboration architecture, the system employs cloud-based model training and edge computing technology to achieve realtime monitoring and data processing, thereby enhancing the accuracy and timeliness of warnings. Integrating machine learning algorithms and pattern recognition technology, the system can automatically analyze and predict the impact of construction machinery on cables. Through a visualization system, it provides real-time warnings and response measures, effectively reducing losses caused by external damage and ensuring the safe operation and reliability of the distribution network and power supply.