Abstract: In recent years, online monitoring and early warning methods such as the transient ground voltage method, the high-frequency current method, and artificial intelligence data-driven model have been applied to distribution cables. However, there are the following problems: the evaluation and early warning of partial discharge through on-site edge monitoring methods such as transient ground voltage method and the high-frequency current method are not comprehensive, and it is difficult to calibrate the health status of distribution cables. Furthermore, uploading all partial discharge high-frequency signals to the cloud main station and integrating other data through artificial intelligence data-driven modeling methods for evaluation and warning results in excessive data volume and high cost. This paper has constructed an online monitoring and early warning framework for distribution cable defects and faults, proposed a data collaboration mechanism for cloud-edge collaboration, and developed an online early warning and age-overdue lean management module for distribution cable defects and faults. The system has been deployed and applied in the actual distribution network, and the effectiveness of the methods proposed in this paper has been verified.