Abstract: In recent years, online monitoring and early warning methods such as transient ground voltage method and high-frequency current method, as well as artificial intelligence data-driven model method, have been applied in 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 high-frequency current method are not comprehensive, and it is difficult to calibrate the health status of distribution cables; However, uploading all high-frequency signals of partial discharge 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. Therefore, how to conduct online evaluation and early warning through cloud edge collaboration is currently a technical challenge for monitoring and early warning of distribution cables. This article constructs an online monitoring and early warning framework for distribution cable defects and faults, proposes a cloud edge collaborative data collaboration mechanism, and develops an online early warning and lean management module for distribution cable defects and faults. The system has been deployed and applied in the actual distribution network, verifying the effectiveness of the method proposed in this paper.