Abstract: To address common anomalies such as sudden deterioration, drift, or loss of monitoring data during smart substation operation and maintenance, this study develops a precise and efficient methodology for tracing and processing abnormal data sources. By employing multivariate spatiotemporal correlation analysis algorithms and multidimensional causal logic mapping techniques, the approach performs feature extraction and correlation modeling on diverse heterogeneous data streams—including substation monitoring systems, relay protection devices, and environmental auxiliary monitoring data—thereby establishing a hierarchical response mechanism that facilitates deep collaboration between operation and maintenance teams. Operational results demonstrate that the proposed method can identify both the location of data anomalies and their root causes within one second, significantly reducing false alarm and missed detection rates, substantially shortening on-site troubleshooting time, and markedly enhancing both the precision management of substation equipment and overall grid security capabilities.