Abstract: To address challenges in power distribution network renovation quality control—including data fragmentation, inconsistent standards, and delayed monitoring—this study proposes a multi-source data fusion-based quality control framework. The system establishes a standardized multi-source data collection taxonomy to integrate design, spatial, and process data. It develops a data-driven digital twin model for real-time construction site mapping, and implements a multi-source integrated quality monitoring system with intelligent anomaly detection. Field applications demonstrate that this technology significantly enhances real-time monitoring accuracy, achieving over 40% improvement in defect detection rates and overall construction quality. These advancements provide robust technical support for high-quality power distribution network renovation projects.