Abstract:To address the challenges of low computational efficiency, insufficient storage management, and large time synchronization errors in high-sampling-rate distribution network online detection, a Synchronized High-Sampling Detection System is proposed. The system employs multi-threaded parallel parsing, indexed frame optimization, and streaming computation to enhance data parsing efficiency, increasing throughput by nearly four times while reducing parsing delay by 85%. By utilizing batch writing, indexed storage, and hot-cold tiered data management strategies, storage and query performance are significantly improved, achieving 12× higher throughput and 87% lower query latency. A window-based alignment and interpolation correction method reduces time synchronization errors to within 950μs, ensuring enhanced alignment consistency of multi-PMU data. Additionally, sliding window feature extraction, LSTM time-series prediction, and density-based clustering are integrated to improve anomaly detection accuracy by 10%-20%. Experimental results demonstrate that the system operates stably under high-sampling-rate conditions, effectively supporting online detection and intelligent monitoring of distribution networks..