Abstract: In response to the practical operational demands of the distribution network in Shandong Province, this study investigates the key technologies for multi-parameter power quality monitoring, encompassing 420 monitoring devices and 1,580 monitoring points. The paper focuses on core technical solutions including high-precision synchronous data acquisition, distributed system architecture design, and optimized network communication frameworks. Additionally, it explores advanced big data processing methodologies, such as large-scale data preprocessing and feature engineering, deep learning-based anomaly pattern recognition, and comprehensive evaluation and predictive analysis. The proposed techniques aim to enhance the accuracy, efficiency, and intelligence of power quality monitoring systems in modern distribution networks.