Abstract: Addressing the issues of delayed response and insufficient multi-parameter collaborative analysis capability in the monitoring of hydropower station auxiliary equipment, this study aims to enhance real-time performance and accuracy. Based on a diversion-type hydropower station project in Fujian, a real-time monitoring system leveraging smart instruments was developed. The study systematically analyzed optimization approaches for data acquisition, transmission, and fusion processing, and proposed a three-layer monitoring architecture encompassing field sensing, network transmission, and data management. Furthermore, a dynamic multi-parameter early-warning mechanism centered on a sliding window algorithm was introduced. Results demonstrate that the system effectively reduces anomaly response time, transitions monitoring from single-parameter to multidimensional collaborative analysis, and enhances operational reliability through tiered early warnings and digital data storage.