As the core component of wind power equipment, the health status of wind turbine blades is crucial for the normal operation of the entire system. In response to the real-time monitoring requirements of blade condition parameters during operation, a distributed real-time monitoring system based on 5G communication has been designed and implemented. The system employs a two-layer distributed architecture, consisting of a multi-node data acquisition unit cluster and upper computer software. A two-level synchronous trigger strategy has been proposed to achieve high-precision synchronous sampling across all data acquisition channels, with the trigger delay being only 0.13% of the minimum sampling period. A dynamic adjustment model of trigger rate and data transmission rate was developed by fusing the compressed sensing technology, and the parameters were optimized accordingly. This approach enables the dynamic matching of data collection and transmission in the variable environment of 5G networks, thereby improving the reliability of system data transmission. Experimental results indicate that the fluctuation of background noise is less than 0.5%, the nonlinear error is below 1.1%, the channel consistency error is under 1%, and the reconstruction accuracy of compressed sensing reaches 98%. This design not only provides an effective solution for the real-time monitoring of wind turbine blades but also serves as a technical reference for the development of other online real-time monitoring systems.