Research on Data Link Monitoring and Comprehensive Evaluation Model Based on Data Central Station

As the core platform for integrating data resources in modern enterprises, the data middle platform's pipeline health status directly impacts the reliability and timeliness of business decision-making. Addressing the limitations of existing monitoring methods in feature sensitivity and anomaly diagnosis efficiency, this study proposes a hybrid feature selection strategy integrating recursive cross-validation with random forest. Combined with a dynamic learning mechanism incorporating Self-Organizing Map (SOM) networks and Particle Swarm Optimization (PSO), an enhanced data pipeline anomaly diagnosis model is constructed. The feature selection algorithm filters critical indicators to reduce redundant interference, while the SOM network captures nonlinear correlation patterns. The PSO algorithm optimizes network topology to enhance convergence precision. Experimental validation demonstrates that this model exhibits significant advantages over conventional methods in diagnostic accuracy and response speed. The comprehensive evaluation system effectively quantifies pipeline status, providing interpretable decision support for data middle platform operation and maintenance, thereby advancing intelligent O&M systems toward adaptive evolution.