Abstract: With the rapid expansion of wind power capacity, traditional manual inspections and threshold-based alarms can no longer support fine-grained operation and maintenance (O&M) under high-frequency, multi-source data. To reduce downtime losses and enhance safety, this study delves into data-driven techniques for intelligent monitoring and O&M of wind farms. We propose a monitoring framework based on spectral-graph dual-domain features and a dual-stream time-frequency coupled residual network, combined with an uncertainty-risk joint loss function and an opportunistic maintenance strategy. This framework enables rolling assessment of turbine health indices and optimal scheduling of maintenance resources. A pilot implementation at a wind farm in northwest China demonstrates the system's ability to provide early fault warnings and significantly improve overall economic performance.