A full-factor large model management and control platform system
By combining physical mechanisms and data-driven dual-brain collaborative reasoning, the problem of anti-physical outputs in large-scale wind power models under extreme weather conditions has been solved, achieving interpretability and high-precision prediction of the model, and improving the safety and operation and maintenance efficiency of wind farms.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2026-04-28
- Publication Date
- 2026-07-17
AI Technical Summary
Existing large-scale wind power models produce results that defy physics under extreme weather conditions, leading to safety hazards and economic losses. Furthermore, the model's decision-making process is unexplainable and has low credibility.
The system employs a comprehensive production model management platform that combines a physical mechanism-based approach with a data-driven approach for collaborative reasoning. Through extreme weather risk level quantification, dynamic weight allocation between the two approaches, physical consistency verification, and the generation of interpretable reasoning chains, it ensures that the output results conform to physical laws and are interpretable.
Improving model prediction accuracy under extreme weather conditions and generating interpretable reasoning chains allows operation and maintenance personnel to verify the correctness of model decisions, thereby enhancing the safety and operation and maintenance efficiency of wind farms.
Smart Images
Figure CN122415067A_ABST