基于AI结合高程三角网算法的风机布置标高优化方法及系统
By combining AI technology with elevation triangulation algorithms, a triangulation graph structure is constructed, and graph convolution and attention networks are used to optimize the wind turbine foundation type. This solves the problems of error amplification and insufficient consistency analysis in traditional design, and achieves efficient and accurate wind turbine foundation layout design.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- ZHUHAI HUACHENG ELECTRIC POWER DESIGN INST CO LTD
- Filing Date
- 2026-05-08
- Publication Date
- 2026-07-17
AI Technical Summary
Traditional wind turbine foundation design suffers from problems such as triangular mesh interpolation errors leading to changes in foundation type, lack of spatial consistency analysis, large workload of manual verification, and low design efficiency.
By combining AI technology with elevation triangulation algorithms, elevation correction is performed by constructing a triangulation graph structure and a graph convolutional network model. Basic type space optimization is performed by combining a graph attention network. The optimal elevation is determined by elevation perturbation simulation and multi-objective optimization, and abnormal nodes are identified and optimized.
It improves the accuracy of elevation calculation, reduces the workload of manual verification, enhances design efficiency, and achieves spatial consistency and automated design of wind turbine foundation layout.
Smart Images
Figure CN122133536B_ABST