一种多模态数据的电网线路规划方法
By integrating multimodal data and optimizing neural networks using deep learning and attention mechanisms, the limitations of data processing in power grid line planning were solved, achieving more efficient and accurate path planning.
CN119026287BActive Publication Date: 2026-07-17HUANGGANG QIANGYUAN POWER DESIGN CO LTD +1
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- HUANGGANG QIANGYUAN POWER DESIGN CO LTD
- Filing Date
- 2024-08-02
- Publication Date
- 2026-07-17
AI Technical Summary
Technical Problem
Existing power grid planning methods have limitations in data processing and integration, making it difficult to effectively utilize complex multi-source data, which affects the accuracy and efficiency of planning.
Method used
By integrating multimodal data (satellite imagery, drone imagery, and ground sensor data), deep learning algorithms and attention mechanisms are used to design loss functions, optimize neural network architecture, and perform power grid line route planning.
Benefits of technology
It improves the accuracy and efficiency of power grid line planning, reduces labor costs, and provides more flexible and efficient planning solutions.
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Figure CN119026287B_ABST
Abstract
本发明提供一种多模态数据的电网线路规划方法，包括以下步骤：首先整合并筛选各种模态的数据源，所述多模态数据包括卫星摇感图像、无人机拍摄的高分辨率视图以及地面传感器的气象数据。然后设计线路优选模型，评估不同路线的可行性并优化线路规划。最后线路规划模型通过地图展示规划后的线路。所述的线路规划模型包括数据处理模块、特征选择模块、注意力机制模块和语义分割预测模块。本发明通过深度学习算法整合并预处理来自多个数据源的信息，结合地理信息系统进行电网线路的路径规划，为电网线路规划提供数据支持和决策工具，大大提高了工作效率。同时减少了人工成本，为线路规划提供更效率和灵活的方案。
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