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基于自适应分辨率网格和改进的 Dijkstra 算法的输电线路规划方法。

Transmission Line-Planning Method Based on Adaptive Resolution Grid and Improved Dijkstra Algorithm.

机构信息

School of Electrical Engineering and Automation, Hefei University of Technology, Hefei 230000, China.

出版信息

Sensors (Basel). 2023 Jul 7;23(13):6214. doi: 10.3390/s23136214.

Abstract

An improved Dijkstra algorithm based on adaptive resolution grid (ARG) is proposed to assist manual transmission line planning, shorten the construction period and achieve lower cost and higher efficiency of line selection. Firstly, the semantic segmentation network is used to change the remote sensing image into a ground object-identification image and the grayscale image of the ground object-identification image is rasterized. The ARG map model is introduced to greatly reduce the number of redundant grids, which can effectively reduce the time required to traverse the grids. Then, the Dijkstra algorithm is combined with the ARG and the neighborhood structure of the grid is a multi-center neighborhood. An improved method of bidirectional search mechanism based on ARG and inflection point-correction is adopted to greatly increase the running speed. The inflection point-correction reduces the number of inflection points and reduces the cost. Finally, according to the results of the search, the lowest-cost transmission line is determined. The experimental results show that this method aids manual planning by providing a route for reference, improving planning efficiency while shortening the duration, and reducing the time spent on algorithm debugging. Compared with the comparison algorithm, this method is faster in running speed and better in cost saving and has a broader application prospect.

摘要

提出了一种基于自适应分辨率网格(ARG)的改进 Dijkstra 算法,以辅助手动输电线路规划,缩短施工周期,实现更低的成本和更高的线路选择效率。首先,语义分割网络将遥感图像转换为地物识别图像,并对该图像的灰度图像进行光栅化。引入 ARG 地图模型可以大大减少冗余网格的数量,从而有效减少遍历网格所需的时间。然后,将 Dijkstra 算法与 ARG 结合,将网格的邻域结构设置为多中心邻域。采用基于 ARG 和拐点修正的改进双向搜索机制方法,大大提高了运行速度。拐点修正减少了拐点数量,降低了成本。最后,根据搜索结果确定最低成本的输电线路。实验结果表明,该方法通过提供路线参考来辅助手动规划,提高规划效率的同时缩短了工期,并减少了算法调试的时间。与对比算法相比,该方法在运行速度、成本节约和应用前景方面都具有优势。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8c6d/10346975/321d471465fa/sensors-23-06214-g001.jpg

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