An Edge Network Resource Allocation Method for Virtual-real Mapping for Digital Twin Power Grid Facilities

HU Ping; HAN Jinglin; CHEN Zhiyong; LI Xiang; LIAN Haoran; XING Zhikun

doi:10.19725/j.cnki.1007-2322.2023.0367

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An Edge Network Resource Allocation Method for Virtual-real Mapping for Digital Twin Power Grid Facilities

更新时间：2025-10-22

- An Edge Network Resource Allocation Method for Virtual-real Mapping for Digital Twin Power Grid Facilities
- Vol. 42, Issue 5, Pages: 878-890(2025)
- 作者机构：
  
  1. 国网河北省电力有限公司,河北省,石家庄市,050000
  2. 国网河北省电力有限公司经济技术研究院,河北省,石家庄市,050000
  3. 华北电力大学电气与电子工程学院, 北京市昌平区,102206
  4. 国网河北省电力有限公司雄安新区供电公司, 河北省雄安新区,071700
- 作者简介：
- 基金信息：
- DOI：10.19725/j.cnki.1007-2322.2023.0367
  CLC： 5
- Published：2025
- 稿件说明：
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HU Ping, HAN Jinglin, CHEN Zhiyong, et al. An Edge Network Resource Allocation Method for Virtual-real Mapping for Digital Twin Power Grid Facilities[J]. 2025, 42(5): 878-890.
DOI：

HU Ping, HAN Jinglin, CHEN Zhiyong, et al. An Edge Network Resource Allocation Method for Virtual-real Mapping for Digital Twin Power Grid Facilities[J]. 2025, 42(5): 878-890. DOI： 10.19725/j.cnki.1007-2322.2023.0367.

摘要

数字孪生电网通过虚实映射实现物理电网的数字化，但目前电网设施的虚实映射过程面临静态模型重建精度低和动态响应实时性差等问题。传统人工建立静态模型效率低，且难度高，不适用于复杂电网的模型构建，传统云计算模式也无法满足模型重建和响应过程中大量随机传输业务的差异化需求。因此，形成了面向数字孪生电网设施虚实映射的边缘网络资源分配方法。首先，提出一种基于Shi-Tomasi和SURF融合算法的双阈值近邻比特征匹配方法，并进一步融合随机抽样一致性（random sample consensus，RANSAC）算法提高相机位姿估计准确度，且提高电网设施三维模型视觉效果以及重建准确度。在此基础上，考虑模型重建和动态响应过程中不同业务的传输需求，构建云-边-端数据交互架构，设计面向不同传输时延以及准确率需求的效用函数，并提出子信道及功率资源分配方法，实现通信资源的按需分配。所提算法与Shen算法和信道平均分配算法相比，在数据传输时效性方面分别提高9.03%和31.30%，同时能够保证数据传输的准确性，有效支撑电网设施准确高效的虚实映射过程。

Abstract

The digital twin power grid digitizes the physical power grid through virtual-real mapping. The current virtual real mapping process of power grid facilities

however

faces challenges in terms of limited accuracy in reconstructing static models and poor real-time dynamic response. The traditional manual establishment of static models is inefficient and challenging

making it unsuitable for construction of model in complex power grid. The traditional cloud computing model fails to meet the diverse demands of numerous randomly transmission services during model reconstruction and response processes. Therefore

in this paper we propose a new edge network resource allocation method for virtual-real mapping of digital twin power grid facilities. Firstly

a feature matching method of double threshold nearest neighbor ratio is proposed based on the fusion of Shi-Tomasi and SURF algorithms. Additionally

the random sample consensus (RANSAC) algorithm is further integrated to enhance the accuracy of camera pose estimation

thereby improving the accuracy of the three-dimensional reconstruction model of power grid facilities. The aforementioned foundation serves as the basis for designing utility functions for different transmission delay and accuracy requirements

while also presents a method for subchannel and power resource allocation so as to achieve on-demand allocation of communication resources. In addition

a cloud-edge-end data interaction architecture is built to support the above process. Compared with Shen algorithm and channel average allocation algorithm

our algorithm can improve data transmission timeliness by 9.03% and 31.30% respectively

while ensuring data transmission accuracy. Moreover

the algorithm effectively facilitates the accurate and efficient virtual-real mapping process of power grid facilities.

关键词

Keywords

references

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