Full-scale data augmentation mining of electric power new energy engineering based on multi-level grid of spatial information

Wang Wei; Cai Si; Ye Xinyang; Li Zan; Lu Ting; Duan Yuxiang

doi:10.11959/j.issn.1000-0801.2026036

您当前的位置：

首页 >

文章列表页 >

Full-scale data augmentation mining of electric power new energy engineering based on multi-level grid of spatial information

Engineering and Application | 更新时间：2026-04-08

- Full-scale data augmentation mining of electric power new energy engineering based on multi-level grid of spatial information
- Telecommunications Science Vol. 42, Issue 3, Pages: 189-199(2026)
- 作者机构：
  
  1.湖北省电力规划设计研究院有限公司，湖北武汉 430040
  2.华中科技大学计算机科学与技术学院，湖北武汉 430074
- 作者简介：
- 基金信息：
  
  The State Grid Corporation of China Headquarters Technology Project(5108-202220034A-1-1-ZN);The Research Project of Hubei Electric Power Planning and Design Institute Co., Ltd(K2024-1-05)
- DOI：10.11959/j.issn.1000-0801.2026036
  CLC： TN223
- Received：01 September 2025，
  
  Revised：2025-09-10，
  
  Accepted：10 October 2025，
  
  Published：20 March 2026
- 稿件说明：
移动端阅览
王伟,蔡思,叶馨阳等.基于空间信息多级网格的电力新能源工程全尺度数据增强挖掘[J].电信科学,2026,42(03):189-199.

Wang Wei,Cai Si,Ye Xinyang,et al.Full-scale data augmentation mining of electric power new energy engineering based on multi-level grid of spatial information[J].Telecommunications Science,2026,42(03):189-199.
王伟,蔡思,叶馨阳等.基于空间信息多级网格的电力新能源工程全尺度数据增强挖掘[J].电信科学,2026,42(03):189-199. DOI： 10.11959/j.issn.1000-0801.2026036.

Wang Wei,Cai Si,Ye Xinyang,et al.Full-scale data augmentation mining of electric power new energy engineering based on multi-level grid of spatial information[J].Telecommunications Science,2026,42(03):189-199. DOI： 10.11959/j.issn.1000-0801.2026036.

摘要

当前电力新能源工程数据呈现出多源异构、时空耦合、空间非平稳性的复杂特性，使全尺度数据挖掘区域边界模糊，导致电力新能源工程全尺度数据挖掘精度下降。为此，提出了基于空间信息多级网格的电力新能源工程全尺度数据增强挖掘方法。通过余弦相似性和皮尔逊相关系数量化数据间的关联性，在此基础上，采用相似度驱动的网格密度峰值计算方法，并结合距离阈值化处理，最终通过空间信息多级网格，实现了对复杂电力新能源数据的精细化空间划分。将划分结果标定行列索引作为特征摘要，并为网格单元特征值添加索引标记，在添加索引标记后利用反距离加权法计算挖掘索引阈值，以实现电力新能源工程的全尺度数据增强挖掘。实验结果表明，所提方法在工程全尺度数据增强挖掘中具有较高的精准度，挖掘结果与目标结果之间的一致性较强，具有较强的实用价值。

Abstract

The current data of electric power new energy engineering presents complex characteristics of multi-source heterogeneity

spatiotemporal coupling

and spatial non-stationarity

which make the boundaries of the full-scale data mining area fuzzy and lead to a decrease in the accuracy of full-scale data mining of electric power new energy engineering. Therefore

a multi-level grid based method for enhancing the mining of full-scale data of electric power new energy engineering based on spatial information was proposed. By quantifying the correlation between data through cosine similarity and Pearson correlation coefficient

a similarity driven grid density peak calculation method was adopted

combined with distance thresholding processing. Finally

a multi-level spatial information grid was used to achieve refined spatial partitioning of complex power new energy data. The row and column indexes of the partition results were calibrated as feature summaries

and the index labels were added to the grid cell feature values. After adding the index labels

the inverse distance weighting method was used to calculate the mining index threshold

in order to achieve full-scale data augmentation mining of power new energy engineering. The experimental data shows that the proposed method has high accuracy in engineering full-scale data augmentation mining

and the consistency between the mining results and the target results is strong. Therefore

the proposed method has strong practical value.

关键词

Keywords

references

王绪亮 , 顾媛丽 , 张鸿儒 , 等 . 基于知识集成流形的电力设备缺陷文本数据增强方法与应用研究 [J ] . 电网技术 , 2024 , 48 ( 4 ): 1690 - 1702 .

Wang X L , Gu Y L , Zhang H R , et al . Data augmentation and application of defect texts for power equipment based on knowledge integration manifold [J ] . Power System Technology , 2024 , 48 ( 4 ): 1690 - 1702 .

李娇 , 张玉清 , 吴亚飚 . 面向网络安全关系抽取的大语言模型数据增强方法 [J ] . 信息网络安全 , 2024 , 24 ( 10 ): 1477 - 1483 .

Li J , Zhang Y Q , Wu Y B . Data augmentation method via large language model for relation extraction in cybersecurity [J ] . Netinfo Security , 2024 , 24 ( 10 ): 1477 - 1483 .

Pu Y , Zhu R , Wang S , et al . City-scale roadside electric vehicle parking and charging capacity: a deep learning augmented street-view-image data mining and analytic framework [J ] . Applied Energy , 2025 , 389 ( 6 ): 256 - 414 .

Iacono F L , Maragna R , Pontone G , et al . A novel data augmentation method for radiomics analysis using image perturbations [J ] . Journal of Imaging Informatics in Medicine , 2024 , 37 ( 5 ): 2401 - 2414 .

张宇波 , 王有元 , 梁玄鸿 , 等 . 电力设备缺陷文本的双通道语义增强网络挖掘方法 [J ] . 高电压技术 , 2024 , 50 ( 5 ): 1923 - 1932 .

Zhang Y B , Wang Y Y , Liang X H , et al . Dual-channel semantic enhancement network mining method for defect text of power equipment [J ] . High Voltage Engineering , 2024 , 50 ( 5 ): 1923 - 1932 .

孟令兵 , 袁梦雅 , 时雪涵 , 等 . 跨模态融合和边界可变形卷积引导的RGB-D显著性目标检测 [J ] . 电子学报 , 2023 , 51 ( 11 ): 3155 - 3166 .

Meng L B , Yuan M Y , Shi X H , et al . RGB-D salient object detection based on cross-modal fusion and boundary deformable convolution guidance [J ] . Acta Electronica Sinica , 2023 , 51 ( 11 ): 3155 - 3166 .

闫机超 , 郑静雅 , 孙胜耀 . 基于最大信息挖掘广域学习系统的混沌时间序列预测 [J ] . 计算机应用与软件 , 2023 , 40 ( 9 ): 253 - 260 .

Yan J C , Zheng J Y , Sun S Y . Chaotic time series prediction based onmaximum information mining broad learning system [J ] . Computer Applications and Software , 2023 , 40 ( 9 ): 253 - 260 .

张红斌 , 侯婧怡 , 石皞炜 , 等 . 联合多头数据增强与多粒度语义挖掘的图像情感分析 [J ] . 控制与决策 , 2024 , 39 ( 6 ): 2013 - 2021 .

Zhang H B , Hou J Y , Shi H W , et al . Image sentiment analysis via multi-head data augmentation and multigranularity semantics mining [J ] . Control and Decision , 2024 , 39 ( 6 ): 2013 - 2021 .

崔广炎 , 王艳辉 , 徐杰 , 等 . 基于改进Faster R-CNN的隧道衬砌中离散实体目标自动检测研究 [J ] . 铁道学报 , 2024 , 46 ( 2 ): 171 - 180 .

Cui G Y , Wang Y H , Xu J , et al . Automatic detection of discrete entity objects in tunnel lining based on improved faster R-CNN [J ] . Journal of the China Railway Society , 2024 , 46 ( 2 ): 171 - 180 .

刘付渝杰 . 基于多尺度工况增强网络及Informer的设备剩余寿命预测 [J ] . 计算机测量与控制 , 2024 , 32 ( 8 ): 115 - 122 .

Liu F Y J . RUL prediction of device based on multi-scale working condition enhancement network and informer [J ] . Computer Measurement &Control , 2024 , 32 ( 8 ): 115 - 122 .

荀亚玲 , 任姿芊 , 闫海博 . 一种有效的周期高效用序列模式增量挖掘算法 [J ] . 计算机应用研究 , 2024 , 41 ( 8 ): 2301 - 2308 .

Xun Y L , Ren Z Q , Yan H B . Effective incremental mining algorithm forperiodic high-utility sequential patterns [J ] . Application Research of Computers , 2024 , 41 ( 8 ): 2301 - 2308 .

陆思洁 , 范頔 , 渐令 , 等 . 集成数据挖掘知识的可解释最优超球体支持向量机 [J ] . 控制理论与应用 , 2024 , 41 ( 3 ): 375 - 384 .

Lu S J , Fan D , Jian L , et al . Interpretable small sphere and large margin support vector machine with integrated data mining knowledge [J ] . Control Theory & Applications , 2024 , 41 ( 3 ): 375 - 384 .

王春枝 , 邢绍文 , 高榕 , 等 . 基于预训练交互式图神经网络的多元时间序列异常检测 [J ] . 中南民族大学学报(自然科学版) , 2023 , 42 ( 4 ): 541 - 550 .

Wang C Z , Xing S W , Gao R , et al . Multivariate time series anomaly detection using pre-training based interactive graph neural network [J ] . Journal of South-central Minzu University (Natural Science Edition) , 2023 , 42 ( 4 ): 541 - 550 .

王梨名 , 祁昆仑 , 杨超 , 等 . 弱监督尺度自适应增强的高分辨率遥感影像场景分类 [J ] . 遥感学报 , 2023 , 27 ( 12 ): 2815 - 2830 .

Wang L M , Qi K L , Yang C , et al . Weakly supervised scale adaptation data augmentation for sceneclassification of high-resolution remote sensing images [J ] . National Remote Sensing Bulletin , 2023 , 27 ( 12 ): 2815 - 2830 .

刘熙鹏 , 罗庆全 , 余涛 , 等 . 基于多尺度特征融合的负荷辨识及其可解释交互增强方法 [J ] . 电力系统自动化 , 2024 , 48 ( 2 ): 105 - 117 .

Liu X P , Luo Q Q , Yu T , et al . Load identification and its interpretable interactive enhancement method based on multi-scale feature fusion [J ] . Automation of Electric Power Systems , 2024 , 48 ( 2 ): 105 - 117 .

李林 . 基于数字孪生的配电网潮流网络模型构建研究 [J ] . 电子设计工程 , 2024 , 32 ( 1 ): 111 - 114 .

Li L . Research on power flow network model construction of distribution network based on digital twin [J ] . Electronic Design Engineering , 2024 , 32 ( 1 ): 111 - 114 .

Views

下载量

CSCD

Alert me when the article has been cited

提交

Tools

Publicity Resources

No data

Related Author

No data

Related Institution

No data

⁰