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《山东大学学报(理学版)》 ›› 2026, Vol. 61 ›› Issue (5): 18-26.doi: 10.6040/j.issn.1671-9352.0.2025.181

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电力安全知识图谱构建技术与应用

汤步洲,胡晗   

  1. 哈尔滨工业大学(深圳)计算机科学与技术学院, 广东 深圳 518067
  • 发布日期:2026-05-15
  • 作者简介:汤步洲(1984— ),男,教授,博士,研究方向为自然语言处理、知识图谱. E-mail:tangbuzhou@hit.edu.cn
  • 基金资助:
    深圳市科技计划基金资助项目(GXWD20231128103819001)

Construction of technology and application of knowledge graph in power safety

TANG Buzhou, HU Han   

  1. School of Computer Science, Harbin Institution of Technology, Shenzhen 518067, Guangdong, China
  • Published:2026-05-15

摘要: 知识图谱技术因其强大的数据管理和推理能力,在诸多领域得到了广泛应用,在电力安全生产和安全管理中也极具应用潜力。针对电力安全生产与管理效率较低的问题,国内外已开始逐步开展面向电力安全生产和管理的知识图谱研究,但仍处于起步阶段。为有效推动电力安全知识图谱构建与应用技术的发展,本文对电力安全知识图谱相关技术系统调研,介绍电力安全生产和管理中面临的问题、电力安全数据的形式与特点、电力安全知识图谱构建、电力安全知识图谱应用。电力安全知识图谱构建介绍本体库构建、实体识别和关系抽取关键技术。电力安全知识图谱应用包括安全隐患预测、岗位安全管理和安全知识学习的场景介绍。最后综合分析现有电力安全知识图谱研究中的不足和未来可能的研究方向。

关键词: 电力安全, 知识图谱, 知识图谱构建, 知识抽取

Abstract: Due to its efficient data management and reasoning capabilities, knowledge graph technology has been widely used in many fields, and has great application potential in power safety production and safety management. Researchers all over the world have began to study knowledge graph for power safety production and management, but the studies are still in the initial stage. In order to effectively promote the construction of power safety knowledge graph and the development of its application, a systematic investigation is conducted on the related technologies of power safety knowledge graph, and the problems faced in the production and management of power safety, the forms and characteristics of power safety data, the status quo and application of power safety knowledge graph construction technology in detail are introduced. In the case of the construction of power safety knowledge graph, the common methods and models are presented, and the application part of power safety knowledge graph mainly includes the safety risk prediction, post safety management and the scene introduction of safety knowledge learning. Finally, we point out the shortcomings of the existing research on the knowledge graph of power safety and the possible research directions in the future through comprehensive analysis.

Key words: power safety, knowledge graph, construction of knowledge graph, knowledge extraction

中图分类号: 

  • TP181
[1] 贾磊. 浅析电力工程安全管理[J]. 中国电力教育,2010(10):249-250. JIA Lei. Analysis of safety management inelectirc power engineering[J]. China Electric Power Education, 2010(10):249-250.
[2] 陈晓波. VR技术在电力安全生产培训中的应用[J]. 光源与照明,2022(7):234-236. CHEN Xiaobo. Application of technology in power safety production training[J]. Lamps & Lighting, 222(7):234-236.
[3] 张思慧,胡广林,魏国旺. 面向电力项目的知识图谱构建及应用研究[J]. 现代信息科技,2024,8(6):115-120. ZHANG Sihui, HU Guanglin, WEI Guowang. Research on knowledge graph construction and application for electricity power projects[J]. Modern Information Technology, 2024, 8(6):115-120.
[4] 陈中秋,张晓丽,胡继亮. 电力工程中的安全管理策略分析[J]. 电子技术,2023,52(9):92-93. CHEN Zhongqiu, ZHANG Xiaoli, HU Jiliang. Analysis of safety management strategies in eletric power engineering[J]. Electronic Technology, 2023, 52(9):92-93.
[5] 王钢. 电力工程安全管理的方法及应用[J]. 时代农机,2018,45(1):54-55. WANG Gang. Methods and applications of safety management in electric powerengineering[J]. Time Agricultural Machinery, 2018, 45(1):54-55.
[6] 刘畅. 某国有电力公司建设项目安全管理研究[D]. 长春:长春工业大学,2024. LIU Chang. Research on the safety management odf the construction project of a state-owned electric power company[D]. Changchun: Changchun Universiry of Technology, 2024.
[7] 郑皓元. 电力工程施工中的进度控制与安全管理分析[J]. 工程建设与设计,2022(2):202-204. ZHENG Haoyuan. Analysis of progress control and safety management in electric power engineering construction[J]. Engineering Construction and Design, 2022(2):202-204.
[8] 王军龙,李永祥,王守长,等. 信息化技术在电力工程施工安全管理中的应用探讨[J]. 中国管理信息化,2020,23(8):87-88. WANG Longjun, LI Yongxiang, WANG Shouchang, et al. Disscusion on the application of information technology in safety management of electric power engineering construction[J]. Chine Management Informationization, 2020, 23(8):87-88.
[9] 刘峤,李杨,段宏,等. 知识图谱构建技术综述[J]. 计算机研究与发展,2016,53(3):582-600. LIU Qiao, LI Yang, DUAN Hong, et al. Knowledge graph construction techniques[J]. Joural of Computer Research and Development, 2016, 53(3):582-600.
[10] 苏青青. 基于知识图谱的电力企业岗位安全管理系统的研究与设计[D]. 北京: 华北电力大学,2023. SU Qingqing. Research and design of power enterprise post safety management system based on knowledge geaph[D]. Beijing: North China Electric Power University, 2023.
[11] 李泽科,陈书里,陈斌,等. 基于知识图谱的电力调度自动化作业工作票生成方法[J]. 福州大学学报(自然科学版),2025,53(2):135-143. LI Zeke, CHEN Shuli, CHEN Bin, et al. Job ticket generation method for power dispatching automation based on knowledge graph [J]. Journal of Fuzhou University(Natural Science Edition), 2025, 53(2):135-143.
[12] 徐鸿飞. 基于语义表征技术的电力安全作业实体关系抽取研究[D]. 昆明:昆明理工大学,2024. XU Hongfei. Research on entity eelation extracting in power safety operations based on semantic representation technology[D]. Kunming: Kunming University of Science and Technology, 2024.
[13] 徐冲,汪凝,倪相生. 基于知识图谱的用户特征-关系推荐模型在电力安全教育中的应用[J]. 电力信息与通信技术, 2024, 22(11):60-66. XU Chong, WANG Ni, NI Xiangsheng. The application of user feature-relationship recommendation model based on knowledge graph in electric power safety education[J]. Electric power Information and Communication Technology, 2024, 22(11):60-66.
[14] LI Jing, SUN Anxin, HAN Jianglei, et al. A survey on deep learning for named entity recognition[J]. IEEE Transactions on Knowledge and Data Engineering, 2022, 34(1):50-70.
[15] 李新鹏,徐建航,郭子明,等. 调度自动化系统知识图谱的构建与应用[J]. 中国电力,2019,52(2):70-77. LI Xinpeng, XU Jianhang, GUO Zihang, et al. Construction and application of knowledge graph in dispatching automation system [J]. China Power, 2019, 52(2):70-77.
[16] 王慧芳,曹靖,罗麟. 电力文本数据挖掘现状及挑战[J]. 浙江电力,2019,38(3):1-7. WANG Huifang, CAO Jing, LUO Lin. The current situation and challenges of power text data mining[J]. Zhejiang Power, 2019, 38(3):1-7.
[17] 袁金斗,潘明明,张腾,等. 基于规则和词典的用电安全领域命名实体识别[J]. 电子技术应用,2022,48(12):22-27. YUAN Jindou, PAN Mingming, ZHANG Teng, et al. Named entity recognition in the field of electrical safety based on rules and dictionaries[J]. Application of Electronic Technology, 2022, 48(12):22-27.
[18] RABINER L R. A tutorial on hidden Markov models and selected applications in speech recognition[J]. Proceedings of the IEEE, 1989, 77(2):257-286.
[19] HUNT E B, MARIN J, STONE P J. Experiments in induction[J]. The American Journal of Psychology, 1966, 80(4). DOI:10.2307/1421207.
[20] 刘梓权,王慧芳. 基于知识图谱技术的电力设备缺陷记录检索方法[J]. 电力系统自动化,2018,42(14):158-164. LIU Ziqvan, WANG Huifang. The retrieval method of power equipment defect records based on knowledge graph technology[J]. Power System Automation, 2018, 42(14):158-164.
[21] 孙玉芹,肖静婷,王海超. 基于多模型融合的电力运检命名实体识别[J]. 科学技术与工程,2023,23(36):15545-15552. SUN Yuqin, XIAO Jingting, WANG Haichao. Named entity recognition for power operation and maintenance based on multi-model fusion[J]. Science Technology and Engineering, 2023, 23(36):15545-15552.
[22] 潘晖,赵岩,李麟,等. 电力客户需求高适配性关联抽取算法[J]. 太赫兹科学与电子信息学报,2023,21(10):1257-1262. PAN Hui, ZHAO Yan, LI Lin, et al. Highly adaptable association extraction algorithm for power customer demands[J]. Journal of Terahertz Science and Electronic Information, 2023, 21(10):1257-1262.
[23] CHO K, VAN Merriënboer B, GULCEHRE C, et al. Learning phrase representations using RNN encoder-decoder for statistical machine translation[C] //Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing(EMNLP 2014). Doha, Qatar. 2014:1724-1734.
[24] HOCHREITER S, SCHMIDHUBER J. Long short-term memory[J]. Neural computation, 1997, 9(8):1735-1780.
[25] VASWANI A, SHAZEER N, PARMAR N, et al. Attention is all you need[C] //Proceedings of the 2017 Conference on Advances in Neural Information Processing Systems. Long Beach: IEEE, 2017:5999-6009.
[26] 李嘉皓,熊威,龚康,等. 融合BERT-WWM与注意力机制的电力设备缺陷实体识别研究[J].电力学报,2024,39(2):126-135. LI Jiahao, XIONG Wei, GONG Kang, et al. Research on defect entity recognition of power equipment integrating BERT-WWM and attention mechanism[J]. Journal of Electric Power, 2024, 39(2):126-135.
[27] 纪鑫,武同心,余婷,等. 基于多任务学习的电力文本信息抽取[J]. 北京航空航天大学学报,2024,50(8):2461-2469. JI Xin, WU Tongxin, YU Ting, et al. Electric power text information extraction based on Multi-task learning[J]. Journal of Beihang University, 2024, 50(8):2461-2469.
[28] 田雪涵,董坤,赵剑锋,等. 基于增强优化预训练语言模型的电力数据实体识别方法[J]. 智慧电力,2024,52(6):100-107. TIAN Xuehan, DONG Kun, ZHAO Jianfeng, et al. Entity recognition method for power data based on enhanced and optimized pre-trained language models[J]. Smart Power, 2024, 52(6):100-107.
[29] BROWN T B, MANN B, RYDER N, et al. Language models are few-shotlearners[J]. Advances in Neural Information Processing Systems, 2020, 33:1877-1901.
[30] 王昕岩,陈建,邓曦. 基于大语言模型的命名实体识别方法研究[J]. 通信与信息技术,2024(6):109-112. WANG Xinyan, CHEN Jian, DENG Xi. Research on named entity recognition method based on large language model[J]. Communication and Information Technology, 2024(6):109-112.
[31] LIU Wenjing, ZHANG Suxiang, SUN Yang, et al. New energy power domain question-method extraction and soft clustering[C] //Proceedings of the 2023 9th International Conference on Communication and Information Processing(ICCIP 2023). Berlin: ACM, 2023:484-491.
[32] 郭素芹,郑建宁,陈坤,等. 基于知识图谱的变电站安全隐患动态分析方法[J]. 电力系统及其自动化学报,2021,33(12):125-133. GUO Suqin, ZHENG Jianning, CHEN Kun, et al. Dynamic analysis method of substation safety hazards based on knowledge graph[J]. Journal of Electric Power System and Its Automation, 2021, 33(12):125-133.
[33] 潘道成,邓卫民,蒋祝巍,等. 基于电网安全隐患知识图谱的智能诊断技术研究及应用[J]. 河北电力技术,2022,41(4):20-24. PAN Daocheng, CHENG Weimin, JIANG Zhuwei, et al. Research and application of intelligent diagnosis technology based on power grid safety hazard knowledge graph[J]. Hebei Electric Power Technology, 2022, 41(4):20-24.
[34] DEVLIN J, CHANG M W, LEE K, et al. Bert: pre-training of deep bidirectional transformers for language understanding[C] //Proceedings of the 2019 conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. Minneapolis: ACM, 2019, 1:4171-4186.
[35] CHI P H, CHUNG P H, WU T H. Audio ALBERT: a lite bert for self-supervised learning of audio representation[C] // Accepted by IEEE Spoken Language Technology Workshop, 2021:08575. https://doi.org/10.48550/arXiv.2005.08575.
[36] 郭宇. 面向电力安全作业实体关系抽取及图谱构建研究[D]. 昆明:昆明理工大学,2023. GUO Yu. Research onentity relationship extraction and graph construction for power safety operations[D]. Kunming:Kunming University of Science and Technology, 2023.
[37] 张滈辰,屈红军,牛雪莹,等. 融合注意力机制的电力集控安全隐患实体识别模型研究[J]. 自动化仪表,2023,44(10):55-59. ZHANG Haochen, QU Hongjun, NIU Xueying, et al. Research on the entity recognition model of power centralized control safety hazards integrating attention mechanism [J]. Automatic Instrument, 2023, 44(10):55-59.
[38] 戴玉艳,章瑶易,安佰龙,等. 结合自然语言处理与知识图谱的电力项目安全管理应用设计[J]. 自动化与仪器仪表,2024(8):198-201. DAI Yuyan, ZHANG Yaoyi, AN Bailong, et al. Application design of power project safety management combining natural language processing and knowledge graph [J]. Automation and Instrumentation, 2024(8):198-201.
[39] JI S X, PAN S, CAMBRIA E, et al. A survey on knowledge graphs: representation, acquisition, and applications[J]. IEEE transactions on neural networks and learning systems, 2022, 33(2):494-514.
[40] ZHONG Zexuan, CHEN Danqi. A frustratingly easy approach for entity and relation extraction[C] //Proceedings of the Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies(NAACL-HLT 2021), 2021:50-61. https://aclanthology.org/2021.naacl-main.5/
[41] YE Deming, LIN Yankai, LI Peng, et al. Packed levitated marker for entity and relation extraction[C] // Proceedings of the Annual Meeting of the Association for Computational Linguistics(ACL 2022), Dublin:Association for Computational Linguistics(ACL), 2022, 1:4904-4917.
[42] SHANG Yuming, HUANG Heyan, MAO Xianling. Onerel: joint entity and relation extraction with one module in one step[C] //Proceedings of the 36th AAAI Conference on Artificial Intelligence(AAAI 2022), 2022, 36:11285-11293. https://ojs.aaai.org/index.php/AAAI/article/view/21379.
[43] ZHENG Hengyi, WEN Rui, CHEN Xi, et al. PRGC: potential relation and global correspondence based joint relational triple extraction[C] //Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing(ACL-IJCNLP 2021), 2021, 1:6225-6235. https://aclanthology.org/2021.acl-long.486/.
[44] 李洪海. 基于大数据分析的电力行业安全生产隐患识别研究[J]. 现代职业安全, 2025(3):14-17. LI Honghai. Research on the identification of safety production hazards in the power industry based on big data analysis[J]. Modern Occupational Safety, 2025(3):14-17.
[45] 张燎原,李英娜. 基于三元组分类器的电力安全领域知识抽取[J]. 电力科学与工程,2024,40(6):11-18. ZHANG Liaoyuan, LI Yingna. Knowledge extraction in the field of power safety based on triple classifier[J]. Electric Power Science and Engineering, 2024, 40(6):11-18.
[46] WANG Yucheng, YU Bowen, ZHANG Yueyang, et al. TPLinker: single-stage joint extraction of entities and relations through token pair linking[C] //Proceedings of the 28th International Conference on Computational Linguistics(COLING 2020), 2020:1572-1582. https://arxiv.org/abs/2010.13415
[47] WEI Zhepei, SU Jianlin, WANG Yue, et al. A novel cascade binary tagging framework for relational triple extraction[C] //Proceedings of the Annual Meeting of the Association for Computational Linguistics(ACL 2020), 2020:1476-1488. https://aclanthology.org/2020.acl-main.136/
[48] 李媛,张志荣,杨晶,等. 基于深度学习算法构建电力安全知识学习系统[J]. 微型电脑应用,2023,39(4):164-167. LI Yuan, ZHANG Zhirong, YANG Jing, et al. A power safety knowledge learning system is constructed based on deep learning algorithms[J]. Microcomputer Applications, 2023, 39(4):164-167.
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