电力监控系统中网络安全大模型决策研究
电子技术应用
张伟1，李季凡2，丁朝晖1，刘腾1，乔一帆3
1.中国大唐集团科学技术研究总院有限公司；2.华北电力大学（保定）；3.浙江大学
摘要： 针对电力监控系统传统安全防护在攻击检测、溯源及未知威胁应对等方面的不足，融合知识图谱与大模型技术构建电力监控决策系统。通过标准化流程处理多源异构数据，运用实体识别与关系抽取构建知识图谱，结合网安专用大模型实现威胁智能检测分析。系统具备威胁检测、攻击溯源等核心功能，可实时监测、精准定位并提供运维建议。实践显示，其攻击检测准确率、未知攻击识别能力及溯源效率优于传统技术，漏洞检测平均准确率达95.5%，提升了系统安全性与决策智能化水平，为电力行业数字化转型提供技术支撑。
中图分类号：TP393.08 文献标志码：A DOI: 10.16157/j.issn.0258-7998.256801
中文引用格式： 张伟，李季凡，丁朝晖，等. 电力监控系统中网络安全大模型决策研究[J]. 电子技术应用，2026，52(5)：74-79.
英文引用格式： Zhang Wei，Li Jifan，Ding Zhaohui，et al. Research on cybersecurity large model decision-making in power monitoring systems[J]. Application of Electronic Technique，2026，52(5)：74-79.
中文引用格式： 张伟，李季凡，丁朝晖，等. 电力监控系统中网络安全大模型决策研究[J]. 电子技术应用，2026，52(5)：74-79.
英文引用格式： Zhang Wei，Li Jifan，Ding Zhaohui，et al. Research on cybersecurity large model decision-making in power monitoring systems[J]. Application of Electronic Technique，2026，52(5)：74-79.
Research on cybersecurity large model decision-making in power monitoring systems
Zhang Wei1，Li Jifan2，Ding Zhaohui1，Liu Teng1，Qiao Yifan3
1.China Datang Corporation Science and Technology Research Institute Co.， Ltd.；2.North China Electric Power University （Baoding）；3.Zhejiang University
Abstract： Aiming at the shortcomings of traditional security protection for power monitoring systems in attack detection, traceability and unknown threat response, this study constructs a power monitoring decision-making system by integrating knowledge graph and large model technologies. Multi-source heterogeneous data is processed through standardized processes, entity recognition and relationship extraction are used to build a knowledge graph, and a special large model for network security is combined to achieve intelligent threat detection and analysis. The system has core functions such as threat detection and attack traceability, and can monitor in real time, locate accurately and provide operation and maintenance suggestions. Practices show that its attack detection accuracy, unknown attack recognition ability and traceability efficiency are better than traditional technologies, with an average vulnerability detection accuracy of 95.5%. It improves the system security and decision-making intelligence level, providing technical support for the digital transformation of the power industry.
Key words : knowledge graph；large model；power monitoring system；cybersecurity；decision-making system
引言
随着电力行业数字化转型加速，电力监控系统作为智能电网的核心，其安全性关乎电力系统稳定运行和国家能源安全。然而，工控系统结构复杂、设备定制化程度高，传统安全防护手段在应对新型网络威胁时存在诸多不足，如攻击检测准确率低、溯源能力弱、对未知威胁检测能力弱等问题[1]。知识图谱[2]和大模型技术[3]的发展为解决这些问题提供了新途径。知识图谱可整合多源数据，挖掘网络威胁与系统要素的关联；大模型凭借强大的语义理解和模式识别能力，能精准分析异常行为。将两者结合应用于电力监控系统，构建决策系统，有助于提升系统的安全性和决策效率，保障电力行业的可靠运行。
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作者信息：
张伟1，李季凡2，丁朝晖1，刘腾1，乔一帆3
（1.中国大唐集团科学技术研究总院有限公司，北京 100040；
2.华北电力大学（保定），河北 保定 071051；
3.浙江大学，浙江 杭州310058）
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