|本期目录/Table of Contents|

[1]冯永康,尹鑫伟,吴祥,等.基于文本挖掘的电网事故风险因素及致因识别*[J].中国安全生产科学技术,2026,22(3):110-117.[doi:10.11731/j.issn.1673-193x.2026.03.014]
　FENG Yongkang,YIN Xinwei,WU Xiang,et al.Identification of risk factors and causation in power grid accidents based on text mining[J].Journal of Safety Science and Technology,2026,22(3):110-117.[doi:10.11731/j.issn.1673-193x.2026.03.014]
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基于文本挖掘的电网事故风险因素及致因识别*

《中国安全生产科学技术》[ISSN:1673-193X/CN:11-5335/TB]

卷:
22
期数:
2026年3期
页码:
110-117
栏目:
安全工程技术
出版日期:
2026-03-30

文章信息/Info

Title:
Identification of risk factors and causation in power grid accidents based on text mining
文章编号:
1673-193X(2026)-03-0110-08
作者:
冯永康; 尹鑫伟; 吴祥; 代宝乾
(1.中国地质大学（北京） 工程技术学院，北京 100083；
2.北京市科学技术研究院城市安全与环境科学研究所，北京 100054)
Author(s):
FENG Yongkang;  YIN Xinwei;  WU Xiang;  DAI Baoqian
（1.School of Engineering and Technology,China University of Geosciences (Beijing),Beijing 100083,China;
2.Institute of Urban Safety and Environmental Science,Beijing Academy of Science and Technology,Beijing 100054,China）
关键词:
文本挖掘; 复杂网络; 电网安全; 风险识别; 事故致因
Keywords:
text mining;  complex network;  power grid security;  risk identification;  accident causation
分类号:
X943
DOI:
10.11731/j.issn.1673-193x.2026.03.014
文献标志码:
A
摘要:
为了提升新型电力系统背景下电网安全风险识别的准确性与系统性，解决传统分析方法在处理高维、非线性事故数据时的不足。采用词语频率-逆文档频率（term frequency-inverse document frequency，TF-IDF）算法与隐含狄利克雷分布（latent dirichlet allocation，LDA）主题模型进行文本挖掘，提取关键风险因素与致因主题，并运用复杂网络分析法构建风险关联网络。研究结果表明：识别出27个关键风险因素和10个核心致因主题；复杂网络分析进一步表明，管理缺失与高风险作业许可管理分别是2个网络中的核心，这证明管理体系缺陷是导致系统性风险的根本原因。研究结果可为电网企业提供1套数据驱动的风险识别与管控方法，所构建的综合分析框架亦可推广至其他工业领域，为系统性安全治理提供决策支持。
Abstract:
In order to improve the accuracy and systematic identification of power grid safety risks under the background of the new power system and to address the limitations of traditional analytical approaches in handling high-dimensional and nonlinear accident data,this study employs the term frequency-inverse document frequency (TF-IDF) algorithm and the latent dirichlet allocation (LDA) topic model for text mining.Key risk factors and causal themes are extracted,and a risk association network is constructed using complex network analysis.The results show that 27 key risk factors and 10 core causal themes are identified.Further complex network analysis indicates that management high-risk work permit management are the central nodes in the two networks,respectively,which demonstrates that deficiencies in the management system constitute the fundamental cause of systemic risk.These findings provide power grid enterprises with a data-driven approach for risk identification and control.The integrated analytical framework developed in this study can also be extended to other industrial sectors to support decision-making in systemic safety governance.

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备注/Memo

备注/Memo:
收稿日期： 2025-11-30
* 基金项目： 国家重点研发计划项目（2022YFC3005805）；北京市科技新星计划资助项目（20230484402）
作者简介： 冯永康，硕士研究生，主要研究方向为安全管理及安全风险识别。
通信作者： 尹鑫伟，博士研究生，助理研究员，主要研究方向为安全风险评估与应急管理。
更新日期/Last Update: 2026-03-31