基于24Model的动火作业事故致因文本挖掘

doi:10.16265/j.cnki.issn1003-3033.2025.03.0757

摘要/Abstract

摘要：

为探究工业动火作业事故的根源,提出一种基于“2-4”模型(24Model)的文本挖掘方法。首先,收集整理220篇动火作业事故报告,并作为数据集,构建基于来自变换器的双向编码器表征量(BERT)的24Model分类器,使用预训练模型训练和评估事故报告数据集,构建分类模型;然后,通过基于BERT的关键字提取算法(KeyBERT)和词频-逆文档频率(TF-IDF)算法的组合权重,结合24Model框架,建立动火作业事故文本关键词指标体系;最后,通过文本挖掘关键词之间的网络共现关系,分析得到事故致因之间的相互关联。结果显示,基于BERT的24Model分类器模型能够系统准确地判定动火作业事故致因类别,通过组合权重筛选得到4个层级关键词指标体系,其中安全管理体系的权重最大,结合共现网络分析得到动火作业事故的7项关键致因。

关键词: “2-4”模型(24Model), 动火作业, 事故致因, 文本挖掘, 指标体系

Abstract:

In order to systematically explore the root causes of industrial hot work accidents through a large amount of text data, a text mining method based on 24Model was proposed. Firstly, 220 hot work accident reports were collected and sorted as datasets, and a 24Model classifier based on Bidirectional Encoder Representations from Transformers (BERT) was constructed. The pre-trained model was used to train and evaluate the accident report dataset to construct a classification model. Then, through the combination weight of the Keyword extraction algorithm based on BERT (KeyBERT) and Term Frequency-Inverse Document Frequency (TF-IDF) algorithms, combined with the 24Model framework, a keyword index system for hot work accident text was established. Finally, the interrelationships between accident causes were obtained through the analysis of the network co-occurrence relationship between text-mining keywords. The results show that the BERT-based 24Model classifier model can systematically and accurately determine the causative categories of hot work accidents. The weight of the safety management system was the largest among the 4-level keyword index systems obtained through the combination of weights. Furthermore, 7 key causative factors of hot work accidents were obtained by combining them with the co-occurrence network analysis. This shows that 24Model can strengthen the interpretability of text mining results, which provides an important reference for the prevention and management of hot work accidents.

Key words: "2-4" model (24Model), hot work, accident causes, text mining, index system

中图分类号:

X928.02事故处理

牛茂辉, 李威君, 刘音, 王璐. 基于24Model的动火作业事故致因文本挖掘[J]. 中国安全科学学报, 2025, 35(3): 151-158.

NIU Maohui, LI Weijun, LIU Yin, WANG Lu. Text mining of causes of hot working accidents based on 24Model[J]. China Safety Science Journal, 2025, 35(3): 151-158.

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层级要素	解释词
安全文化	文化建设、思想、责任、价值观、投入、态度、理念、方针、指导思想
安全管理体系	许可证、培训、监管、规程、风险管控、隐患排查、管理体系、标准、资质、规章、制度、岗位责任
安全知识	教育、技能、知识、能力、资格、经验
安全意识	不重视、疏忽、逃避、动机
安全习惯	多次违章、多次违反
安全心理	冒险、压力、紧张、情绪
安全生理	疲劳、迟钝、年龄、性别、体重
不安全动作	未正确使用、违法采用、违规操作、违反操作规程、整改不力、违章作业、违章指挥
不安全状态	条件、环境、防护用品、工具、设备、附件、安全防护措施、标志

层级要素	P	R	F₁
安全文化	0.750 0	0.750 0	0.750 0
安全管理体系	0.918 4	0.882 4	0.900 0
安全知识	1.000 0	0.800 0	0.888 9
安全意识	0.750 0	0.857 1	0.800 0
安全习惯	1.000 0	0.500 0	0.667 0
安全心理	0.833 0	1.000 0	0.909 1
安全生理	1.000 0	1.000 0	1.000 0
不安全动作	0.666 7	0.909 1	0.769 2
不安全状态	0.727 3	0.761 9	0.744 2
噪声文本	0.761 9	0.695 7	0.727 3
A	0.820 1

关键词	W_K	W_T	W
安全防护措施	0.074 9	0.028 1	0.051 5
安全管理	0.05 94	0.026 5	0.042 9
安全制度不完善	0.054 6	0.022 5	0.038 6
教育和培训	0.039 9	0.028 3	0.034 1
安全意识淡薄	0.051 0	0.016 3	0.033 6
动火作业审批	0.041 7	0.013 3	0.027 5
动火作业许可证	0.038 2	0.016 1	0.027 2
安全生产主体责任	0.036 0	0.012 1	0.024 1
现场监护不力	0.027 3	0.020 0	0.023 7
违规动火作业	0.032 2	0.011 4	0.021 8

层级要素	分层权重	关键词	归一化权重	层级要素	分层权重	关键词	归一化权重
A	0.075 3	A₁	0.070 0	—	—	D₅	0.002 4
		A₂	0.002 0	E	0.0142	E₁	0.008 4
		A₃	0.002 3			E₂	0.002 8
		A₄	0.001 0			E₃	0.003 0
B	0.480 0	B₁	0.088 2	F	0.001 8	F₁	0.001 8
		B₂	0.048 6	G	0.005 8	G₁	0.005 8
		B₃	0.055 9	H	0.128 2	H₁	0.044 8
		B₄	0.056 6			H₂	0.033 1
		B₅	0.049 5			H₃	0.010 6
		B₆	0.040 5			H₄	0.018 6
		B₇	0.079 3			H₅	0.008 9
		B₈	0.039 3			H₆	0.012 2
		B₉	0.022 1	I	0.186 0	I₁	0.105 9
C	0.017 6	C₁	0.010 2			I₂	0.024 0
		C₂	0.003 2			I₃	0.018 5
		C₃	0.002 1			I₄	0.014 4
		C₄	0.002 1			I₅	0.010 2
D	0.091 1	D₁	0.069 2			I₆	0.007 9
		D₂	0.007 3			I₇	0.003 6
		D₃	0.006 2			I₈	0.001 5
		D₄	0.006 0

关键词	C_D	C_C	C_B	关键词	C_D	C_C	C_B
B₈	40	1.00	22.25	H₂	36	0.91	9.91
A₁	39	0.98	18.20	H₃	32	0.83	3.99
I₁	39	0.98	18.73	H₆	32	0.83	5.00
B₁	40	1.00	22.25	H₅	32	0.83	5.11
B₇	40	1.00	22.25	H₄	34	0.87	6.54
B₉	38	0.95	18.03	I₅	28	0.77	2.44
B₆	34	0.87	8.15	E₁	32	0.83	4.62
B₂	36	0.91	12.54	D₃	25	0.73	0.61
B₄	35	0.89	9.06	I₄	24	0.71	1.21
B₃	34	0.87	8.01	C₂	28	0.77	1.84
B₅	32	0.83	3.62	D₄	20	0.67	1.31
I₂	35	0.89	11.46	C₁	25	0.73	0.74
D₁	38	0.95	16.99	D₅	22	0.69	0.28
I₃	34	0.87	9.87	E₃	25	0.73	0.96
H₁	32	0.83	6.96	D₂	28	0.77	2.18