基于NLP的煤矿事故原因分类研究

doi:10.16265/j.cnki.issn1003-3033.2023.06.1416

摘要/Abstract

摘要：

为有效提升分析和处理煤矿事故文本的效率,融合自然语言处理(NLP)技术与事故致因模型,构建一个自动化的事故原因分类框架。首先以事故致因“2-4”模型(24Model)为事故分类依据,分析87份煤矿事故调查报告,得到煤矿事故原因分类框架,构建每类事故原因的语料库;然后利用NLP技术分别处理语料库中各类原因文本,将其用于训练fastText模型,自动识别事故原因文本并分类;最后对比分析fastText模型与TextCNN等其他3种经典模型的分类效果。结果表明:共得到21类事故原因和6 684条训练语料,训练后的fastText模型对煤矿事故原因分类的识别正确率能够达到98.92%,综合性能优于其他3种分类模型。基于24Model和NLP技术开发的事故文本挖掘系统,能够快速分析处理事故文本信息,进一步细化事故调查报告中的原因,便于进行事故案例学习和统计分析。

关键词: 自然语言处理(NLP), 事故原因分类, “2-4”模型(24Model)

Abstract:

In order to improve the efficiency of analyzing and processing coal mine accident text effectively, NLP and accident cause models were used for building an automatic classification framework of accident cause. Based on 24Model, 87 typical mine accident investigation reports were analyzed, and a framework of mine accident cause classifications was obtained. A corpus was constructed for each type of accident cause constructed. The NLP was used for processing each type of cause text of the corpus and training the fastText model to realize the automatic recognition and classification of accident cause text. The method proposed was compared with TextCNN and the other two classical models. The results show that a total of 21 types of accident causes and 6 684 training corpus are obtained, the accuracy of fastText after training can reach 98.92%, and the comprehensive performance is better than the other three methods. The accident text mining system is developed based on 24Model and NLP, which can analyze and process the accident text information quickly and further detail the cause of the accident investigation report, which is convenient for the case study and statistical analysis.

Key words: natural language processing(NLP), classification of accidents causes, "2-4″model(24Model), fastText

张江石, 李泳暾, 冒香凝, 胡馨月, 潘雨, 王梓伊. 基于NLP的煤矿事故原因分类研究[J]. 中国安全科学学报, 2023, 33(6): 20-26.

ZHANG Jiangshi, LI Yongtun, MAO Xiangning, HU Xinyue, WANG Ziyi. Study on classification of coal mine accident causes based on NLP[J]. China Safety Science Journal, 2023, 33(6): 20-26.

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24Model事故原因分类体系			细化后的分类
组织内因素	直接原因	不安全动作	1.违章指挥
			2.违规作业
			3.操作失误
		不安全物态	4.不安全物态
	间接原因	安全知识不足	5.安全知识不足
		安全意识不高	6.安全意识不高
		安全习惯不佳	7.安全习惯不佳
		心理状态不佳	8.心理状态不佳
		生理状态不佳	9.生理状态不佳
	根本原因	安全管理体系欠缺	10.未建立体系文件
			11.体系文件不健全
			12.设计缺陷
			13.配套设施不齐全
			14.人员组织不合理
			15.制度落实不到位
			16.安全培训不到位
			17.隐患排查不到位
	根源原因	安全文化欠缺	18.安全文化欠缺
组织外因素			19.政府部门疏于监管
			20.第三方问题
			21.其他因素

模型	A /%	P /%	R /%	F₁ /%	运行时间/s
fastText	98.92	98.58	99.09	98.84	1.9
SVM	98.82	99.43	93.26	94.60	8.28
NBC	98.49	99.43	93.75	94.78	0.17
TextCNN	98.49	98.82	96.63	97.49	176.83