煤矿事故原因智能分析方法研究与应用  

Research and application of intelligent methods for analyzing the causes of coal mining accidents

作　　者：张江石[1,2] 李泳暾 吴静茹 任晓锋 潘雨 张琪[1] ZHANG Jiangshi;LI Yongtun;WU Jingru;REN Xiaofeng;PAN Yu;ZHANG Qi(School of Emergency Management and Safety Engineering,China University of Mining and Technology-Beijing,Beijing 100083,China;State Key Laboratory for Fine Exploration and Intelligent Development of Coal Resources,China University of Mining and Technology-Beijing,Beijing 100083,China;School of Safety Science,Tsinghua University,Beijing 100084,China)

机构地区：[1]中国矿业大学(北京)应急管理与安全工程学院,北京100083 [2]中国矿业大学(北京)煤炭精细勘探与智能开发全国重点实验室,北京100083 [3]清华大学安全科学学院,北京100084

出　　处：《清华大学学报(自然科学版)》2025年第3期555-568,共14页Journal of Tsinghua University(Science and Technology)

基　　金：国家自然科学基金项目(72204139);中国博士后科学基金项目(2023T160371)。

摘　　要：有效的事故致因分析是预防煤矿事故发生的有效途径,由于人工分析事故时受人员主观影响较强,且面对海量的事故和风险文本数据,人工分析存在局限,因此该文针对煤矿领域,基于集成命名实体识别(named entity recognition,NER)、语义依存分析(semantic dependency parsing,SDP)、文本分类(text classification,TC)和事故致因“2-4”模型(24Model),提出了一种煤矿事故原因智能分析方法。该文首先利用NER识别事故文本中的主要实体信息,结合SDP识别实体信息之间的语义关系,提取个体不安全动作和组织原因的文本表示模式;其次,利用TC构建了个体能力原因分类模型,用于识别个体能力方面的因素;最后,开发了相关应用程序,将所提方法应用于现场事故案例分析和学习。研究结果表明:该文构建的NER和TC模型精确率均较高,结合SDP能自动根据24Model分析和梳理事故原因,并识别动作的发出者、作业工序和物资设备等信息。该文所提方法可促进事故致因理论在煤矿企业的应用,提升事故案例分析和学习的有效性,从而预防相关事故发生。[Objective]An effective causal analysis of accidents is essential for learning from and preventing coal mine accidents.Manual analysis of accidents is strongly influenced by the subjectivity of the personnel involved and becomes inefficient for analyses involving large volumes of accident and risk text data.Although considerable research has been conducted in the area of accident text mining,most studies directly apply data mining techniques to extract accident information and factors from texts without considering accident causation theories.This approach leads to results that lack systematic and logical coherence.[Methods]To address the aforementioned issues,this paper proposes a method for the intelligent identification of accident causes in the coal mining sector.This method integrates entity recognition,semantic dependency analysis,text classification,and the accident causation“2-4”model(24Model).Specific implementation steps for this method are also provided.Accident causation theory is crucial for ensuring the effectiveness and scientific validity of accident analysis.This paper introduces the 24Model as a theoretical basis for accident cause identification,and the advantages of the model in the intelligent analysis of accident causes are highlighted.Entity recognition technology is employed to identify key entity information in accident texts,including information on personnel,organizational structures,accidents,abnormal characteristics,values,safety management,building facilities,environments,equipment and materials,safety policies,procedural documents,and operational processes.To effectively identify this information,this paper integrates the bidirectional encoder representations from transformers(BERT)-bidirectional long short-term memory(BiLSTM)-conditional random fields(CRF)model and trains the combined model using 660 accident texts.This paper utilizes semantic dependency analysis technology to identify the semantic relationships among entity information.Text representation patterns were extract

关 键 词：事故致因分析 命名实体识别 语义依存分析 文本分类 事故致因“2-4”模型 

分 类 号：X936[环境科学与工程—安全科学]