BERT

Accident analysis is a crucial aspect of accident prevention, and natural language processing (NLP) techniques can efficiently be applied to analyze the causes of accidents. However, existing analysis methods primarily rely on text clustering and lack the application of accident causation theories, leading to a lack of specific accident cause analysis results. Combining text classification techniques with accident causation theories is an effective approach to address this issue. In this study, we integrated text classification techniques with accident causation theories and utilized coal mine gas explosion accidents as an example. We constructed a corpus, trained a BERT model, and evaluated its performance to obtain a text classification model for accident cause analysis. The results indicated that the BERT model-based text classification algorithm had an accuracy and macro-average F1 value of 0.9878 and 0.7792, respectively, significantly outperforming the control model. The application of this approach demonstrated that combining accident causation theories with text classification techniques for accident cause analysis can improve the efficiency of accident analysis while ensuring the richness of details in analyzing accident causes. By efficiently analyzing a large number of accident cases, this approach can provide a data foundation for data-driven accident prevention and technical support for integrated accident prevention.

事故分析是事故预防的关键环节，自然语言处理（Natural Language Processing，NLP）技术可高效应用于事故致因分析。然而，现有分析方法主要依赖文本聚类，且未结合事故致因理论开展研究，导致无法获取针对性的事故致因分析结果。将文本分类技术与事故致因理论相结合，是解决该问题的有效路径。本研究将文本分类技术与事故致因理论相结合，以煤矿瓦斯爆炸事故为研究案例，构建了事故文本语料库，训练了BERT模型并对其性能进行评估，最终得到用于事故致因分析的文本分类模型。实验结果表明，基于BERT模型的文本分类算法准确率达0.9878，宏平均F1值为0.7792，性能显著优于对照模型。该方法的应用结果表明，将事故致因理论与文本分类技术相结合开展事故致因分析，既可提升事故分析效率，又能保障事故致因分析细节的丰富性。通过高效分析海量事故案例，该方法可为数据驱动的事故预防工作提供数据基础，同时为综合事故预防体系提供技术支撑。

搜集汇总

背景与挑战

背景概述

该数据集源于一项研究，旨在将事故致因理论与文本分类技术结合，以BERT模型分析煤矿瓦斯爆炸事故原因。研究通过构建语料库并训练模型，取得了较高的准确率和F1值，从而提升了事故分析效率，为数据驱动的事故预防提供了支持。