在许多调查报告中已经揭示,人和组织因素(HOF)是煤矿事故的根本原因。然而,煤矿各种事故原因,缺乏对特定HOF内因果关系的系统分析可能导致有缺陷的事故预防措施。因此,本研究以数据驱动概念为中心,选取2011-2020年883份煤矿事故报告作为原始数据,发现具体HOF的影响路径。首先,通过文本分割提取了55种具有煤矿事故特征的表现形式。第二,根据他们自己的属性,所有表现都被映射到人为因素分析和分类系统(HFACS),在中国煤炭开采业中形成5类改良的HFACS-CM框架,19个小类,42个不安全因素。最后,应用Apriori关联算法发现外部影响之间的因果关联规则,组织影响,不安全的监督,不安全行为和直接不安全行为的前提条件,在HAFCS-CM中暴露四个明显的事故原因“轨迹”。本研究有助于建立分析煤矿事故原因的系统因果模型,有助于直接客观地形成相应的风险防范措施。
It has been revealed in numerous investigation reports that human and organizational factors (HOFs) are the fundamental causes of coal mine accidents. However, with various kinds of accident-causing factors in coal mines, the lack of systematic analysis of causality within specific HOFs could lead to defective accident precautions. Therefore, this
study centered on the data-driven concept and selected 883 coal mine accident reports from 2011 to 2020 as the original data to discover the influencing paths of specific HOFs. First, 55 manifestations with the characteristics of the coal mine accidents were extracted by text segmentation. Second, according to their own attributes, all manifestations were mapped into the Human Factors Analysis and Classification System (HFACS), forming a modified HFACS-CM framework in China\'s coal-mining industry with 5 categories, 19 subcategories and 42 unsafe factors. Finally, the Apriori association algorithm was applied to discover the causal association rules among external influences, organizational influences, unsafe supervision, preconditions for unsafe acts and direct unsafe acts layer by layer, exposing four clear accident-causing \"trajectories\" in HAFCS-CM. This
study contributes to the establishment of a systematic causation model for analyzing the causes of coal mine accidents and helps form corresponding risk prevention measures directly and objectively.