Establishment of a Key Hidden Danger Factor System for Electric Power Personal Casualty Accidents Based on Text Mining

Abstract

Based on actual safety management difficulties and needs, this paper aims to screen and extract the key accident potential factors of personal injuries and deaths within the electric power industry to provide a reference for electric power companies’ accident prevention effort. First, this document sorts out and analyzes all of the causes and influencing elements that may lead to the occurrence of electric personal injuries and deaths, based on which rough accident potential factors are initially identified and combined with the definition of accident potentials. Second, this paper mines and analyzes relevant accident report texts using text-mining technologies such as term count, word cloud, and term frequency–inverse document frequency (TF-IDF), and thus a system of key accident potential factors for personal injuries and deaths within the electric power industry, including three key factors (human, material, and management), is finally constructed. Workers’ habitual violation behavior, in particular, has a larger risk than other key accident potential components, implying that additional steps should be made to eradicate this type of critical accident potential in time.

Keywords

• text mining
• word cloud
• TF-IDF
• safety management
• personal injuries and deaths
• potential factors