基于矿工大数据的不安全行为主题挖掘与语义分析

Topic mining and semantic analysis of unsafe behavior based on miner big data

为科学防控煤矿安全事故,深度挖掘不安全行为数据隐藏的信息和知识;基于Python算法、LDA主题模型和NetDraw工具,选取2017—2021年陕西省某大型煤矿集团的44 069条不安全行为数据进行分词处理、主题提取,绘制矿工不安全行为语义网络图并对矿工不安全行为语义网络的中心性进行分析;研究得出5个矿工不安全行为高频主题和3个矿工不安全行为高发地点。

In order to prevent and control coal mine safety accidents,we should deeply mine the hidden information and knowledge of unsafe behavior data.Based on Python,LDA and NetDraw,44069 pieces of unsafe behavior data of a large coal mine group in Shaanxi Province from 2017 to 2021 are selected for word segmentation and topic extraction,and the miner unsafe behavior semantic network diagram is drawn to analyze the centrality of miner unsafe behavior semantic network.5 miners’unsafe behavior high frequency topics and 3 miners’unsafe behavior high incidence sites are obtained.