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Title
Topic mining and semantic analysis of unsafe behavior based on miner big data
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作者
李琰刘珍陈南希
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Author
LI Yan;LIU Zhen;CHEN Nanxi
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单位
西安科技大学管理学院，陕西西安710000
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Organization
School of Management, Xi’an University of Science and Technology
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摘要
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44069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。
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Abstract
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.
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关键词
矿工大数据不安全行为LDA语义网络图安全管理主题挖掘语义分析
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KeyWords
miner big data;unsafe behavior;LDA;semantic network graph;safety management;topic mining;semantic analysis
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基金项目(Foundation)
国家自然科学基金资助项目（51604216）；教育部人文社科资助项目（21YJA630050）；陕西省社会科学基金资助项目（2020R010）；西安市社会科学规划基金资助项目（GL14，22GL38）
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DOI
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引用格式
李琰，刘珍，陈南希. 基于矿工大数据的不安全行为主题挖掘与语义分析[J]. 煤矿安全，2023，54（9）：254−257
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Citation
LI Yan, LIU Zhen, CHEN Nanxi. Topic mining and semantic analysis of unsafe behavior based on miner big data[J]. Safety in Coal Mines, 2023, 54（9）: 254−257
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相关文章
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图表
煤矿安全 
2023年第09期 
