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智能化建设对井下煤矿工人劳动强度的影响研究 被引量:2

Impact of intelligent construction on the workload of underground coal miners
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摘要 煤矿井下工作环境危险而艰苦,长期的重体力劳动和危险性工作对矿工的身体和心理健康产生许多负面影响。以煤矿智能化转型为背景,通过在山西某国有煤矿展开问卷调查,构建了智能设备应用对井下矿工工作负荷、疲惫感和疼痛感的影响研究框架;并利用层次回归以及Bootstrap方法,对最终回收的202份有效问卷进行了数据分析。研究结果表明,煤矿智能化建设通过减轻矿工的体力劳动强度和改善工作环境,显著降低了矿工的身体负荷,减轻了疲惫感和疼痛感,其中身体负荷改善起到中介作用。本研究可以为煤矿企业和管理者制定合理的智能化建设策略和人力资源管理政策提供借鉴,从而促进煤矿工人福祉和工作满意度的提升。 Working in underground coal mines is dangerous and arduous,with long-term physical labor and hazardous conditions having numerous negative effects on miners’physical and mental health.This study,conducted against the backdrop of the intelligent transformation of coal mines,used a questionnaire survey at a state-owned coal mine in Shanxi,China.It established a research framework to investigate the impact of intelligent equipment application on the workloads,fatigue,and pain experienced by underground miners.Data analysis was performed using hierarchical regression and Bootstrap method on 202 valid questionnaires.The results showed that the implementation of intelligent systems in coal mining significantly improved miners’physical burden by reducing physical labor and enhancing the working environment.This,in turn,reduced feelings of fatigue and pain,with the improvement in physical burden acting as a mediating factor.These findings can provide valuable guidance for coal mining companies and managers in formulating appropriate strategies for intelligent development and human resource management policies,ultimately promoting the well-being and job satisfaction of coal miners.
作者 王雷 张硕 徐泽鹏 Wang Lei;Zhang Shuo;Xu Zepeng(National Energy Group Institute of Technology and Economics,Beijing 102211,China;China University of Mining and Technology,Xuzhou 221116,China)
出处 《煤炭经济研究》 2023年第11期72-79,共8页 Coal Economic Research
基金 国家能源集团2021年度十大科技攻关项目(GJNY-21-25)
关键词 煤矿智能化 煤矿工人 劳动强度 层次回归 BOOTSTRAP方法 coal mine intelligent coal miners workload hierarchical regression Bootstrap method
作者简介 王雷(1981—),男,河南虞城人,硕士,教授级高级工程师,主要从事煤炭及相关领域政策、经济、企业战略规划等研究工作。E-mail:20026412@ceic.com
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