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基于数据挖掘的中国煤矿事故研究中的方差分析。

Variance Analysis in China's Coal Mine Accident Studies Based on Data Mining.

机构信息

Institute of Geographic Sciences and Natural Resources Research, Beijing 100101, China.

University of Chinese Academy of Sciences, Beijing 100049, China.

出版信息

Int J Environ Res Public Health. 2022 Dec 9;19(24):16582. doi: 10.3390/ijerph192416582.

Abstract

The risk of coal mine accidents rises significantly with mining depth, making it urgent for accident prevention to be supported by both scientific analysis and advanced technologies. Hence, a comprehensive grasp of the research progress and differences in hotspots of coal mine accidents in China serves as a guide to find the shortcomings of studies in the field, promote the effectiveness of coal mine disaster management, and enhance the prevention and control ability of coal mine accidents. This paper analyzes Chinese and foreign literature based on data mining algorithms (LSI + Apriori), and the findings indicate that: (1) 99% of the available achievements are published in Chinese or English-language journals, with the research history conforming to the stage of Chinese coal industry development, which is characterized by "statistical description, risk evaluation, mechanism research, and intelligent reasoning". (2) Chinese authors are the primary contributors that lead and contribute to the continued development of coal mine accident research in China globally. Over 81% of the authors and over 60% of the new authors annually are from China. (3) The emphasis of the Chinese and English studies is different. Specifically, the Chinese studies focus on the analysis of accident patterns and causes at the macroscale, while the English studies concentrate on the occupational injuries of miners at the small-scale and the mechanism of typical coal mine disasters (gas and coal spontaneous combustion). (4) The research process in Chinese is generally later than that in English due to the joint influence of the target audience, industrial policy, and scientific research evaluation system. After 2018, the Chinese studies focus significantly on AI technology in deep mining regarding accident rules, regional variation analysis, risk monitoring and early warning, as well as knowledge intelligence services, while the hotspots of English studies remain unchanged. Furthermore, both Chinese and English studies around 2019 focus on "public opinion", with Chinese ones focusing on serving the government to guide the correct direction of public opinion while English studies focus on critical research of news authenticity and China's safety strategy.

摘要

煤矿事故风险随着开采深度的增加而显著上升,因此迫切需要科学分析和先进技术的支持来预防事故。因此,全面掌握中国煤矿事故的研究进展和热点差异,是发现该领域研究不足、促进煤矿灾害管理有效性、增强煤矿事故预防和控制能力的指导。本文基于数据挖掘算法(LSI+Apriori)对中外文献进行分析,结果表明:(1)可用成果的 99%发表在中文或英文期刊上,研究历史符合中国煤炭工业发展阶段,特点是“统计描述、风险评价、机理研究、智能推理”。(2)中国作者是全球引领和推动煤矿事故研究不断发展的主要贡献者,每年作者中有 81%以上,新作者中有 60%以上来自中国。(3)中、英文研究的侧重点不同,中文研究侧重于宏观尺度的事故模式和原因分析,而英文研究侧重于矿工职业伤害和典型煤矿灾害(瓦斯和煤自燃)的机理。(4)由于目标受众、产业政策和科研评价体系的共同影响,中国的研究过程通常比英语国家滞后。2018 年后,中国的研究重点主要集中在人工智能技术在深部开采中的应用,涉及事故规律、区域变化分析、风险监测预警以及知识智能服务,而英语研究的热点保持不变。此外,2019 年前后,中、英文研究都集中在“舆情”上,中文研究侧重于为政府服务,引导正确的舆情方向,而英文研究则侧重于新闻真实性和中国安全战略的批判性研究。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/63b9/9778805/614b6b0711e5/ijerph-19-16582-g001.jpg

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