Study on coal mine risk hidden danger evaluation system

doi:10.16265/j.cnki.issn1003-3033.2023.S2.0027

Abstract

Abstract:

The untimely disposal of the hidden danger, the weak correlation among the various hidden dangers, and the insufficient quantification between the hidden danger and the accident risk in coal mines are major problems to be solved. First, by collecting the data of the ″trinity″ system of coal mines, semantic recognition was performed, and the disaster accident chain in coal mines and the risk evaluation model were established to quantify and analyze the risk in coal mines. Then, a risk evaluation system for coal mines was built, including comprehensive risk dynamic evaluation, professional risk dynamic evaluation, hidden danger time distribution, hidden danger location distribution, self-checking risk evaluation coincidence analysis, repeated hidden danger data analysis, hidden danger importance analysis, and hidden danger year and keyword analysis of coal mines. The results show that the quantitative expression, visual display, and pre-warning of all kinds of disasters and risks in coal mines can be realized by the semantic recognition of hidden risks in coal mines, providing technical support for reducing the accident risk and ensuring timely closed-loop disposal of hidden dangers.

Key words: risk hidden danger, ″trinity″, semantic recognition, accident chain, coal mines disaster

CLC Number:

X936

LI Lin, ZHANG Jinpeng, FU Ensan, LIU Guangwei. Study on coal mine risk hidden danger evaluation system[J]. China Safety Science Journal, 2023, 33(S2): 1-6.

Figures/Tables 11

Fig.1

Tab.1

Tab.2

Fig. 2

Tab.3

Tab.4

Tab.5

Tab.6

Fig.3

Fig.4

Tab.7

References 14

[1]	付恩三, 白润才, 刘光伟, 等. “十三五”期间我国煤矿事故特征及演变趋势分析[J]. 中国安全科学学报, 2022, 32(12):88-94. doi: 10.16265/j.cnki.issn1003-3033.2022.12.0076
	FU Ensan, BAI Runcai, LIU Guangwei, et al. Analysis on characteristics and evolution trend of coal mine accidents in our country during ″13^th five-year″ plan period[J]. China Safety Science Journal, 2022, 32(12):88-94. doi: 10.16265/j.cnki.issn1003-3033.2022.12.0076
[2]	付恩三, 刘光伟, 白润才, 等. 大数据时代矿山协同监管监察新思路[J]. 中国安全科学学报, 2023, 33(8):39-44. doi: 10.16265/j.cnki.issn1003-3033.2023.08.2035
	FU Ensan, LIU Guangwei, BAI Runcai, et al. Exploration and research of mine multi-element cooperative supervision new thinking in era of big data[J]. China Safety Science Journal, 2023, 33(8):39-44. doi: 10.16265/j.cnki.issn1003-3033.2023.08.2035
[3]	谭章禄, 陈晓, 宋庆正, 等. 基于文本挖掘的煤矿安全隐患分析[J]. 安全与环境学报, 2017, 17(4):1262-1 266.
	TAN Zhanglu, CHEN Xiao, SONG Qingzheng, et al. Analysis for the potential hazardous risks of the coal mines based on the so-called text mining[J]. Journal of Safety and Environment, 2017, 17(4):1262-1 266.
[4]	游兰, 曾晗, 韩凡宇, 等. 基于BERT-BiGRU集成学习的情感语义识别[J]. 计算机技术与发展, 2023, 33(5):159-166.
	YOU Lan, ZENG Han, HAN Fanyu, et al. Sentiment semantic recognition based on BERT-BiGRU ensemble learning[J]. Computer Technology and Development, 2023, 33(5):159-166.
[5]	李蕾, 许晶, 龚媛, 等. 基于全信息自然语言理解的语音识别后文本处理[J]. 北京邮电大学学报, 2006, 29(增2): 192-196.
	LI Lei, XU Jing, GONG Yuan, et al. CI-NLU based text processing after speech recognition[J]. Journal of Beijing University of Posts and Telecommunications, 2006, 29(S2): 192-196. doi: 10.13190/jbupt.2006s2.192.lil
[6]	周潇, 许银彪, 史益. 基于深度学习与语义挖掘的技术创新组合识别与追踪[J]. 图书情报工作, 2022, 66(10): 33-44. doi: 10.13266/j.issn.0252-3116.2022.10.003
	ZHOU Xiao, XU Yinbiao, SHI Yi. Identifing and tracing technological innovation combination based on deep learning and semantic mining[J]. Library and Information Service, 2022, 66(10): 33-44. doi: 10.13266/j.issn.0252-3116.2022.10.003
[7]	高明霞, 刘椿年. 基于约束的自然语言问题到 OWL 的语义映射方法研究[J]. 电子学报, 2007, 35(8): 1 598.
	GAO Mingxia, LIU Chunnian. A constraints-based semantic mapping method from natural language questions to OWL[J]. Acta Electronica Sinica, 2007, 35(8): 1 598.
[8]	黄红, 陈翀, 张婧莹. 科技文献内容语义识别研究综述[J]. 情报学报, 2022, 41(9):991-1 002.
	HUANG Hong, CHEN Chong, ZHANG Jingying. Review on identifying the semantics of scientific literature content[J]. Journal of the China Society for Scientific and Technical Information, 2022, 41(9):991-1 002.
[9]	刘妍. 基于语义挖掘的文本情感分析[D]. 成都: 电子科技大学, 2023.
	LIU Yan. Text Sentiment analysis based on semantic mining[D]. Chengdu: University of Electronic Science and Technology of China, 2023.
[10]	赵宏, 傅兆阳, 王乐. 基于特征融合的中文文本情感分析方法[J]. 兰州理工大学学报, 2022, 48(3):94-102.
	ZHAO Hong, FU Zhaoyang, WANG Le. SentimentanalysisofChinesetextbasedonfeaturefusion[J]. Journal of Lanzhou University of Technology, 2022, 48(3):94-102.
[11]	王颖. 一种改进卷积神经网络的语义情感识别应用研究[J]. 软件, 2019, 40(2):67-69.
	WANG Ying. Application research on semantic emotion recognition based on improved convolutional neural network[J]. Computer Engineering & Software, 2019, 40(2):67-69.
[12]	段江丽, 胡新. 自然语言问答中的语义关系识别[J]. 山东大学学报:工学版, 2020, 50(3):1-7.
	DUAN Jiangli, HU Xin. Semantic relation recognition for natural language question answering[J]. Journal of Shandong University:Engineering Science, 2020, 50(3):1-7.
[13]	付恩三, 刘光伟, 赵浩, 等. 智慧露天矿山总体框架及关键技术研究[J]. 工况自动化, 2021, 47(8):27-32.
	FU Ensan, LIU Guangwei, ZHAO Hao, et al. Research on the overall framework and key technologies of intelligent open-pit mines[J]. Industry and Mine Automation, 2021, 47(8):27-32.
[14]	付恩三, 刘光伟, 白润才, 等. 智能露天矿山评价体系研究[J]. 煤炭工程, 2022, 54(5):1-8.
	FU Ensan, LIU Guangwei, BAI Runcai, et al. Evaluation system of intelligent open-pit mine[J]. Coal Engineering, 2022, 54(5):1-8.

模型	数据准确率/%
模型	20	40	80	100
SVM	25.23	38.89	84.22	98.29
贝叶斯	17.32	33.34	78.97	91.11

原事故树事件符号	原图形形状	新灾害时空事故链事件符号	事件类型
矩形符号			顶端事件中间事件
圆形符号			基本因素
半圆符号			正常事件

管控状态	低风险	一般风险	较大风险	重大风险
管控有效	1	2	3	4
存在缺陷	2	4	6	8
存在漏洞	3	6	9	12
管控失效	4	8	12	16

序号	概率系数	发生概率
1	(0,0.05]	发生概率极小,几乎不发生
2	[0.05, 0.10)	很少发生
3	[0.10,0.20)	容易或偶然发生
4	[0.20,0.30)	很容易发生,相当可能发生
5	[0.30,0.35]	频繁发生、经常发生

序号	隐患因素数	危险系数	概率系数
1	工作面上隅角瓦斯浓度超限	8	0.5
2	电气火花及井下明火	8	0.3
3	井下违章爆破	8	0.2
4	风筒漏风	4	0.4
5	风机故障	8	0.05