Big data analysis and visualization of potential hazardous risks of the mine based on text mining

GUO Dui-ming; LI Guo-qing; HU Nai-lian; HOU Jie

doi:10.13374/j.issn2095-9389.2020.10.23.004

Volume 44 Issue 3

Jan. 2022

Turn off MathJax

Article Contents

Article Navigation > Chinese Journal of Engineering > 2022 > 44(3): 328-338

GUO Dui-ming, LI Guo-qing, HU Nai-lian, HOU Jie. Big data analysis and visualization of potential hazardous risks of the mine based on text mining[J]. Chinese Journal of Engineering, 2022, 44(3): 328-338. doi: 10.13374/j.issn2095-9389.2020.10.23.004

Citation:

GUO Dui-ming, LI Guo-qing, HU Nai-lian, HOU Jie. Big data analysis and visualization of potential hazardous risks of the mine based on text mining[J]. Chinese Journal of Engineering, 2022, 44(3): 328-338. doi: 10.13374/j.issn2095-9389.2020.10.23.004

Citation:

PDF( 1587 KB)

Big data analysis and visualization of potential hazardous risks of the mine based on text mining

doi: 10.13374/j.issn2095-9389.2020.10.23.004

GUO Dui-ming^{1, 2},
LI Guo-qing^{1, 2
,
,},
HU Nai-lian^{1, 2},
HOU Jie^{1, 2}

1.
School of Civil and Resource Engineering, University of Science and Technology Beijing, Beijing 100083, China
2.
Key Laboratory of High-Efficient Mining and Safety of Metal Mines, Ministry of Education, Beijing 100083, China

More Information

Corresponding author: E-mail: qqlee@ustb.edu.cn
Received Date: 2020-10-23
Available Online: 2021-02-08
Publish Date: 2022-01-08

Abstract

Abstract

Compared with other production industries, metal mine is recognized as a high accident rate and the highest casualty rate due to the bad working environment. Therefore, safety production is the key concern of mining enterprises. With the attention of enterprises to safety problems and the increasing improvement of mine safety management system, many mines have established secure big data platform to effectively manage production and ensure the safety of underground operation, receiving the safety hazard information from daily safety inspection into the platform. However, due to the data of security risks are unstructured short texts with the operation of the enterprise, including the data recorded in the platform presents the characteristics of complex data content, large data scale, and non-standard data records. Moreover, due to the lack of an effective text analysis model, a small part of the security risk data is only used for simple analysis such as report analysis and data statistics, whereas more data is stored in a secure big data platform. Thus, the data did not play a guiding role in production, resulting in a waste of these valuable data resources. In order to explore the internal relationship between hidden danger data and the rule of hidden danger occurrence, based on big data analysis technology, this paper constructed a multi-dimensional analysis model of mine safety hidden danger. We analyzed the distribution law of hidden danger in two dimensions of time and space, used the topic mining model to classify hidden danger information, and obtained 13 hidden danger topics, using association rules to mine hidden danger. The model explores the internal relationship between different hidden dangers and uses an R programming language to visualize the above results. The results made full use of the mine hidden danger data and avoided the waste of data resources through the analysis and research of the hidden danger with a certain guiding value for preventing mine accidents.
- mine safety,
- text mining,
- data of hidden danger,
- data analysis,
- data visualization

FullText(HTML)

References(29)

References

[1]	畢洪濤, 李國清. 黃金礦山安全生產閉環管理體系研究. 黃金, 2014, 35(8):1 doi: 10.11792/hj20140801 Bi H T, Li G Q. Research on the closed circuit management system of safety production in gold mines. Gold, 2014, 35(8): 1 doi: 10.11792/hj20140801
[2]	孟現飛, 李克業, 劉飛. 基于3級嵌套安全管理模式的煤礦安全風險預控研究. 中國安全科學學報, 2013, 23(4):102 Meng X F, Li K Y, Liu F. Study on coal mine safety risk pre-control based on three-levels nested management mode. China Saf Sci J, 2013, 23(4): 102
[3]	趙東風, 申玉琪, 趙志強, 等. 基于事故發展與控制的隱患分級方法. 中國安全科學學報, 2012, 22(4):71 doi: 10.3969/j.issn.1003-3033.2012.04.013 Zhao D F, Shen Y Q, Zhao Z Q, et al. Risk classification method for accident potential based on development and control measures of accident. China Saf Sci J, 2012, 22(4): 71 doi: 10.3969/j.issn.1003-3033.2012.04.013
[4]	Martin E B, Morris A J. Non-parametric confidence bounds for process performance monitoring charts. J Process Control, 1996, 6(6): 349 doi: 10.1016/0959-1524(96)00010-8
[5]	Martin E B, Morris A J. An overview of multivariate statistical process control in continuous and batch process performance monitoring. Trans Inst Meas Control, 1996, 18(1): 51 doi: 10.1177/014233129601800107
[6]	Dunia R, Qin S J, Edgar T F. Identification of faulty sensors using principal component analysis. AIChE J, 1996, 42(10): 2797 doi: 10.1002/aic.690421011
[7]	李季, 翟勃, 汪群, 等. 煤礦重大災害辯識和控制信息系統 // 創新型煤炭企業發展與信息化高峰論壇論文集. 蘭州, 2010: 56 Li J, Cui B, Wang Q, et al. Information system for identification and control of major coal mine disasters // Innovative coal enterprise development and informatization Summit Forum. Lanzhou, 2010: 56
[8]	秦文靜. 煤礦瓦斯爆炸危險源三維辨識研究及應用[學位論文]. 太原: 太原理工大學, 2015 Qin W J. The Research and Application of the Three-Dimensional Method in Coalmine Gas Explosion Hazard Identifiction [Dissertation]. Taiyuan: Taiyuan University of Technology, 2015
[9]	張寶隆, 王向前, 何葉榮, 等. 基于本體的煤礦事故隱患辨識排查系統構建. 煤礦安全, 2018, 49(5):239 Zhang B L, Wang X Q, He Y R, et al. Construction of safety hazard identification and investigation system of coal mine based on ontology. Saf Coal Mines, 2018, 49(5): 239
[10]	馬小平, 代偉. 大數據技術在煤炭工業中的研究現狀與應用展望. 工礦自動化, 2018, 44(1):50 Ma X P, Dai W. Research status and application prospect of big data technology in coal industry. Ind Mine Autom, 2018, 44(1): 50
[11]	孫繼平. 煤礦事故分析與煤礦大數據和物聯網. 工礦自動化, 2015, 41(3):1 Sun J P. Accident analysis and big data and Internet of Things in coal mine. Ind Mine Autom, 2015, 41(3): 1
[12]	譚章祿, 陳曉, 宋慶正, 等. 基于文本挖掘的煤礦安全隱患分析. 安全與環境學報, 2017, 17(4):1262 Tan Z L, Chen X, Song Q Z, et al. Analysis for the potential hazardous risks of the coal mines based on the socalled text mining. J Saf Environ, 2017, 17(4): 1262
[13]	錢宇虹. 數據挖掘算法在瓦斯安全預測中的應用. 煤炭技術, 2018, 37(5):207 Qian Y H. Application of data mining algorithm in gas safety prediction. Coal Technol, 2018, 37(5): 207
[14]	石記斌, 石記紅. 關聯分析數據挖掘在煤礦瓦斯安全監測預警中的應用. 能源與環保, 2017, 39(8):1 Shi J B, Shi J H. Application of correlation analysis on data mining in coal mine gas safety monitoring and early warning. China Energy Environ Prot, 2017, 39(8): 1
[15]	雷煜斌, 陳兆波, 曾建潮, 等. 基于關聯規則的煤礦瓦斯事故致因鏈研究. 煤礦安全, 2016, 47(8):240 Lei Y B, Chen Z B, Zeng J C, et al. Research on causal chain of coal mine gas accidents based on association rule. Saf Coal Mines, 2016, 47(8): 240
[16]	劉海濱, 李春賀. 智慧礦山職業健康安全監管信息系統研究. 煤炭科學技術, 2019, 47(3):87 Liu H B, Li C H. Research on occupational health and safety management information system in intelligent mine. Coal Sci Technol, 2019, 47(3): 87
[17]	Henriques V, Malekian R. Mine safety system using wireless sensor network. IEEE Access, 2016, 4: 3511 doi: 10.1109/ACCESS.2016.2581844
[18]	周雪田. 礦山安全監測預警與綜合管理信息系統. 世界有色金屬, 2017(19):26 Zhou X T. Mine safety monitoring, early warning and integrated management information system. World Nonferrous Met, 2017(19): 26
[19]	張大偉. 基于OLAM的煤礦企業安全隱患趨勢分析. 煤炭工程, 2015, 47(5):139 doi: 10.11799/ce201505045 Zhang D W. Analysis of coal mine safety hidden danger trends based on OLAM. Coal Eng, 2015, 47(5): 139 doi: 10.11799/ce201505045
[20]	姚慶國, 趙麗霞, 張學睦. 煤礦安全管理信息系統模糊綜合評價模型. 礦業安全與環保, 2017, 44(6):120 doi: 10.3969/j.issn.1008-4495.2017.06.027 Yao Q G, Zhao L X, Zhang X M. Fuzzy comprehensive evaluation model of coal mine safety management information system. Min Saf Environ Prot, 2017, 44(6): 120 doi: 10.3969/j.issn.1008-4495.2017.06.027
[21]	Cheng J W, Yang S Q. Data mining applications in evaluating mine ventilation system. Saf Sci, 2012, 50(4): 918 doi: 10.1016/j.ssci.2011.08.003
[22]	唐曉波, 向坤. 基于LDA模型和微博熱度的熱點挖掘. 圖書情報工作, 2014, 58(5):58 Tang X B, Xiang K. Hotspot mining based on LDA model and microblog heat. Libr Inf Serv, 2014, 58(5): 58
[23]	Blei D M, Ng A Y, Jordan M I, Lafferty J. Latent Dirichlet Allocation. J Mach Learn Res, 2003, 3: 993
[24]	馬廷斌, 徐芬. 關聯規則挖掘中Apriori算法的研究與改進. 蘭州工業高等專科學校學報, 2010, 17(1):13 Ma T B, Xu F. Research and improvement for apriori algorithm of association rule mining. J Lanzhou Polytech Coll, 2010, 17(1): 13
[25]	崔妍, 包志強. 關聯規則挖掘綜述. 計算機應用研究, 2016, 33(2):330 doi: 10.3969/j.issn.1001-3695.2016.02.002 Cui Y, Bao Z Q. Survey of association rule mining. Appl Res Comput, 2016, 33(2): 330 doi: 10.3969/j.issn.1001-3695.2016.02.002
[26]	Krause S, Busch F. New insights into road accident analysis through the use of text mining methods // 2019 6th International Conference on Models and Technologies for Intelligent Transportation Systems (MT-ITS). Cracow, 2019: 1
[27]	張勇. 煤礦安全生產事故隱患自查自報系統建立與應用. 煤炭工程, 2014, 46(11):150 doi: 10.11799/ce201411047 Zhang Y. Establishment and application of hidden danger automatic investigation and automatic report system to coal mine safety production accident. Coal Eng, 2014, 46(11): 150 doi: 10.11799/ce201411047
[28]	中華人民共和國應急管理部. 安全生產事故隱患排查治理暫行規定[EB/OL]. 中華人民共和國應急管理部 (2008-01-10) [2020-10-23]. http://www.mem.gov.cn/gk/gwgg/zjl_01/200801/t20080110_233738.shtml Ministry of Emergency Management of People’s Republic of China. Interim Provisions on the Investigation and Treatment of Hidden Dangers of Work Safety [EB/OL]. Ministry of Emergency Management of the People’s Republic of China (2008-01-10) [2020-10-23]. http://www.mem.gov.cn/gk/gwgg/zjl_01/200801/t20080110_233738.shtml
[29]	中華人民共和國應急管理部. 金屬非金屬礦山重大生產安全事故隱患判定標準(試行)[EB/OL]. 中華人民共和國應急管理部 (2017-09-05) [2020-10-23].https://www.mem.gov.cn/gk/gwgg/201709/t20170905_241758.shtml Ministry of Emergency Management of People’s Republic of China. Judgment Standards for Hidden Dangers of Major Production Safety Accidents in Metal and Nonmetal Mine (Trial) [EB/OL]. Ministry of Emergency Management of People’s Republic of China (2017-09-05) [2020-10-23].https://www.mem.gov.cn/gk/gwgg/201709/t20170905_241758.shtml