煤炭工业监控大数据平台建设与数据处理应用技术
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摘要
为保障煤矿的安全高效生产,及时准确地预测可能发生的故障及灾害,通过分析兖矿集团煤矿大数据的现状及存在的问题,探讨了在大数据时代下,煤炭工业监控大数据平台的建设和使用方法。依托互联网、云计算和大数据技术,采集、整理和存储海量数据。通过数据挖掘技术和视频分析技术来探索发掘现场生产规律,从而提高煤矿应对潜在灾害和发现安全隐患的能力。以煤矿带式输送机工况监控系统大数据和视频监控系统大数据分析为例,分别使用流形空间的非平衡处理技术和分水岭等视觉技术对数据进行处理。试验结果表明:流形空间的过采样技术可以提高AUC指标20%左右,且基于机器视觉的煤流检测也能达到较好的效果。同时,探讨了大数据与煤炭工业监控系统的结合,并提出了报警数据融合应用的具体解决方法。
In order to ensure the safe and efficient production of coal mines,and forecast the faults and the disa sters timely and accurately,this paper analyzes the current situation and existing problems of coal mines big data in Yankuang Group,and explores the construction and application of big data platform for coal industry monitoring. Massive data is collected,processed and stored by using the internet,cloud computing and big data technology. Through data mining technology and video analysis technology to explore the production rules of the site,the ability of coal mines can be improved in dealing with potential disasters and identifying potential safety hazards. Taking the big data analysis of the coal mine belt conveyor monitoring system and video monitoring system as examples,the data is processed by using the manifold space imbalance processing technology and watershed algorithm. The experimental results show that the manifold space imbalance processing technology can increase the AUC index by about 20%,and the coal flow detection based on machine vision can also achieve better performance. Meanwhile,the combination of big data and coal industry monitoring system is discussed,and the specific solution of alarm data fusion application is proposed.
In order to ensure the safe and efficient production of coal mines,and forecast the faults and the disa sters timely and accurately,this paper analyzes the current situation and existing problems of coal mines big data in Yankuang Group,and explores the construction and application of big data platform for coal industry monitoring. Massive data is collected,processed and stored by using the internet,cloud computing and big data technology. Through data mining technology and video analysis technology to explore the production rules of the site,the ability of coal mines can be improved in dealing with potential disasters and identifying potential safety hazards. Taking the big data analysis of the coal mine belt conveyor monitoring system and video monitoring system as examples,the data is processed by using the manifold space imbalance processing technology and watershed algorithm. The experimental results show that the manifold space imbalance processing technology can increase the AUC index by about 20%,and the coal flow detection based on machine vision can also achieve better performance. Meanwhile,the combination of big data and coal industry monitoring system is discussed,and the specific solution of alarm data fusion application is proposed.
引文
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[2]孙继平.煤矿事故分析与煤矿大数据和物联网[J].工矿自动化,2015,41(3):1-5.SUN Jiping.Accident analysis and big data and internet of things in coal mine[J].Industry and Mine Automation,2015,41(3):1-5.
[3]孙继平.煤与瓦斯突出报警方法[J].工矿自动化,2014,40(11):1-5.SUN Jiping.Alarm methods of coal and gas outburst[J]. Industry and Mine Automation,2014,40(11):1-5.
[4]姜福兴,曲效成,王颜亮,等.基于云计算的煤矿冲击地压监控预警技术研究[J].煤炭科学技术,2018,46(1):199-206,244.JIANG Fuxing,QU Xiaocheng,WANG Yanliang,et al. Study on monitoring&control and early warning technology of mine pressure bmp based on cloud computing[J].Coal Science and Technology,2018,46(1):199-206,244.
[5]程健,杨凌凯,王全魁,等.基于半监督过采样非平衡学习的矿山微震信号识别[J].煤炭科学技术,2018,46(2):213-218,202.CHENG Jian,YANG Lingkai,WANG Quankui,et al. Mine microseismic detection based on semi-supervised over-sampling framework[J]. Coal Science and Technology,2018,46(2):213-218,202.
[6]张科利,王建文,曹豪.互联网+煤矿开采大数据技术研究与实践[J].煤炭科学技术,2016,44(7):123-128.ZHANG Keli,WANG Jianwen,CAO Hao.Study and practice on big data technology of internet plus coal mining[J]. Coal Science and Technology,2016,44(7):123-128.
[7]孙杰,陈美英,唐朝苗,等.我国煤炭资源勘查现状跟踪研究[J].中国煤炭地质,2017,29(11):1-8.SUN Jie,CHEN Meiying,TANG Chaomiao,et al.Tracking study of coal resources exploration status quo in China[J].Coal Geology of China,2017,29(11):1-8.
[8]马小平,代伟.大数据技术在煤炭工业中的研究现状与应用展望[J].工矿自动化,2018,44(1):50-54.MA Xiaoping,DAI Wei.Research status and application prospect of big data technology in coal industry[J]. Industry and Mine Automation,2018,44(1):50-54.
[9]刘香兰.煤矿安全生产大数据分析与管理平台设计研究[J].煤炭工程,2017,49(6):32-35.LIU Xianglan.Design and research of big data analysis and management platform for coal mine safety[J].Coal Engineering,2017,49(6):32-35.
[10]王海军,武先利.“互联网+”时代煤矿大数据应用分析[J].煤炭科学技术,2016,44(2):139-143.WANG Haijun,WU Xianli. Analysis on application of coal mine big data in age of ‘Internet+’[J]. Coal Science and Technology,2016,44(2):139-143.
[11]杨林,刘坤.兖矿集团煤矿工业大数据发展应用刍议[C].济南:中国煤矿信息化与自动化高层论坛,2017.
[12]李飒.大数据挖掘技术在煤炭企业安全管理中的应用[J].煤炭技术,2016,35(3):323-324.LI Sa.Application of big data mining technology in coal enterprise safety management[J].Coal Technology,2016,35(3):323-324.
[13]程健,杨凌凯,崔宁,等.基于流形嵌入过采样的非平衡数据分类方法[J].中国矿业大学学报,2018,47(6):1331-1339.CHENG Jian,YANG Lingkai,CUI Ning,et al. A novel classification method for imbalanced data set based on manifold embedded over-sampling[J]. Journal of China University of Mining&Technology,2018,47(6):1331-1339.
[14] GALAR M,FERNANDEZ A,BARRENECHEA E,et al.A review on ensembles for the class imbalance problem:bagging,boosting,and hybrid-based approaches[J]. IEEE Transactions on Systems Man&Cybernetics Part C Applications&Reviews,2012,42(4):463-484.
[15] LAW M H C,JAIN A K.Incremental nonlinear dimensionality reduction by manifold learning[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2006,28(3):377-391.
[16] TENENBAUM J B,DE S V,LANGFORD J C.A global geometric framework for nonlinear dimensionality reduction[J]. Science,2000,290:2319-2322.
[17] ROWEIS S T,SAUL L K.Nonlinear dimensionality reduction by locally linear embedding[J].Science,2000,290:2323-2326.
[18] SCHOLKOPF B,PLATT J,HOFMANN T.MLLE:modified locally linear embedding using multiple weights[J]. Advances in Neural Information Processing Systems,2006,19:1593-1600.
[19] HE X,NIYOGI P.Locality preserving projections[C]//Advances in neural information processing systems. MIT Press,British Columbia,2004:153-160.
[20] CHAWLA N V,BOWYER K W,HALL L O,et al.SMOTE:synthetic minority over-sampling technique[J]. Journal of Artificial Intelligence Research,2011,16(1):321-357.
[21] HAN H,WANG W Y,MAO B H. Borderline-SMOTE:a new over-sampling method in imbalanced data sets learning[J].Lecture Notes in Computer Science,2005(5):878-887.
[22] HE H,BAI Y,GARCIA E A,et al.ADASYN:adaptive synthetic sampling approach for imbalanced learning[C]. 2008International Joint Conference on Neural Networks,Hongkong,China,2008.