Development and Application of Visualization System of Gas Geological Dynamic Characteristics under Big Data Framework
In this study, coal and gas outbursts are the “biggest killer” of mine safety production. With the deepening of mining, the mine gas disaster is becoming more and more serious. From the perspective of big data, the establishment of a dynamic visualization system of gas geology integrating gas geolog...
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description | In this study, coal and gas outbursts are the “biggest killer” of mine safety production. With the deepening of mining, the mine gas disaster is becoming more and more serious. From the perspective of big data, the establishment of a dynamic visualization system of gas geology integrating gas geology, gas drainage, dynamic outburst prevention, and other information can effectively improving the defects of gas disaster prevention and control in the coal industry, such as insufficient advance, lack of systematic identification methods and backward information collection methods, which is of great significance to improve the ability of gas hazard identification. Based on the precise detection of geology, structure, and gas, this paper proposes to use information technology, the Internet of things, big data analysis, and other technologies to comprehensively analyze the changes in gas occurrence and coal seam occurrence on the basis of the causes of mine gas geological outburst, fully consider the logical relationship between different factors and outburst and adopt the disciplinary advantages of grey theory, fault tree theory, BP neural network, and so on. The tree of coal and gas outburst accidents with general significance is constructed by 24 relatively independent factors. The input vector is determined as the matrix composed of eight main factors affecting and controlling outburst, including gas pressure, coal mechanical strength, comprehensive characteristic coefficient of coal fragmentation, the permeability coefficient of coal, comprehensive characteristic coefficient of coal seam bifurcation and combination, comprehensive characteristic coefficient of coal thickness and coal thickness change, fault complexity coefficients and interlayer sliding comprehensive characteristic coefficient. The geological data affecting coal and gas outbursts are analyzed and calculated scientifically so that the gas geological data can be updated in time, and the change of gas geological laws is presented dynamically, so as to guide the mine to predict the gas disaster more scientifically and reliably. |
doi_str_mv | 10.1155/2023/2393411 |
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With the deepening of mining, the mine gas disaster is becoming more and more serious. From the perspective of big data, the establishment of a dynamic visualization system of gas geology integrating gas geology, gas drainage, dynamic outburst prevention, and other information can effectively improving the defects of gas disaster prevention and control in the coal industry, such as insufficient advance, lack of systematic identification methods and backward information collection methods, which is of great significance to improve the ability of gas hazard identification. Based on the precise detection of geology, structure, and gas, this paper proposes to use information technology, the Internet of things, big data analysis, and other technologies to comprehensively analyze the changes in gas occurrence and coal seam occurrence on the basis of the causes of mine gas geological outburst, fully consider the logical relationship between different factors and outburst and adopt the disciplinary advantages of grey theory, fault tree theory, BP neural network, and so on. The tree of coal and gas outburst accidents with general significance is constructed by 24 relatively independent factors. The input vector is determined as the matrix composed of eight main factors affecting and controlling outburst, including gas pressure, coal mechanical strength, comprehensive characteristic coefficient of coal fragmentation, the permeability coefficient of coal, comprehensive characteristic coefficient of coal seam bifurcation and combination, comprehensive characteristic coefficient of coal thickness and coal thickness change, fault complexity coefficients and interlayer sliding comprehensive characteristic coefficient. The geological data affecting coal and gas outbursts are analyzed and calculated scientifically so that the gas geological data can be updated in time, and the change of gas geological laws is presented dynamically, so as to guide the mine to predict the gas disaster more scientifically and reliably.</description><identifier>ISSN: 1024-123X</identifier><identifier>EISSN: 1563-5147</identifier><identifier>DOI: 10.1155/2023/2393411</identifier><language>eng</language><publisher>New York: Hindawi</publisher><subject>Automation ; Back propagation networks ; Big Data ; Coal gas outbursts ; Coal mining ; Coefficients ; Data analysis ; Digital technology ; Disasters ; Dynamic characteristics ; Engineering ; Fault trees ; Gas pressure ; Geology ; Hazard identification ; Identification methods ; Information technology ; Interlayers ; Internet of Things ; Mathematical analysis ; Metadata ; Mines ; Mining engineering ; Occupational safety ; Prevention ; Safety management ; Technology utilization ; Thickness ; Visualization</subject><ispartof>Mathematical problems in engineering, 2023-01, Vol.2023 (1)</ispartof><rights>Copyright © 2023 Xiao Liu et al.</rights><rights>Copyright © 2023 Xiao Liu et al. This is an open access article distributed under the Creative Commons Attribution License (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. https://creativecommons.org/licenses/by/4.0</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><cites>FETCH-LOGICAL-c1391-aeafdcb280f34323a240d39683536ef614bc0321480a42c63e6cb713debbcb3c3</cites><orcidid>0000-0002-0766-514X ; 0000-0001-7327-0878</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>314,780,784,27924,27925</link.rule.ids></links><search><contributor>Liu, Conghu</contributor><contributor>Conghu Liu</contributor><creatorcontrib>Liu, Xiao</creatorcontrib><creatorcontrib>Xu, Sen</creatorcontrib><creatorcontrib>Lin, Haixiao</creatorcontrib><creatorcontrib>Ni, Xiaoming</creatorcontrib><creatorcontrib>Xuan, Dequan</creatorcontrib><title>Development and Application of Visualization System of Gas Geological Dynamic Characteristics under Big Data Framework</title><title>Mathematical problems in engineering</title><description>In this study, coal and gas outbursts are the “biggest killer” of mine safety production. With the deepening of mining, the mine gas disaster is becoming more and more serious. From the perspective of big data, the establishment of a dynamic visualization system of gas geology integrating gas geology, gas drainage, dynamic outburst prevention, and other information can effectively improving the defects of gas disaster prevention and control in the coal industry, such as insufficient advance, lack of systematic identification methods and backward information collection methods, which is of great significance to improve the ability of gas hazard identification. Based on the precise detection of geology, structure, and gas, this paper proposes to use information technology, the Internet of things, big data analysis, and other technologies to comprehensively analyze the changes in gas occurrence and coal seam occurrence on the basis of the causes of mine gas geological outburst, fully consider the logical relationship between different factors and outburst and adopt the disciplinary advantages of grey theory, fault tree theory, BP neural network, and so on. The tree of coal and gas outburst accidents with general significance is constructed by 24 relatively independent factors. The input vector is determined as the matrix composed of eight main factors affecting and controlling outburst, including gas pressure, coal mechanical strength, comprehensive characteristic coefficient of coal fragmentation, the permeability coefficient of coal, comprehensive characteristic coefficient of coal seam bifurcation and combination, comprehensive characteristic coefficient of coal thickness and coal thickness change, fault complexity coefficients and interlayer sliding comprehensive characteristic coefficient. The geological data affecting coal and gas outbursts are analyzed and calculated scientifically so that the gas geological data can be updated in time, and the change of gas geological laws is presented dynamically, so as to guide the mine to predict the gas disaster more scientifically and reliably.</description><subject>Automation</subject><subject>Back propagation networks</subject><subject>Big Data</subject><subject>Coal gas outbursts</subject><subject>Coal mining</subject><subject>Coefficients</subject><subject>Data analysis</subject><subject>Digital technology</subject><subject>Disasters</subject><subject>Dynamic characteristics</subject><subject>Engineering</subject><subject>Fault trees</subject><subject>Gas pressure</subject><subject>Geology</subject><subject>Hazard identification</subject><subject>Identification methods</subject><subject>Information technology</subject><subject>Interlayers</subject><subject>Internet of Things</subject><subject>Mathematical analysis</subject><subject>Metadata</subject><subject>Mines</subject><subject>Mining engineering</subject><subject>Occupational safety</subject><subject>Prevention</subject><subject>Safety management</subject><subject>Technology utilization</subject><subject>Thickness</subject><subject>Visualization</subject><issn>1024-123X</issn><issn>1563-5147</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2023</creationdate><recordtype>article</recordtype><sourceid>RHX</sourceid><sourceid>ABUWG</sourceid><sourceid>AFKRA</sourceid><sourceid>AZQEC</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><sourceid>DWQXO</sourceid><sourceid>GNUQQ</sourceid><recordid>eNp9kMtOwzAQRS0EEqWw4wMssYRQ25M46bK0tCAhseAhdtHEcVqXJA520qp8PanaNau5ujq6Ix1Crjm75zyKRoIJGAkYQ8j5CRnwSEIQ8TA-7TMTYcAFfJ2TC-_XjAke8WRANjO90aVtKl23FOucTpqmNApbY2tqC_ppfIel-T0Ubzvf6mrfL9DThbalXfZwSWe7Giuj6HSFDlWrnfGtUZ52da4dfTBLOsMW6dxhpbfWfV-SswJLr6-Od0g-5o_v06fg5XXxPJ28BIrDmAeoschVJhJWQAgCUIQsh7FMIAKpC8nDTDEQPEwYhkJJ0FJlMYdcZ5nKQMGQ3Bx2G2d_Ou3bdG07V_cv0340jqWImeypuwOlnPXe6SJtnKnQ7VLO0r3ZdG82PZrt8dsDvjJ1jlvzP_0H2-N49Q</recordid><startdate>20230101</startdate><enddate>20230101</enddate><creator>Liu, Xiao</creator><creator>Xu, Sen</creator><creator>Lin, Haixiao</creator><creator>Ni, Xiaoming</creator><creator>Xuan, Dequan</creator><general>Hindawi</general><general>Hindawi Limited</general><scope>RHU</scope><scope>RHW</scope><scope>RHX</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7TB</scope><scope>8FD</scope><scope>8FE</scope><scope>8FG</scope><scope>ABJCF</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>ARAPS</scope><scope>AZQEC</scope><scope>BENPR</scope><scope>BGLVJ</scope><scope>CCPQU</scope><scope>CWDGH</scope><scope>DWQXO</scope><scope>FR3</scope><scope>GNUQQ</scope><scope>HCIFZ</scope><scope>JQ2</scope><scope>K7-</scope><scope>KR7</scope><scope>L6V</scope><scope>M7S</scope><scope>P5Z</scope><scope>P62</scope><scope>PIMPY</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><scope>PTHSS</scope><orcidid>https://orcid.org/0000-0002-0766-514X</orcidid><orcidid>https://orcid.org/0000-0001-7327-0878</orcidid></search><sort><creationdate>20230101</creationdate><title>Development and Application of Visualization System of Gas Geological Dynamic Characteristics under Big Data Framework</title><author>Liu, Xiao ; Xu, Sen ; Lin, Haixiao ; Ni, Xiaoming ; Xuan, Dequan</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c1391-aeafdcb280f34323a240d39683536ef614bc0321480a42c63e6cb713debbcb3c3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2023</creationdate><topic>Automation</topic><topic>Back propagation networks</topic><topic>Big Data</topic><topic>Coal gas outbursts</topic><topic>Coal mining</topic><topic>Coefficients</topic><topic>Data analysis</topic><topic>Digital technology</topic><topic>Disasters</topic><topic>Dynamic characteristics</topic><topic>Engineering</topic><topic>Fault trees</topic><topic>Gas pressure</topic><topic>Geology</topic><topic>Hazard identification</topic><topic>Identification methods</topic><topic>Information technology</topic><topic>Interlayers</topic><topic>Internet of Things</topic><topic>Mathematical analysis</topic><topic>Metadata</topic><topic>Mines</topic><topic>Mining engineering</topic><topic>Occupational safety</topic><topic>Prevention</topic><topic>Safety management</topic><topic>Technology utilization</topic><topic>Thickness</topic><topic>Visualization</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Liu, Xiao</creatorcontrib><creatorcontrib>Xu, Sen</creatorcontrib><creatorcontrib>Lin, Haixiao</creatorcontrib><creatorcontrib>Ni, Xiaoming</creatorcontrib><creatorcontrib>Xuan, Dequan</creatorcontrib><collection>Hindawi Publishing Complete</collection><collection>Hindawi Publishing Subscription Journals</collection><collection>Hindawi Publishing Open Access Journals</collection><collection>CrossRef</collection><collection>Mechanical & Transportation Engineering Abstracts</collection><collection>Technology Research Database</collection><collection>ProQuest SciTech Collection</collection><collection>ProQuest Technology Collection</collection><collection>Materials Science & Engineering Collection</collection><collection>ProQuest Central (Alumni Edition)</collection><collection>ProQuest Central UK/Ireland</collection><collection>Advanced Technologies & Aerospace Collection</collection><collection>ProQuest Central Essentials</collection><collection>ProQuest Central</collection><collection>Technology Collection</collection><collection>ProQuest One Community College</collection><collection>Middle East & Africa Database</collection><collection>ProQuest Central Korea</collection><collection>Engineering Research Database</collection><collection>ProQuest Central Student</collection><collection>SciTech Premium Collection</collection><collection>ProQuest Computer Science Collection</collection><collection>Computer Science Database</collection><collection>Civil Engineering Abstracts</collection><collection>ProQuest Engineering Collection</collection><collection>Engineering Database</collection><collection>Advanced Technologies & Aerospace Database</collection><collection>ProQuest Advanced Technologies & Aerospace Collection</collection><collection>Access via ProQuest (Open Access)</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>ProQuest Central China</collection><collection>Engineering Collection</collection><jtitle>Mathematical problems in engineering</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Liu, Xiao</au><au>Xu, Sen</au><au>Lin, Haixiao</au><au>Ni, Xiaoming</au><au>Xuan, Dequan</au><au>Liu, Conghu</au><au>Conghu Liu</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Development and Application of Visualization System of Gas Geological Dynamic Characteristics under Big Data Framework</atitle><jtitle>Mathematical problems in engineering</jtitle><date>2023-01-01</date><risdate>2023</risdate><volume>2023</volume><issue>1</issue><issn>1024-123X</issn><eissn>1563-5147</eissn><abstract>In this study, coal and gas outbursts are the “biggest killer” of mine safety production. With the deepening of mining, the mine gas disaster is becoming more and more serious. From the perspective of big data, the establishment of a dynamic visualization system of gas geology integrating gas geology, gas drainage, dynamic outburst prevention, and other information can effectively improving the defects of gas disaster prevention and control in the coal industry, such as insufficient advance, lack of systematic identification methods and backward information collection methods, which is of great significance to improve the ability of gas hazard identification. Based on the precise detection of geology, structure, and gas, this paper proposes to use information technology, the Internet of things, big data analysis, and other technologies to comprehensively analyze the changes in gas occurrence and coal seam occurrence on the basis of the causes of mine gas geological outburst, fully consider the logical relationship between different factors and outburst and adopt the disciplinary advantages of grey theory, fault tree theory, BP neural network, and so on. The tree of coal and gas outburst accidents with general significance is constructed by 24 relatively independent factors. The input vector is determined as the matrix composed of eight main factors affecting and controlling outburst, including gas pressure, coal mechanical strength, comprehensive characteristic coefficient of coal fragmentation, the permeability coefficient of coal, comprehensive characteristic coefficient of coal seam bifurcation and combination, comprehensive characteristic coefficient of coal thickness and coal thickness change, fault complexity coefficients and interlayer sliding comprehensive characteristic coefficient. The geological data affecting coal and gas outbursts are analyzed and calculated scientifically so that the gas geological data can be updated in time, and the change of gas geological laws is presented dynamically, so as to guide the mine to predict the gas disaster more scientifically and reliably.</abstract><cop>New York</cop><pub>Hindawi</pub><doi>10.1155/2023/2393411</doi><orcidid>https://orcid.org/0000-0002-0766-514X</orcidid><orcidid>https://orcid.org/0000-0001-7327-0878</orcidid><oa>free_for_read</oa></addata></record> |
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subjects | Automation Back propagation networks Big Data Coal gas outbursts Coal mining Coefficients Data analysis Digital technology Disasters Dynamic characteristics Engineering Fault trees Gas pressure Geology Hazard identification Identification methods Information technology Interlayers Internet of Things Mathematical analysis Metadata Mines Mining engineering Occupational safety Prevention Safety management Technology utilization Thickness Visualization |
title | Development and Application of Visualization System of Gas Geological Dynamic Characteristics under Big Data Framework |
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