Article(id=1321893279264780592, tenantId=1146029695717560320, journalId=1225396423026438145, issueId=1321893272490987695, articleNumber=null, orderNo=null, doi=null, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1776614400000, receivedDateStr=2026-04-20, revisedDate=null, revisedDateStr=null, acceptedDate=null, acceptedDateStr=null, onlineDate=1793093517366, onlineDateStr=2026-10-27, pubDate=1781712000000, pubDateStr=2026-06-18, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1793093517366, onlineIssueDateStr=2026-10-27, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1793093517366, creator=13701087609, updateTime=1793093517366, updator=13701087609, issue=Issue{id=1321893272490987695, tenantId=1146029695717560320, journalId=1225396423026438145, year='2026', volume='46', issue='6', pageStart='1', pageEnd='312', issueExtLink='null', onlineDate='null', pubDate='1781712000000', pubDateStr='2026-06-18', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1793093515751, creator='13701087609', updateTime=1793094575775, updator='13701087609', preIssue=null, nextIssue=null, articleTotal=null, ext={EN=IssueExt(id=1321897718625194101, tenantId=1146029695717560320, journalId=1225396423026438145, issueId=1321893272490987695, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1321897718625194102, tenantId=1146029695717560320, journalId=1225396423026438145, issueId=1321893272490987695, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null, downloadFileDto=null}, startPage=82, endPage=92, ext={EN=ArticleExt(id=1321893279428358449, articleId=1321893279264780592, tenantId=1146029695717560320, journalId=1225396423026438145, language=EN, title=Research on Multi-Scenario Analysis and Mathematical Method Fusion Path of Mine Safety Risk Intelligent Early Warning, columnId=null, journalTitle=Mining Research and Development, columnName=null, runingTitle=null, highlight=null, articleAbstract=

Intelligent early warning of mine safety risk is the key technical means to ensure mine safety production. In the face of challenges such as complex mining environment, strong multi-factor coupling of disasters, and large amount of multi-source heterogeneous data, systematic research was carried out on the typical application scenarios and fusion path of mathematical methods. Starting from three typical application scenarios: dynamic prediction of disaster risk, real-time perception and intelligent control of human-machine-environment total factors, and emergency decision-making optimization of accident chain traceability and simulation, the evolution rules of mine safety early warning technology "from single to multiple, from traditional to intelligent" "from discrete monitoring to closed-loop contro" and "from experience-driven to intelligent assistance" were sorted out. On this basis, the mathematical methods supporting the above application scenarios were summarized, including basic statistics and spatio-temporal analysis, machine learning and deep learning, complex system modeling, mechanism-data hybrid driving model and digital twin model. The collaborative framework of application scenarios and mathematical methods was constructed, and the selection and combination of mathematical methods in different application scenarios were clarified. The research results can provide technical reference and method support for the transformation of mine safety risk early warning to "active prevention and control".

, authors=Daheng Zeng^{1, 2}, authorsList=Daheng Zeng, authorCompany=null, correspAuthors=null, authorNote=null, correspAuthorsNote=null, copyrightStatement=null, copyrightOwner=null, extLink=null, articleAbsUrl=null, sourceXml=null, magXml=null, pdfUrl=null, pdf=null, pdfFileSize=null, pdfExtLink=null, richHtmlUrl=null, mobilePdfUrl=null, reviewReport=null, pdfFirstPage=null, abstractGraph=null, abstractGraphContent=null, abstractVideo=null, citation=null, cebUrl=null, magXmlContent=null, mapNumber=null, fund=null), CN=ArticleExt(id=1321893279684210994, articleId=1321893279264780592, tenantId=1146029695717560320, journalId=1225396423026438145, language=CN, title=矿山安全风险智能预警多场景分析与数学方法融合路径研究, columnId=1226462295417598973, journalTitle=矿业研究与开发, columnName=矿山安全与环保, runingTitle=null, highlight=null, articleAbstract=

矿山安全风险智能预警是保障矿山安全生产的关键技术手段。面对矿山开采环境复杂、灾害多因素耦合性强、多源异构数据量大等挑战，围绕典型应用场景与数学方法融合路径开展了系统研究。从灾害风险动态预测、人-机-环全要素实时感知与智能管控、事故链追溯与模拟的应急决策优化三个典型应用场景出发，梳理了矿山安全预警技术“从单一到多元、从传统到智能”“从离散监测到闭环控制”“从经验驱动到智能辅助”的演进规律。在此基础上，对支撑上述应用场景实现的数学方法进行了归纳，涵盖基础统计与时空分析、机器学习与深度学习、复杂系统建模、机理-数据混合驱动模型及数字孪生模型，并构建了应用场景与数学方法协同框架，明确了不同应用场景下数学方法的选择与组合方式。研究成果可为矿山安全风险预警向“主动防控”转型提供技术参考与方法支持。

, authors=曾大恒^{1, 2}, authorsList=曾大恒, authorCompany=null, correspAuthors=null, authorNote=, correspAuthorsNote=null, copyrightStatement=null, copyrightOwner=null, extLink=null, articleAbsUrl=null, sourceXml=4LqCaSg8nVnJ/sW9c042sQ==, magXml=1LbqkT9qhy0bwQKGUoxp4w==, pdfUrl=null, pdf=/uDXzsPgAYWgvM17c5Nuag==, pdfFileSize=1724586, pdfExtLink=null, richHtmlUrl=null, mobilePdfUrl=null, reviewReport=null, pdfFirstPage=null, abstractGraph=null, abstractGraphContent=null, abstractVideo=null, citation=null, cebUrl=null, magXmlContent=WgVsZDskVLp2iwut0iFFwg==, mapNumber=null, fund=null)}, authors=[Author(id=1321893281621979450, tenantId=1146029695717560320, journalId=1225396423026438145, articleId=1321893279264780592, orderNo=0, firstName=null, middleName=null, lastName=null, nameCn=null, orcid=null, stid=null, country=null, authorPic=null, dead=0, email=834664616@qq.com, emailSecond=null, emailThird=null, correspondingAuthor=0, authorType=1, ext={EN=AuthorExt(id=1321893281701671229, tenantId=1146029695717560320, journalId=1225396423026438145, articleId=1321893279264780592, authorId=1321893281621979450, language=EN, stringName=Daheng Zeng, firstName=Daheng, middleName=null, lastName=Zeng, prefix=null, suffix=null, authorComment=null, nameInitials=null, affiliation=null, department=null, xref=^{1, 2}, address=^1.Safety Training Center of the Hunan Bureau of the National Mine Safety Administration, Changsha, Hunan 410151, China
^2.Hunan Vocational Institute of Safety Technology, Changsha, Hunan 410151, China, bio=null, bioImg=null, bioContent=null, aboutCorrespAuthor=null), CN=AuthorExt(id=1321893281777168702, tenantId=1146029695717560320, journalId=1225396423026438145, articleId=1321893279264780592, authorId=1321893281621979450, language=CN, stringName=曾大恒, firstName=null, middleName=null, lastName=null, prefix=null, suffix=null, authorComment=null, nameInitials=null, affiliation=null, department=null, xref=^{1, 2}, address=^1．国家矿山安全监察局湖南局安全培训中心，湖南 长沙　410151
^2．湖南安全技术职业学院，湖南 长沙　410151, bio={"content":"

曾大恒（1971—），男，湖南汉寿人，硕士，副教授，主要从事矿山安全培训与高职教育方面的工作。E-mail:834664616@qq.com

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曾大恒（1971—），男，湖南汉寿人，硕士，副教授，主要从事矿山安全培训与高职教育方面的工作。E-mail:834664616@qq.com

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