Article(id=1321897397861597761, tenantId=1146029695717560320, journalId=1225396423026438145, issueId=1321897310649434924, 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=1772553600000, receivedDateStr=2026-03-04, revisedDate=null, revisedDateStr=null, acceptedDate=null, acceptedDateStr=null, onlineDate=1793094499315, onlineDateStr=2026-10-27, pubDate=1790265600000, pubDateStr=2026-09-25, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1793094499315, onlineIssueDateStr=2026-10-27, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1793094499315, creator=13701087609, updateTime=1793094499315, updator=13701087609, issue=Issue{id=1321897310649434924, tenantId=1146029695717560320, journalId=1225396423026438145, year='2026', volume='46', issue='9', pageStart='1', pageEnd='346', issueExtLink='null', onlineDate='null', pubDate='1790265600000', pubDateStr='2026-09-25', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=1, specialIssue=null, createTime=1793094478523, creator='13701087609', updateTime=1793094618231, updator='13701087609', preIssue=null, nextIssue=null, articleTotal=null, ext={EN=IssueExt(id=1321897896698561428, tenantId=1146029695717560320, journalId=1225396423026438145, issueId=1321897310649434924, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1321897896698561429, tenantId=1146029695717560320, journalId=1225396423026438145, issueId=1321897310649434924, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null, downloadFileDto=null}, startPage=235, endPage=243, ext={EN=ArticleExt(id=1321897398352331330, articleId=1321897397861597761, tenantId=1146029695717560320, journalId=1225396423026438145, language=EN, title=Technological Evolution of Intelligent Mine Large Models and Layered Collaborative Architecture for Safety Governance, columnId=null, journalTitle=Mining Research and Development, columnName=null, runingTitle=null, highlight=null, articleAbstract=

The engineering basis for introducing large models in mines is established through the long-term accumulation of digitalization, automation, and specialized models. However, the integrated understanding of complex risks by safety systems is still hindered by fragmented data, isolated knowledge, and the disconnection between early warning and response. Based on the technological evolution of intelligent mines and engineering practices, the prior foundations, including platform interfaces, specialized tools, multi-source safety evidence, and domain knowledge, were examined, and the differentiated requirements of high-risk scenarios such as goaf, tailings pond, and hoisting system were analyzed. The study suggests that mine safety large models should not replace control systems and on-site responsibility chains, but rather serve as a "cognition-and-proposal layer" to participate in multimodal understanding, risk assessment, mechanism interpretation, and solution generation. Its outputs should be checked by data evidence, professional rules, mechanistic models, protection interlocks, and human review, and routes for rejection and safe fallback shall be retained. Data governance and knowledge base, cognition and proposal, constraint checking, on-site response, and outcome feedback are incorporated into the same accountability chain by the resulting layered collaborative architecture. The engineering value of mine safety large models should possess interpretability, traceability, verifiability, and fallback capability.

, authors=Xiangyu Kou^{1, 2}, Huihui Guo³, Cao Gao^{1, 2}, Chen Chen^{1, 4}, Shuang Huang^{1, 4}, authorsList=Xiangyu Kou, Huihui Guo, Cao Gao, Chen Chen, Shuang Huang, authorCompany=null, correspAuthors=Huihui Guo, 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=1321897398897590851, articleId=1321897397861597761, tenantId=1146029695717560320, journalId=1225396423026438145, language=CN, title=智能矿山大模型技术演进及安全治理分层协同架构, columnId=1226462295946085268, journalTitle=矿业研究与开发, columnName=矿山机电与矿业智能化, runingTitle=null, highlight=null, articleAbstract=

数字化、自动化和专业模型的长期积累，使矿山具备了引入大模型的工程基础，但数据分散、知识孤立以及预警与处置脱节，仍限制着安全系统对复杂风险的整体认知。结合智能矿山技术演进和工程实践，梳理了平台接口、专用工具、多源安全证据和领域知识等前序基础，分析了采空区、尾矿库和提升系统等高风险场景的差异化需求。研究认为，矿山安全大模型不宜替代控制系统和现场责任链，而应作为“认知与提议层”参与跨模态理解、风险研判、机理解释和方案生成；其输出须经过数据证据、专业规则、机理模型、保护联锁和人工复核等环节校核，并保留拒绝执行和安全回退路径。由此形成的分层协同架构，将数据治理与知识底座、认知提议、约束校核、现场处置和结果反馈纳入同一责任链。矿山安全大模型的工程价值应具备可解释性、可追溯性、可校核性和可回退能力。

, authors=寇向宇^{1, 2}, 郭晖晖³, 高操^{1, 2}, 陈谌^{1, 4}, 黄爽^{1, 4}, authorsList=寇向宇, 郭晖晖, 高操, 陈谌, 黄爽, authorCompany=null, correspAuthors=郭晖晖, authorNote=, correspAuthorsNote=, copyrightStatement=null, copyrightOwner=null, extLink=null, articleAbsUrl=null, sourceXml=0uYVxwC8Zh2oN2qqytPleg==, magXml=mNzC6ITQu9jwOgt0twdBZw==, pdfUrl=null, pdf=RinYAUKi5u4C3t2HUZeblw==, pdfFileSize=2434076, pdfExtLink=null, richHtmlUrl=null, mobilePdfUrl=null, reviewReport=null, pdfFirstPage=null, abstractGraph=null, abstractGraphContent=null, abstractVideo=null, citation=null, cebUrl=null, magXmlContent=/XdFocKKlc9+nmK97781dQ==, mapNumber=null, fund=null)}, authors=[Author(id=1321897399681925713, tenantId=1146029695717560320, journalId=1225396423026438145, articleId=1321897397861597761, orderNo=0, firstName=null, middleName=null, lastName=null, nameCn=null, orcid=null, stid=null, country=null, authorPic=null, dead=0, email=kouxiangyu2008@163.com, emailSecond=null, emailThird=null, correspondingAuthor=0, authorType=1, ext={EN=AuthorExt(id=1321897399782589012, tenantId=1146029695717560320, journalId=1225396423026438145, articleId=1321897397861597761, authorId=1321897399681925713, language=EN, stringName=Xiangyu Kou, firstName=Xiangyu, middleName=null, lastName=Kou, prefix=null, suffix=null, authorComment=null, nameInitials=null, affiliation=null, department=null, xref=^{1, 2}, address=^1.Changsha Institute of Mining Research Co., Ltd., Changsha, Hunan 410012, China
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寇向宇（1985—），河南南阳人，博士，正高级工程师，主要从事智能采矿工艺、岩石力学、矿山数字信息技术等方面的研究工作。E-mail:kouxiangyu2008@163.com

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寇向宇（1985—），河南南阳人，博士，正高级工程师，主要从事智能采矿工艺、岩石力学、矿山数字信息技术等方面的研究工作。E-mail:kouxiangyu2008@163.com

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