Article(id=1321897312440398458, 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=1774627200000, receivedDateStr=2026-03-28, revisedDate=null, revisedDateStr=null, acceptedDate=null, acceptedDateStr=null, onlineDate=1793094478949, onlineDateStr=2026-10-27, pubDate=1790265600000, pubDateStr=2026-09-25, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1793094478949, onlineIssueDateStr=2026-10-27, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1793094478949, creator=13701087609, updateTime=1793094478949, 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=212, endPage=224, ext={EN=ArticleExt(id=1321897312616559227, articleId=1321897312440398458, tenantId=1146029695717560320, journalId=1225396423026438145, language=EN, title=A Review of Mine Blasting Fragmentation Predictive Analysis and Intelligent Recognition Technology, columnId=null, journalTitle=Mining Research and Development, columnName=null, runingTitle=null, highlight=null, articleAbstract=

Blasting fragmentation is regarded as an important basis for evaluating blasting effects and optimizing blasting parameters, and the subsequent operations such as shoveling, transportation, and crushing and grinding are directly affected. With blasting fragmentation prediction and post-blast fragmentation recognition as the main line, the formation mechanism and main influencing factors of blasting fragments were introduced. Prediction methods, including empirical and semi-empirical models, mechanism analysis and numerical simulation, statistical learning and artificial intelligence, as well as recognition techniques, including traditional detection, 2D image recognition, 3D point cloud, and photogrammetry, were systematically reviewed. The characteristics of different methods in terms of prediction accuracy, mechanism interpretability, computational efficiency, field applicability, and implementation cost were compared. The analysis results show that existing methods have their own advantages and applicable boundaries, and that accurate prediction, stable recognition, and low-cost application are still difficult to achieve simultaneously under complex mine conditions. It is concluded through analysis that current research is mainly constrained by the lack of model interpretability and cross-scene generalization ability, the vulnerability of recognition results to field environments, and the underdevelopment of data standards and engineering application systems. The synergy among mechanistic models, data-driven methods, and multi-source recognition technologies should be strengthened in future research, with emphasis placed on real-time fragmentation prediction based on multi-source data fusion, precise recognition and control in complex environments, and coordinated optimization of intelligent blasting equipment and prediction models, so that support can be provided for establishing a closed-loop control system comprising pre-blast prediction, post-blast recognition, effect evaluation, and parameter feedback.

, authors=Renze Ou^{1, 2}, Ning Yang^{1, 2}, Zhouyun Fu^{1, 2}, Mingdong Tang^{1, 2}, Yuejin Peng^{1, 2}, authorsList=Renze Ou, Ning Yang, Zhouyun Fu, Mingdong Tang, Yuejin Peng, authorCompany=null, correspAuthors=Ning Yang, 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=1321897312859828860, articleId=1321897312440398458, tenantId=1146029695717560320, journalId=1225396423026438145, language=CN, title=矿山爆破块度预测分析与智能识别技术研究综述, 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=

爆破块度是评价矿山爆破效果和优化爆破参数的重要依据，直接影响铲装、运输及破碎磨矿等后续工序。以爆破块度预测和爆后块度识别为主线，介绍了爆破块体形成机理及主要影响因素；系统梳理了经验与半经验模型、机理分析与数值模拟、统计学习和人工智能等预测方法，以及传统检测、二维图像识别、三维点云与摄影测量等识别技术；比较了不同方法在预测精度、机理解释性、计算效率、现场适用性和实施成本等方面的特点。分析结果表明，现有方法各具优势和适用边界，在复杂矿山条件下仍难以同时实现准确预测、稳定识别与低成本应用。分析认为：当前研究主要受制于模型可解释性和跨场景泛化能力不足、识别结果易受现场环境影响，以及数据标准和工程化应用体系不完善；后续研究应加强机理模型、数据驱动方法与多源识别技术的协同，重点研究多源数据融合的块度实时预测、复杂环境下的精准识别与控制，以及智能爆破装备与预测模型联动优化，为形成“爆前预测—爆后识别—效果评价—参数反馈”的闭环管控提供支撑。

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欧任泽（1982—），男，湖南郴州人，硕士，主要从事采矿工程技术研究。E-mail:ourz0@minmetals.com

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欧任泽（1982—），男，湖南郴州人，硕士，主要从事采矿工程技术研究。E-mail:ourz0@minmetals.com

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