Article(id=1308454735246282783, tenantId=1146029695717560320, journalId=1146123302524792850, issueId=1308454712513156008, articleNumber=null, orderNo=null, doi=10.3969/j.issn.1672-6073.2026.04.011, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1756224000000, receivedDateStr=2025-08-27, revisedDate=1767542400000, revisedDateStr=2026-01-05, acceptedDate=null, acceptedDateStr=null, onlineDate=1789889518796, onlineDateStr=2026-09-20, pubDate=null, pubDateStr=null, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1789889518796, onlineIssueDateStr=2026-09-20, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1789889518796, creator=13701087609, updateTime=1789889518796, updator=13701087609, issue=Issue{id=1308454712513156008, tenantId=1146029695717560320, journalId=1146123302524792850, year='2026', volume='39', issue='4', pageStart='1', pageEnd='197', issueExtLink='null', onlineDate='null', pubDate='1786291200000', pubDateStr='2026-08-10', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=1, specialIssue=null, createTime=1789889513376, creator='13701087609', updateTime=1789889839088, updator='13701087609', preIssue=null, nextIssue=null, articleTotal=null, ext={EN=IssueExt(id=1308456079029986178, tenantId=1146029695717560320, journalId=1146123302524792850, issueId=1308454712513156008, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1308456079029986179, tenantId=1146029695717560320, journalId=1146123302524792850, issueId=1308454712513156008, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null, downloadFileDto=null}, startPage=100, endPage=106, ext={EN=ArticleExt(id=1308454735950925856, articleId=1308454735246282783, tenantId=1146029695717560320, journalId=1146123302524792850, language=EN, title=Research on Intelligent Inspection Methods and Technologies for Urban Rail Transit Equipment Rooms Based on Large Vision Models, columnId=1308453883261514080, journalTitle=Urban Rapid Rail Transit, columnName=Artifical Intelligence, runingTitle=null, highlight=null, articleAbstract=

To address the challenges of weak feature extraction capability and poor adaptability to multi-source data encountered by by intelligent inspection algorithms in urban rail transit equipment rooms under complex environments, this paper proposes an innovative inspection scheme based on large vision models. This scheme leverages the powerful deep semantic modeling capability of large vision models to perform multi-granularity feature extraction equipment-room monitoring images, while simultaneously introducing an adaptive multi-scale fusion module to optimize feature representation capability. To balance computational efficiency and detection accuracy, a cloud-edge collaborative architecture is designed. The cloud hosts the complete large vision model and is responsible for high-precision inference in complex scenarios and model iteration, while the edge side deploys a lightweight inference module for preprocessing, real-timedetection, and fault early warning of inspection images, ensuring the timeliness and safety of equipment room operation and maintenance. To validate the effectiveness of the proposed scheme, tests are conducted using real operational data from the equipment room of Dalian North Station on Dalian Metro Line 1, covering various equipment types. Experimental results show that the algorithm achieves an mAP@50 of 0.923 and an F1-score of 0.915 in equipment state detection tasks, outperforming mainstream inspection algorithms in overall detection performance. Economic benefit analysis indicates that the scheme incurs an average annual operating cost of approximately RMB 440,000 yuan, representing an annual saving of about RMB 460,000 yuan per year compared with traditional manual and periodic maintenance approaches, demonstrating sound economic feasibility. Furthermore, the scheme adopts a general-purpose industrial hardware acquisition architecture, standardized data access protocols, and a modular algorithm design, offering strong scenario adaptability. It is not limited to urban rail transit equipment rooms and can be extended to other rail transit scenarios such as subway tunnels and stations.

, authors=Lianbao Yang, Zhuangzhuang Miao, Xin Xie, authorsList=Lianbao Yang, Zhuangzhuang Miao, Xin Xie, 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=1308454736194195489, articleId=1308454735246282783, tenantId=1146029695717560320, journalId=1146123302524792850, language=CN, title=基于视觉大模型的城轨机房智能巡检方法与技术研究, columnId=1308453883420897634, journalTitle=都市快轨交通, columnName=人工智能, runingTitle=null, highlight=null, articleAbstract=

针对城轨机房智能巡检算法在复杂环境中特征提取能力弱、多源数据适应性差的问题，本文提出一种基于视觉大模型的城轨机房智能巡检创新方案。该方案利用视觉大模型强大的深层语义建模能力，对机房监控图像进行多粒度特征提取，同时引入自适应多尺度融合模块，以优化特征表征能力。为兼顾计算效率与识别精度，本方案设计云-边协同架构。云端部署完整视觉大模型，负责复杂场景下的高精度推理与模型迭代，边缘端则部署轻量化推理模块，实现巡检图像的预处理、实时检测和故障预警，保障机房运维的即时性与安全性。为验证所提方案的有效性，基于大连地铁1号线大连北站机房真实运营数据开展测试验证，涵盖多种设备类型。实验结果表明，该算法在设备状态识别任务中mAP@50达0.923, F1分数为0.915，综合检测性能优于主流巡检算法。经济效益分析显示，方案年均运营成本约44万元，较传统人工及定期维护方案年均节约46万元，具备良好的经济可行性。此外，本方案采用通用工业硬件采集架构、标准化数据接入协议与模块化算法设计，场景适配性强，不局限于城轨机房，还可拓展应用于地铁隧道、车站等轨道交通场景。

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杨连报，男，博士，高级工程师，研究方向为大数据与人工智能等新技术应用，yanglianbao@hollysys.com

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杨连报，男，博士，高级工程师，研究方向为大数据与人工智能等新技术应用，yanglianbao@hollysys.com

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