Article(id=1281608108518641878, tenantId=1146029695717560320, journalId=1146031787341344770, issueId=1281608054340829934, articleNumber=null, orderNo=null, doi=10.16265/j.cnki.issn1003-3033.2026.04.1106, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1763049600000, receivedDateStr=2025-11-14, revisedDate=1769011200000, revisedDateStr=2026-01-22, acceptedDate=null, acceptedDateStr=null, onlineDate=1783488784197, onlineDateStr=2026-07-08, pubDate=1777305600000, pubDateStr=2026-04-28, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1783488784197, onlineIssueDateStr=2026-07-08, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1783488784197, creator=13701087609, updateTime=1783488784197, updator=13701087609, issue=Issue{id=1281608054340829934, tenantId=1146029695717560320, journalId=1146031787341344770, year='2026', volume='36', issue='4', pageStart='1', pageEnd='286', issueExtLink='null', onlineDate='null', pubDate='1777305600000', pubDateStr='2026-04-28', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1783488771281, creator='13701087609', updateTime=1783588648031, updator='13701087609', preIssue=null, nextIssue=null, articleTotal=null, ext={EN=IssueExt(id=1282026967885124001, tenantId=1146029695717560320, journalId=1146031787341344770, issueId=1281608054340829934, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1282026967885124002, tenantId=1146029695717560320, journalId=1146031787341344770, issueId=1281608054340829934, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null, downloadFileDto=null}, startPage=19, endPage=27, ext={EN=ArticleExt(id=1281608108711579863, articleId=1281608108518641878, tenantId=1146029695717560320, journalId=1146031787341344770, language=EN, title=Recognition of construction workers' unsafe behaviors based on a multi-component topology graph convolutional network, columnId=1277328386947150682, journalTitle=China Safety Science Journal, columnName=Safety Science Theories and Methods, runingTitle=null, highlight=null, articleAbstract=

In order to effectively identify the unsafe behaviors of construction workers in high-altitude environments, a recognition model based on MCT-GCN was proposed. Firstly, a data preprocessing module was designed to convert video surveillance data into three-dimensional skeleton data. Secondly, a tri-component dynamic adjacency graph convolution was constructed. It integrated learnable structural prior topology, channel correlation adaptive topology, and relative position encoding topology to dynamically build adjacency matrices adapted to different actions. Furthermore, a multi-scale separable temporal convolution was proposed to decompose standard convolutions into deep temporal convolution and point-by-point convolution, thereby independently modeling the temporal characteristics and spatial distribution characteristics of construction workers' actions. Finally, experimental verification and comparative analysis were conducted on both public datasets and a self-built dataset of workers' unsafe behaviors. The results demonstrate that the proposed model outperforms existing methods in terms of recognition accuracy and cross-scene generalization. On the self-built dataset, the model achieved a peak recognition accuracy of 95.8%, significantly enhancing the detection of workers' unsafe behaviors in complex and dynamic construction environments, and making a significant contribution to the development of intelligent monitoring and control in the construction industry.

, authors=Yang Yu¹, Lin Jiang¹, Qijun Hu^{**, 2}, Leping He³, Qijie Cai³, Yu Bai³, authorsList=Yang Yu, Lin Jiang, Qijun Hu, Leping He, Qijie Cai, Yu Bai, authorCompany=null, correspAuthors=Qijun Hu, 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=1281608109214896346, articleId=1281608108518641878, tenantId=1146029695717560320, journalId=1146031787341344770, language=CN, title=基于多元拓扑图卷积网络的建筑工人不安全行为识别, columnId=1277328387270112092, journalTitle=中国安全科学学报, columnName=安全科学理论与方法, runingTitle=null, highlight=null, articleAbstract=

为有效识别高空作业环境下建筑工人的不安全行为，提出一种基于多元拓扑图卷积网络（MCT-GCN）的工人不安全行为识别模型。首先，设计数据预处理模块，将视频监控数据转换为三维骨架数据；其次，构建三元动态邻接图卷积，融合可学习结构先验拓扑、通道相关性自适应拓扑和相对位置编码拓扑，动态构建出适应不同动作的邻接矩阵；然后，提出多尺度可分离时间卷积，将标准卷积分解为深度时序卷积与逐点卷积，分别对施工人员的动作时序特征和空间分布特征独立建模；最后，在公共数据集和自建的工人不安全行为数据集上进行试验验证和对比分析。结果表明：该模型在识别准确率和跨场景泛化能力上均优于现有方法；在自建的数据集上，该模型达到95.8%的最高识别准确率，能够显著提升复杂动态施工环境下工人不安全行为的识别准确度。

, authors=余洋讲师¹, 蒋琳¹, 胡启军教授^{**, 2}, 何乐平副教授³, 蔡其杰讲师³, 白羽讲师³, authorsList=余洋讲师, 蒋琳, 胡启军教授, 何乐平副教授, 蔡其杰讲师, 白羽讲师, authorCompany=null, correspAuthors=胡启军教授, authorNote=, correspAuthorsNote=, copyrightStatement=null, copyrightOwner=null, extLink=null, articleAbsUrl=null, sourceXml=L9qz6SbwTldCpGYgEwQ5Bg==, magXml=GoRMazI+Y5NMlQpuHwHyng==, pdfUrl=null, pdf=A8kO/f3iwcoU5PWcLyorNA==, pdfFileSize=2131524, pdfExtLink=null, richHtmlUrl=null, mobilePdfUrl=null, reviewReport=null, pdfFirstPage=null, abstractGraph=null, abstractGraphContent=null, abstractVideo=null, citation=null, cebUrl=null, magXmlContent=PoWP70bKWLVTRrcEKcnQYg==, mapNumber=null, fund=null)}, authors=[Author(id=1281608109730795749, tenantId=1146029695717560320, journalId=1146031787341344770, articleId=1281608108518641878, orderNo=0, firstName=null, middleName=null, lastName=null, nameCn=null, orcid=null, stid=null, country=null, authorPic=null, dead=0, email=sccdyuyang@163.com, emailSecond=null, emailThird=null, correspondingAuthor=0, authorType=1, ext={EN=AuthorExt(id=1281608109806293223, tenantId=1146029695717560320, journalId=1146031787341344770, articleId=1281608108518641878, authorId=1281608109730795749, language=EN, stringName=Yang Yu, firstName=Yang, middleName=null, lastName=Yu, prefix=null, suffix=null, authorComment=null, nameInitials=null, affiliation=null, department=null, xref=¹, address=¹School of Computer Science and Software Engineering, Southwest Petroleum University, Chengdu Sichuan 610500, China, bio=null, bioImg=null, bioContent=null, aboutCorrespAuthor=null), CN=AuthorExt(id=1281608109869207785, tenantId=1146029695717560320, journalId=1146031787341344770, articleId=1281608108518641878, authorId=1281608109730795749, language=CN, stringName=余洋讲师, firstName=null, middleName=null, lastName=null, prefix=null, suffix=null, authorComment=null, nameInitials=null, affiliation=null, department=null, xref=¹, address=¹西南石油大学 计算机与软件学院，四川 成都 610500, bio={"content":"

余洋（1987—），男，四川泸州人，博士，讲师，硕士生导师，主要从事人因安全、工程安全、工业互联网、工业大数据等方面的研究。E-mail: sccdyuyang@163.com。

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余洋（1987—），男，四川泸州人，博士，讲师，硕士生导师，主要从事人因安全、工程安全、工业互联网、工业大数据等方面的研究。E-mail: sccdyuyang@163.com。

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