Article(id=1281608155574538834, tenantId=1146029695717560320, journalId=1146031787341344770, issueId=1281608117335068953, articleNumber=null, orderNo=null, doi=10.16265/j.cnki.issn1003-3033.2026.03.0477, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1757779200000, receivedDateStr=2025-09-14, revisedDate=1764691200000, revisedDateStr=2025-12-03, acceptedDate=null, acceptedDateStr=null, onlineDate=1783488795416, onlineDateStr=2026-07-08, pubDate=1774627200000, pubDateStr=2026-03-28, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1783488795416, onlineIssueDateStr=2026-07-08, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1783488795416, creator=13701087609, updateTime=1783488795416, updator=13701087609, issue=Issue{id=1281608117335068953, tenantId=1146029695717560320, journalId=1146031787341344770, year='2026', volume='36', issue='3', pageStart='1', pageEnd='270', issueExtLink='null', onlineDate='null', pubDate='1774627200000', pubDateStr='2026-03-28', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1783488786300, creator='13701087609', updateTime=1783588670896, updator='13701087609', preIssue=null, nextIssue=null, articleTotal=null, ext={EN=IssueExt(id=1282027063758533259, tenantId=1146029695717560320, journalId=1146031787341344770, issueId=1281608117335068953, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1282027063758533260, tenantId=1146029695717560320, journalId=1146031787341344770, issueId=1281608117335068953, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null, downloadFileDto=null}, startPage=58, endPage=65, ext={EN=ArticleExt(id=1281608157122237011, articleId=1281608155574538834, tenantId=1146029695717560320, journalId=1146031787341344770, language=EN, title=Cascading failure evolution model of safety risks in UAV logistics distribution based on complex network, columnId=1277328386947150682, journalTitle=China Safety Science Journal, columnName=Safety Science Theories and Methods, runingTitle=null, highlight=null, articleAbstract=

To enhance the safety risk management level of UAV logistics distribution, a cascading failure evolution model for UAV logistics distribution safety risks was constructed based on complex network theory. Through simulated evolution experiments, the risk evolution characteristics in specific scenarios of UAV logistics distribution were revealed. By analyzing 63 interview transcripts, safety risk factors and their interrelationships were identified, and the credibility of these findings was verified by comparing them with relevant literature analysis results. The safety risk factors were categorized into five types: personnel, machinery, materials, methods, and environment. A directed weighted complex network with 69 nodes and 469 edges was then constructed. Using the Gephi platform, the overall topological structure characteristics of the complex network were calculated and analyzed, confirming the necessity of dynamic risk evolution analysis. The importance levels of nodes in the complex network were classified as high, medium, and general, and the development stages were classified as latent, diffusion, and occurrence, which laid the foundation for the construction of the dynamic cascading failure evolution model. Risk propagation probability and risk load redistribution rules were defined to quantitatively characterize risk evolution. A Python program was designed to conduct simulation evolution experiments. Key experimental groups were defined, and the evolution network of UAV logistics distribution safety risks was constructed. The simulated evolution results were analyzed from dimensions such as key evolution nodes and key evolution paths. The results show that in the evolution network, nodes s15, s17, and s18 in the machinery category are the most important, and the key evolution paths starting from this category of factors are the most abundant. The loss of control of nodes s54 and s57 in the environment category is the primary cause of control failure in the machinery category nodes. Therefore, strategies should be formulated to focus on controlling factors in the environment and machinery categories, such as immature external supervision and equipment signal issues, to achieve forward shifting of safety risk control.

, authors=Pei QIU¹, Fan LUO^{**, 1}, Cheng MA², authorsList=Pei QIU, Fan LUO, Cheng MA, authorCompany=null, correspAuthors=Fan LUO, 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=1281608157696856662, articleId=1281608155574538834, tenantId=1146029695717560320, journalId=1146031787341344770, language=CN, title=基于复杂网络的无人机物流配送安全风险级联失效演化模型, 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=

为提升无人机（UAV）物流配送安全风险管理水平，基于复杂网络理论构建无人机物流配送安全风险级联失效演化模型，运用仿真演化试验揭示无人机物流配送特定场景中的风险演化特征。针对63份访谈文本分析安全风险因素及其作用关系，对比相关文献的分析结果以验证访谈文本分析结果的可信度；将安全风险因素梳理为人、机、料、法、环（4MIE）5类，构建69个节点、469条连边组成的有向加权复杂网络；基于Gephi平台计算分析复杂网络整体拓扑结构特征，明确风险动态演化分析的必要性；将复杂网络中节点的重要度分为高度、中度、一般，所处的发展阶段划分为潜伏、扩散、发生，为动态级联失效演化模型构建奠定基础；定义风险传播概率、风险负荷重分配规则等，以定量刻画风险演化，设计Python程序进行仿真演化试验；设置关键试验组并构建无人机物流配送安全风险的演化网络，从关键演化节点、关键演化路径等维度分析仿真演化结果。研究结果表明：演化网络中“机”类s15、s17、s18等节点重要度最高，且该类因素为起点的关键演化路径最丰富；“环”类节点s54、s57失控是导致“机”类节点失控的主要源头，故应制定策略重点管控环、机类因素，如外部监管不成熟、设备信号问题等，实现安全风险管控关口前移。

, authors=邱佩¹, 罗帆教授^{**, 1}, 马成², authorsList=邱佩, 罗帆教授, 马成, authorCompany=null, correspAuthors=罗帆教授, authorNote=, correspAuthorsNote=, copyrightStatement=null, copyrightOwner=null, extLink=null, articleAbsUrl=null, sourceXml=a0tjH0E7LA92vFpLI/jjsw==, magXml=yuIIfhXCe0C+WwXq9bxz4g==, pdfUrl=null, pdf=EEDLadDXF16kNaOl5maX4g==, pdfFileSize=6394830, pdfExtLink=null, richHtmlUrl=null, mobilePdfUrl=null, reviewReport=null, pdfFirstPage=null, abstractGraph=null, abstractGraphContent=null, abstractVideo=null, citation=null, cebUrl=null, magXmlContent=N2PvGTB9Du2h2KW4y8RIfA==, mapNumber=null, fund=null)}, authors=[Author(id=1281608158099509854, tenantId=1146029695717560320, journalId=1146031787341344770, articleId=1281608155574538834, orderNo=0, firstName=null, middleName=null, lastName=null, nameCn=null, orcid=null, stid=null, country=null, authorPic=null, dead=0, email=3516771628@qq.com, emailSecond=null, emailThird=null, correspondingAuthor=0, authorType=1, ext={EN=AuthorExt(id=1281608158195978848, tenantId=1146029695717560320, journalId=1146031787341344770, articleId=1281608155574538834, authorId=1281608158099509854, language=EN, stringName=Pei QIU, firstName=Pei, middleName=null, lastName=QIU, prefix=null, suffix=null, authorComment=null, nameInitials=null, affiliation=null, department=null, xref=¹, address=¹School of Management, Wuhan University of Technology, Wuhan Hubei 430070, China, bio=null, bioImg=null, bioContent=null, aboutCorrespAuthor=null), CN=AuthorExt(id=1281608158267282017, tenantId=1146029695717560320, journalId=1146031787341344770, articleId=1281608155574538834, authorId=1281608158099509854, language=CN, stringName=邱佩, firstName=null, middleName=null, lastName=null, prefix=null, suffix=null, authorComment=null, nameInitials=null, affiliation=null, department=null, xref=¹, address=¹武汉理工大学 管理学院，湖北 武汉 430070, bio={"content":"

邱佩（2000—），女，江西赣州人，硕士研究生，主要研究方向为安全风险与应急管理等方面。E-mail:3516771628@qq.com。

"}, bioImg=null, bioContent=

邱佩（2000—），女，江西赣州人，硕士研究生，主要研究方向为安全风险与应急管理等方面。E-mail:3516771628@qq.com。

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