Article(id=1281608090743193622, tenantId=1146029695717560320, journalId=1146031787341344770, issueId=1281608054340829934, articleNumber=null, orderNo=null, doi=10.16265/j.cnki.issn1003-3033.2026.04.1582, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1765641600000, receivedDateStr=2025-12-14, revisedDate=1771862400000, revisedDateStr=2026-02-24, acceptedDate=null, acceptedDateStr=null, onlineDate=1783488779960, onlineDateStr=2026-07-08, pubDate=1777305600000, pubDateStr=2026-04-28, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1783488779960, onlineIssueDateStr=2026-07-08, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1783488779960, creator=13701087609, updateTime=1783488779960, 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=271, endPage=280, ext={EN=ArticleExt(id=1281608091196178456, articleId=1281608090743193622, tenantId=1146029695717560320, journalId=1146031787341344770, language=EN, title=Quality and safety risk analysis of intelligent consumer products based on LLMs and KG, columnId=1281608091074543639, journalTitle=China Safety Science Journal, columnName=Intelligent Safety Technology, runingTitle=null, highlight=null, articleAbstract=

To effectively identify and control quality and safety risk of intelligent consumer products, a KG for intelligent consumer product quality and safety risk was constructed based on LLMs. Complex network analysis methods were integrated to systematically analyze risk distribution characteristics and propagation mechanisms. Data on intelligent consumer products were collected through multiple channels; the quality and safety risk issues in product accident cases were sorted out. Based on safety theories and the system structure of intelligent products, a multi-level and extensible knowledge ontology for quality and safety risks was constructed. Under the constraints of this ontology, LLMs were utilized to achieve automated knowledge extraction, and a KG containing 16 611 nodes and 32 178 edges was constructed. The KG was mapped into a risk network. Based on complex network theory, centrality indicators of three key node types—hazard factors, accident injuries, and safety events—were calculated to identify critical nodes and propagation paths in risk transmission. The research results show that among various intelligent consumer products, intelligent home products account for the highest proportion of risk entities; physical hazards and information hazards are the main risk types; property damage and physical injury are the primary accident consequences. Typical direct propagation paths and cascading propagation paths can be identified by the risk network.

, authors=Yuexiang Yang¹, Xuewen Liu¹, Xinyu Tu¹, Huaicheng Zheng¹, Yingcheng Xu^{**, 2}, authorsList=Yuexiang Yang, Xuewen Liu, Xinyu Tu, Huaicheng Zheng, Yingcheng Xu, authorCompany=null, correspAuthors=Yingcheng Xu, 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=1281608091963736092, articleId=1281608090743193622, tenantId=1146029695717560320, journalId=1146031787341344770, language=CN, title=基于LLMs与KG的智能消费产品质量安全风险分析, columnId=1281608091288453145, journalTitle=中国安全科学学报, columnName=智能安全技术, runingTitle=null, highlight=null, articleAbstract=

为有效识别和控制智能消费产品质量安全风险，基于大语言模型（LLMs）构建智能消费产品质量安全风险知识图谱（KG），融合复杂网络分析方法，系统分析风险分布特征和传播机制。通过多渠道采集智能消费产品相关数据，梳理产品事故案例中的质量安全风险问题，结合安全理论与智能产品系统结构，构建多层次、可扩展的质量安全风险知识本体。在本体约束下利用LLMs实现知识自动化抽取，构建包含16 611个节点和32 178条边的KG。将KG映射为风险网络，基于复杂网络理论，计算危害因素、事故伤害、安全事件3类关键节点的中心性指标，识别风险传播的关键节点和传播路径。结果表明：在各类智能消费产品中，智能家居类产品的风险实体数量占比最高，物理危害和信息危害为主要风险类型，财产损失和物理伤害为主要事故后果；通过风险网络能够识别典型直接传播路径和级联传播路径。

, authors=杨跃翔教授¹, 刘学文¹, 涂新雨¹, 郑怀城¹, 许应成研究员^{**, 2}, authorsList=杨跃翔教授, 刘学文, 涂新雨, 郑怀城, 许应成研究员, authorCompany=null, correspAuthors=许应成研究员, authorNote=, correspAuthorsNote=, copyrightStatement=null, copyrightOwner=null, extLink=null, articleAbsUrl=null, sourceXml=iT8XzmqOYdj9CIA9rbEETQ==, magXml=/FXktvH1nUlatAcuoxqhoA==, pdfUrl=null, pdf=UpPk98ekvX/4HLNqgZBfug==, pdfFileSize=9393091, pdfExtLink=null, richHtmlUrl=null, mobilePdfUrl=null, reviewReport=null, pdfFirstPage=null, abstractGraph=null, abstractGraphContent=null, abstractVideo=null, citation=null, cebUrl=null, magXmlContent=8IFuzUFhcRnoyHVDfkj0Ig==, mapNumber=null, fund=null)}, authors=[Author(id=1281608092429303844, tenantId=1146029695717560320, journalId=1146031787341344770, articleId=1281608090743193622, orderNo=0, firstName=null, middleName=null, lastName=null, nameCn=null, orcid=null, stid=null, country=null, authorPic=null, dead=0, email=201901@cumtb.edu.cn, emailSecond=null, emailThird=null, correspondingAuthor=0, authorType=1, ext={EN=AuthorExt(id=1281608093662429222, tenantId=1146029695717560320, journalId=1146031787341344770, articleId=1281608090743193622, authorId=1281608092429303844, language=EN, stringName=Yuexiang Yang, firstName=Yuexiang, middleName=null, lastName=Yang, prefix=null, suffix=null, authorComment=null, nameInitials=null, affiliation=null, department=null, xref=¹, address=¹School of Management, China University of Mining and Technology-Beijing, Beijing 100083, China, bio=null, bioImg=null, bioContent=null, aboutCorrespAuthor=null), CN=AuthorExt(id=1281608093733732391, tenantId=1146029695717560320, journalId=1146031787341344770, articleId=1281608090743193622, authorId=1281608092429303844, language=CN, stringName=杨跃翔教授, firstName=null, middleName=null, lastName=null, prefix=null, suffix=null, authorComment=null, nameInitials=null, affiliation=null, department=null, xref=¹, address=¹中国矿业大学（北京） 管理学院，北京 100083, bio={"content":"

杨跃翔（1976—），男，黑龙江齐齐哈尔人，博士，研究员，主要从事质量安全、人工智能、知识图谱、数据挖掘等方面的研究。E-mail:201901@cumtb.edu.cn。

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杨跃翔（1976—），男，黑龙江齐齐哈尔人，博士，研究员，主要从事质量安全、人工智能、知识图谱、数据挖掘等方面的研究。E-mail:201901@cumtb.edu.cn。

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