Article(id=1282037456337879645, tenantId=1146029695717560320, journalId=1146031787341344770, issueId=1282037390130790473, articleNumber=null, orderNo=null, doi=10.16265/j.cnki.issn1003-3033.2025.12.0065, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1750003200000, receivedDateStr=2025-06-16, revisedDate=1759075200000, revisedDateStr=2025-09-29, acceptedDate=null, acceptedDateStr=null, onlineDate=1783591148694, onlineDateStr=2026-07-09, pubDate=1766851200000, pubDateStr=2025-12-28, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1783591148694, onlineIssueDateStr=2026-07-09, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1783591148694, creator=13701087609, updateTime=1783591148694, updator=13701087609, issue=Issue{id=1282037390130790473, tenantId=1146029695717560320, journalId=1146031787341344770, year='2025', volume='35', issue='12', pageStart='1', pageEnd='259', issueExtLink='null', onlineDate='null', pubDate='1766851200000', pubDateStr='2025-12-28', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1783591132910, creator='13701087609', updateTime=1783593783109, updator='13701087609', preIssue=null, nextIssue=null, articleTotal=null, ext={EN=IssueExt(id=1282048505929777979, tenantId=1146029695717560320, journalId=1146031787341344770, issueId=1282037390130790473, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1282048505929777980, tenantId=1146029695717560320, journalId=1146031787341344770, issueId=1282037390130790473, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null, downloadFileDto=null}, startPage=204, endPage=212, ext={EN=ArticleExt(id=1282037456526623326, articleId=1282037456337879645, tenantId=1146029695717560320, journalId=1146031787341344770, language=EN, title=Research on automobile safety risk discrimination utilizing LLMs-based agents: driven by complaint text, columnId=1149733270084042840, journalTitle=China Safety Science Journal, columnName=Public safety, runingTitle=null, highlight=null, articleAbstract=

To address the limitations of relying on manual extraction of knowledge from complaint texts in automotive safety risk management, this study employed LLMs for automated risk discrimination. First, over 50 000 complaint texts covering eight major subsystems such as the engine were collected. A demonstration sampling method based on Bilingual and Crosslingual Embedding(BCEmbedding) model and community detection algorithm was proposed to construct a diverse and high-quality example knowledge base. Secondly, prompts were designed from perspectives such as scenarios, skills, and examples to develop agents capable of performing risk keyword extraction and expansion, as well as subsystem risk categorization. Finally, by analyzing reasoning knowledge from retrieved texts and utilizing chain of thought techniques, a risk level knowledge base for retrieved texts was established. A consensus-seeking multi-agent(MA) system based on LLMs was designed, resulting in a discriminative model for automotive safety risk levels. The results show that the model not only reduces labor costs but also achieves high accuracy and efficiency. It effectively supports risk term extraction, risk categorization, and risk level assessment in complaint incidents, thereby enhancing safety risk management.

, authors=Yi YANG^{1, 2}, Donglin WANG¹, Zhensong CHEN^{**, 3}, authorsList=Yi YANG, Donglin WANG, Zhensong CHEN, authorCompany=null, correspAuthors=Zhensong CHEN, 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=1282037456975413857, articleId=1282037456337879645, tenantId=1146029695717560320, journalId=1146031787341344770, language=CN, title=基于大语言模型智能体的汽车安全风险判别：以投诉文本为驱动, columnId=1149733271510106222, journalTitle=中国安全科学学报, columnName=公共安全, runingTitle=null, highlight=null, articleAbstract=

为解决汽车安全风险管理中依赖人工提取投诉文本知识的局限性，应用大语言模型（LLMs）智能判别事件风险。首先，采集包含发动机等8大子系统的5万余条投诉文本，基于双语与跨语言嵌入（BCEmbedding）模型与社区发现算法，提出演示采样方法，构建多样化高质量示例集知识库；然后，从场景、技能以及示例等方面设计LLMs提示词，构建实现风险词抽取与扩充、子系统风险归类的智能体；最后，分析召回文本的推理知识，运用思维链技术构建召回文本风险等级知识库，设计基于共识寻求的LLMs多智能体（MA）系统，建立汽车安全风险等级判别模型。结果表明：该模型在节约人工成本的同时具有较好的准确性和效率，能够有效支持投诉事件中的风险词抽取、风险归类和风险等级判别，提升安全风险管理水平。

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²湘江实验室，湖南 长沙 410205, bio={"content":"

杨艺（1990—），男，湖南邵阳人，博士，副教授，主要从事大语言模型智能体、多层复杂网络、拓扑聚类、在线服务平台大数据挖掘等方面的研究。E-mail: yangyihnuc2018@163.com。

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杨艺（1990—），男，湖南邵阳人，博士，副教授，主要从事大语言模型智能体、多层复杂网络、拓扑聚类、在线服务平台大数据挖掘等方面的研究。E-mail: yangyihnuc2018@163.com。

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