Article(id=1281627236386189998, tenantId=1146029695717560320, journalId=1146031787341344770, issueId=1281627156769903305, articleNumber=null, orderNo=null, doi=10.16265/j.cnki.issn1003-3033.2026.01.0157, 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=1763568000000, revisedDateStr=2025-11-20, acceptedDate=null, acceptedDateStr=null, onlineDate=1783493344636, onlineDateStr=2026-07-08, pubDate=1769529600000, pubDateStr=2026-01-28, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1783493344636, onlineIssueDateStr=2026-07-08, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1783493344636, creator=13701087609, updateTime=1783493344636, updator=13701087609, issue=Issue{id=1281627156769903305, tenantId=1146029695717560320, journalId=1146031787341344770, year='2026', volume='36', issue='1', pageStart='1', pageEnd='280', issueExtLink='null', onlineDate='null', pubDate='1769529600000', pubDateStr='2026-01-28', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1783493325654, creator='13701087609', updateTime=1783588714348, updator='13701087609', preIssue=null, nextIssue=null, articleTotal=null, ext={EN=IssueExt(id=1282027246013624992, tenantId=1146029695717560320, journalId=1146031787341344770, issueId=1281627156769903305, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1282027246013624993, tenantId=1146029695717560320, journalId=1146031787341344770, issueId=1281627156769903305, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null, downloadFileDto=null}, startPage=249, endPage=256, ext={EN=ArticleExt(id=1281627236583322287, articleId=1281627236386189998, tenantId=1146029695717560320, journalId=1146031787341344770, language=EN, title=A clustering algorithm-based approach to pilot psychological health profiling, columnId=1149735805633081985, journalTitle=China Safety Science Journal, columnName=Occupational Health, runingTitle=null, highlight=null, articleAbstract=

In order to ensure flight safety and enhance operational efficiency, a classification method for civil aviation pilots' psychological health characteristics was developed based on machine learning clustering algorithms. Firstly, 215 active airline pilots were selected as research subjects. Psychological health data across two dimensions—psychological traits and psychological states—were collected using a pilot psychological competency assessment system. Subsequently, principal component analysis was employed for dimensionality reduction, and the elbow method was utilized to determine the optimal number of clusters. The K-means algorithm was applied to classify the samples into four distinct psychological health categories, facilitating the analysis of psychological characteristic differences among groups. Finally, visualization techniques were implemented based on the clustering outcomes to construct individual and group psychological health profiles. Radar charts were adopted to illustrate the distribution patterns of psychological characteristics across different pilot categories. The psychological health structure of pilots has group heterogeneity and diverse expression patterns. The proposed clustering algorithm can transform pilots' psychological health data into observable group feature portraits, achieving systematic identification and visualization of pilot psychological states at the operational level.

, authors=Lei WANG, Qiyu YANG, Ruiyuan HONG, authorsList=Lei WANG, Qiyu YANG, Ruiyuan HONG, 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=1281627237124387506, articleId=1281627236386189998, tenantId=1146029695717560320, journalId=1146031787341344770, language=CN, title=基于聚类算法的民航飞行员心理健康画像研究, columnId=1281608104106242978, journalTitle=中国安全科学学报, columnName=职业健康, runingTitle=null, highlight=null, articleAbstract=

为保障飞行员的飞行安全和提升工作效率，基于机器学习聚类算法，提出民航飞行员心理健康特征分类方法。首先，选取215名现役航线飞行员作为被试，通过飞行员心理胜任力测评系统，采集心理品质和心理状态2个维度的心理健康数据；然后，采用主成分分析法对数据进行特征降维，并结合肘部法确定最优聚类簇数，运用K-means算法实现样本分类，将飞行员划分为4类心理健康状况，用以分析各类群体的心理特征差异；最后，基于聚类结果进行可视化操作，进一步表征飞行员心理健康个体与群体画像，采用雷达图呈现不同类别飞行员的心理特征分布模式。结果表明：飞行员心理健康结构具有群体异质性和多元表现模式，提出的聚类算法可将抽象的飞行员心理健康数据转化为可观测的群体特征画像，在操作层面实现对飞行员心理状态的系统化辨识与可视化表达。

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汪磊（1982—），男，安徽霍山人，博士，研究员，博士生导师，主要从事航空安全与人为因素等方面的研究。E-mail: wanglei0564@hotmail.com。

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汪磊（1982—），男，安徽霍山人，博士，研究员，博士生导师，主要从事航空安全与人为因素等方面的研究。E-mail: wanglei0564@hotmail.com。

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