差分拉曼光谱结合PCA-RCSC-Transformer对快递面单的检验研究

快递面单检验

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关键词：差分拉曼光谱；快递面单；正交约束主成分分析；元素比例-余弦相似度聚类中图分类号：TS77；0657.3 文献标识码：A DOI：10.11980/j. issn. 0254-508X.2025.11.023

Differential Raman Spectroscopy Combined with PCA-RCSC and Improved Transformer for Courier Face Sheets Inspection Research

JIANG Hong1，2.3.*MA Xingyu² （1.DepartmentofCriminalScienceandTechnology，Hu’nanPoliceCollege，Changsh，Hu’nanProvince，4138;.Collegeof Investigation，People'sPublicSecurityUniverstyofChinBeijing，Oo38;3.CenterofFrensicSienceofBeijingHuiZhengZuYue Technology Co.，Ltd.，Beijing，102446） （*E-mail： jiangh2001@163.com）

Abstract：Addressingthhallengesofasilyfdinghandwitingandstableflrcomponentsintealpaper-basedcourierfaceshets， thisstudycollteddatafro73exprssdlrybelsamplesfovarousansandprintingdatestroughdiferetialaaso copy，andproposedaovelmethodintegratingmodifiedortogonallonstrainedprincipalcomponntanalysis（CA），ndelntati cosinesimilariyustering（RCSC），ombinedwithaTansforerodelicorporatingasparseaentiomechanismfordataclasification prediction.TeresultssowedthatorthogoallonsraiedPCAeducedthedimensonofdierentialRamanspectralataandsultein a compression rate of 95.6% ， while RCSC supplemented by manual validation，categorized the samples into four classes.Further classification using the sparse attention-based Transformer model achieved an forecast accuracy of 90.0% ，significantly outperforming traditional methods such as random forest and support vector machines.

Keywords：DiferentialRamanspctrosopy；ourfcesets；rogoallyonstainedprincialomponntaalysis；lmetalati cosine similarity clustering

随着快递业的迅速发展，快递面单的使用日渐普遍。（剩余8480字）