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<article xsi:noNamespaceSchemaLocation="http://jats.nlm.nih.gov/publishing/1.1/xsd/JATS-journalpublishing1-mathml3.xsd" dtd-version="1.1" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"><front><journal-meta><journal-id journal-id-type="publisher-id">ERA</journal-id><journal-title-group><journal-title>Engineering Research and Application</journal-title></journal-title-group><issn>2995-3154</issn><eissn>2993-2742</eissn><publisher><publisher-name>Art and Technology</publisher-name></publisher></journal-meta><article-meta><article-id pub-id-type="doi">10.61369/ERA.2026060042</article-id><article-categories><subj-group subj-group-type="heading"><subject>Article</subject></subj-group></article-categories><title>基于预训练模型适配的烟草营销领域智能查询增强技术研究</title><url>https://artdesignp.com/journal/ERA/4/6/10.61369/ERA.2026060042</url><author>李一平,舒刚,张滔,祝凯,程康</author><pub-date pub-type="publication-year"><year>2026</year></pub-date><volume>4</volume><issue>6</issue><history><date date-type="pub"><published-time>2026-06-20</published-time></date></history><abstract>针对XX 烟草企业营销数据孤岛严重、业务人员数据分析能力不强、需求响应迟缓等问题，本文结合业务需求及现有技术，提出融合湖仓一体架构与改进型NL2SQL 技术的查询增强方案，以满足企业技术需求。</abstract><keywords>查询增强,NL2SQL,维度建模,烟草营销</keywords></article-meta></front><body/><back><ref-list><ref id="B1" content-type="article"><label>1</label><element-citation publication-type="journal"><p>[1] Gartner. Hype Cycle for Data Management, 2022[R]. Stamford: Gartner, 2022.[2] Zaharia M, Ghodsi A, Xin R, et al. Lakehouse: A New Generation of Open Platforms that Unify Data Warehousing and Advanced Analytics[C]// Proceedings of the 11th Conference on Innovative Data Systems Research (CIDR). Online: www.cidrdb.org, 2021.[3] Elizabeth M. Pierce, Song I Y. Dimensional modeling: Identification, classification, and evaluation of patterns[J]. Decision Support Systems, 2008, 45(1): 59-76.[4] Wang B, Shin R, Liu X, et al. RAT-SQL: Relation-Aware Schema Encoding and Linking for Text-to-SQL Parsers[C]// Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics (ACL). Online: ACL, 2020: 7567-7578.[5] Sun Z, Deng Z H, Nie J Y, Tang J. RotatE: Knowledge Graph Embedding by Relational Rotation in Complex Space[C]// Proceedings of the 7th International Conference on Learning Representations (ICLR). New Orleans: OpenReview.net, 2019.[6] Hu E J, Shen Y, Wallis P, et al. LoRA: Low-Rank Adaptation of Large Language Models[C]// Proceedings of the 10th International Conference on Learning Representations (ICLR). Online: OpenReview.net, 2022.</p><pub-id pub-id-type="doi"/></element-citation></ref></ref-list></back></article>
