信息通信技术与政策2025，Vol.51Issue(10)：7-13,7.DOI:10.12267/j.issn.2096-5931.2025.10.002
基于本地算力基座的RAG-大模型融合物料主数据治理研究
Research on material master data governance based on the integration of large language models and RAG technology with local computing infrastructure
苏成金 1黄伟 2王骏成 2傅韵 2杨东旭3
作者信息
- 1. 国家能源集团物资有限公司,北京 102200
- 2. 中国信息通信研究院信息化与工业化融合研究所,北京 100191
- 3. 信通院(青岛)科技创新中心有限公司,青岛 266100
- 折叠
摘要
Abstract
As the cornerstone of enterprise digital transformation,the governance quality of Material Master Data(MMD)directly impacts an enterprise's operational efficiency and decision-making accuracy.When dealing with massive and heterogeneous datasets,traditional MMD governance methods generally face challenges like low automation,inefficiency,and high governance costs.To address these problems,this paper proposes an innovative framework integrating large language models and retrieval-augmented generation technology,conjunction with the actual business context of National Energy Group Materials Co.,Ltd..Built on a local computing architecture,the framework designs a clear technical implementation path and establishes a four-tier technical architecture,including computing infrastructure layer,model layer,data layer,and application capability hub layer.It achieves three core functions:duplicate detection for legacy materials,intelligent classification with context-aware recommendations,and automated parameter validation.In specific data governance scenarios,the solution significantly improves the accuracy rate and processing efficiency of data governance while effectively controlling governance costs.It provides solid technical support for the enterprise's digital transformation and aligns with the future trend of MMD management towards intelligence and automation.关键词
物料主数据/大模型/增强检索生成/智能分类/数据治理Key words
material master data/large language model/retrieval-augmented generation/data governance/intelligent classification分类
信息技术与安全科学引用本文复制引用
苏成金,黄伟,王骏成,傅韵,杨东旭..基于本地算力基座的RAG-大模型融合物料主数据治理研究[J].信息通信技术与政策,2025,51(10):7-13,7.
