软件导刊2025，Vol.24Issue(10)：1-14,14.DOI:10.11907/rjdk.251555
工业大模型应用技术:综述与展望
Survey and Perspective on Application Technologies of Industrial Foundation Models
摘要
Abstract
Industrial foundation models(IFMs)represent a new generation of artificial intelligence technologies deeply integrated with indus-trial scenarios,providing powerful technical support for reshaping the intelligent manufacturing ecosystem.With the advancement of the Fourth Industrial Revolution,IFMs characterized by excellent data-processing capabilities,cross-modal fusion features,and intelligent decision-making—have become the core technologies driving industrial digital-intelligence transformation and industrial intelligence.Howev-er,their deployment within industry remains at an early stage and faces challenges such as data bias,lack of domain-specific knowledge,and high computational costs;facilitating rapid adoption and widespread application has thus become an urgent problem to solve.Existing surveys tend to focus on the macro-level theoretical architectures,training methodologies,and representative industry case studies of large models,but they lack a systematic examination of the key technologies and application patterns in concrete industrial scenarios,making it difficult to offer comprehensive guidance for typical use cases.By summarizing the key enabling technologies of IFMs and reviewing major domestic and international application advances,this paper further elucidates the challenges encountered in practical deployments.We have analyzed the ar-chitectural framework of industrial foundation models.These models provide a theoretical foundation,essential technologies,and representa-tive applications as valuable references for future research.关键词
工业大模型/工业智改数转/工业智能/工业大模型架构体系/数据偏差Key words
Industrial Foundation Models(IFMs)/industrial digital-intelligence transformation/industrial intelligence/architectural frame-work of IFMs/data bias分类
计算机与自动化引用本文复制引用
秦小林,王宇,李弟诚,徐海文..工业大模型应用技术:综述与展望[J].软件导刊,2025,24(10):1-14,14.基金项目
国家重点研发计划项目(2023YFB3308601) （2023YFB3308601）
四川省委组织部人才专项(2024) （2024）
