arXiv 2409.012072024-12-27cs.LG

迈向通用工业智能：工业物联网中持续大模型综述

Towards General Industrial Intelligence: A Survey of Continual Large Models in Industrial IoT

Shien-Ming Wu School of Intelligent Engineering, South China University of Technology（华南理工大学伍舜德智能工程学院）；School of Mechanical and Automotive Engineering, South China University of Technology（华南理工大学机械与汽车工程学院）；Pazhou Lab（琶洲实验室）；ZJU-UIUC Institute, Zhejiang University（浙江大学ZJU-UIUC学院）；Key Laboratory of Maritime Intelligent Network Information Technology, Ministry of Education, Hohai University（河海大学海洋智能网络信息技术教育部重点实验室）

Jiao Chen, Jiayi He, Fangfang Chen, Zuohong Lv, Jianhua Tang, Weihua Li, Zuozhu Liu, Howard H. Yang, Guangjie Han

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AI总结：

本文综述了工业物联网与大模型的融合，系统梳理语言、视觉、时间序列和多模态四类工业大模型在数据基础、训练、连接与持续演化四个生命周期阶段中的关键作用，为工业智能演进提供理论框架。

摘要：

工业人工智能正从传统深度学习模型向基于Transformer的大规模架构过渡，工业物联网（IIoT）在其中发挥着关键作用。IIoT从简单的数据管道演进为智能基础设施，赋能并增强这些先进的人工智能系统。本综述探讨了IIoT与大模型（LMs）的融合及其在工业环境中的潜在应用。我们聚焦于四类主要的工业大模型：基于语言的、基于视觉的、时间序列的和多模态模型。大模型的生命周期被划分为四个关键阶段：数据基础、模型训练、模型连接和持续演化。首先，我们分析了IIoT如何提供丰富多样的数据资源，支持大模型的训练和微调。其次，我们讨论了IIoT如何在低延迟和带宽优化的环境中提供高效的训练基础设施。第三，我们强调了大模型在IIoT中的部署优势，突出IIoT作为连接枢纽的作用，通过模块化设计、动态路由和模型合并促进涌现智能，以增强系统的可扩展性和适应性。最后，我们展示了IIoT如何支持持续学习机制，使大模型能够适应动态工业条件并确保长期有效性。本文强调了IIoT在基于大模型的工业智能演进中的关键作用，为未来研究提供了理论框架和可操作的见解。

英文摘要：

Industrial AI is transitioning from traditional deep learning models to large-scale transformer-based architectures, with the Industrial Internet of Things (IIoT) playing a pivotal role. IIoT evolves from a simple data pipeline to an intelligent infrastructure, enabling and enhancing these advanced AI systems. This survey explores the integration of IIoT with large models (LMs) and their potential applications in industrial environments. We focus on four primary types of industrial LMs: language-based, vision-based, time-series, and multimodal models. The lifecycle of LMs is segmented into four critical phases: data foundation, model training, model connectivity, and continuous evolution. First, we analyze how IIoT provides abundant and diverse data resources, supporting the training and fine-tuning of LMs. Second, we discuss how IIoT offers an efficient training infrastructure in low-latency and bandwidth-optimized environments. Third, we highlight the deployment advantages of LMs within IIoT, emphasizing IIoT's role as a connectivity nexus fostering emergent intelligence through modular design, dynamic routing, and model merging to enhance system scalability and adaptability. Finally, we demonstrate how IIoT supports continual learning mechanisms, enabling LMs to adapt to dynamic industrial conditions and ensure long-term effectiveness. This paper underscores IIoT's critical role in the evolution of industrial intelligence with large models, offering a theoretical framework and actionable insights for future research.

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