用大模型让工业自动化系统能听懂人话，自动应对突发情况。
Control Industrial Automation System with Large Language Model Agents
- 设计工业任务专用智能体，结合实时事件驱动机制
- 支持自然语言指令生成生产计划并控制设备运行
- 适合想简化工业系统操作的工程师和研究人员
传统工业自动化系统操作需专业技能，新流程调整需复杂重编程。大语言模型（LLM）可提升其灵活性与易用性，但其在工业场景的应用仍较少。本文提出一个端到端控制框架，核心包括专为工业任务设计的智能体系统、结构化提示方法，以及事件驱动的信息建模机制，为LLM推理提供多语义层级的实时数据。该框架使LLM能够解析信息、生成生产计划并控制自动化系统，同时支持特定任务数据集的结构化构建，用于微调与测试。我们贡献了系统正式设计、概念验证实现及下游应用的数据生成方法。该方案使自动化系统更具适应性，能响应突发事件，并通过自然语言实现更直观的人机交互。项目演示视频与数据已公开于GitHub：https://github.com/YuchenXia/LLM4IAS。
原文摘要 · Abstract (English)
Traditional industrial automation systems require specialized expertise to operate and complex reprogramming to adapt to new processes. Large language models offer the intelligence to make them more flexible and easier to use. However, LLMs' application in industrial settings is underexplored. This paper introduces a framework for integrating LLMs to achieve end-to-end control of industrial automation systems. At the core of the framework are an agent system designed for industrial tasks, a structured prompting method, and an event-driven information modeling mechanism that provides real-time data for LLM inference. The framework supplies LLMs with real-time events on different context semantic levels, allowing them to interpret the information, generate production plans, and control operations on the automation system. It also supports structured dataset creation for fine-tuning on this downstream application of LLMs. Our contribution includes a formal system design, proof-of-concept implementation, and a method for generating task-specific datasets for LLM fine-tuning and testing. This approach enables a more adaptive automation system that can respond to spontaneous events, while allowing easier operation and configuration through natural language for more intuitive human-machine interaction. We provide demo videos and detailed data on GitHub: https://github.com/YuchenXia/LLM4IAS.
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