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

翻译：传统的工业自动化系统需要专业知识进行操作，且需复杂的重新编程以适应新流程。大型语言模型提供了使其更灵活、更易用的智能能力。然而，LLMs在工业环境中的应用尚未得到充分探索。本文介绍了一种集成LLMs以实现工业自动化系统端到端控制的框架。该框架的核心包括一个为工业任务设计的智能体系统、一种结构化提示方法，以及一个为LLM推理提供实时数据的事件驱动信息建模机制。该框架为LLMs提供不同上下文语义层级的实时事件，使其能够解释信息、生成生产计划并控制自动化系统的操作。它还支持为LLMs在此下游应用上的微调创建结构化数据集。我们的贡献包括一个正式的系统设计、概念验证实现，以及一种为LLM微调和测试生成任务特定数据集的方法。该方法实现了一个更具适应性的自动化系统，能够响应自发事件，同时允许通过自然语言进行更简便的操作和配置，从而实现更直观的人机交互。我们在GitHub上提供了演示视频和详细数据：https://github.com/YuchenXia/LLM4IAS