This paper presents an innovative approach to integrating Large Language Models (LLMs) with Arduino-controlled Electrohydrodynamic (EHD) pumps for precise color synthesis in automation systems. We propose a novel framework that employs fine-tuned LLMs to interpret natural language commands and convert them into specific operational instructions for EHD pump control. This approach aims to enhance user interaction with complex hardware systems, making it more intuitive and efficient. The methodology involves four key steps: fine-tuning the language model with a dataset of color specifications and corresponding Arduino code, developing a natural language processing interface, translating user inputs into executable Arduino code, and controlling EHD pumps for accurate color mixing. Conceptual experiment results, based on theoretical assumptions, indicate a high potential for accurate color synthesis, efficient language model interpretation, and reliable EHD pump operation. This research extends the application of LLMs beyond text-based tasks, demonstrating their potential in industrial automation and control systems. While highlighting the limitations and the need for real-world testing, this study opens new avenues for AI applications in physical system control and sets a foundation for future advancements in AI-driven automation technologies.

翻译：本文提出了一种创新方法，将大语言模型与基于Arduino控制的电液动力泵集成，用于自动化系统中的精确色彩合成。我们提出了一种新颖框架，采用微调后的大语言模型解析自然语言指令，并将其转换为电液动力泵控制的具体操作指令。该方法旨在增强用户与复杂硬件系统的交互，使其更直观高效。该技术路线包含四个关键步骤：使用色彩规格及对应Arduino代码数据集对语言模型进行微调；开发自然语言处理接口；将用户输入翻译为可执行的Arduino代码；控制电液动力泵实现精确色彩混合。基于理论假设的概念实验结果表明，该方法在精确色彩合成、高效语言模型解析以及电液动力泵稳定运行方面展现出巨大潜力。本研究将大语言模型的应用拓展至文本任务之外，彰显了其在工业自动化与控制系统中的应用潜力。在指出局限性并强调真实环境测试必要性的同时，本研究为人工智能在物理系统控制中的应用开辟了新途径，并为未来人工智能驱动自动化技术的发展奠定了坚实基础。