Spec2Control：利用大语言模型从自然语言需求自动化实现PLC/DCS控制逻辑工程——多工厂评估
Spec2Control: Automating PLC/DCS Control-Logic Engineering from Natural Language Requirements with LLMs - A Multi-Plant Evaluation
AI总结：
本研究提出高度自动化的LLM工作流Spec2Control，可直接从自然语言需求生成DCS图形化控制逻辑，实验验证其准确率和人力节省效率优异，已推进工业应用落地并开源供验证。
摘要：
分布式控制系统（Distributed Control System, DCS）管理着众多工业生产流程（如发电厂、炼油厂、钢厂）的自动化运行。为这类系统编写软件程序目前仍以人工手动方式为主，过程繁琐，大型设施的相关成本高达数百万美元。研究发现大语言模型（Large Language Model, LLM）可辅助生成DCS控制逻辑，催生了商业辅助编程工具已商业化落地。当前这类工具聚焦于文本表示法，自动化程度有限，且尚未在包含真实测试用例的大规模数据集上接受过测试。我们提出了Spec2Control，这是一种高度自动化的LLM工作流，可直接从自然语言用户需求生成图形化控制逻辑。我们使用包含10个控制叙述和65个复杂测试用例的公开数据集开展实验，结果表明，Spec2Control能够成功识别控制策略，可自主生成98.6%的正确控制策略连接，还能节省94%-96%的人力。Spec2Control正被集成到ABB的商业工程工具中，同时也提供开源版本以供独立验证。
英文摘要：
Distributed control systems (DCS) manage the automation for many industrial production processes (e.g., power plants, chemical refineries, steel mills). Programming the software for such systems remains a largely manual and tedious process, incurring costs of millions of dollars for extensive facilities. Large language models (LLMs) have been found helpful in generating DCS control logic, resulting in commercial copilot tools. Today, these tools are focused on textual notations, they provide limited automation, and have not been tested on large datasets with realistic test cases. We introduce Spec2Control, a highly automated LLM workflow to generate graphical control logic directly from natural language user requirements. Experiments using an open dataset with 10 control narratives and 65 complex test cases demonstrate that Spec2Control can successfully identify control strategies, can generate 98.6% of correct control strategy connections autonomously, and can save between 94-96% of human labor. Spec2Control is being integrated into commercial ABB engineering tools, but is also available as an open-source variant for independent validation.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
