让自然语言直接找到实验设备的控制信号，提升运维效率。
From Natural Language to Control Signals: A Conceptual Framework for Semantic Channel Finding in Complex Experimental Infrastructure
- 提出四类信号定位方法，从查字典到智能代理探索。
- 在四大真实设施上实现90%-97%准确率，覆盖不同规模系统。
- 适合科研运维人员、AI系统集成者使用，解决命名混乱问题。
现代实验平台如粒子加速器、聚变装置、望远镜和工业控制系统，积累了数十万至百万级的控制与诊断通道，历经数十年演进。当前操作员与人工智能系统依赖非正式专家经验、不一致命名规范和碎片化文档来定位信号，严重制约可靠性、可扩展性及基于语言模型的交互界面发展。本文首次将‘语义通道查找’——即从自然语言意图映射到具体控制信号——形式化为复杂实验基础设施中的通用问题，并提出四范式框架，指导不同数据环境下的系统架构选择：(i) 基于精选通道词典的上下文直接查找；(ii) 通过结构化树状层级的约束导航；(iii) 使用迭代推理与工具查询的交互式智能体探索；(iv) 基于本体的语义搜索，解耦信号含义与设施特异性命名。我们在四个运行中设施上实现了概念验证，涵盖从紧凑型自由电子激光器到大型同步辐射光源，跨越两个数量级的规模和多样化的控制系统架构（从清晰层次结构到遗留系统）。所有实现均在专家标注的操作查询上达到90%-97%的准确率。
原文摘要 · Abstract (English)
Modern experimental platforms such as particle accelerators, fusion devices, telescopes, and industrial process control systems expose tens to hundreds of thousands of control and diagnostic channels accumulated over decades of evolution. Operators and AI systems rely on informal expert knowledge, inconsistent naming conventions, and fragmented documentation to locate signals for monitoring, troubleshooting, and automated control, creating a persistent bottleneck for reliability, scalability, and language-model-driven interfaces. We formalize semantic channel finding-mapping natural-language intent to concrete control-system signals-as a general problem in complex experimental infrastructure, and introduce a four-paradigm framework to guide architecture selection across facility-specific data regimes. The paradigms span (i) direct in-context lookup over curated channel dictionaries, (ii) constrained hierarchical navigation through structured trees, (iii) interactive agent exploration using iterative reasoning and tool-based database queries, and (iv) ontology-grounded semantic search that decouples channel meaning from facility-specific naming conventions. We demonstrate each paradigm through proof-of-concept implementations at four operational facilities spanning two orders of magnitude in scale-from compact free-electron lasers to large synchrotron light sources-and diverse control-system architectures, from clean hierarchies to legacy environments. These implementations achieve 90-97% accuracy on expert-curated operational queries.
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