AI 中文总结
本研究提出基于检索增强大语言模型的KG-DML框架，实现了复杂系统动态主逻辑模型的自动化构建，经低压冷却剂注入系统验证，可转化技术文档为可执行功能模型用于诊断分析。
AI 中文摘要
动态主逻辑（Dynamic Master Logic, DML）提供了一个分层框架，用于通过将功能目标与底层结构元素关联来表示系统行为。然而，DML的构建通常依赖专家对技术文档的解读，这限制了其在复杂系统中的可扩展性。本研究提出了一种从系统描述自动构建DML模型并将其表示为知识图谱（KG-DML）的框架，使用检索增强生成和大语言模型作为支撑工具。在先前针对小规模系统研究的基础上，该框架将自动化KG-DML的构建与评估扩展到规模更大、更复杂的系统。模型构建过程在DML分层结构中进行，采用定向检索，同时保留功能依赖关系和显式逻辑关系。生成的KG-DML支持诊断推理、安全评估、向上故障传播分析和向下依赖追踪。多层面验证方法评估了各层的精确率和召回率、逻辑门一致性以及整体结构完整性。将该方法应用于已退役沸水反应堆的低压冷却剂注入系统，结果显示多次运行均实现了一致的重构。研究表明，自动化KG-DML构建可将技术文档转化为可执行的功能模型，用于诊断和可靠性分析。
英文摘要
Dynamic Master Logic (DML) provides a hierarchical framework for representing system behavior by linking functional objectives to underlying structural elements. However, DML construction typically relies on expert interpretation of technical documentation, limiting scalability for complex systems. This study presents a framework for automated construction of DML models from system descriptions and their representation as Knowledge Graphs (KG-DML), using Retrieval-Augmented Generation and Large Language Models as enabling tools. Building on prior work with small-scale systems, the framework extends automated KG-DML construction and evaluation to substantially larger and more complex systems. Model construction proceeds across the DML hierarchy using targeted retrieval while preserving functional dependencies and explicit logical relationships. The resulting KG-DML supports diagnostic reasoning, safety assessment, upward failure propagation, and downward dependency tracing. A multi-level validation methodology evaluates layer-specific precision and recall, logical gate consistency, and overall structural integrity. Application to the Low-Pressure Coolant Injection system of a decommissioned Boiling Water Reactor demonstrates consistent reconstruction across repeated runs. The results show that automated KG-DML construction can transform technical documentation into executable functional models for diagnostic and reliability analysis.
Comments36 Pages, 8 Figures
