动态主逻辑(DML) 通过将功能目标链接到底层结构元素来提供表示系统行为的分层框架。然而, DML 构建通常依赖于技术文档的专家解释, 限制了复杂系统的可扩展性。本研究提出了一个框架，使用检索增强生成和大型语言模型作为支持工具，从系统描述及其表示形式的知识图 (KG-DML), 中自动构建 DML 模型。该框架以之前小型系统, 的工作为基础，将自动化 KG-DML 构建和评估扩展到更大、更复杂的系统。使用有针对性的检索跨 DML 层次结构进行模型构建，同时保留功能依赖性和显式逻辑关系。生成的 KG-DML 支持诊断推理, 安全评估, 向上故障传播, 和向下依赖性跟踪。多级验证方法可评估特定层的精度和召回, 逻辑门一致性, 以及整体结构完整性。在退役沸水反应堆的低压冷却剂注入系统中的应用表明，在重复运行中重建的一致性。结果表明，自动化 KG-DML 构建可以将技术文档转换为可执行的功能模型，用于诊断和可靠性分析。
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.
科目:人工智能(cs.AI)
Subjects: Artificial Intelligence (cs.AI)