用知识图谱和大模型自动生成流程安全的因果逻辑，减少人工出错。
Automating Cause-Effect Specification with Knowledge Graphs and Large Language Models
- 用知识图谱+受限大模型构建可机器理解的流程语义
- 自动生成操作员可用的安全说明与可验证规则
- 适合工业安全系统开发人员快速构建标准化规范
互锁、报警合理性表和因果（C&E）矩阵等工程规范在过程控制与安全中仍至关重要，但其创建主要依赖人工文档，易出现不一致。本文提出一种语义-AI框架，通过结合知识图谱（KG）与受约束的大语言模型（LLM），自动化生成C&E逻辑。知识图谱基于已有的模块化对齐本体，以机器可读形式表示流程结构、运行模式、故障、症状、原因及缓解措施。大语言模型在此基础上，生成符合本体与词汇约束的操作员可用安全叙述和语义网络规则语言（SWRL）规则，确保生成内容扎根于底层语义模型。该工作在模块化流程装置上实现演示，展示了如何从统一知识表示中生成工程语义、诊断关系与机器可验证规范，显著降低人工投入。
原文摘要 · Abstract (English)
Engineering specifications such as interlocks, alarm rationalization tables, and cause-and-effect (C&E) matrices remain central to process control and safety, yet their creation is still predominantly manual, document-driven, and prone to inconsistency. This paper presents a semantic-AI framework that automates the generation of C&E logic by combining a knowledge graph (KG) with a constrained large language model (LLM) layer. The KG builds on an established modular alignment ontology to represent process structure, operating modes, faults, symptoms, causes, and mitigation actions in a machine-interpretable form. The LLM then transforms this information into operator-ready safety narratives and Semantic Web Rule Language (SWRL) rules under strict ontology and vocabulary constraints, grounding the generated artifacts in the underlying semantic model. The workflow is demonstrated on a modular process plant, showing how engineering semantics, diagnostic relations, and machine-verifiable specifications can be generated from a unified knowledge representation with reduced manual effort.
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