用大模型+知识图谱自动算人因失误概率，省时更准。
KRAIL: A Knowledge-Driven Framework for Base Human Reliability Analysis Integrating IDHEAS and Large Language Models
- 两阶段框架融合大模型与知识图谱，实现半自动化人因失误概率计算
- 在信息不全条件下，对基线失误概率的估计性能优于现有方法
- 适合安全评估、工程可靠性领域研究人员快速建模
人因可靠性分析（HRA）对于评估和提升复杂系统的安全性至关重要。近年来研究聚焦于估算人因失误概率（HEP），但现有方法高度依赖专家知识，存在主观性强、耗时长的问题。受大语言模型（LLMs）在自然语言处理中成功应用的启发，本文提出一种新型两阶段知识驱动分析框架——KRAIL，该框架整合了IDHEAS与大语言模型。该创新框架实现了基线HEP值的半自动化计算。此外，通过知识图谱作为检索增强生成（RAG）手段，提升了框架高效检索与处理相关数据的能力。在权威人因可靠性数据集上系统开展实验并评估，结果表明，所提方法在部分信息条件下进行可靠性评估时，对基线HEP估计具有更优性能。
原文摘要 · Abstract (English)
Human reliability analysis (HRA) is crucial for evaluating and improving the safety of complex systems. Recent efforts have focused on estimating human error probability (HEP), but existing methods often rely heavily on expert knowledge,which can be subjective and time-consuming. Inspired by the success of large language models (LLMs) in natural language processing, this paper introduces a novel two-stage framework for knowledge-driven reliability analysis, integrating IDHEAS and LLMs (KRAIL). This innovative framework enables the semi-automated computation of base HEP values. Additionally, knowledge graphs are utilized as a form of retrieval-augmented generation (RAG) for enhancing the framework' s capability to retrieve and process relevant data efficiently. Experiments are systematically conducted and evaluated on authoritative datasets of human reliability. The experimental results of the proposed methodology demonstrate its superior performance on base HEP estimation under partial information for reliability assessment.
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