摘要：

油气生产系统中的管道、压力容器和水下生产装置等关键装备，长期服役于高温、高压及腐蚀等极端恶劣环境，其全生命周期的设计、检测与维护工作高度依赖设备的完整性管理标准。然而，由于多来源标准往往由不同机构制定，在文本结构、专业术语定义及信息颗粒度上存在显著差异，导致标准知识呈碎片化分布，条款之间关联脉络不清，极大地增加了工程师的检索难度，难以支撑跨设备类型的一致性决策。为此，本文应用GraphRAG技术构建了油气设备完整性标准知识图谱，并开发出面向工程问题的智能检索与推理方法，实现了跨标准知识的统一组织、显式关联与证据可追溯。具体研究流程包括：首先，对多源标准文本进行数字化处理，统一版式并完成语义切分；其次，系统抽取设备、部件、材料、失效模式、检测方法和维护策略等核心要素及其关联关系，构建以“设备—部件—风险—事件—策略”为主线的语义网络，进而开展实体对齐与融合；最终，基于图谱路径聚合跨文档条款，输出精准的推理结论及对应的依据条款。实验表明，语料覆盖一百余份标准文档，形成约5万条语义片段与10万余条实体关系；多跳问答准确率87.3%，证据溯源覆盖率91.5%。结果表明，该方法可提升标准知识整合效率与推理透明度，为完整性评估、检测方案选取与维护策略制定提供支撑。

关键词: GraphRAG, 知识图谱, 设备完整性管理, 标准体系, 语义推理

Abstract:

Key equipment in oil and gas production systems, including pipelines, pressure vessels, and subsea production facilities, operates for extended periods under extremely harsh environmental conditions characterized by high temperature, high pressure, and severe corrosion. These extreme conditions pose significant risks to equipment safety, operational efficiency, and the overall reliability of the production process. Consequently, their design, inspection, and maintenance activities throughout the entire lifecycle are highly dependent on comprehensive equipment integrity management standards. However, since multi-source standards are often formulated by different organizations, significant discrepancies exist in their document structures, professional terminology definitions, and information granularity. This inherent heterogeneity leads to fragmented standard knowledge and ambiguous associations between clauses, which significantly increases the difficulty of information retrieval for engineers and makes it extremely challenging to support consistent decision-making across different equipment types, ultimately leading to inefficiencies in maintenance planning. To address these challenges, this study applies Graph Retrieval-Augmented Generation (GraphRAG) technology to construct a knowledge graph specifically for oil and gas equipment integrity standards. Furthermore, it develops an intelligent retrieval and reasoning method tailored for practical engineering problems, thereby achieving unified organization of cross-standard knowledge, explicit semantic association, and full traceability of evidence. The specific research workflow includes three well-defined steps. First, multi-source standard texts are digitized, with layout normalization and semantic segmentation applied to transform the heterogeneous raw data into a structured corpus. Second, core elements—such as equipment, components, materials, failure modes, inspection methods, and maintenance strategies—along with their intrinsic relationships are systematically extracted. Based on this extraction, a comprehensive semantic network with an “equipment-component-risk-event-strategy” backbone is constructed. Furthermore, entity alignment and fusion techniques are applied to resolve conflicts and eliminate redundant information, ensuring the consistency of the knowledge base. Finally, cross-document clauses are aggregated along the pathways of the knowledge graph. By leveraging sophisticated reasoning algorithms, the system outputs precise reasoning conclusions accompanied by their corresponding supporting clauses, providing clear and reliable evidence. Experimental results demonstrate that the constructed corpus covers more than one hundred standard documents, yielding approximately fifty thousand semantic fragments and over one hundred thousand entity-relation pairs. In comprehensive evaluation tests, the system achieves an accuracy of 87.3% in multi-hop question-answering tasks, and the evidence traceability coverage reaches a high level of 91.5%. These findings indicate that the proposed method effectively improves the efficiency of standard knowledge integration and enhances reasoning transparency. Ultimately, this research provides robust technical support for equipment integrity assessment, scientific inspection plan selection, and the strategic formulation of maintenance plans in complex industrial environments.

Key words: GraphRAG, knowledge graph, equipment integrity management, standard system, semantic reasoning

中图分类号: 

• TE832

• X937