基于GraphRAG的地面建设项目智能管控应用构建方法
Construction method for intelligent management application in ground construction projects based on GraphRAG
浏览(140) 下载(0)
- 引用格式:
-
孙晓磊,宋旭,陈怡玥,翟纪宇,杜志虎.基于GraphRAG的地面建设项目智能管控应用构建方法[J].天然气与石油,2026,44(3):109-117.doi:10.3969/j.issn.1006-5539.2026.03.014
Sun Xiaolei, Song Xu, Chen Yiyue, Zhai Jiyu, Du Zhihu.Construction method for intelligent management application in ground construction projects based on GraphRAG[J].Natural Gas and Oil,2026,44(3):109-117.doi:10.3969/j.issn.1006-5539.2026.03.014
- DOI:
- 10.3969/j.issn.1006-5539.2026.03.014
- 作者:
- 孙晓磊1 宋旭2 陈怡玥2 翟纪宇2 杜志虎2
Sun Xiaolei1, Song Xu2, Chen Yiyue2, Zhai Jiyu2, Du Zhihu2
- 作者单位:
- 1. 大庆油田有限责任公司技术监督中心,黑龙江 大庆 163000; 2. 中国石油集团安全环保技术研究院有限公司, 北京 102200
1. Technical Supervision Center of Daqing Oilfield Co., Ltd., Daqing, Heilongjiang, 163000, China; 2. CNPC Research Institute of Safety & Environment Technology Co., Ltd., Beijing, 102200, China
- 关键词:
- 大语言模型;检索增强生成;智能体;知识图谱;地面建设
Large language model; Retrieval-augmented generation; Intelligent agent; Knowledge graph; Ground construction
- 摘要:
- 大庆油田年均实施1 000余项地面建设项目,类型复杂、周期长、参建方多、质量安全环保风险高,导致违章与事故频发,显著增大整体合规管控难度。针对上述痛点,提出构建融合“领域知识库+行业大模型”的地面建设项目合规管控智能解决方案,结合油田地面建设专业管理实践,通过本体设计、知识抽取、知识表示、知识存储、知识融合、知识计算及知识运维七步构建领域知识图谱;设计知识推荐/智能问答智能体、智能判标/合规审查智能体、临期预警/超期报警智能体三类核心智能体;从前期方案审查、施工图设计、招标管理、开工管理等维度构建智能应用体系,可提升知识赋能与决策支持能力、优化效率与成本控制、增强质量与合规管控、强化安全环保风险防控,为油田地面建设项目合规管控提供了一条从“经验驱动”迈向“知识驱动”的系统性技术路径。但在实际转化应用过程中仍需攻克多源异构数据治理、复杂工程逻辑关系建模、知识图谱动态演化、智能体参数优化、大小模型协同等关键技术挑战。
Daqing oilfield implements over more than 1 000 surface construction projects annually, characterized by complex types, long cycles, multiple participating parties, and high risks in quality, safety, and environmental protection, leading to frequent violations and accidents, significantly increasing the overall compliance management difficulty. Addressing these pain points, this paper proposes an intelligent compliance management solution for surface construction projects by integrating a “domain knowledge base+industry large model”. Combining professional management practices in oilfield surface construction, the solution constructs a domain knowledge graph through seven steps: ontology design, knowledge extraction, knowledge representation, knowledge storage, knowledge fusion, knowledge computation, and knowledge maintenance. It also designs three core intelligent agents: knowledge recommendation/intelligent Q & A agent, intelligent bid evaluation/compliance review agent, and expiration warning/overdue alarm agent. Constructing an intelligent application system from the dimensions of preliminary plan review, construction drawing design, bidding management, and commencement management can enhance knowledge empowerment and decision support capabilities, optimize efficiency and cost control, strengthen quality and compliance management, and reinforce safety and environmental risk prevention, and provide a systematic technical path from “experience driven” to “knowledge driven” compliance control for oilfield surface construction projects. However, in the actual implementation process, key technical challenges remain to be overcome: multi-source heterogeneous data governance, complex engineering logic modeling, dynamic evolution of knowledge graphs, intelligent agent parameter optimization, and large-small model collaboration.
