能源运营中传统企业资产管理的人工智能辅助知识获取：一个实用检索系统
AI-Assisted Knowledge Access for Legacy Enterprise Asset Management in Energy Operations: A Practical Retrieval System
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中文总结 AI 辅助
针对能源运营中传统企业资产管理平台更换成本高、干扰运营的问题，提出检索助手，结合意图理解等运行时方法及语义丰富的数据准备管道，经试点测试，提升了检索质量和用户成果，为传统环境提供运营价值并奠定基础。
中文摘要 AI 辅助
能源公用事业仍在长期的企业资产管理平台上运行工程工作管理、工程采购和库存流程。更换这些平台成本高昂且会干扰运营，因此需要实用的改进层。本文提出了一种检索助手，可在三种操作模式下改善日常知识获取：供应商文档问答、运营数据存储（ODS）模式问答以及用户界面使用和操作方法问答。运行时方法结合了意图理解、查询重写、混合语义和向量检索、令牌限制下的上下文工程、有根据的答案生成以及面板标识符和引用文档的确定性超链接转换。数据准备管道强调语义丰富，通过添加表和字段描述、跨源标准化首字母缩写词以及在有用时索引代表性行级上下文作为主要质量杠杆。一项实测试点表明检索质量和用户成果持续提升。精度在五位时从0.56提高到0.72，平均倒数排名从0.43提高到0.58，五位时的归一化折损累计增益（nDCG）从0.51提高到0.66。任务完成时间中位数从14.2分钟降至8.3分钟，而有用性和信心在五分制上均提高到4.0。结果基于小样本并作为试点结果报告，但表明意图理解和语义丰富可以在传统环境中提供有意义的运营价值，同时也为未来的分析和自动化工具建立可重用的基础。
英文摘要
Energy utilities still run engineering work management, engineering procurement, and inventory processes on long-lived enterprise asset management platforms. Replacing these platforms is often cost prohibitive and operationally disruptive, so practical improvement layers are required. This paper presents a retrieval assistant that improves day-to-day knowledge access across three operational modes: vendor documentation question answering, operational data store (ODS) schema question answering, and user interface usage and how-to question answering. The runtime method combines intent understanding, query rewriting, hybrid semantic and vector retrieval, context engineering under token limits, grounded answer generation, and deterministic hyperlink conversion for panel identifiers and cited documentation. The data preparation pipeline emphasizes semantic enrichment as the primary quality lever by adding table and field descriptions, normalizing acronyms across sources, and indexing representative row-level context when useful. A measured pilot shows consistent gains in retrieval quality and user outcomes. Precision at five improved from 0.56 to 0.72, mean reciprocal rank from 0.43 to 0.58, and normalized discounted cumulative gain (nDCG) at five from 0.51 to 0.66. Median task completion time dropped from 14.2 to 8.3 minutes, while usefulness and confidence both increased to 4.0 on a five-point scale. Results are based on a small sample and are reported as pilot findings, but they indicate that intent understanding and semantic enrichment can deliver meaningful operational value in legacy environments while also establishing reusable foundations for future analytics and automation tools.
发表机构
- Ontario Power Generation（安大略发电公司）
- Dalhousie University（达尔豪斯大学）
机构由 AI 辅助整理，请以论文原文为准。
