用AI保存能源行业专家经验，让离职不再导致知识流失。
Expert Mind: A Retrieval-Augmented Architecture for Expert Knowledge Preservation in the Energy Sector
- 结合访谈与多模态数据，构建可查询的专家知识库
- 通过向量检索+对话接口，实现经验快速获取
- 重视知情同意与隐私权，兼顾伦理设计
工业组织中专业人员的离职导致难以文档化的隐性知识永久流失。本文提出Expert Mind系统，利用检索增强生成（RAG）、大语言模型（LLMs）和多模态采集技术，保存、结构化并使组织知识持有者的深度专长可被查询。针对能源领域因员工老龄化而面临数十年运营经验消失的风险，我们描述了系统架构、处理流程、伦理框架与评估方法。通过结构化访谈、实时思考过程记录及文本语料摄入，将知识嵌入向量存储，并通过对话界面进行查询。初步设计表明，该系统可显著降低知识传递延迟，提升新人入职效率。知情同意、知识产权与删除权等伦理问题被列为首要设计约束。
原文摘要 · Abstract (English)
The departure of subject-matter experts from industrial organizations results in the irreversible loss of tacit knowledge that is rarely captured through conventional documentation practices. This paper proposes Expert Mind, an experimental system that leverages Retrieval-Augmented Generation (RAG), large language models (LLMs), and multimodal capture techniques to preserve, structure, and make queryable the deep expertise of organizational knowledge holders. Drawing on the specific context of the energy sector, where decades of operational experience risk being lost to an aging workforce, we describe the system architecture, processing pipeline, ethical framework, and evaluation methodology. The proposed system addresses the knowledge elicitation problem through structured interviews, think-aloud sessions, and text corpus ingestion, which are subsequently embedded into a vector store and queried through a conversational interface. Preliminary design considerations suggest Expert Mind can significantly reduce knowledge transfer latency and improve onboarding efficiency. Ethical dimensions including informed consent, intellectual property, and the right to erasure are addressed as first-class design constraints.
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