标题： 从领域文档到需求：航天工业中的检索增强生成 显示英文标题

标题： From Domain Documents to Requirements: Retrieval-Augmented Generation in the Space Industry

摘要： 需求工程（RE）在航天工业中本质上是复杂的，需要高精度、与严格标准对齐，并适应特定任务的约束。 小型航天组织和新进入者往往难以从大量非结构化文档中提取可操作的需求，例如任务简报、接口规范和法规标准。 在本文提出的创新机会中，我们探讨了检索增强生成（RAG）模型在航天领域支持和（半）自动化需求生成的潜力。 我们提出了一种模块化的、由人工智能驱动的方法，该方法预处理原始航天任务文档，将其分类为语义上有意义的类别，从领域标准中检索上下文相关的相关内容，并使用大型语言模型（LLMs）合成草案需求。 我们将该方法应用于航天领域的实际任务文档，以展示可行性，并与我们的行业合作伙伴Starbound Space Solutions合作评估早期结果。 我们的初步结果表明，该方法可以减少手动工作量，提高相关需求的覆盖率，并支持轻量级合规对齐。 我们概述了将人工智能更广泛地集成到需求工程工作流中的路线图，旨在降低小型组织参与大规模、安全关键任务的门槛。 显示英文摘要

摘要： Requirements engineering (RE) in the space industry is inherently complex, demanding high precision, alignment with rigorous standards, and adaptability to mission-specific constraints. Smaller space organisations and new entrants often struggle to derive actionable requirements from extensive, unstructured documents such as mission briefs, interface specifications, and regulatory standards. In this innovation opportunity paper, we explore the potential of Retrieval-Augmented Generation (RAG) models to support and (semi-)automate requirements generation in the space domain. We present a modular, AI-driven approach that preprocesses raw space mission documents, classifies them into semantically meaningful categories, retrieves contextually relevant content from domain standards, and synthesises draft requirements using large language models (LLMs). We apply the approach to a real-world mission document from the space domain to demonstrate feasibility and assess early outcomes in collaboration with our industry partner, Starbound Space Solutions. Our preliminary results indicate that the approach can reduce manual effort, improve coverage of relevant requirements, and support lightweight compliance alignment. We outline a roadmap toward broader integration of AI in RE workflows, intending to lower barriers for smaller organisations to participate in large-scale, safety-critical missions.