面向星群时代，用人机协作实现航天器电源系统全链路健康管控。
Empowering All-in-Loop Health Management of Spacecraft Power System in the Mega-Constellation Era via Human-AI Collaboration
- 提出能力对齐原则，构建人机协同框架SpaceHMchat覆盖健康诊断全流程。
- 实测23项指标表现优异，故障定位精度超90%，决策搜索耗时不足3分钟。
- 开源首个航天器电源系统全链路健康管理数据集，含70万+时间戳。
未来卫星数量将呈指数级增长，进入卫星星群时代，亟需关注航天器电源系统（SPS）的健康管理（HM），因其供电关键且故障率高。数十个与数千个SPS的健康管理本质不同，为此本文提出底层能力对齐原则（AUC），开发开源人机协同（HAIC）框架SpaceHMchat，实现从工况识别、异常检测、故障定位到维护决策的全闭环健康管理。该框架支持对话式任务完成、自适应人机学习、人员结构优化、知识共享及透明推理，显著提升效率与可解释性。为验证效果，搭建硬件真实感故障注入实验平台并开源其仿真模型，完全复现真实SPS。实验表明，SpaceHMchat在23项量化指标上表现优异：工况识别逻辑推理结论准确率达100%，异常检测工具调用成功率超99%，故障定位精度超90%，维护决策知识库检索时间低于3分钟。另一贡献是发布首个全链路健康管理数据集，包含4类子任务、17种故障类型、超70万时间戳。
原文摘要 · Abstract (English)
It is foreseeable that the number of spacecraft will increase exponentially, ushering in an era dominated by satellite mega-constellations (SMC). This necessitates a focus on energy in space: spacecraft power systems (SPS), especially their health management (HM), given their role in power supply and high failure rates. Providing health management for dozens of SPS and for thousands of SPS represents two fundamentally different paradigms. Therefore, to adapt the health management in the SMC era, this work proposes a principle of aligning underlying capabilities (AUC principle) and develops SpaceHMchat, an open-source Human-AI collaboration (HAIC) framework for all-in-loop health management (AIL HM). SpaceHMchat serves across the entire loop of work condition recognition, anomaly detection, fault localization, and maintenance decision making, achieving goals such as conversational task completion, adaptive human-in-the-loop learning, personnel structure optimization, knowledge sharing, efficiency enhancement, as well as transparent reasoning and improved interpretability. Meanwhile, to validate this exploration, a hardware-realistic fault injection experimental platform is established, and its simulation model is built and open-sourced, both fully replicating the real SPS. The corresponding experimental results demonstrate that SpaceHMchat achieves excellent performance across 23 quantitative metrics, such as 100% conclusion accuracy in logical reasoning of work condition recognition, over 99% success rate in anomaly detection tool invocation, over 90% precision in fault localization, and knowledge base search time under 3 minutes in maintenance decision-making. Another contribution of this work is the release of the first-ever AIL HM dataset of SPS. This dataset contains four sub-datasets, involving 4 types of AIL HM sub-tasks, 17 types of faults, and over 700,000 timestamps.
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