让卫星在轨实时分析遥感数据，提升灾情监测响应速度
OrbitChain: Orchestrating In-orbit Real-time Analytics of Earth Observation Data
- 将分析任务拆解为流水线函数，在多颗卫星间协同执行
- 实验显示结果可在分钟级输出，处理能力提升60%，通信开销降45%
- 适合应急响应、搜救等对时效性要求高的遥感应用
地球观测分析有望变革多个领域，但受限于地面连接能力，当前遥感数据下载与分析需数小时至数天，难以满足灾害监测、搜救等时效性应用需求。为此，我们提出OrbitChain——一种面向在轨多卫星的地球观测分析框架。该框架采用流水线设计，将分析流程分解为可调度的分析函数，并统筹星群资源完成实时分析任务。它能为地球感知应用提供及时洞察，支持在轨目标提示与引导（tip-and-cue）等高级工作流。硬件在环实验表明，OrbitChain可在分钟内交付分析结果，相比现有框架支持高达60%更多的分析负载，且星间通信开销减少最多45%。
原文摘要 · Abstract (English)
Earth observation analytics have the potential to transform many sectors. However, due to limited ground connections, it currently takes hours to days to download and analyze Earth observation data, diminishing the value of data for time-sensitive applications like disaster monitoring or search-and-rescue. To enable real-time analytics, we propose OrbitChain, an in-orbit multi-satellite Earth analytics framework. OrbitChain uses a pipelined design to decompose workflows into analytics functions, and orchestrates constellation-wide resources to finish real-time analytics tasks. It provides timely insights to Earth sensing applications and enables advanced workflows like in-orbit tip-and-cue. Hardware-in-the-loop experiments show that OrbitChain can deliver analytics results in minutes, supports up to 60% more analytics workload than existing frameworks, and reduces inter-satellite communication overhead by up to 45%.
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