自动化深空网络数据系统；通过智能体AI实现自适应异常检测的案例研究
Automating the Deep Space Network Data Systems; A Case Study in Adaptive Anomaly Detection through Agentic AI
AI总结：
面向NASA深空网络天线与发射机的异常检测需求，本文构建数据流水线、机器学习重建模型、强化学习分级与LLM解释模块，并由智能体AI统一推理，形成可随人工反馈迭代的自适应异常检测系统。
摘要：
深空网络（Deep Space Network，DSN）是NASA最大的天线设施网络，这些设施会产生大量多变量时间序列数据。这些设施包含DSN天线和发射机，它们会在长时期内发生退化，进而可能造成代价高昂的数据流中断，并威胁数十颗依靠深空网络维持生命线的航天器与地球的连接。本研究的目的是试验不同方法，以协助JPL工程师通过所收集的数据直接定位异常和设备退化，并继续为未来宇宙中的空间任务开展DSN的维护与运行。因此，我们研究了多种机器学习技术，这些技术能够通过预测性分析完整重建数据，并通过统计计算和阈值确定实时数据集中的异常数据条目。在经过完整训练和测试的机器学习模型之上，我们还集成了一个强化学习子系统，用于按严重程度等级对识别出的异常进行分类；以及一个大型语言模型，用于为每个异常数据条目标注解释；所有这些都可以随着时间推移通过人类反馈/输入得到改进和微调。具体而言，针对DSN发射机，我们还实现了一个完整的数据流水线系统，将数据提取、解析和处理工作流连接在一起，因为此前并不存在用于执行这些任务的连贯程序或脚本。借助该数据流水线系统，我们随后还能够连接由DSN天线数据训练出的模型，从而完成DSN异常检测的数据工作流。这一切又由一个智能体AI系统封装并进一步连接，在该系统中，复杂推理被用于确定异常数据的分类和预测。
英文摘要：
The Deep Space Network (DSN) is NASA's largest network of antenna facilities that generate a large volume of multivariate time-series data. These facilities contain DSN antennas and transmitters that undergo degradation over long periods of time, which may cause costly disruptions to the data flow and threaten the earth-connection of dozens of spacecraft that rely on the Deep Space Network for their lifeline. The purpose of this study was to experiment with different methods that would be able to assist JPL engineers with directly pinpointing anomalies and equipment degradation through collected data, and continue conducting maintenance and operations of the DSN for future space missions around our universe. As such, we have researched various machine learning techniques that can fully reconstruct data through predictive analysis, and determine anomalous data entries within real-time datasets through statistical computations and thresholds. On top of the fully trained and tested machine learning models, we have also integrated the use of a reinforcement learning subsystem that classifies identified anomalies based on severity level and a Large Language Model that labels an explanation for each anomalous data entry, all of which can be improved and fine-tuned over time through human feedback/input. Specifically, for the DSN transmitters, we have also implemented a full data pipeline system that connects the data extraction, parsing, and processing workflow all together as there was no coherent program or script for performing these tasks before. Using this data pipeline system, we were able to then also connect the models trained from DSN antenna data, completing the data workflow for DSN anomaly detection. This was all wrapped around and further connected by an agentic AI system, where complex reasoning was utilized to determine the classifications and predictions of anomalous data.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
