SERVIMON：面向天文台的AI驱动预测性维护与实时监控
SERVIMON: AI-Driven Predictive Maintenance and Real-Time Monitoring for Astronomical Observatories
AI总结：
ServiMon提出一种结合云原生技术与机器学习的可扩展智能流水线，用于天文望远镜系统的预测性维护、实时异常检测与监控，有效提升系统弹性与科学数据质量。
摘要：
目标：ServiMon旨在为数据收集与审计提供可扩展且智能的流水线，以监控如ASTRI Mini-Array等分布式天文系统。该系统负责望远镜运行的质量控制、预测性维护和实时异常检测。方法：ServiMon集成了云原生技术（包括Prometheus、Grafana、Cassandra、Kafka和InfluxDB）用于遥测数据的收集与处理。它采用机器学习算法（ notably Isolation Forest）来检测Cassandra性能指标中的异常。读取/写入延迟、吞吐量和内存使用等关键指标被持续监控，作为时间序列数据存储，并经过预处理用于特征工程。模型检测到的异常记录在InfluxDB v2中，并通过Flux访问，以实现实时监控可视化。结果：基于AI的异常检测通过在早期阶段识别性能下降、最小化停机时间和优化望远镜运行，增强了系统弹性。此外，ServiMon通过将遥测与观测数据相关联来支持天文统计分析，从而提升科学数据质量。AI生成的警报还改善了实时监控，实现主动的系统管理。结论：ServiMon的可扩展框架被证明对天文基础设施的预测性维护和实时监控有效。通过利用云计算和边缘计算，它可适应未来大规模实验，优化性能与成本。机器学习与大数据分析的结合使ServiMon成为现代及下一代观测天文学的稳健且灵活的解决方案。
英文摘要：
Objective: ServiMon is designed to offer a scalable and intelligent pipeline for data collection and auditing to monitor distributed astronomical systems such as the ASTRI Mini-Array. The system enhances quality control, predictive maintenance, and real-time anomaly detection for telescope operations. Methods: ServiMon integrates cloud-native technologies-including Prometheus, Grafana, Cassandra, Kafka, and InfluxDB-for telemetry collection and processing. It employs machine learning algorithms, notably Isolation Forest, to detect anomalies in Cassandra performance metrics. Key indicators such as read/write latency, throughput, and memory usage are continuously monitored, stored as time-series data, and preprocessed for feature engineering. Anomalies detected by the model are logged in InfluxDB v2 and accessed via Flux for real-time monitoring and visualization. Results: AI-based anomaly detection increases system resilience by identifying performance degradation at an early stage, minimizing downtime, and optimizing telescope operations. Additionally, ServiMon supports astrostatistical analysis by correlating telemetry with observational data, thus enhancing scientific data quality. AI-generated alerts also improve real-time monitoring, enabling proactive system management. Conclusion: ServiMon's scalable framework proves effective for predictive maintenance and real-time monitoring of astronomical infrastructures. By leveraging cloud and edge computing, it is adaptable to future large-scale experiments, optimizing both performance and cost. The combination of machine learning and big data analytics makes ServiMon a robust and flexible solution for modern and next-generation observational astronomy.
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。
