📄 中文摘要
该研究展示了如何利用亚马逊 SageMaker AI 构建和部署深度学习模型，以检测来自欧洲航天局 STIX 仪器的太阳耀斑数据。通过使用长短期记忆（LSTM）网络，模型能够有效分析和预测太阳耀斑的发生。研究中详细介绍了数据预处理、模型训练和评估的过程，强调了 STIX 数据在提高检测准确性方面的重要性。最终，系统的部署为实时监测太阳活动提供了新的解决方案，具有重要的科学和应用价值。
基于 SageMaker AI LSTM 网络和 ESA STIX 数据构建太阳耀斑检测系统
📄 English Summary
Build a solar flare detection system on SageMaker AI LSTM networks and ESA STIX data
This research demonstrates the use of Amazon SageMaker AI to build and deploy a deep learning model for detecting solar flares using data from the European Space Agency's STIX instrument. By employing Long Short-Term Memory (LSTM) networks, the model effectively analyzes and predicts solar flare occurrences. The study details the processes of data preprocessing, model training, and evaluation, highlighting the significance of STIX data in enhancing detection accuracy. Ultimately, the deployment of the system offers a new solution for real-time monitoring of solar activity, holding substantial scientific and practical value.