基于改进的RIME-XGBoost的NSRT备份结构温度场的实时重建
Shi-Jiao Zhang1,2, Qian Xu1,3,4, Hui Wang1,2
1Xinjiang Astronomical Observatory, Chinese Academy of Sciences, Urumqi 830011, China.
Sensors (Basel, Switzerland)
|December 31, 2025
概括
准确预测天线备用结构 (BUS) 温度至关重要. 改进的RIME-XGBoost模型使用环境数据进行精确的公共汽车温度预测,为传统方法提供了替代方案.
科学领域:
- 天文学和天体物理学
- 机械工程 机械工程
- 数据科学数据科学数据科学
背景情况:
- 准确的温度场数据对于分析天线热变形至关重要.
- 传统的热力学模拟为热分布提供了较低的计算精度.
- 现有的温度测量系统面临着繁的布线和难以维护的挑战.
研究的目的:
- 开发一个改进的RIME-XGBoost模型,用于预测南山26米射电望远镜 (NSRT) 的备用结构 (BUS) 的温度.
- 提供一种更准确,更有效的方法来重建大光圈射电望远镜结构的热分布.
主要方法:
- 开发了一个改进的RIME-XGBoost模型用于BUS温度预测.
- 使用环境传感数据,包括环境温度,太阳辐射角度和天线方向,作为输入.
- 将模型的性能与实际的BUS温度测量进行了验证.
主要成果:
- RIME-XGBoost模型在NSRT的公共汽车温度方面实现了97.15%的相对预测准确度.
- 该模型表明预测误差小于0.897K (根均平方误差).
- 仅根据环境因素成功预测了BUS温度分布.
结论:
- 改进的RIME-XGBoost模型为大型光圈射电望远镜的实时热分布重建提供了一个可行的替代方案.
- 这种方法克服了热力学模拟的低精度和物理测量系统的维护问题的局限性.
- 该研究强调了机器学习在提高无线电天文学运营效率和数据采集方面的潜力.
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