In response to the need for a space security situation assessment during orbit, the multi-satellite space environmental risk prediction and early warning system is based on the detection results of the space weather payload of the Fengyun 4A and 4B satellites, as well as the prediction results of the National Space Weather Center, for the first time. By comprehensively utilizing some open-source data, it is the first time that we have achieved a 24 h advanced prediction of the space environment high-energy proton, low-energy particle, and high-energy electron risks for the safety of the Fengyun-series high-orbit satellites, and a real-time warning of satellite single-event upset, surface charging, and deep charging risks. The automation system has preliminarily achieved an intelligent space risk assessment for the safety of multiple stationary meteorological satellites, effectively improving the application efficiency of the space environmental data and the products of Fengyun-series satellites. The business status is stable in operation, and the resulting error between the predicted results of various risk indices and the measured data was less than one level. The warning accuracy was better than 90%. This article uses the system for the first time, to use Fengyun satellite data to, accurately and in a timely manner, predict and warn us about the low-energy particles and surface charging high-risk levels of the Fengyun 4A and 4B satellites during the typical space weather event on 21 April 2023, in response to the impact of complex spatial environmental factors on the safety of Fengyun-series high-orbit satellites. The construction and operation of a multi-satellite space environmental risk prediction and early warning system can provide a reference for the safety work of subsequent satellite ground system operations.

根据空间天气的状态，调整大气模型的相关输入参数能够减小模型的计算误差.通过对比CHAMP卫星在轨大气密度探测数据与NRLMSISE-00模式的计算结果发现，通过调整F_10.7的输入，使轨道大气密度积分的模式计算结果与探测结果之间的误差达到最小，此时的F_10.7被称为理想F_10.7输入（F^*）.进一步的分析发现，F^*与太阳紫外辐射MgII指数存在很好的相关性，因此可以选择其他的太阳紫外辐射代理参数取代F_10.7，从而减小模型计算误差.本文采用神经网络技术，建立新的太阳紫外辐射代理参量F_euv与MgII，F_10.7等的对应模型，能够根据当日参数值计算F_euv.研究结果表明，新的代理参数能够有效减小NRLMSISE-00的计算误差.

通过地面μ介子望远镜可以探测不同方向到达的宇宙线通量，得到从外空间入射的宇宙线受到不均匀结构的调制情况，判断CME的特征.通过分析，发现大地磁暴前Nagoya台站东向和南向的探测数据存在固定的2h时间差，认为这是由两个入射方向的宇宙线粒子先后穿越CME结构引起的.分别计算了两个方向相同时间和南向相位后移2h后通量探测数据的相关系数，以及两种情形下通量差的变化幅度，定量描述了CME接近地球过程中两个方向通量的相关特征.通过比较发现，CME接近地球过程中，经过相位变换的两个方向的相关系数明显高于未经变换的情况，经过相位变换的两个方向的通量差幅度明显小于未经变换的情况；CME到达地球后，两种情形的相关系数和通量差幅度则趋于相同.对2003-2005年K_p=9的地磁暴事件的分析均发现了这种现象.对2006年12月14日大地磁暴前的μ介子通量特征进行了分析，也完全符合上述特征.

The short-term disturbance of galactic cosmic rays(GCRs) caused by coronal cass ejections (CMEs) can be quantified to indicators of geomagnetic storms. We proposed a model takes GCRs time series slice as input and generates two geomagnetic storm prediction indexes as output. This new model is based on Hilbert-Huang Transform (HHT), utilizing Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) instead of Empirical Mode Decomposition (EMD), and extends 2D spectrum result of HHT to 3D form.Two prediction indexes is constructed from the sensitive components in GCRs. Finally, this model is tested on 11 great geomagnetic storms(Kp>=8) during solar cycle 23 and 24. Accuracy of this model turned out to be 82%, and prediction lead time ranges from 9 to 24 hours.