基于集成学习的空间科学卫星工作模式识别
doi: 10.11728/cjss2023.04.20220301022 cstr: 32142.14.cjss2023.04.20220301022
Recognition of Working Pattern of Space Science Satellite Based on Ensemble Learning
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摘要: 针对空间科学卫星遥测参数数据量大且特征维度高、需要消耗大量人力资源预先设置海量阈值、预先设置的阈值可能不再适用、现有监测手段可扩展性低等问题，提出了一种基于集成学习的空间科学卫星工作模式识别方法。该方法采用相关系数统计特性和互信息理论对遥测参数数据进行筛选降维，使用数据重采样技术解决数据集中存在的类别不平衡问题，构建集成学习模型，实现空间科学卫星工作模式的识别。借助某型号科学卫星真实遥测参数数据对该方法进行验证，在短时内便可构建完成算法模型，模型对整体类别的识别正确率高达99.67%，可正确识别多数类样本和少数类样本，为地面运控人员判断空间科学卫星工作模式提供了决策依据。Abstract: Aiming at the issues of space science satellite telemetry parameters, such as large amount of data, high dimension, the need of numerous artificial resource consumption for preset massive thresholds, the preset thresholds that may not be applicable, and the current monitoring methods with low scalability, a working pattern recognition method is proposed for scientific satellite based on ensemble learning. Correlation coefficient statistical characteristics and mutual information theory are used to screen and reduce the dimension of telemetry parameter data. Data resampling technology is used to solve the problem of category imbalance for the dataset. An integrated learning model is used to identify the working mode of space science satellite. The method is verified with the real telemetry parameter data of quantum science satellites. And the algorithm model can be constructed in a short time, and the overall recognition accuracy rate reaches 99.67%, which can correctly identify the majority and minority class samples. The method can provide decision-making basis for ground personnel to judge the working mode of space science satellites.
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表 1 皮尔逊系数与相关程度的关系
Table 1. Relationship between Pearson coefficient and correlation degree
皮尔逊相关系数绝对值$ |\rho | $取值 相关程度 $ 0 \leqslant |\rho | < 0.3 $ 低相关 $ 0.3 \leqslant |\rho | < 0.8 $ 中相关 $ 0.8 \leqslant |\rho | \leqslant 1 $ 高相关 
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表 2 实验环境信息
Table 2. Experimental environment information
类目 详细信息 CPU 8核i7-1165 G7 2.8 GHz 内存 16 GByte 硬盘 500 GByte 操作系统 Windows 10家庭中文版 编程语言 Python 编程工具 Pycharm Community 2021.3
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表 3 随机森林模型（基学习器数19）在不同数据集上的性能表现
Table 3. Performance of random forest model (number of base learners 19) on different datasets
数据集类别 正确率 F1-score 原始数据集 0.9966 0.9894 随机过采样后数据集 0.9967 0.9911 SMOTE过采样后数据集 0.9967 0.9908 SMOTE-Tomek过采样后数据集 0.9967 0.9914 注 黑体数字表示该数据在所列中表现最优。
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表 4 不同机器学习模型的性能表现
Table 4. Performance of different machine learning models
机器学习模型类别 正确率 F1-score 训练时间/s 朴素贝叶斯 0.7456 0.6176 0.18 逻辑回归 0.9309 0.8664 6.42 支持向量机 0.9169 0.8483 144.36 CART决策树 0.9959 0.9896 2.88 随机森林（基学习器数19） 0.9967 0.9914 4.36 注 黑体数字表示该数据在所列中表现最优。
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