| 曹杨锁,卢晓伟,庞景月.多粒度时频域特征融合的温度遥测概率性预测[J].电子测量与仪器学报,2025,39(8):189-199 |
| 多粒度时频域特征融合的温度遥测概率性预测 |
| Probabilistic prediction of temperature telemetry based on multi-granularitytime-frequency domain feature fusion |
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| DOI： |
| 中文关键词: 卫星温度遥测序列 概率性预测 时频特征提取 去噪扩散模型 |
| 英文关键词:satellite temperature telemetry sequence probabilistic prediction time frequency feature extraction denoising diffusion model |
| 基金项目:国家自然科学基金（62001069）、重庆市教委科学技术研究项目（KJQN202300841，KJQN202100821）、重庆市博士“直通车”科研项目（CSTB2022BSXM-JSX0008）、高层次人才科研启动项目（2056009）资助 |
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| 摘要点击次数: 524 |
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| 中文摘要: |
| 卫星温度遥测数据预测对于地面运维系统监测卫星状态及故障预警具有重要的研究与应用价值。但传统预测方法存在精度低、鲁棒性不足且无法提供概率区间表达的局限性。为此，提出了一种针对卫星温度遥测序列的多元时序概率性预测模型TFM-Diff。首先，构建了一种基于门控循环单元与离散余弦变换的混合架构，以更准确地识别遥测数据中的时频域动态模式。然后通过融合多粒度特征实现对温度遥测数据短期波动与长期趋势的复杂建模，以有效解析卫星温度数据的多尺度特性。最后，结合去噪扩散模型全面分析数据的潜在分布模式，实现预测结果的概率区间表达。基于4组真实卫星温度数据集的实验验证表明，针对概率性预测的连续排序概率评分总和指标，相对于其他主流方法，所提出模型的预测性能提升6.26%~27.77%，验证了其在空间应用场景下具有优越的预测性能、良好的适用性和通用性。 |
| 英文摘要: |
| The prediction of satellite temperature telemetry data has important research and application value for monitoring satellite status and fault warning in ground operation and maintenance systems. However, traditional prediction methods have limitations such as low accuracy, insufficient robustness, and inability to provide probabilistic interval expressions. Therefore, this study proposes a multivariate temporal probabilistic prediction model TFM Diff for satellite temperature telemetry sequences. Firstly, a hybrid architecture based on gated recurrent units and discrete cosine transform was constructed to more accurately identify time-frequency domain dynamic patterns in telemetry data. Next, by integrating multi granularity features, complex modeling of short-term fluctuations and long-term trends in temperature telemetry data can be achieved, effectively analyzing the multi-scale characteristics of satellite temperature data. Finally, by combining the denoising diffusion model to comprehensively analyze the potential distribution patterns of the data, the probability interval expression of the prediction results can be achieved. Experimental verification based on four sets of real satellite temperature datasets shows that the continuous ranking probability score sum index for probabilistic prediction has improved the predictive performance of the proposed model by 6.26% to 27.77% compared to other mainstream methods, verifying its superior predictive performance, good applicability, and universality in space application scenarios. |
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