摘要： 在空天地多层次算力网络背景下,针对数字孪生逻辑靶场中因负载数据复杂性和非平稳特征带来的精准预测挑战,提出融合格拉姆转场(GAF)、卷积神经网络(CNN)、通道注意力机制的压缩与激励网络(SENet)和门控循环单元(GRU)的GCSG模型。GCSG模型通过GAF将一维负载数据转换为二维图像,利用CNN提取局部特征,使用SENet优化特征重要性,采用GRU捕捉时序特征,实现了高效的特征融合和精准预测。此外,GCSG模型采用融合麻雀搜索算法(SSA)的变分模态分解(VMD)对负载数据进行平稳化处理,进一步提高了预测性能。实验结果表明,GCSG模型在不同数据长度下均表现出优异的预测精度和稳定性,且在多步预测任务中同样表现突出。因此,GCSG模型显著提升了负载数据的预测精度,为空天地算力网络中的数字孪生系统负载预测提供了强有力的解决方案。

关键词: 空天地多层次算力网络, 数字孪生, 逻辑靶场, 负载预测, 变分模态分解

Abstract: In multitier space-air-ground computing power networks, the complexity and non-stationary characteristics of load data in a digital twin logical testbed hinder load data prediction accuracy. This study proposes the GCSG model, which integrates the Gramian Angular Field (GAF) transformation, a Convolutional Neural Network (CNN), a Squeeze-and-Excitation Network (SENet) with a channel attention mechanism, and a Gated Recurrent Unit (GRU) to achieve efficient feature fusion and precise prediction, to address this issue. The GAF transforms one-dimensional load data into two-dimensional images, enabling the CNN to extract local features. SENet optimizes feature importance through attention mechanisms, while GRU captures temporal dependencies, ensuring robust feature integration. In addition, the model employs the Variational Mode Decomposition (VMD) enhanced by the Sparrow Search Algorithm (SSA) to stabilize the load data and further improve prediction performance. Experimental results demonstrate that the GCSG model achieves superior prediction accuracy and stability across varying data lengths and excels in multistep prediction tasks. Thus, the GCSG model significantly enhances load data prediction accuracy, offering a powerful solution for load forecasting in digital twin systems within space-air-ground computing power networks.

Key words: space-air-ground multitier computing power network, digital twin, logical range, load prediction, Variational Mode Decomposition (VMD)

中图分类号: 

• TP311