## 3 Proposed Methodology
### Data Restoration Enhancement Module  
Considering the data loss after compression, this paper proposes using a deep learning network model to enhance the restoration of compressed data, making it closer to the original data and thereby improving the accuracy of spatiotemporal data reconstruction. Given the vastness of spatiotemporal data and the inherent correlations between different variables, the choice of a deep learning network is crucial. In this paper, we choose the ConvNeXt network [28] for feature extraction, which integrates the design principles of the Swin Transformer, enhancing the feature extraction capability of the traditional residual neural network (ResNet). We chose the ConvNeXt network for our restoration tasks due to its superior performance in handling complex image restoration problems. ConvNeXt, as a convolutional neural network (CNN) architecture, incorporates advanced design principles from both traditional CNNs and vision transformers, resulting in a robust and efficient model for image restoration. Its ability to capture fine details and contextual information makes it particularly well-suited for our application. While other networks, such as ResNet or Swin Transformer, are also popular choices for image restoration, ConvNeXt offers several advantages. For example, compared to ResNet, ConvNeXt's integration of dynamic convolution and enhanced normalization layers improves parameter efficiency and global context capture, boosting accuracy over ResNet's fixed-size convolutional kernels. When compared to the Swin Transformer, which excels at capturing long-range dependencies with its hierarchical attention mechanism but suffers from high computational complexity and memory usage, ConvNeXt balances convolutional efficiency and transformer improvements, delivering high performance without excessive overhead. These comparisons underscore our decision to choose ConvNeXt for our restoration tasks. It combines the strengths of both convolutional and transformer architectures, providing a robust, efficient, and scalable solution for high-quality image restoration. The ConvNeXt network draws inspiration from a series of design principles from the Swin Transformer while maintaining the simplicity of the network as a standard ConvNet, without introducing any attention-based modules. These design principles can be summarized as follows: macro design, group convolution from ResNeXt [29], inverted bottleneck, large kernel size, and various levels of micro design. In Figure 1, we illustrate the ConvNeXt block, where DConv2D(.) represents depth-wise separable 2D convolution, LayerNorm represents layer normalization, Dense(.) represents a densely connected neural network layer, and GELU represents the activation function. In the macro design of the model, the Conv4_x part of the original ResNet has the most stacked blocks, while the ratio of stacked blocks from stage 1 to stage 4 is (3, 4, 6, 3) (i.e., 1:1:2:1). In comparison, the ratios in the Swin Transformer are more balanced, with Swin-T being 1:1:3:1 and Swin-L being 1:1:9:1. Given that Swin Transformer has more stacked blocks in stage 3, we consider adopting a similar ratio to design the number of ConvNeXt Blocks, such as 1:1:9:1. We adjusted the stage computation ratio to better adapt to the feature learning of spatiotemporal data. Increasing the number of stages helps the model to understand the data at a deeper level and better capture abstract features in the data. Additionally, we introduced the Patchify operation, designing the model's Stem layer as a convolution operation with a kernel size of 4 and a stride of 4, to more flexibly adapt to the complexity of spatiotemporal data. The ConvNeXt model employs depth-wise separable convolution, where the number of groups matches the number of channels. This is similar to the weighted sum operation in self-attention, which can only interact with spatial information on each channel.