The field of weather forecasting makes extensive use of radar big data to extract information about precipitation, storms, lightning, and other weather phenomena to aid in the prediction and monitoring of weather changes. To improve the quality of radar data, machine learning and fuzzy logic algorithms are often used to identify and classify non-meteorological clutter in weather data. However, these methods often require dozens of texture features as inputs and need to manually adjust the thresholds to cope with different clutter types, which leads to significant time costs. In this paper, we propose a multi-scale weighted connected UNet to address these challenges by combining the channel attention feature fusion module and the UNet structure model. The task of recognizing non-meteorological clutter is regarded as a semantic segmentation problem, which eliminates the need to manually set thresholds for clutter pixel-level classification. Additionally, the channel-focused feature fusion mechanism is able to analyze the deep latent features of the input parameters and suppress the useless features, so that only six polarization parameters are required as inputs. Furthermore, the model incorporates full-scale deep supervision to improve the edge segmentation accuracy of clutter and meteorological echoes. Experiments confirm that our proposed model outperforms the compared models in clutter identification with Critical Success Index (CSI) of 0.808.

Digital twinning and edge computing are attractive solutions to support computing-intensive and service-sensitive Internet of Vehicles applications. Most of the existing Internet of Vehicles service offloading solutions only consider edge–cloud collaboration, but the collaboration between small cell eNodeB (SCeNB) should not be ignored. Service delays far lower than offloading tasks to the cloud can be obtained through reasonable collaborative computing between nodes. The proposed framework realizes and maintains the simulation of collaboration between SCeNB nodes by constructing a digital twin that maintains SCeNB nodes in the central controller, thereby realizing user task offloading positions, sub-channel allocation, and computing resource allocation. Then an algorithm named AUC-AC is proposed, based on the dominant actor–critic network and the auction mechanism. In order to obtain a better command of global information, the convolutional block attention mechanism (CBAM) is used in the digital twin of each SCeNB node to observe its environment and learn strategies. Numerical results show that our experimental scheme is better than several baseline algorithms in terms of service delay.