## 3 System Architecture
### Computing Power Allocation System  
Given the high real-time requirements and huge computational demands of image segmentation tasks in smart vehicles, coupled with the insufficiency of terminal computing power, this study has designed a computing power allocation system within the CPN. The system composition is shown in Figure 2. This computing power allocation system is not only highly adaptable and compatible but also stable and reliable. Through this system, it is possible to significantly enhance edge computing efficiency, reduce the energy consumption of connected cars, ensure the timeliness of task completion, and improve the overall capability of edge computing in various IoV computing-power allocation scenarios. Each server acts as both a computing node and a computing-power allocation model. The computing power allocation system is deployed on edge servers. As shown in Figure 2, the overall architecture of the system consists of several key components, each designed to enhance the efficiency and rational allocation of computing power. First, the system initializes and manages environmental parameters, which include server performance metrics, communication line performance metrics, and the size of task data, among other specific simulation settings. This is to ensure the rationality of resource allocation, enabling the proposed PER-iSAC to function more effectively in the real world. Second, this paper simulates a real-world cloud-edge network architecture. Its role is to mimic the actual network environment to generate environmental parameter data. Specifically, it simulates the working scenarios of vehicles, tasks, servers, and communication links. In this environment, PER-iSAC can dynamically obtain parameter data, forming a state space. The vehicular and edge servers make decisions based on the state space and action space parameters, and PER-iSAC provides agents with rewards and states for the next time slot based on the actions taken by them. Additionally, the agent is the core of decision making. It is designed to accommodate various algorithms and this paper takes the PER-iSAC algorithm as an example, which can generate the optimal allocation strategy through experience replay and environmental state analysis, thereby reducing time consumption and ensuring that vehicle demands are met, while also enhancing edge computing power. Moreover, PER-iSAC can also perceive the changes in the environment after an agent makes a move. Finally, the agent adjusts the allocation strategy in real time based on the rewards returned by the environment, gradually improving the overall optimization effect of PER-iSAC. In this architecture, cloud servers play a crucial role in model retraining and distribution. When edge servers reach their computational capacity, tasks are offloaded to cloud servers to  
Figure 2: Computing power allocation system. ensure continuous processing. This offloading occurs in scenarios where the computational demands exceed the available resources at the edge, such as during peak usage times or when handling particularly large datasets. By leveraging cloud resources, the system can maintain performance and meet the real-time requirements of image segmentation tasks, even under high load conditions. The allocation of computing power is essentially a data transfer process that involves reasonably assigning tasks to various edge servers and cloud servers based on task requirements. Taking image segmentation tasks as an example, the specific process is as follows. First, the vehicle collects a certain batch of images through its sensing devices (such as cameras and lidars for 3D point clouds) and performs preliminary processing. Since the vehicle has some computing power, it can handle preliminary processing tasks such as image denoising and cropping. The size of the preprocessed image data, selected model, and computing power requirements are then uploaded to the nearest edge server as the task parameter information.