The computing power network (CPN) offers exceptional computational capabilities and reliable network services, with significant potential for future applications. To achieve ubiquitous coverage and efficient computational resource allocation, CPN can be seamlessly coordinated with low-cost autonomous aerial vehicle (AAV)-based mobile computing platforms. This article investigates an efficient low-altitude AAV-assisted computing power and resource allocation mechanism tailored for urban environments. The aim is to ensure seamless scheduling and efficient processing of computational tasks across various computing devices at different layers of the CPN, while minimizing AAV energy consumption and ensuring flight safety. First, this article proposes an Urban AAV-assisted CPN task-allocation and AAV trajectory-management decision-making problem. The AAV works until it safely lands, aiming to minimize overall task processing delay and AAV energy consumption while ensuring fairness in task allocation. Then, a novel AAV-protection-based multiagent deep deterministic policy gradient (UP-MADDPG) algorithm is introduced. It offers dynamic management of secure computing and communication flight paths when facing building blockages. Finally, we compared the proposed algorithm with baseline algorithms across various metrics. Experimental results demonstrate that the proposed algorithm achieves lower and more balanced task execution delay and AAV energy consumption while also improving fairness.

In the contemporary landscape of computationally intensive applications, Computing Power Network (CPN) offers a solution to enhance computational efficiency and cost-effectiveness by integrating and sharing computing resources. However, with the surge in task volume within multi-user environments, effectively scheduling these tasks to optimize system profit and delay presents a significant challenge. This paper introduces an optimization approach leveraging Deep Deterministic Policy Gradient (DDPG) to enhance CPN performance through task decomposition and computing path optimization. We initially construct a multi-layer CPN system model encompassing cloud computing, edge computing, and terminal device layers. Subsequently, we integrate a novel mechanism for convex optimization-based task decomposition, enabling intelligent subdivision of tasks into sub-tasks and dynamic allocation to suitable nodes within the network. Furthermore, we devise a Convex Optimization Task Decomposition-based Multi-Agent Deep Deterministic Policy Gradient (CO-MADDPG) algorithm, empowering multiple computing tasks as independent agents to learn and identify optimal offloading paths and computing nodes, thereby minimizing delay and maximizing system profit. A series of simulation experiments validate the effectiveness of the CO-MADDPG algorithm in handling concurrent tasks, demonstrating its capability to reduce task completion times, enhance system revenue, and maintain adaptability and stability across varying task demands.