Inspired by these challenges, we investigate the problem of task splitting and resource allocation under the collaborative network of HAP drones and LEO satellites, as shown in Figure 1. In this paper, we propose that HAP drones can not only cooperate with satellites but also with each other to jointly provide computing services for ground IoT devices. This solution is conducive to expanding the task processing capabilities of the double-layer network and improving the resource utilization of the network. Correspondingly, we considered factors such as the task processing tolerance delay of ground IoT devices, the limited computing resources of satellites, HAP drones, and IoT devices, and the limited communication resources between devices, and we formulated the problem of minimizing the total task processing delay. In order to minimize the total task processing delay, we propose an intelligent online offloading and resource allocation algorithm based on a combination of deep reinforcement learning and convex optimization. We first decompose the original optimization problem into a task splitting optimization sub-problem and a resource allocation optimization sub-problem. For the task splitting optimization sub-problem, we designed an intelligent solving algorithm based on the DDPG algorithm. For the resource allocation optimization sub-problem, we used a convex optimization algorithm to solve it. Through joint optimization of the two sub-problems, the efficiency of task offloading and resource allocation can be improved, and the total task processing delay can be reduced. Compared with typical reinforcement learning solutions, the algorithm proposed in this paper does not use deep reinforcement learning methods to directly output task offloading and resource-scheduling strategy, which objectively reduces the scale of the neural network, thereby reducing the training difficulty of deep reinforcement learning and improving the efficiency of neural networks, and improving the convergence speed of the network. Finally, we conduct simulation experiments to verify the convergence and performance of the proposed algorithm. The results demonstrate that, under various scenarios, such as different task data sizes and different CPU cycles required per bit, the proposed algorithm can reduce the total task processing delay compared to baseline algorithms. The main contributions of this paper are summarized as follows:  
1. We construct an edge computing framework for multi-HAP drones and multi-LEO collaboration. Under this framework, ground IoT devices' tasks can be dynamically allocated to multiple HAP drones and LEO satellites for processing. In addition, the communication and computing resources of each node under this framework can also be dynamically allocated. 2. Considering the task splitting constraints, available resource constraints, and the maximum tolerated delay constraint of the tasks, we construct a task splitting and resource allocation problem to minimize the total system delay. This is a non-convex continuous optimization problem. 3. We propose a joint optimization algorithm of deep reinforcement learning and convex optimization. We design a task splitting optimization algorithm based on the deep deterministic policy gradient (DDPG) method and solve the optimal resource allocation strategy through a convex optimization algorithm. We design the structure of the actor network to ensure that the actions of DRL are effective. 4. We verify the convergence and effectiveness of the algorithm proposed in this paper through experiments. By comparing the algorithm convergence under different discount factors and learning rates, we select reasonable neural network parameters. By comparing our algorithm with three other baseline schemes, we verify that the algorithm proposed in this paper can effectively reduce the total system delay. Figure 1: The scenario of LEO-HAP drone collaborative networks. The rest of the paper is structured as follows. The related works are presented in Section 2.