With the development of 6G, the use of unmanned aerial vehicles (UAVs) to assist the Internet of Things (IoT), has attracted tremendous attention due to its broad applications in recent years. This paper proposes the double-layer UAV framework for real-time distributed collaborative tasks, which need to be processed separately and then aggregated for processing jointly to form a global conclusion. We aim to minimize the total time delay of executing the tasks in the IoT network, including the data collection delay, distributed processing delay, data aggregation delay, and centralized processing delay, by jointly optimizing the location deployment, task offloading, and computing resource allocation of double-layer UAVs.To overcome the challenge of non-convexity in the original problem, we adopt a method that integrates block coordinate descent, successive convex approximation, and the PSO algorithm by breaking it down into three interdependent sub-problems, which are solved in an alternating manner until convergence is achieved. The simulation results indicate that the proposed framework substantially minimizes the overall time delay in UAV-assisted IoT networks compared to the baseline approach.