Research on cloud-edge-end distributed collaborative computing based on deep reinforcement learning

With the large-scale integration of renewable energy sources, the number of data acquisition terminals and the sampling frequency in distribution networks have increased rapidly, placing higher demands on the system’s computing and communication resource scheduling capabilities. This paper focuses on collaborative resource management and intelligent scheduling under a cloud–edge–device distributed architecture, and conducts a study on collaborative computing based on deep reinforcement learning. Firstly, an optimization model is formulated with the objective of maximizing the volume of data collaboratively processed by the cloud–edge–device system. The Lyapunov optimization theory is introduced to transform the long-term optimization problem into an online optimization problem that relies only on current time-slot information, enabling a joint guarantee of queuing delay control and long-term average data acquisition. Secondly, an improved Deep Q-Network (DQN) algorithm is proposed. By incorporating a greedy strategy-based Q-value sorting mechanism and a double experience replay mechanism, the algorithm enhances sample diversity and training stability, thereby improving convergence performance and decision robustness in multi-terminal resource scheduling scenarios. This also effectively mitigates resource conflicts caused by processing coupling among terminals. Finally, simulation results demonstrate that the proposed algorithm can effectively adapt to high-density, high-frequency data acquisition loads, providing excellent scheduling adaptability and system performance assurance. This work offers theoretical support and technical pathways for intelligent perception via cloud–edge collaboration in distribution networks.