In order to meet the real-time and efficient computing power scheduling requirements of hot and cold services, a computational load prediction model (abbreviated as C-TCN model) based on adaptive noise complete set empirical mode decomposition(CEEMDAN) and time convolutional network(TCN) is proposed, and a resource cooperative scheduling algorithm(CTQ algorithm) based on C-TCN and Q learning is designed. The C-TCN model is used to sense the load change at the next time in advance, and the optimal wavelength partitioning and edge storage allocation scheme is found through Q learning. The experimental results show that the CTQ algorithm not only has better scheduling performance than the existing scheduling algorithms,but also can meet the requirements of hot and cold service scheduling performance, and improve the wavelength utilization rate.