随着新一代信息通信技术，如5G、云计算和人工智能的不断演进，世界正迅速迈入数字经济的快车道。针对数据中心中可再生能源和工作负载预测的不确定性，提出了一种基于多智能体近端策略网络的数据中心双层优化调度方法。首先，建立了数据中心双层时空优化调度框架，对数据中心工作负载、IT设备、空调设备进行详细建模；在此基础上，提出数据中心的双层优化调度模型，上层以互联网数据中心（Internet data center，IDC）运营管理商总运营成本最小为目标进行时间维度调度，下层以各IDC运行成本最低为目标进行空间维度调度；然后，介绍多智能体近端策略网络算法原理，设计数据中心双层优化调度模型的状态空间、动作空间和奖励函数。最后，针对算例进行离线训练和在线调度决策，仿真结果表明，所提模型和方法能够有效降低系统成本和能耗，实现工作负载的最佳分配，具有较好的经济性和鲁棒性。

With the continuous evolution of new generation information and communication technologies such as 5G, cloud computing, and artificial intelligence, the world is rapidly entering the fast lane of the digital economy. A dual-layer optimization scheduling method for data centers based on multi-agent proximal strategy network is proposed to address the uncertainty of renewable energy and workload prediction in data centers. Firstly, a dual-layer spatiotemporal optimization scheduling framework for data centers is established, which provides detailed modeling of data center workloads, IT equipment, and air conditioning equipment; On this basis, a dual-layer optimization scheduling model for data centers is proposed. The upper layer schedules the time dimension with the goal of minimizing the total operating cost of IDC operators, while the lower layer schedules the space dimension with the goal of minimizing the operating cost of each IDC. Then, the principle of multi-agent proximal strategy network algorithm is introduced, and the state space, action space, and reward function of the dual-layer optimization scheduling model for data centers are designed. Finally, offline training and online scheduling decisions are conducted for the examples. Simulation results show that the proposed model and method can effectively reduce system costs and energy consumption, achieve optimal workload allocation, and have good economy and robustness.