In this study，a deterministic adaptive resource management strategy for 5G power private networks is developed to ensure stable performance and quality of service(QoS)for power users. First，for typical power-service scenarios where traffic is accessed through 5G and transported over optical communication bearing networks，to achieve fine-grained segmentation of electric service resources，a slicing system model for 5G power virtual private networks is developed. This model dynamically adjusts the slicing resources reserved for services and establishes resource constraints on a large time scale. Additionally，the age of information is adopted on a small time scale to ensure deterministic transmission delay and flexible configuration of power and bandwidth. Second，the problem of maximizing the long-term system utility while ensuring QoS is discussed herein. This problem is split into dual time-scale subproblems. Finally，to solve the optimal resource management strategy，the Double Deep Q Network algorithm is used to improve resource allocation decisions over large time scales，and the Preferred Empirical Replay Multi-Agents Composite Action Actor Critic algorithm is used to dynamically schedule resources over small time scales. The results show that the proposed algorithm can effectively reduce the delay and optimize load penalty and cost. The proposed strategy can provide a reference for deterministic resource management in coordinated scenarios of 5G power virtual private networks and optical communication bearing networks.