With the development of 5G communications and mobile edge computing (MEC), a variety of new demands for power Internet of things (PIoT) have emerged. On the one hand, these new PIoT applications usually require for high quality of service (QoS) guarantee. On the other hand, service providers are desired to have elastic framework for diverse service level agreements (SLA). The feature technologies of network slicing and MEC have constituted a practical viable framework for solving these challenges. This paper firstly defined a metric to measure the reliability of PIoT services. Based on this index, a resource management method of 5G edge network slicing is proposed, which ensures the latency and reliability of the PIoT services while minimizing their energy and communication consumption through using deep reinforcement learning. Through simulation experiments, the slicing management approach is demonstrated to outperform the traditional baseline approach.