In modern grid systems which is a typical cyber-physical System (CPS), information space and physical space are closely related. Once the communication link is interrupted, it will make a great damage to the power system. If the service path is too concentrated, the risk will be greatly increased. In order to solve this problem, this paper constructs a route planning algorithm that combines node load pressure, link load balance and service delay risk. At present, the existing intelligent algorithms are easy to fall into the local optimal value, so we chooses the deep reinforcement learning algorithm (DRL). Firstly, we build a risk assessment model. The node risk assessment index is established by using the node load pressure, and then the link risk assessment index is established by using the average service communication delay and link balance degree. The route planning problem is then solved by a route planning algorithm based on DRL. Finally, experiments are carried out in a simulation scenario of a power grid system. The results show that our method can find a lower risk path than the original Dijkstra algorithm and the Constraint-Dijkstra algorithm.

With the development of the edge computing and cloud computing technology, the cloud-edge collaboration framework is proposed as a new effective computing architecture and applied in many fields. However, due to the openness of the edge networks, the security of cloud-edge framework is an unavoidable problem and most recent trust mechanism could not resist mixed malicious attacks at the same time. In this work, a light-weight and reliable trust mechanism based on the improved LightGBM algorithm is originally proposed to evaluate the credibility of edge devices. First, we design a light-weight trust mechanism for edge devices to process raw interaction data and extract trust features, which reduces the amount of data transmission and the pressure on the communication networks. In addition, an evaluation algorithm based on the entropy weight method (EWM) and punishment factors is designed for edge brokers to distinguish the malicious devices from the normal ones, which performs great against mixed malicious attacks. At last, we propose an improved LightGBM algorithm developed in the centralized cloud to learn other researchers' evaluation methods and check the evaluation uploaded from edge brokers, which could make the punishment factors of edge networks weighted adaptively with the change of edge networks. The experimental results show the proposed trust mechanism outperforms existing methods in the accuracy and discriminating speed under mixed malicious attacks.