With the rapid development of the Internet of Things and machine learning, deep neural networks have been widely applied and developed in multiple fields. At the same time, thanks to the development of mobile edge computing, the deployment of DNN reasoning tasks sinks from the remote central cloud to the edge side. However, the failure probability of edge computing nodes is much higher than that of central cloud. Therefore, when an edge server fails, how to better reconstruct and resume the execution of DNN inference tasks is a key issue. To address the aforementioned issues, we propose an edge fault oriented DNN inference task reconstruction method. Firstly, we established a problem model for the redeployment of edge fault oriented DNN inference tasks based on cloud edge network environment. The problem model optimizes the reliability and delay weights of the reconstructed DNN inference tasks. Then we designed a meta heuristic method based on simulated annealing algorithm to find the optimal redeployment scheme for DNN inference tasks. Finally, we evaluated the performance of our proposed algorithm through simulation. The results showed that our algorithm outperformed Random, LB, and RMA algorithms by an average of 36.4%, 22.5%, and 21.8%, respectively, in terms of optimization objectives. The above experimental results indicate that the algorithm proposed in this paper can provide an effective redeployment scheme for DNN inference tasks caused by edge faults.