Edge computing has been widely adopted across various domains due to its low latency and high processing efficiency. As core foundational software in edge intelligence scenarios, the runtime reliability of the KubeEdge platform is critically important. However, software on edge systems may experience software aging after prolonged operation, leading to delayed system responses or even service interruptions, which may negatively impact user experience and potentially cause accidents. While rejuvenation operations can mitigate aging effects, current research on aging in edge systems remains relatively limited, and existing rejuvenation methods cannot be directly applied to edge systems. To address these challenges, this study proposes a comprehensive aging state determination and rejuvenation method named GIP-MI for the KubeEdge edge system. The method first employs the GCN-Informer approach to model spatial correlations and temporal dependencies among system metrics, enabling more accurate and stable predictions of future trends in these metrics compared to conventional methods. The predicted data is then fed into the deep learning method ParNet, which leverages multi-timepoint slicing and multi-resolution feature fusion to achieve more precise identification of dynamic aging states of system resources. Finally, a task offloading method based on the multi-objective evolutionary algorithm based on decomposition and information feedback model (MOEA/D-IFM) is introduced as a rejuvenation mechanism, effectively avoiding system downtime and ensuring service continuity. Experimental results demonstrate that GIP-MI outperforms baseline methods in both aging prediction and state identification accuracy. Moreover, compared to traditional rejuvenation approaches, it shows significant advantages in key metrics such as downtime, enabling effective recovery of system states