提出新模型检测能源物联网中的时间异常，防时钟漂移和2038年故障

Time integrity across distributed Internet of Things (IoT) devices is fundamental to reliable sensing, control, and security in energy cyber-physical systems. However, operational energy IoT systems remain vulnerable to clock-drift escalation, time-synchronization manipulation, and catastrophic timestamp discontinuities, e.g., the Year 2038 (Y2K38) Unix epoch overflow. These failures violate timestamp monotonicity, distort temporal ordering, and introduce structured inconsistencies in system observations. Conventional anomaly detection models, which typically assume reliable and uniformly ordered timestamps, are therefore ill-equipped to capture timing-layer failures. This paper introduces STGAT (Spatio-Temporal Graph Attention Network), a clock-dynamics-aware anomaly detection solution that jointly models temporal distortion and inter-device consistency in energy IoT systems. STGAT integrates drift-aware temporal embeddings and temporal self-attention to capture non-uniform and corrupted time evolution within individual device streams, while graph attention models the spatial propagation of timing inconsistencies across interconnected nodes. A Jacobian-regularized latent representation further promotes geometric separation between nominal clock evolution and anomalous temporal deformation caused by drift escalation, synchronization offsets, jitter accumulation, and epoch-overflow events. Experimental evaluation on energy IoT telemetry augmented with controlled timing-layer perturbations shows that STGAT achieves 95.7% accuracy, 94.0% precision, 92.0% recall, 93.0% F1-score, and 0.97 AUC under the primary test setting. STGAT also reduces detection delay to 2.3 time steps, corresponding to a 26% improvement over the closest baseline, while maintaining stable performance under overflow-induced discontinuities, stealthy drift escalation, and temporally induced physical inconsistencies.