Cloud–Edge Collaborative Hardware Sensing with Dynamic Reliability Estimation and Evidence-Constrained Large Language Model Reasoning

Banking data centers generate heterogeneous IT operational streams from servers, network devices, edge nodes, and business terminals. Existing detectors are vulnerable to asynchronous sampling and unreliable channels, while separate root-cause and language-model modules cannot preserve an end-to-end evidence trail. This study presents a cloud–edge–end framework that couples sensor-quality-aware temporal encoding, topology- and chronology-constrained event-graph reasoning, and evidence-constrained language generation. At the edge, dynamic reliability scores suppress missing, noisy, delayed, drifting, and frozen channels. At the cloud layer, the model ranks root causes and identifies propagation relations and onset times from device topology, logs, alarms, and maintenance records. The language model then generates traceable incident interpretations from the resulting evidence package and rejects claims when support is insufficient or conflicting. Experiments use IT operational data collected from the headquarters and disaster-recovery data centers of a commercial bank between 2024 and 2025; the dataset contains no customer financial or transaction records. The proposed method achieves 95.38% Accuracy, 95.06% Precision, 94.71% Recall, a 94.88% F1-score, and a 97.41% AUC for hardware anomaly detection. Root-cause localization accuracy, Top-3 Accuracy, event-relation F1-score, and temporal localization error are 91.84%, 97.26%, 90.73%, and 1.82 s, respectively. The complete model attains a 94.68% evidence support rate and a 4.26% unsupported statement rate. These results show that reliability is propagated from sensing through system-level diagnosis to evidence-grounded interpretation.