<p>This study proposes a comprehensive framework that integrates deep learning, event-triggering mechanism, and UIO technology to address the problem of false data injection attacks faced by multi-regional interconnected power systems. In this framework, deep learning techniques are applied to the construction of attack detection models, which detect potential anomalies and attacks in real-time by monitoring and analyzing power system data. Meanwhile, design and implement event-triggering strategies based on deep learning to dynamically adjust triggering conditions and improve system response speed and accuracy. Combining UIO technology for system state estimation and isolation of unknown inputs, combined with deep learning prediction results, to provide more accurate system state information. By optimizing the integration method of the overall framework, the resilience and security of multi-regional interconnected power systems against false data injection attacks can be improved. This study demonstrates the important role of deep learning technology in the field of power system security and provides useful references for future-related research.</p>