基于大模型的公安智能预警系统研究与应用OA

The research proposes a large-scale model-based modeling method for public security intelligent early warning systems to meet the challenges of multi-source and heterogeneous data in complex urban security scenarios.The system integrates structured and unstructured data,uses an improved Transformer to model event chain,and introduces a comparative learning mechanism to improve the model's ability to identify fine-grained risk differences.Through model compression,knowledge distillation and streaming processing architecture,the system can be efficiently deployed in actual combat scenarios.The experimental verification model is superior to traditional methods in terms of accuracy,response time and interpretability,showing good practical adaptability and promotion potential.