面向分布式数据安全共享的高速公路路网拥堵监测OA北大核心

The application of artificial intelligence(AI)technology for monitoring the condi-tion of expressway networks has become a prominent research area.However,challenges such as data silos and privacy protection hinder intelligent decision-making in this domain.To address these issues and enable secure sharing of distributed data for intelligent decision-making,particularly with regard to congestion,a strategy based on federated learning is proposed.This strategy employs real-time camera data and utilizes a fully homomorphic encryption scheme within the federated learning framework.This enables the establishment of an encrypted,intelligent decision-making architecture to develop a congestion status monitoring model based on optimized road segments.The results indicate that,while ensuring the security and privacy of distributed data,this approach can effec-tively monitor expressway congestion.