支持隐私保护的自主无人系统分布式智能运维框架OA北大核心CSTPCD

Constructing intelligent operation method is crucial to the autonomy,safety and reliability of autonomous unmanned systems,and is challenged by the issues of distributed operation and privacy protection.Without considering the limitation of computation resources and the requirement of quantitative privacy protection,the existing works can not solve the above issues for autonomous unmanned systems.Therefore,in this paper,a distributed intelligent operating framework supporting privacy protection for autonomous unmanned systems is proposed.The essential idea is combining offline learning and online monitoring to build both local and global operating models,building operation models by federated learning,sharing models based on the blockchain network,and protecting local models by differential privacy techniques,such that the issues caused by the limited computing resource and privacy protection are resolved.The experimental results show that the proposed framework can support the construction of efficient intelligent operation methods for autonomous unmanned systems and the method designed within the framework is effective.