Intelligent unmanned system is a type of computing system that can autonomously perform real-time inferences and decisions,and formulate action plans in complex environments. The key to realizing real-time decision-making in intelligent unmanned systems lies in the real-time processing of streaming data. However,with the rapid devel-opment of artificial intelligence technology and sensor technology,the scale of data that intelligent unmanned sys-tems need to process is constantly increasing,and the types of data are becoming more complex. Faced with the growing demand for data processing performance,intelligent unmanned systems need a well-optimized dedicated streaming data processing framework to enhance their data processing performance. To address this problem,this article proposes a stream data processing framework for intelligent unmanned systems called OODAFlow,which in-tegrates the hardware features of intelligent unmanned systems and the data features of intelligent computing tasks with the observe-orient-decide-act ( OODA) model to enable functions such as OODA task creation,task schedu-ling,and resource scheduling,it can schedule heterogeneous resources of intelligent unmanned systems and process intelligent computing tasks. This paper builds an OODA task processing system on intelligent unmanned systems to verify the feasibility of OODAFlow framework. Then,by proposing methods such as image preprocessing optimiza-tion,pipeline optimization,and orient node multi-process parallel optimization,the data throughput performance and resource utilization of the system have been improved. The experimental results in unmanned aerial vehicle in-telligent control tasks show that when the proposed OODAFlow framework is used,the data processing performance of the intelligent unmanned system is improved by 73 times.
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