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OODAFlow:面向智能无人系统的流式数据处理框架

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智能无人系统是一种能够在复杂环境中自主进行实时推理、决策和制定行动方案的计算系统。智能无人系统实现实时决策的关键在于对流式数据的实时处理，然而随着人工智能技术和传感器技术的快速发展，智能无人系统需要处理的数据规模不断增长，数据类型变得更加复杂。面对不断增长的数据处理性能需求，智能无人系统需要一个充分优化的专用流式数据处理框架来提升其数据处理性能。针对该问题，本文提出了一种面向智能无人系统的流式数据处理框架OODAFlow，该框架将智能无人系统的硬件特征和智能计算任务的数据特征与观察-判断-决策-行动(OODA)模型思想相融合，实现了OODA任务创建、任务调度、资源调度等功能，能够实现对智能无人系统异构资源的调度和智能计算任务的处理。本文在智能无人系统上搭建了一套OODA任务处理系统，验证了所提OODAFlow框架的可行性。通过提出的图像预处理过程优化、流水线优化以及判断节点并行加速优化等方法，提高了系统的数据吞吐性能和资源利用率。无人机智能控制任务的实验表明，采用本文提出的OODAFlow框架后，智能无人系统的数据处理性能提升了73倍。
OODAFlow:streaming data processing framework for intelligent unmanned systems
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.

intelligent unmanned systemdeep learning accelerator cardobserve-orient-decide-act ( OODA)streaming data processing frameworkheterogeneous computing resource

全振宇、尹龙祥、陈晓明、韩银和

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中国科学院计算技术研究所智能计算机研究中心 北京 100190

中国科学院大学 北京 100190

智能无人系统 深度学习加速卡 观察-判断-决策-行动(OODA) 流式数据处理框架 异构计算资源

国家重点研发计划中国科学院战略性先导科技专项(B类)中国科学院计算技术研究所创新课题

2022YFB4501600XDB44000000E261040

2024

10.3772/j.issn.1002-0470.2024.09.001

高技术通讯
中国科学技术信息研究所

高技术通讯

CSTPCD北大核心
影响因子：0.19
ISSN：1002-0470
年,卷(期)：2024.34(9)