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[ "胡志强(1970- ),男,江苏自动化研究所研究员,主要研究方向为海上作战指挥控制系统、大数据指挥控制理论与技术。" ]
网络出版日期:2021-03,
纸质出版日期:2021-03-15
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胡志强. 大数据智能指挥控制内在机理框架模型研究[J]. 智能科学与技术学报, 2021,3(1):101-109.
Zhiqiang HU. The framework model on internal mechanism of big data intelligent command and control[J]. Chinese journal of intelligent science and technology, 2021, 3(1): 101-109.
胡志强. 大数据智能指挥控制内在机理框架模型研究[J]. 智能科学与技术学报, 2021,3(1):101-109. DOI: 10.11959/j.issn.2096-6652.202111.
Zhiqiang HU. The framework model on internal mechanism of big data intelligent command and control[J]. Chinese journal of intelligent science and technology, 2021, 3(1): 101-109. DOI: 10.11959/j.issn.2096-6652.202111.
从信息时代到大数据时代,信息化指挥控制向智能指挥控制发展已成为趋势。针对大数据时代智能指挥控制发展面临的挑战,提出了“智能心源”和大数据“智能算法高架”两个新概念。在此基础上,提出大数据智能决策网,进而构建大数据智能指挥控制概念模型;通过模型分析,揭示智能指挥控制的内在机理。最后,以“智能仿真引擎”为依托,通过一个简化的模型仿真设计,说明该模型的可行性。
From the information age to the big data era
the development of information-based command and control to intelligent command and control has become a trend.In view of the challenges faced by the development of intelligent command and control in the era of big data
two new concepts of “intelligent source of soul” and big data “intelligent algorithm viaduct” were proposed.On this basis
the big data intelligent decision-making network was proposed
and then the concept model of big data intelligent command and control was constructed
and the internal intelligent mechanism of intelligent command and control was revealed through the model analysis.Finally
based on the “intelligent simulation engine”
the feasibility of the model research was demonstrated through a simplified model simulation design.
胡晓峰 , 郭圣明 , 贺筱媛 . 指挥信息系统的智能化挑战——“深绿”计划及AlphaGo带来的启示与思考 [J ] . 指挥信息系统与技术 , 2016 , 7 ( 3 ): 1 - 7 .
HU X F , GUO S M , HE X Y . Challenges to intelligent command information system:reason and revelation on Deep Green plan and AlphaGo [J ] . Command Information System and Technology , 2016 , 7 ( 3 ): 1 - 7 .
郭圣明 , 贺筱媛 , 胡晓峰 , 等 . 军用信息系统智能化的挑战和趋势 [J ] . 控制理论与应用 , 2016 , 33 ( 12 ): 1562 - 1571 .
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张钹 . 人工智能进入后深度学习时代 [J ] . 智能科学与技术学报 , 2019 , 1 ( 1 ): 4 - 6 .
ZHANG B . Artificial intelligence is entering the post deep-learning era [J ] . Chinese Journal of Intelligent Science and Technology , 2019 , 1 ( 1 ): 4 - 6 .
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PROVOST F , FAWCETT T . Data science and its relationship to big data and data-driven decision making [J ] . Big Data , 2013 , 1 ( 1 ): 51 - 59 .
胡志强 . 基于“三个世界”理论的大数据指挥与控制本体论 [J ] . 指挥与控制学报 , 2015 , 1 ( 2 ): 232 - 237 .
HU Z Q . Ontology of big data command and control based on theory of “three worlds” [J ] . Journal of Command and Control , 2015 , 1 ( 2 ): 232 - 237 .
SVENMARCK P , LUOTSINEN L , NILSSON M , et al . Possibilities and challenges for artificial intelligence in military applications [C ] // Proceeding of the NATO Big Data and Artificial Intelligence for Military Decision Making Specialists’ Meeting .[S.l.:s.n. ] , 2018 : 1 - 5 .
LUOTSINEN L J , KAMRANI F , HAMMAR P , et al . Evolved creative intelligence for computer generated forces [C ] // Proceeding of the IEEE International Conference on Systems . Piscataway:IEEE Press , 2017 .
朱丰 , 胡晓峰 , 吴琳 , 等 . 从态势认知走向态势智能认知 [J ] . 系统仿真学报 , 2018 , 30 ( 3 ): 761 - 771 .
ZHU F , HU X F , WU L , et al . From situation cognition stepped into situation intelligent cognition [J ] . Journal of System Simulation , 2018 , 30 ( 3 ): 761 - 771 .
SCHUBERT J , BRYNIELSSON J , NILSSON M , et al . Artificial intelligence for decision support in command and control systems [C ] // Proceeding of the 23th International Command and Control Research and Technology Symposium .[S.l.:s.n. ] , 2018 .
胡晓峰 . 态势智能认知的问题与探索——AlphaGo 的启示与我们的实践 [J ] . 中国指挥与控制学会通讯 , 2019 , 4 ( 2 ): 7 - 16 .
HU X F . Problems and exploration of situation intelligent cognition—inspiration from AlphaGo and our practice [J ] . Communications of the CICC , 2019 , 4 ( 2 ): 7 - 16 .
唐振韬 , 邵坤 , 赵冬斌 , 等 . 深度强化学习进展:从 AlphaGo 到AlphaGo Zero [J ] . 控制理论与应用 , 2017 , 34 ( 12 ): 1529 - 1546 .
TANG Z T , SHAO K , ZHAO D B , et al . Recent progress of deep reinforcement learning:from AlphaGo to AlphaGo Zero [J ] . Control Theory and Applications , 2017 , 34 ( 12 ): 1529 - 1546 .
SAMEK W , WIEGAND T , MÜLLER K . Explainable artificial intelligence:understanding,visualizing and interpreting deep learning models [J ] . arXiv preprint , 2017 ,arXiv:1708.08296.
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