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Study on the Construction Scheme of Mega Interconnected Knowledge Systems in Deep Space Exploration
- LIU Jizhong1,2, GE Ping1,2, KANG Yan1,2, ZHANG Tianxin1,2, JIANG Yichen2, MA Ke2, SHAO Yanli2
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1. Lunar Exploration and Space Engineering Center, Beijing 100041, China;
2. Deep Space Exploration Laboratory, Beijing 100195, China
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History
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Received |
Revised |
Published |
13 Nov 2023 |
08 Dec 2023 |
26 Mar 2024 |
Issue Date |
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26 Mar 2024 |
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Abstract
China’s deep space exploration has gradually developed from technology and science-driven to the stage dominated by science, leading to technological advances. Under our demand for high-quality development in deep space exploration, the Mega Interconnected Knowledge System in Deep Space Exploration (MIKSE) was innovatively proposed with its concept and scheme conceiving. Centering around the scientific goals, deep space exploration engineering, science, technology, and big data in the application were collected, techniques including artificial intelligence and cloud computing were utilized to perform organic organization, information association, and knowledge mining on relevant elements, a large model with genealogical associations and networks of connections was built, and an intelligent big knowledge platform was established. With the help of this platform, historical data and its current capabilities can be fully utilized to support generative knowledge and information for the future planning and development of deep space exploration and promote the paradigm shift of data-driven deep space research.
Keywords
mega interconnected knowledge systems /
scientific leading /
large model in deep space exploration /
data driving
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LIU Jizhong, GE Ping, KANG Yan, ZHANG Tianxin, JIANG Yichen, MA Ke, SHAO Yanli.
Study on the Construction Scheme of Mega Interconnected Knowledge Systems in Deep Space Exploration. Journal of Deep Space Exploration, 2024, 11(1): 79‒89 https://doi.org/10.15982/j.issn.2096-9287.2024.20230169
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References
[1] 刘继忠,胡朝斌,庞涪川,等. 深空探测发展战略研究[J]. 中国科学:技术科学,2020,50(9):1126-1139.
LIU J Z,HU C B,PANG F C,et al. Strategy of deep space exploration[J]. Scientia Sinica Technologica,2020,50(9):1126-1139.
[2] 吴伟仁,刘继忠,唐玉华,等. 中国探月工程[J]. 深空探测学报(中英文),2019,6(5):405-416.
WU W R,LIU J Z,TANG Y H,et al. China lunar exploration program[J]. Journal of Deep Space Exploration,2019,6(5):405-416.
[3] 于登云,马继楠. 中国深空探测进展与展望[J]. 前瞻科技,2022,1(01):17-27.
YU D Y,MA J N. Progress and prospect of deep space exploration in China[J]. Science and Technology Foresight,2022,1(1):17-27.
[4] 李春来,刘建军,耿言,等. 中国首次火星探测任务科学目标与有效载荷配置[J]. 深空探测学报(中英文),2018,5(5):406-413.
LI C L,LIU J J,GENG Y,et al. Scientific objectives and payload configuration of China’s first Mars exploration mission[J]. Journal of Deep Space Exploration,2018,5(5):406-413.
[5] 葛平,张天馨,康焱,等. 2021年深空探测进展与展望[J]. 中国航天,2022(2):9-19.
GE P,ZHANG T X,KANG Y,et al. Progress and prospects of deep space exploration in 2021[J]. Aerospace China,2022(2):9-19.
[6] 张荣桥. “天问”一号开启我国行星探测新征程[J]. 中国航天,2021(6):9-10.
ZHANG R Q. Tianwen-1 launches a new journey of planetary exploration in China[J]. Aerospace China,2021(6):9-10.
[7] 张荣桥,黄江川,赫荣伟,等. 小行星探测发展综述[J]. 深空探测学报(中英文),2019,6(5):417-423,455.
ZHANG R Q,HUANG J C,HE R W,et al. The development overview of asteroid exploration[J]. Journal of Deep Space Exploration,2019,6(5):417-423,455.
[8] 裴照宇,刘继忠,王倩,等. 月球探测进展与国际月球科研站[J]. 科学通报,2020,65(24):2577-2586.
PEI Z Y,LIU J Z,WANG Q,et al. Overview of lunar exploration and International Lunar Research Station[J]. Chinese Science Bulletin,2020,65(24):2577-2586.
[9] 张熇,顾征,韩承志. 小行星撞击防御任务分析与设计[J]. 深空探测学报(中英文),2023,10(4):387-396.
ZHANG H,GU Z,HAN C Z. Analysis and design of asteroid impact defense mission[J]. Journal of Deep Space Exploration,2023,10(4):387-396.
[10] 刘慧根,赵海斌,周济林. 近地小天体调查、防御与开发[J]. 科学通报,2020,65(9):757-763.
LIU H G,ZHAO H B,ZHOU J L. Survey,defence and resource development of NEO[J]. Chinese Science Bulletin,2020,65(9):757-763.
[11] 刘继忠,尚海滨,刘勇,等. 深空探测全域轨迹优化设计平台研究与实现[J]. 宇航学报,2023,44(7):998-1007.
LIU J Z,SHANG H B,LIU Y,et al. Global trajectory optimization design platform for deep space exploration[J]. Journal of Astronautics,2023,44(7):998-1007.
[12] 关锋,葛平,邵艳利,等. 基于MBSE的月球科研站任务分析[J]. 航空工程进展,2023,14(3):84-99.
GUAN F,GE P,SHAO Y L,et al. Mission analysis of lunar scientific research station based on MBSE[J]. Advances in Aeronautical Science and Engineering,2023,14(3):84-99.
[13] 关锋,葛平,周国栋,等. MBSE发展趋势与中国探月工程并行协同论证[J]. 空间科学学报,2022,42(2):183-190.
GUAN F,GE P,ZHOU G D,et al. Development trend of MBSE and investigation of concurrent collaborative demonstration for Chinese lunar exploration program[J]. Chinese Journal of Space Science,2022,42(2):183-190.
[14] 秦涛,杜尚恒,常元元,等. ChatGPT工作原理、关键技术及未来发展趋势[J]. 西安交通大学学报,2024(1):1-11.
QIN T,DU S H,CHANG Y Y,et al. Running principles,key technologies and developing trends of ChatGPT[J]. Journal of Xi'an Jiaotong University,2024(1):1-11.
[15] 周毅,刘峥,粟小青,等. 融合多层次数据的问答知识图谱本体模型构建[J]. 图书情报工作,2022,66(5):8.
ZHOU Y,LIU Z,SU X Q,et al. Ontology model construction question-answering knowledge graph integrating multi-level data[J]. Library And Information Service,2022,66(5):8.
[16] 田玲,张谨川,张晋豪,等. 知识图谱综述——表示,构建,推理与知识超图理论[J]. 计算机应用,2021,41(8):26.
TIAN L,ZHANG J C,ZHANG J H,et al. Knowledge graph survey:representation,construction,reasoning and knowledge hypergraph theory[J]. Journal of Computer Applications,2021,41(8):26.
[17] 丁效,吴婷婷,杜理等. 基于双曲空间的事理图谱增强的因果推理方法及系统:中国,202210131870[P]. 2023-10-22.
[18] LI Z,DING X,LIU T. Constructing narrative event evolutionary graph for script event prediction[C]//Proceedings of the 27th International Joint Conference on Artificial Intelligence. Stockholm, Sweden: [s. n.], 2018:4201-4207.
[19] SINGH N,AGGARWAL S. Review on knowledge extraction using classifier[J]. International Journal for Scientific Research and Development,2014,6(2):415-417.
[20] VASWANI A,SHAZEER N,PARMAR N,et al. Attention is all you need[C]//Advances in Neural Information Processing Systems. Long Beach, CA, USA: Curran Associates Inc., 2017.
[21] DELVIN J,CHANG M W,LEE K,et al. Bert:pre-training of deep bidirectional transformers for language understanding[C]//Proceedings of NAACL-HLT. Minneapolis: Association for Computational Linguistics, 2019.
[22] BLEI D M,NG A Y,JORDAN M I. Latent dirichlet allocation[J]. Journal of Machine Learning Research,2003(3):993-1022.