“大数据&人工智能”驱动的空间天气科研范式变革初步探索

doi:10.11871/jfdc.issn.2096-742X.2023.02.002

数据与计算发展前沿 ›› 2023, Vol. 5 ›› Issue (2): 24-36.

CSTR: 32002.14.jfdc.CN10-1649/TP.2023.02.002

doi: 10.11871/jfdc.issn.2096-742X.2023.02.002

• 专刊：“人工智能&大数据”科研范式变革专刊（上） • 上一篇下一篇

“大数据&人工智能”驱动的空间天气科研范式变革初步探索

胡晓彦^1,^2,⁴(),徐寄遥^1,^3,^*(),邹自明^1,^4,^*()

1.中国科学院国家空间科学中心，北京 100190
2.中国科学院大学，北京 100049
3.太阳活动与空间天气重点实验室，北京 100190
4.国家空间科学数据中心，北京 100190

收稿日期:2023-02-13 出版日期:2023-04-20 发布日期:2023-04-24
通讯作者: 徐寄遥,邹自明
作者简介:胡晓彦，中国科学院国家空间科学中心，高级工程师，中国科学院青年创新促进会成员。主要从事科学数据治理与空间科学大数据智能应用研究，组织团队在科学数据治理概念建模、空间科学数据互操作、基于机器学习的知识挖掘等方向开展关键技术突破。主持或参与国家自然科学基金、国家重点研发计划、国家重大科技基础设施、中国科学院战略性先导科技专项、中国科学院信息化专项等多个科研项目。
本文主要承担工作：调研与论文撰写。
HU Xiaoyan is a senior engineer at National Space Science Center of Chinese Academy of Sciences and a member of Youth Innovation Promotion Association of Chinese Academy of Sciences. She is mainly engaged in the research of scientific data governance and intelligent application of big data in space science, and organizes the team to focus on key technology breakthroughs in the direction of scientific data governance concept modeling, space science data interoperability, and machine learning based knowledge mining. She has hosted or participated in a number of research projects including National Natural Science Foundation of China, National Key R&D Program, National Major Science and Technology Inf-rastructure, Strategic Priority Program of Chinese Academy of Sciences, and Informatization Project of Chinese Academy of Sciences.
In this paper, she is mainly responsible for literature research and manuscript writing.
E-mail: huxiaoyan@nssc.ac.cn|徐寄遥，中国科学院国家空间科学中心，研究员，博士生导师，国务院特殊津贴专家。主要从事中高层大气光化学和动力学以及辐射过程，中高层大气探测技术、信息处理以及分析方法，空间物理学探测和实验方案的设计与实现等方向研究。2002年度国家杰出青年基金获得者，2007年入选中国科学院“百人计划”，曾获得中国科学院自然科学二等奖等奖项。
本文主要承担工作：论文总体架构和主要思路。
XU Jiyao is a researcher at National Space Science Center of Chinese Academy of Sciences, a Ph.D. mentor and received Special Government Allowances from the State Council. He is mainly engaged in the research of mid- and upper-level atmospheric photochemistry and dynamics and radiation pro-cesses, mid- and upper-level atmospheric sounding techniques, information processing and analysis methods, and the des-ign and implementation of space physics sounding and exper-imental programs, etc. He is a recipient of the National Disting-uished Youth Fund in 2002, and was selected as one of the “100 Talent Program” of Chinese Academy of Sciences in 2007. He also has been awarded the Second Prize of Natural Science of Chinese Academy of Sciences.
In this paper, he is responsible for conceptualization, and methodology.
E-mail: jyxu@spaceweather.ac.cn|邹自明，中国科学院国家空间科学中心副主任，研究员，博士生导师，国家空间科学数据中心主任。主要研究领域为空间科学信息学，在空间科学数据处理，宇宙空间信息的组织、检索与互操作，空间信息系统工程，大数据与人工智能技术在领域知识发现中的应用等方面开展研究。曾获军队科技进步一等奖两项，中国科学院载人航天工程重要贡献奖和中国科学院杰出科技成就奖等奖项。
本文主要承担工作：论文总体架构和研究指导。
ZOU Ziming is the deputy director of National Space Science Center of Chinese Academy of Sciences, a researcher, a Ph.D. mentor, and the director of National Space Science Data Center. His main research interests are space science informatics, with research in space science data processing, the organization, retrieval and interoperability of cosmic space information, space information system engineering, and application of big data and artificial intelligence technology in domain knowledge discovery. He has obtained two Second Class Prizes of Military Scientific and Technological Progress, and won the Important Contribution Award of Manned Space Engineering of Chinese Academy of Sciences and the Outstanding Scientific and Technological Achievement Award of the Chinese Academy of Sciences.
In this paper, he is responsible for the overall structure of the paper and research guidance.
E-mail: mzou@nssc.ac.cn
基金资助:
国家重点研发计划“空间科学大数据智能管理与分析挖掘关键技术及应用”项目(2022YFF0711400);中国科学院网信专项“空间天气典型事件知识挖掘与智能建模研究”项目(CAS-WX2022SF-0103)

Preliminary Study on Paradigm Shift in Space Weather Research Driven by Big Data and Artificial Intelligence

HU Xiaoyan^1,^2,⁴(),XU Jiyao^1,^3,^*(),ZOU Ziming^1,^4,^*()

1. National Space Science Center, Chinese Academy of Sciences, Beijing 100190, China
2. University of Chinese Academy of Sciences, Beijing 100049, China
3. Key Laboratory of Solar Activity and Space Weather, Beijing 100190, China
4. Chinese National Space Science Data Center, Beijing 100190, China

Received:2023-02-13 Online:2023-04-20 Published:2023-04-24
Contact: XU Jiyao,ZOU Ziming

摘要/Abstract

摘要：

【目的】总结大数据与人工智能驱动的空间天气科研范式变革现状，并探讨可能的未来发展趋势。【方法】本文分析了空间天气大数据场景下传统科研模式面临的挑战和人工智能技术带来的机遇，广泛调研了国内外相关战略规划和研究进展，并对典型应用案例进行深入分析，归纳总结了领域科研范式变革现状与特点。【结果】人工智能技术在空间天气领域的多种科学任务类型中均得到了有效尝试，提升了科研效率，解决了大数据场景困难，空间天气领域已呈现科研范式变革的萌芽。【局限】本文侧重于对重要文献和案例的归纳和未来发展整体趋势的分析，未来希望通过文献计量学等方法对现状进行进一步研究，并针对未来发展中需解决的关键问题进行深入探讨。【结论】空间天气领域科研范式正在发生变革，大数据、人工智能与领域知识的融合有望形成新范式。

关键词: 空间天气, 大数据, 人工智能, 科研范式, 领域知识, AI就绪度

Abstract:

[Objective] This paper summarizes the current status of the paradigm shift in space weather research driven by big data and artificial intelligence, and discusses possible future trends. [Methods] This paper analyzes the challenges faced by the traditional research paradigm and the opportunities brought by artificial intelligence in the space weather big data scenario, investigates the relevant strategies and research progress at home and abroad, conducts a deep analysis of typical cases, and summarizes the current status and characteristics of the paradigm shift in space weather research. [Results] AI technologies have been effectively experimented with in a variety of scientific task types in space weather research, improving scientific efficiency, solving big data scenario difficulties, and spurring a research paradigm shift in the field of space weather. [Limitations] This paper focuses on the summarization of important literature and cases and the analysis of the overall trend of future development. In the future, we plan to further study the status through bibliometrics and other methods, and conduct in-depth discussions on the key issues that need to be addressed in future development. [Conclusions] The research in the field of space weather is undergoing a paradigm shift, whereby the convergence of big data, AI, and domain knowledge is expected to lead to a new paradigm

Key words: space weather, big data, artificial intelligence, research paradigm, domain knowledge, AI-ready

胡晓彦,徐寄遥,邹自明. “大数据&人工智能”驱动的空间天气科研范式变革初步探索[J]. 数据与计算发展前沿, 2023, 5(2): 24-36.

HU Xiaoyan,XU Jiyao,ZOU Ziming. Preliminary Study on Paradigm Shift in Space Weather Research Driven by Big Data and Artificial Intelligence[J]. Frontiers of Data and Computing, 2023, 5(2): 24-36, https://cstr.cn/32002.14.jfdc.CN10-1649/TP.2023.02.002.

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重大任务	数据类型	预期数据规模
子午工程二期（建设中）	太阳射电成像	5类设备，常规模式5分钟1张图（圆环阵1分钟1张图），>90万张图像/年
	地磁探测	秒采样数据，约90套设备，>2.8X10⁹记录/年
	全天空气辉成像	常规模式1分钟1张图，双通道，12个台站，>2.8X10⁶张图片/年
	电离层频高图	常规模式15分钟1张图，15个台站，>50万张图像/年
ASO-S（在轨测试中）	日冕成像	常规模式30秒1张图，>100万张图像/年
	白光全日面成像	常规模式10秒1张图，>300万张图像/年
	莱曼阿尔法全日面成像	常规模式10秒1张图，>300万张图像/年
SMILE（研制中）	紫外极光成像	常规模式1分钟1张图，>50万张图像/年
SMILE（研制中）	软X射线成像	常规模式1分钟1张图，>50万张图像/年

特定科学需求	数据模态	核心问题	模型算法	当前技术指标水平
太阳喷流识别与特征提取	图像数据	分类问题、语义分割问题	Region Growing	准确率>75%
日冕物质抛射事件识别与特征提取	图像数据	分类问题、语义分割问题	Marr-Hildreth+CNN	准确率~70%
极盖区极光弧识别与特征提取	图像数据	目标检测问题	YOLO模型	准确率>80% mIoU>50%
等离子体泡识别与形态提取	图像数据	分类问题、语义分割问题	迁移学习+Resnet18 PSPNet	准确率>97% mIoU>0.88
扩展F事件识别与分类	图像数据	分类问题	DCNN	准确率>93%
电离层行进式扰动事件识别与特征提取	图像数据	目标检测问题、语义分割问题	Faster R-CNN 边缘检测	准确率>95% mIoU>70%
地磁波动事件识别	数值数据	异常检测问题	经验模态分解+小波变换 1D ResNet	准确率>90%
地磁波动频谱分类	数值数据	分类问题	贝叶斯信息准则判据	准确率>93%

	处理原则	效率	主观误差	缺点	适用场景
人工	基于经验和知识	低	大	占用专业人员精力长时间专业人才培养	小规模、典型数据
传统机器	基于先验知识	高	小	先验知识可能有遗漏难以处理复杂型态	大规模、典型数据
人工智能	基于模型学习	高	小	目前大部分模型是黑箱、灰箱	大规模、复杂数据

“大数据&人工智能”驱动的空间天气科研范式变革初步探索

Preliminary Study on Paradigm Shift in Space Weather Research Driven by Big Data and Artificial Intelligence

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