数据与计算发展前沿 ›› 2020, Vol. 2 ›› Issue (2): 20-30.doi: 10.11871/jfdc.issn.2096-742X.2020.02.002

• 专刊: 数据分析技术与应用 • 上一篇    下一篇

大数据驱动的资源学科领域数据分析前沿与应用

王卷乐1,*(),程凯1,2,韩雪华1,2,张敏1,2   

  1. 1. 中国科学院地理科学与资源研究所,资源与环境信息系统国家重点实验室,北京 100101
    2. 中国科学院大学,北京 100049
  • 收稿日期:2020-01-15 出版日期:2020-04-20 发布日期:2020-06-03
  • 通讯作者: 王卷乐 E-mail:wangjl@igsnrr.ac.cn
  • 作者简介:王卷乐, 中国科学院地理科学与资源研究所,博士,博士生导师,地球数据科学与共享研究室副主任,世界数据中心可再生资源与环境数据中心主任。主要研究方向为资源环境数据集成与共享、一带一路空间信息系统、防灾减灾知识服务。
    本文中负责总体统稿、资源学科领域创新应用平台与典型应用。
    Wang Juanle, Ph.D., is a professor and doctoral supervisor of Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences. His recent research interest areas follow: scientific data sharing in resources and environmental field, the space information system of Belt and Road Initiatives, and knowledge service system of disaster risk reduction.
    He is responsible for the final compilation, edition of the manuscript, implementation of the Resource Discipline Innovation Platform and design of the typical applications.|程凯,中国科学院地理科学与资源研究所,博士研究生。研究方向为遥感地学分析、机器学习在遥感领域的应用等。
    本文中负责资源遥感监测技术、资源调查技术、部分资源综合分析技术的撰写。
    Cheng Kai is a doctor student of Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences. His research interests include Geo-analysis of Remote Sensing, Machine Learning Application in Remote Sensing.
    He wrote the parts of the resource remote sensing technologies, resource surveying technologies and comprehensive analysis technologies.
    E-mail: chengk@lreis.ac.cn|韩雪华,中国科学院地理科学与资源研究所,博士研究生。研究方向为灾害数据挖掘、社交媒体数据挖掘、公众行为分析等。
    本文中负责资源网络挖掘技术、摘要、引言等内容的撰写。
    Han Xuehua is a doctor student of Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, majoring in cartography and geographic information systems. Her main research interests include natural disaster social media data mining and public behavior analysis during natural disasters.
    She completed the parts of the resource net mining technologies, abstract and introduction.
    E-mail: hanxh@lreis.ac.cn|张敏,中国科学院地理科学与资源研究所,博士研究生。目前主要从事灾害数据管理与共享、知识图谱构建的研究工作。
    在本文中承担资源综合分析技术部分。
    Zhang Min is currently an Ph.D student in National Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences (IGSNRR,CAS). Her research interests focus on disaster data management and sharing, and knowledge graph construction.
    Her contribution to this paper is the part of comprehensive resource analysis technologies.
    E-mail: zhangmin@lreis.ac.cn
  • 基金资助:
    中国科学院“十三五”信息化专项科学大数据工程项目(XXH13505-07)

Big Data Driven Data Technology Analysis Frontier and Application in Resource Discipline

Wang Juanle1,*(),Cheng Kai1,2,Han Xuehua1,2,Zhang Min1,2   

  1. 1. State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China
    2. University of Chinese Academy of Sciences, Beijing 100049, China
  • Received:2020-01-15 Online:2020-04-20 Published:2020-06-03
  • Contact: Juanle Wang E-mail:wangjl@igsnrr.ac.cn

摘要:

【目的】在大数据驱动和信息技术支持下,使得资源科学综合研究这一学科灵魂问题的突破和解决成为可能,催生和促进资源科学的新发展,促进资源学科领域的创新应用。【方法】基于资源学科领域需求,阐述了资源学科领域数据分析技术前沿,包括资源遥感监测、资源调查、资源网络挖掘以及资源综合分析等技术。以中国科学院“十三五”信息化专项科学大数据工程项目“大数据驱动的资源学科创新示范平台”为例,展示其典型应用架构。【结果】基于应用案例,展现了中蒙俄经济走廊交通与管线生态风险防控、京津冀资源环境承载力评价、大数据驱动的美丽中国全景评价三个典型资源学科领域科研活动应用中的大数据驱动场景。【结论】大数据驱动的资源学科领域数据分析技术具有巨大潜力且已有部分应用展示,但仍需要更多适应资源学科领域发展的新方法和新模式,促进其向综合科学研究的范式转变。

关键词: 资源学科, 大数据, 数据分析, 数据驱动, 示范平台

Abstract:

[Objective] Driven by big data and supported by information technology, it makes it possible to break through and solve the soul problem of the comprehensive study of resource science, promoting the new development and innovative application of resource science, and the innovative application of resource science. [Methods] Based on the domain demand of the resource discipline, this paper expounds the frontier of data analysis technology in the resource discipline, including remote sensing monitoring, resource surveying, resource network mining and resource comprehensive analysis, and takes the “The Big Data Driven Resource Discipline Innovation Platform” supported by 13th Five-year Informatization Plan of Chinese Academy of Sciences as an example to demonstrate its typical application architecture. [Results] Based on application cases, three big data-driven scenarios in the typical application of scientific research activities in resource discipline are presented, including ecological risk prevention of transportation and pipeline control in China-Mongolia-Russia economic corridor, assessment of the carrying capacity of resources and environment in Beijing-Tianjin-Hebei region, and assessment of the beautiful China driven by big data. [Conclusions] The data analysis technologies driven by big data in the field of resource discipline have great potential and some of them have been applied in reality. However, more new methods and models adapted to the development of resource discipline are needed to promote its paradigm shift to comprehensive scientific research.

Key words: resource discipline, big data, data driven, demonstration platform