地球信息科学学报 ›› 2020, Vol. 22 ›› Issue (1): 11-20.doi: 10.12082/dqxxkx.2020.190479

• 专辑:地理智能 • 上一篇    下一篇

地理空间传感网融合服务技术与应用

陈能成1,*(), 肖长江2, 杨超3, 王伟1   

  1. 1. 武汉大学测绘遥感信息工程国家重点实验室,武汉 430079
    2. 同济大学测绘与地理信息学院,上海 200092
    3. 中国地质大学(武汉)地理与信息工程学院, 武汉 430074
  • 收稿日期:2019-08-31 修回日期:2019-11-01 出版日期:2020-01-25 发布日期:2020-04-08
  • 通讯作者: 陈能成 E-mail:cnc@whu.edu.cn
  • 作者简介:陈能成(1974— ),男,福建德化人,教授、博士生导师,研究方向为对地观测传感网、地理智能、网络GIS和智慧城市。
  • 基金资助:
    国家重点研发项目(2018YFB2100500);国家自然科学基金项目(41890822);湖北省自然科学基金创新群体项目(2016CFA003)

Technology and Application of the Fusion Service of Geospatial Sensor Web

CHEN Nengcheng1,*(), XIAO Changjiang2, YANG Chao3, WANG Wei1   

  1. 1. State Key Laboratory for Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, China
    2. College of Surveying and Geo-Informatics, Tongji University, Shanghai 200092, China
    3. School of Geography and Information Engineering, China University of Geosciences, Wuhan 430074, China
  • Received:2019-08-31 Revised:2019-11-01 Online:2020-01-25 Published:2020-04-08
  • Contact: CHEN Nengcheng E-mail:cnc@whu.edu.cn
  • Supported by:
    National Key Research and Development Program(2018YFB2100500);National Natural Science Foundation of China (NSFC) Program(41890822);Creative Research Groups of Natural Science Foundation of Hubei Province of China(2016CFA003)

摘要:

随着遥感数据网、传感网、物联网和人工智能的发展,逐渐形成空天地海立体化、集成化和一体化的地理空间传感网。地理空间传感网感知资源呈现出多源、异构和分散的特征,面向多层次用户个性化、即时化和智能化应用需求,存在异构资源共享管理、多协议实时接入、时空无缝感知、自动化感知和精准预测等技术挑战。静态地理信息服务由于无法提供鲜活的地理信息,难以满足地理事件的综合监测、决策预警和聚焦应用需求,急需发展地理空间传感网融合服务技术和实时动态地理信息服务平台。本文围绕信息物理网环境下空天地海观测平台的观测高效共享和融合服务问题,提出了传感网观测共享信息模型和点面观测协同无缝重建模型,突破了观测在线接入、集成管理、星地融合、时空预测和聚焦服务等地理空间传感网融合服务关键技术,研制了包含“感—联—知—控”等功能的传感网时空信息网络感知服务系统GeoSensor,介绍了GeoSensor在流域、海洋和城市等典型应用。未来将进一步发展“人—水—城”智能感知认知理论,突破“空天地海人”群智感知、空间智能和认知服务技术,开展长江经济带应用。

关键词: 地理空间传感网, 在线接入, 集成管理, 星地融合, 时空预测, 聚焦服务

Abstract:

The space-air-ground-sea stereo and integrated geospatial sensor web have gradually formed along with the development of remote sensing data network, sensor web, internet of things (IoT), and artificial intelligence. The sensing resources of the geospatial sensor web are of multiple sources, heterogeneity, and dispersion. These characteristics result in the grand technical challenges of sharing and managing heterogeneous resources, real-time access of geospatial information with multiple protocols, spatiotemporally seamless and autonomous sensing of geospatial information, and accurate prediction of key parameters, especially when facing the personalized, instant, and smart application needs of multi-level users. It is hard for static geographical information service to meet the demands of geo-events for integrated monitoring, early warning and decision support, and focusing application. Therefore, there are urgent needs to develop fusion service technologies of the geospatial sensor web, as well as real-time dynamic geographical information service platforms. To solve these problems, this paper proposed online access, integrated management, space-ground fusion, spatiotemporal prediction, and focusing service models and methods. With the online access, dynamic management methods for sensing spectrum resources, and transparent access methods based on heterogeneous sensor protocols pool were proposed; a cyber-physical spatiotemporal information service environment was established, which realized the efficient access of spatiotemporal information with heterogeneous protocols. With the integrated management, sharable and interoperable information models including sensor observation process information description model, observation data description model, observation event description model, and dynamic observation capability index model were proposed, which tackled the coupling problem of sensor web and GIS, and realized large-scale integrated management and sharing of space-air-ground-sea platforms and sensors for the integrated monitoring of fairway, hydrology, soil, meteorology, and ocean. With space-ground fusion, a point-surface-collaboration and seamless reconstruction model, and evaluation-collaboration-reconstruction, cross-scale, seamless and continuous sensing methods were proposed, which improved the sensing quality by 14 times with respect to using satellite only and meanwhile keeping sensing frequency the same as the station networks, providing new ways for continuous monitoring of resources, environments, and disasters. For spatiotemporal predictions, ensemble models of multiple machine learning models, ensemble models of statistical models and dynamic models, and a spatiotemporal deep learning model were proposed, which realized high-resolution and high-precision predictions of meteorological parameters at regional scales. For the focusing service, a geo-control method based on instant sensing feedback, a time-continuous maximal covering location model, and an automatic aggregation sensing method were proposed, which improved the spatiotemporal coverage of sensing by 18%, and realized active and on-demand sensing of spatiotemporal information. Based on the sensor web observation information models and using the architecture of satellite-ground-collaboration spatiotemporal information sensing as a service, a geospatial sensor web spatiotemporal information sensing and service system named GeoSensor was developed, which has the functions of sensing, access, cognition, and control. The GeoSensor has been successfully applied to the sensing management and service of spatiotemporal information in the Yangtze River, the ocean and the smart city. In the future, the theory of smart sensing and cognition of people, water, and city will be further developed, the technology of crowd-sourced sensing, spatial intelligence, and cognition service of space-air-ground-sea-people will be developed, and large-scale applications in the Yangtze River Economic Zone will be conducted as well.

Key words: geospatial sensor web, online access, integrated management, satellite-ground fusion, spatiotemporal prediction, focusing service