调整 OGC 的 SensorThings API 和数据模型,支持环境传感器的数据管理和共享

IF 4.8 2区 环境科学与生态学 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS
Jeffery S. Horsburgh , Kenneth Lippold , Daniel L. Slaugh
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引用次数: 0

摘要

软件对于管理环境传感器数据至关重要。开放地理空间联盟(OGC)制定了 "OGC SensorThings API"(STA)标准,以解决传感器、观测变量、平台和协议之间的差异,促进传感和物联网应用的开发。本文详细介绍了STA应用编程接口(API)的Python/Django实现和STA数据模型的PostgreSQL/Timescale实现,从而提高了用于管理和共享环境传感器数据的强大软件的可用性。STA 提供带有 JSON 数据编码的 RESTful 接口,符合现代开发模式并促进互操作性。观测数据模型的元数据集成可确保数据得到充分描述和解释。STA 的灵活性允许轻量级的查询响应或全面的元数据包含,而互补的数据管理应用程序接口(API)增强了 STA 在多用户系统中的使用。GitHub 上的开源代码和部署说明可支持独立部署或云部署,从而提高了研究人员和从业人员的可访问性和可用性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Adapting OGC’s SensorThings API and Data Model to Support Data Management and Sharing for Environmental Sensors
Software is critical in managing environmental sensor data. The Open Geospatial Consortium (OGC) developed the “OGC SensorThings API” (STA) standard to address variability across sensors, observed variables, platforms, and protocols, facilitating development of sensing and Internet of Things applications. This paper details a Python/Django implementation of the STA application programming interface (API) and a PostgreSQL/Timescale implementation of the STA data model, enhancing availability of robust software for management and sharing of environmental sensor data. STA offers a RESTful interface with JSON data encoding, aligning with modern development patterns and facilitating interoperability. Integration of metadata from the Observations Data Model ensures data can be adequately described and interpreted. STA’s flexibility allows lightweight query responses or comprehensive metadata inclusion, and a complementary data management API enhances use of STA within multi-user systems. Open-source code and deployment instructions in GitHub enable standalone or cloud deployments, enhancing accessibility and usability for researchers and practitioners.
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来源期刊
Environmental Modelling & Software
Environmental Modelling & Software 工程技术-工程:环境
CiteScore
9.30
自引率
8.20%
发文量
241
审稿时长
60 days
期刊介绍: Environmental Modelling & Software publishes contributions, in the form of research articles, reviews and short communications, on recent advances in environmental modelling and/or software. The aim is to improve our capacity to represent, understand, predict or manage the behaviour of environmental systems at all practical scales, and to communicate those improvements to a wide scientific and professional audience.
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