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开放式且具有成本效益的湖泊水质监测数字生态系统。

Open and Cost-Effective Digital Ecosystem for Lake Water Quality Monitoring.

机构信息

Department of Earth and Environmental Sciences (DSTA), University of Pavia, Via Ferrata 9, 27100 Pavia, Italy.

Institute of Earth Sciences, Department of Environment, Construction and Design, University of Applied Sciences of Southern Switzerland (SUPSI), Campus Mendrisio, Via Francesco Catenazzi 23, 6850 Mendrisio, Switzerland.

出版信息

Sensors (Basel). 2022 Sep 4;22(17):6684. doi: 10.3390/s22176684.

Abstract

In some sectors of the water resources management, the digital revolution process is slowed by some blocking factors such as costs, lack of digital expertise, resistance to change, etc. In addition, in the era of Big Data, many are the sources of information available in this field, but they are often not fully integrated. The adoption of different proprietary solutions to sense, collect and manage data is one of the main problems that hampers the availability of a fully integrated system. In this context, the aim of the project is to verify if a fully open, cost-effective and replicable digital ecosystem for lake monitoring can fill this gap and help the digitalization process using cloud based technology and an Automatic High-Frequency Monitoring System (AHFM) built using open hardware and software components. Once developed, the system is tested and validated in a real case scenario by integrating the historical databases and by checking the performance of the AHFM system. The solution applied the edge computing paradigm in order to move some computational work from server to the edge and fully exploiting the potential offered by low power consuming devices.

摘要

在水资源管理的某些领域,数字革命进程受到一些阻碍因素的影响,例如成本、缺乏数字专业知识、对变革的抵制等。此外,在大数据时代,该领域有许多信息来源,但这些来源往往没有得到充分整合。采用不同的专有解决方案来感知、收集和管理数据是阻碍实现完全集成系统的主要问题之一。在这种情况下,该项目旨在验证一个完全开放、具有成本效益且可复制的湖泊监测数字生态系统是否可以填补这一空白,并利用基于云的技术和使用开源硬件和软件组件构建的自动高频监测系统(AHFM)来帮助实现数字化进程。该系统开发完成后,通过整合历史数据库和检查 AHFM 系统的性能,在实际案例场景中进行了测试和验证。该解决方案应用了边缘计算范例,以便将一些计算工作从服务器转移到边缘,并充分利用低功耗设备提供的潜力。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b388/9459782/f2fc72662587/sensors-22-06684-g001.jpg

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