数据-模型耦合驱动的水质监测技术发展与未来展望

Development and Prospect of Water Quality Monitoring Technology Driven by Data-model Coupling

随着大数据时代的到来,水质监测技术也迎来了快速发展,从以往的人工监测逐步转变为新型的实时监测和系统管理。当前,大数据技术已经在水质监测技术的多个方面得到广泛应用,主要包括数据采集、实时监测系统、数据存储与管理、数据分析以及风险预判5个方面。无线传感器网络和遥感逐渐成为数据采集的主流技术,而云平台和多种数据库的进一步使用促进了水质实时监测的发展。此外,时空大数据混合存储显著提升了数据存储效率。在水质分析和预测方面,机器学习和专家系统等人工智能手段则发挥了重要作用。未来,通过和物联网等新兴技术结合、提高设备集成化水平、构建混合机器学习模型,大数据将进一步助力水质监测的发展。

With the advent of the big data era,water quality detection technology has rapidly advanced,transitioning from traditional manual testing to new real-time monitoring and system management.Currently,big data technology has been widely applied in various aspects of water quality monitoring,including data collection,real-time monitoring system,data storage and management,data analysis,and risk assessment.Wireless sensor networks and remote sensing have gradually become the mainstream technologies for data collection,while the further use of cloud platforms and various databases has enhanced the development of real-time water quality monitoring.Moreover,the hybrid storage of spatio-temporal big data has significantly improved data storage efficiency.In terms of water quality analysis and prediction,artificial intelligence techniques,such as machine learning and expert systems,have played a significant role.In the future,big data will further facilitate the development of water quality monitoring by integrating with such emerging technologies as the Internet of Things(IoT),upgrading equipment integration,and developing hybrid machine learning models.