影像法测定水文流量中的试验研究

Experimental study on image-based method for measuring hydrological flow rates

流量测验是水文站点进行数据监测的重要内容,为了实现数据的自动化精准监测,国内外科研机构和水文相关企业相继设计研发出以微波雷达、声学多普勒效应等为原理的各种监测设备。但黄河汛期河道水流流速快、含沙量高、漂浮物多,使用雷达和多普勒设备存在一定的困难和精度问题,且容易受淤泥、水草等影响。本文研究了一种基于影像法的流量测速方法,并进行了试验探究。智能摄像机采用基于AI的视频测流技术,对水面实时的影像进行特征分析,通过像素间距和帧间隔的换算,得出位移和时间,进而计算出水面实时流速值,对高流速、高含沙量和多漂浮物的河道具有良好的适应性。本研究可为水文流量测验提供一种新的测量思路和技术手段。

Flow rate measurement is a crucial aspect of data monitoring at hydrological stations.In order to achieve automated and precise data monitoring,research institutions and hydrology-related companies both domestically and internationally have designed and developed various monitoring devices based on principles such as microwave radar and acoustic Doppler effect.However,during the flood season in the Yellow River,the river’s fast flow,high sediment content,and abundance of floating debris pose challenges and accuracy issues when using radar and Doppler equipment,as they are susceptible to interference from silt and aquatic vegetation.This paper explores a flow rate measurement method based on image analysis and conducts experimental research.Intelligent cameras employ AI-based video flow measurement technology to analyze real-time images of the water surface.By converting pixel spacing and frame intervals,displacement and time are determined,allowing for the calculation of real-time water flow velocity.This method demonstrates good adaptability for rivers with high flow rates,high sediment content,and abundant floating debris.This research provides a new measurement approach and technological solution for hydrological flow rate measurement.