图像法测流系统开发与应用
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摘要
基于市面上现有的视频监控摄像机开发一套图像法测流系统,实现非接触式的水尺水位、表面流速和断面流量远程在线监测。测流系统采用风光互补供电,并运用虚拟局域网(VPN)技术和4G移动通信网络实现远程访问,可在野外自治运行长达7个阴雨天。通过集成少量低成本的辅助测量设备及对测量算法在现场应用中的优化设计,能够有效抑制水面耀光、阴影等噪声的干扰,并可直接利用天然漂浮物或水面模式进行表面流速测量。测试结果表明,测流系统能够在数分钟内完成一次测量,得到现场的视频图像、稳定的时均水位、测点高度密集的流速分布,以及子断面高达上百个的断面流量。相比传统测流仪器具有时空分辨率高的优势,有利于捕获暴雨、山洪等极端条件下的水文过程。
Using the existing video surveillance camera, an image-based flow measurement system is developed to realize on-line telemetry of non-contact water level, surface velocity and discharge. The system is powered by a wind and photovoltaic hybrid power source and remotely accessed via the 4 G mobile communication network with VPN technology. It can automatically run in the wild for up to 7 rainy days. A small number of low-cost auxiliary measuring devices are integrated and the measuring algorithms are optimized for field application. On account of this, the influence of water surface noises like glares and shadows is effectively decreased, and the surface velocities can be directly measured by tracing of nature floating debris or free-surface patterns. Testing results show that, the system can perform single measurement in several minutes, output data including video and image on site, stable timeaveraged water level, high density velocity distribution and discharge with subsections up to hundreds. Compared with traditional flow measurement instruments, it is superior in high spatial and temporal resolution, thus has great potential to record the hydrological processes under extreme conditions, such as rainstorm and flash flood.
Using the existing video surveillance camera, an image-based flow measurement system is developed to realize on-line telemetry of non-contact water level, surface velocity and discharge. The system is powered by a wind and photovoltaic hybrid power source and remotely accessed via the 4 G mobile communication network with VPN technology. It can automatically run in the wild for up to 7 rainy days. A small number of low-cost auxiliary measuring devices are integrated and the measuring algorithms are optimized for field application. On account of this, the influence of water surface noises like glares and shadows is effectively decreased, and the surface velocities can be directly measured by tracing of nature floating debris or free-surface patterns. Testing results show that, the system can perform single measurement in several minutes, output data including video and image on site, stable timeaveraged water level, high density velocity distribution and discharge with subsections up to hundreds. Compared with traditional flow measurement instruments, it is superior in high spatial and temporal resolution, thus has great potential to record the hydrological processes under extreme conditions, such as rainstorm and flash flood.
引文
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[16]严锡君,张振,陈哲,等.基于FHT-CC的流场图像自适应运动矢量估计方法[J].仪器仪表学报,2014,35(1):50-58.
[17]张振,徐立中,韩华,等.基于径向基神经网络的明渠流量软测量方法[J].仪器仪表学报,2012,32(12):2648-2655.
[18]张振,严锡君,樊棠怀,等.近红外成像的便携式大尺度粒子图像测速仪[J].仪器仪表学报,2012,33(12):2840-2850.
[19]ZHANG Z,WANG X,FAN T H,et al.River surface target enhancement and background suppression for unseeded LSPIV[J].Flow Measurement and Instrumentation,2013,30:99-111.
[20]张振,陈哲,吕莉,等.基于视觉感受野的自适应背景抑制方法[J].仪器仪表学报,2014,35(1):191-199.
[2]徐立中,张振,严锡君,等.非接触式明渠水流监测技术的发展现状[J].水利信息化,2013(3):37-44.
[3]张振,徐枫,王鑫,等.河流水面成像测速研究进展[J].仪器仪表学报,2015,36(7):1441-1450.
[4]LE COZ J,HAUET A,PIERREFEU G,et al.Performance of image-based velocimetry(LSPIV)applied to flash-flood discharge measurements in Mediterranean rivers[J].Journal of Hydrology,2010,394(1):42-52.
[5]TSUBAKI R,FUJITA I,TSUTSUMI S.Measurement of the flood discharge of a small-sized river using an existing digital video recording system[J].Journal of Hydro-environment Research,2011,5(4):313-321.
[6]TAURO F,PORFIRI M,GRIMALDI S.Orienting the camera and firing lasers to enhance large scale particle image velocimetry for streamflow monitoring[J].Water Resources Research,2014,50(9):7470-7483.
[7]DOBSON D W,HOLLAND K T,CALANTONI J.Fast,large-scale,particle image velocimetry-based estimations of river surface velocity[J].Computers&Geosciences,2014,70:35-43.
[8]BOLOGNESI M,FARINA G,ALVISI S,et al.Measurement of surface velocity in open channels using a lightweight remotely piloted aircraft system[J].Geomatics Natural Hazards&Risk,2016:1-14.
[9]RAN Q H,LI W,LIAO Q,et al.Application of an automated LSPIV system in a mountainous stream for continuous flood flow measurements[J].Hydrological Processes,2016,30(17):3014-3029.
[10]张振,吕莉,石爱业,等.基于物像尺度变换的河流水面流场定标方法[J].仪器仪表学报,2017,38(9):2273-2281.
[11]ZHANG Z,ZHOU Y,LI Y C,et al.An IP camera-based LSPIV system for on-line monitoring of river flow[C]//IEEE International Conference on Electronic Measurement&Instruments(ICEMI 2017).Yangzhou:IEEE Press,2017:357-363.
[12]张振,陈红,嵇阳,等.基于视频监控系统的非接触式水位测量方法[C]//水利遥感应用创新论坛.广州:中国水利学会遥感专业委员会,2017:21-30.
[13]张振,徐立中,王慧斌.河流水面成像测速中的水流示踪物综述[J].水利水电科技进展,2014,34(3):81-88.
[14]张振,顾朗朗,陈哲,等.基于时空图像的河流水面成像测速方法研究[C]//第十六届全国水利量测技术综合学术研讨会.郑州:黄河水利出版社,2016:29-41.
[15]张振,王慧斌,严锡君,等.时空图像测速法的敏感性分析及不确定度评估[J].仪器仪表学报,2017,38(7):1763-1771.
[16]严锡君,张振,陈哲,等.基于FHT-CC的流场图像自适应运动矢量估计方法[J].仪器仪表学报,2014,35(1):50-58.
[17]张振,徐立中,韩华,等.基于径向基神经网络的明渠流量软测量方法[J].仪器仪表学报,2012,32(12):2648-2655.
[18]张振,严锡君,樊棠怀,等.近红外成像的便携式大尺度粒子图像测速仪[J].仪器仪表学报,2012,33(12):2840-2850.
[19]ZHANG Z,WANG X,FAN T H,et al.River surface target enhancement and background suppression for unseeded LSPIV[J].Flow Measurement and Instrumentation,2013,30:99-111.
[20]张振,陈哲,吕莉,等.基于视觉感受野的自适应背景抑制方法[J].仪器仪表学报,2014,35(1):191-199.