Search

CN-121995076-A - Radar flow measurement method based on video assistance

CN121995076ACN 121995076 ACN121995076 ACN 121995076ACN-121995076-A

Abstract

The invention relates to a radar flow measurement method based on video assistance. The method comprises the steps of S1) measuring the vertical distance between the water level radar device and the liquid level, and sending the vertical distance value to a vision system with a video acquisition device, S2) obtaining a first water surface flow rate by using the vision system, and transmitting the first water surface flow rate to the flow rate radar device, S3) setting a speed measurement interval based on the first water surface flow rate, calculating a Doppler frequency shift interval corresponding to the speed measurement interval by the flow rate radar device, finding a frequency reflecting the water flow rate in the Doppler frequency shift interval, and calculating the final water surface flow rate according to the frequency.

Inventors

  • HAN DONG
  • ZHANG YUZHEN
  • JIN LEI
  • XI JIANFENG

Assignees

  • 深圳市华聚科学仪器有限公司

Dates

Publication Date
20260508
Application Date
20260305

Claims (9)

  1. 1. A radar current measurement method based on video assistance, comprising: S1) measuring the vertical distance between the water level radar device and the water surface by using the water level radar device, and sending the vertical distance value to a vision system with a video acquisition device; S2) acquiring a first water surface flow rate using the vision system, transmitting the first water surface flow rate to a flow rate radar device, wherein the step of acquiring the first water surface flow rate includes: S2-1) acquiring video data of the water surface, converting the video data into single-channel image data in a gray level image form, and extracting the single-channel image data of the region of interest by using a mask method; s2-2) carrying out smooth filtering on single-channel image data of the region of interest to remove abnormal noise points; s2-3) carrying out self-adaptive local Gaussian threshold segmentation on the single-channel image data with abnormal noise removed by filtering to obtain a binarized image; s2-4) selecting feature points from the binarized image by adopting an ORB feature point detection method; s2-5) calculating sparse optical flow for the feature points by adopting a pyramid-based Lucas-Kanade method to obtain pixel displacement of the feature points; s2-6) calculating a first water surface flow rate by using the pixel displacement, the pixel-to-actual length conversion coefficient and the time interval; S3) setting a speed measurement interval based on the first water surface flow velocity, calculating a Doppler frequency shift interval corresponding to the speed measurement interval by using a flow velocity radar device, finding out the frequency reflecting the water flow velocity in the Doppler frequency shift interval, and calculating according to the frequency to obtain the final water surface flow velocity.
  2. 2. The video-assisted radar flow measurement method according to claim 1, wherein the method for extracting the single-channel image data of the region of interest by using the masking method comprises masking the single-channel image with a binary image having the same size as the original video number, and using a frame of video data as a set of processing data during each masking operation, wherein a white region having a value of 255 corresponds to the region of interest and a black region having a value of 0 corresponds to the background region.
  3. 3. The video-assisted radar current measurement method according to claim 1, wherein gaussian blur filtering is employed for smooth filtering.
  4. 4. The video-assisted radar streaming method according to claim 1, wherein the step S2-3 comprises presetting a neighborhood window and a threshold bias value, taking a difference of a Gaussian weighted average value of pixels in the neighborhood window minus the threshold bias value as a threshold value of one pixel, comparing a gray level value of a current pixel with the threshold value of the pixel, setting the pixel as 255 in a gray level image if the gray level value of the pixel is greater than or equal to the threshold value of the pixel, otherwise setting as 0, and converting the gray level image into a binary image.
  5. 5. The video-assisted radar flow measurement method according to claim 1, wherein the feature point selection process using the ORB feature point detection method includes detecting corner points in an image as key points by using a FAST algorithm, detecting key points on each pyramid layer by constructing an image pyramid, calculating a main direction of the key points, rotating pixel points around the key points to the main direction, generating a binary character string as descriptors by comparing brightness of randomly selected pixel pairs around the key points, generating BRIEFF descriptors for each key point, measuring similarity between two BRIEFF descriptors by using a hamming distance, and using a nearest point pair as a successfully matched feature point pair.
  6. 6. The video-assisted radar flow measurement method according to claim 1, wherein the step S2-6 comprises obtaining a first water surface flow rate by dividing a product of a pixel-to-actual length conversion coefficient and a pixel displacement by a time interval, wherein the vertical distances between a water level radar device and a video acquisition device in a vision system are set to be equal in advance, the vertical distance between the water level radar device and the water surface is the vertical distance between the video acquisition device and the water surface, the actual physical distance corresponding to a water surface picture shot by the video acquisition device is calculated, the quotient of dividing the actual physical distance by the vertical resolution of the picture is the pixel-to-actual length conversion coefficient, and the time interval is the shooting time difference of two continuous frames of video images.
  7. 7. The video-assisted radar flow measurement method according to claim 1, wherein the Doppler shift interval corresponding to the speed measurement interval is calculated by setting a water flow velocity variation value DeltaV using the first water flow velocity as a reference value V ref , thereby setting a speed measurement interval [ V ref –ΔV,V ref + DeltaV ], and calculating the Doppler shift interval corresponding to the speed measurement interval Wherein: ; ; Wherein f 0 is the radar wave frequency emitted by the flow rate radar device, c is the light velocity, θ is the angle between the line connecting the moving target and the flow rate radar device and the actual direction of the velocity, V ref is the first water flow rate, and Δv is the preset water flow rate variation value.
  8. 8. The method for finding a frequency reflecting a flow velocity of a water flow in a Doppler shift interval according to claim 7, wherein the method for finding the frequency reflecting the flow velocity of the water flow in the Doppler shift interval comprises the steps that the flow velocity radar device transmits radar waves to the water surface and receives reflected waves, the difference between the transmitted radar wave frequency and the reflected wave frequency is used as an acquired offset frequency, a plurality of complex numbers are obtained by performing fast Fourier transform operation on the acquired offset frequencies, each complex number corresponds to a frequency value and corresponding amplitude and phase, the frequency value falling in the Doppler shift interval is recorded, when the plurality of frequencies exist, the frequency with the largest amplitude is selected as the frequency reflecting the flow velocity of the water flow, and when only 1 frequency value exists, the frequency is selected as the frequency reflecting the flow velocity of the water flow.
  9. 9. The video-assisted radar-based current measurement method according to claim 1, wherein the video acquisition device has a lens capable of 360 ° omni-directional rotation.

Description

Radar flow measurement method based on video assistance Technical Field The invention belongs to the technical field of water flow velocity measuring equipment, and particularly relates to a radar flow measuring method based on video assistance. Background The water surface flow velocity measurement is a core link of hydrologic water conservancy monitoring, and the data accuracy directly influences the results of the works such as flow measurement, water resource scheduling, flood early warning, river basin treatment and the like. Currently, radar flow measurement technology has been widely used in the field of hydrologic water conservancy monitoring by virtue of the outstanding advantages of non-contact measurement, convenience in installation and maintenance, strong capability of resisting severe environments and the like. The core principle of the technology is based on radar Doppler effect, namely after microwave signals emitted by a radar are irradiated to a moving water surface (containing ripples), reflected waves are received by the radar, the received radar wave signals generate frequency offset relative to the emitted wave signals, and the water surface flow velocity can be calculated by calculating the frequency offset. However, the existing radar flow rate measurement technology has the following technical bottlenecks to be solved: 1) The radar speed measurement is dependent on the effective reflection of radar waves by the roughness (ripples and waves) of the water surface. When the water flow rate is very low, for example, the water flow rate is less than 0.3 m/s, the water surface tends to be calm, the water surface wave is characterized by small amplitude and small quantity, at the moment, the reflected signal is extremely weak, so that a radar can hardly identify the frequency component of the correct reflected signal, and therefore, the situation of false alarm or inaccurate measurement often occurs in a low flow rate scene, for example, the flow rate of 0 m/s can be false alarm to be 0.15m/s. 2) The technical bottleneck that the wind disturbance (wind wave) error is remarkable is that when the wind speed on the water surface is more than or equal to 3m/s, the wind force action can cause the water surface to generate a large number of irregular ripples or waves, the motion direction of the ripples is always inconsistent with the direction of a water flow main body, the radar cannot distinguish the motion of the wind wave from the motion of the water flow driving wave, the reflected wave received by the radar contains a wind disturbance component and a water flow component, and the wind disturbance component and the water flow component are mixed. When the wind speed is high and the water flow is slow, the surface flow velocity measured by the radar actually comprises a velocity component of the wind-driven water wave motion, so that the measured value is far higher than the motion velocity of the water body, for example, when the water flow is approximately static (the flow velocity is approximately equal to 0), the water flow velocity is 0.2-0.5 m/s, and serious forward error is generated. 3) The disturbance of the complex motion mode of the water surface is that complex flow states of which a plurality of motion components such as main flow, backflow, vortex and turbulent flow coexist in the natural water body. The radar beam irradiates an area (typically several to ten meters in diameter) whose echoes are the result of the integration of all the motion components in the area. The "main stream" component representing the overall transport of the body of water cannot be effectively identified and separated. In complex flow regimes, the measured values may differ significantly from the actual main flow rate, with a maximum deviation of over 0.3 m/s. Disclosure of Invention In order to solve the above problems, the present invention provides a radar current measurement method based on video assistance, comprising: S1) measuring the vertical distance between the water level radar device and the water surface by using the water level radar device, and sending the vertical distance value to a vision system with a video acquisition device; S2) acquiring a first water surface flow rate using the vision system, transmitting the first water surface flow rate to a flow rate radar device, wherein the step of acquiring the first water surface flow rate includes: S2-1) acquiring video data of the water surface, converting the video data into single-channel image data in a gray level image form, and extracting the single-channel image data of the region of interest by using a mask method; s2-2) carrying out smooth filtering on single-channel image data of the region of interest to remove abnormal noise points; s2-3) carrying out self-adaptive local Gaussian threshold segmentation on the single-channel image data with abnormal noise removed by filtering to obtain a binarized image; s2-4) selecting featu