Method for measuring movable and inclined surface flow field of long water tank

Abstract

The invention relates to a long water tank mobile surface flow field measurement method, which comprises the following steps: utilizing a river channel local scouring experiment model, the model is a water tank, and the middle part of the water tank is provided with a vertical support rod for simulating a building component; a constant-speed motion traction camera device is built above a water tank, so that a mobile camera moves at a constant speed at a certain inclination angle, meanwhile, water flow in the water tank is shot, and a water flow surface flow field is obtained through calculation according to a shooting result. The method comprises the following steps: performing perspective transformation processing on an obtained image to obtain a continuous front view image, identifying checkerboard control points to position each frame of corrected image, and cutting a tracer particle image by using coordinates of intelligent identification side wall control points. After surface tracer particles are extracted through a morphological method, flow field splicing is carried out on instantaneous flow fields at different moments to obtain a surface flow field in a large range, the problems that traditional fixed flow field measurement is low in precision and small in range are effectively solved, and an effective technical means is provided for hydraulic structure research and water body monitoring.

Description

Technical Field

[0001] The invention relates to a mobile surface flow field measurement method for a local scouring experiment of a long water channel bridge pier. Background Art

[0002] Surface water velocity is an important parameter for river dynamics and hydrological observations. It is the basis for analyzing the internal structure of turbulence (such as coherent structure) and the movement of water-related structures and riverbed sediment. Its measurement has important guiding significance for riverbed evolution prediction, river scouring and siltation control, ship berthing safety, and the construction and design of related water conservancy projects.

[0003] To rapidly measure surface flow fields over large areas, PIV and FMV methods can identify and track tracer particles scattered into the water and calculate the surface flow field based on the conversion relationship between image coordinates and actual coordinates. PIV technology, after improvements in 1998, was first applied to large-scale surface flow field measurements and has been widely used in flow field testing of hydraulic and river engineering physical models. In recent years, the FMV method, based on matching correlation operations with water surface feature points, has been used to calculate and analyze surface flow fields in natural river channels. This method offers robust feature point matching and relatively strong adaptability, but its application still requires further research.

[0004] At present, for the research on surface flow field measurement, fixed camera devices are mostly used to obtain the surface flow field of a specific area. Due to the limited pixel accuracy of the camera, the current shooting range should not be too large, otherwise the flow field accuracy will be low, and it will not be able to meet the flow field measurement in a larger area, thus affecting the analysis of the fluid-solid coupling characteristic mechanism. With the advancement of high-speed camera methods and image analysis technology, conditions have been provided for mobile shooting of large-scale flow fields to analyze water flow structures, but its implementation process still needs to be continuously improved. Traditional surface flow field measurement technology is usually limited to fixed measurement positions and ranges, and it is difficult to meet the needs of large-scale, high-precision measurements. In recent years, although there have been some attempts at mobile measurement technologies, there are still problems such as low accuracy and complex data processing. Therefore, how to achieve high-precision mobile surface flow field measurement has become a problem that those skilled in the art need to consider and solve. Summary of the Invention

[0005] To address the current challenges in surface flow field measurement, fixed cameras are often used to capture the surface flow field in a specific area. Due to the limited pixel accuracy of cameras, the current capture range should not be too large. Otherwise, the flow field accuracy will be low, making it impossible to measure flow fields in larger areas, thus affecting the analysis of the fluid-structure interaction mechanism.

[0006] The technical solution of the present invention is as follows: a method for measuring the surface flow field of a long water flume movable type, including a local scour test model of a river channel, wherein the local scour test model of the river channel includes a water flume, the bottom of the water flume is paved with gravel, and a vertical support rod simulating a building component is fixed in the middle of the water flume; a slide rail is arranged above the water flume along the length direction of the water flume, and a slide seat is slidably fitted on the slide rail, and a high-speed camera is fixed on the slide seat; the slide seat is driven by a driving mechanism to move the slide rail so that the high-speed camera moves at a uniform speed along the direction of water flow, and the high-speed camera is tilted toward the water flume to facilitate shooting of the water flow in the water flume, and the high-speed camera is connected to a control computer, one end of the water flume is a water inlet and the other end has a water inlet, and one end of the water flume has a water outlet; The following steps are included: S10: A number of control points are attached perpendicular to the glass wall on the outside of the water tank along the direction of the slide rail for image position calibration. S20: placing tracer particles in the water tank, driving the high-speed camera to move at a constant speed along the positive direction of the water tank, and continuously shooting at a certain tilt angle to obtain images of the tracer particles on the local surface of the water tank; S30: Performing perspective transformation processing on the image obtained by shooting at an oblique angle, performing a linear transformation and a translation on the four corner coordinate points (x0, y0), (x1, y1), (x2, y2), and (x3, y3) in the source image from one vector space to another vector space, so that the original image plane is transformed to the new target image plane. The corresponding four corner coordinate points in the target image are (Y'0, Y'0), (Y'1, Y'1), (Y'2, Y'2), and (Y'3, Y'3), thereby obtaining n consecutive orthographic images; S40: After the control points in the continuous n corrected images are identified frame by frame and the moving distance Di between two adjacent frames is calculated, the original image is cropped to obtain the overlapping area between two adjacent frames of the moving image; S50: Extract and identify the tracer particles in the two corrected consecutive frames of images, use particle tracking velocimetry technology and image feature point recognition velocimetry to calculate the instantaneous surface flow field; and grid and interpolate the continuous flow field to obtain accurate flow field information of the entire shooting area.

[0007] Based on the above flow field measurement method, it is used to analyze the uniform movement of the high-speed camera at an inclined angle along the positive direction of the water tank and obtain the surface flow field results of the test water flow.

[0008] Preferably, black and white checkerboards are used as control points.

[0009] Preferably, step S30: performing perspective transformation processing on the image obtained by shooting at an oblique angle to obtain n consecutive front-view images. During the processing, the projection mapping matrix is ​​calculated based on the four corner coordinate points (x0, y0), (x1, y1), (x2, y2), (x3, y3) of the source image and the four corner coordinate points (Y'0, Y'0), (Y'1, Y'1), (Y'2, Y'2), (Y'3, Y'3) of the corresponding target image. The parameters in the mapping matrix are used to solve a set of two-variable linear equations to obtain the source image coordinates. The transformation of the perspective transformation matrix is ​​shown in formula (1): (1); in Represents the target point to move to, represents the perspective transformation matrix, Represents the source and target points; The image is converted from two-dimensional space to three-dimensional space, and the image is in the two-dimensional plane, so it is divided by Z. Represents a point on the image, as shown in formula (2): (2); In order to obtain X' and Y', let a 33 =1, expanding formula (2) yields a point as shown in formula (3): (3); According to the four coordinate points on the source image and the target image, eight equations can be listed to solve the perspective transformation matrix A as shown in formula (4): (4); Preferably, step S40: identifying the control points in the n consecutive corrected images frame by frame, using a checkerboard detection method based on the change of image grayscale curvature to identify the center coordinates of the control points; to obtain clear center coordinates, binarizing the original image and deleting smaller areas; The pixel coordinates of the center of the control point chessboard in the image can be obtained by the checkerboard detection method. The coordinates of the calibration points GCP Pi and Pi+1 of the i-th frame and i+1-th frame images are obtained by cyclic calculation. The distance the camera moves in the Δt time is Di = Pi+1-Pi, that is, the pixel distance between the calibration points of two adjacent frames of images. After determining the moving distance between two adjacent frames of images, the original image is cropped to obtain the overlapping area between the two adjacent frames of motion images; The image width is set to B. The B×Di range at the front of the i-th frame image Mi and the B×Di pixel area at the end of the i+1-th frame image Mi+1 need to be truncated to obtain a new pair of images with the same pixel size, namely the aligned image Ai,i and image Ai,i+1; N consecutive frames of images can be obtained through cyclic operation (N-1) pairs. The image cropping and alignment calculation is shown in formula (5): (5); Preferably, in step S50: The specific steps include: S51: Flow field correlation calculations require extraction and identification of tracer particles in two consecutive frames of rectified images. The original images are processed using morphological methods: first, the uneven background color of the image is processed using top-hat transformation, and then the image is binarized. On this basis, erosion and dilation are used to remove noise interference to obtain independent images of each tracer particle. The tracer particles still contain a small amount of fine impurities. A uniform threshold is given to remove small areas of the image to obtain clear tracer particle images. S52: Select two consecutive frames of tracer particle images processed by S51, i.e., at time T and time T+1, and use the SURF operator in the PTV algorithm and the FMV algorithm to perform feature point matching on the tracer particles. The matching accuracy is determined by calculating the Euclidean distance between the two feature points and introducing the Hessian matrix trace. Feature point matching is performed based on the Euler distance of the feature point operator. After calculating the displacement of the feature points in the images from time T to time T+1, the pixel scale is converted to the physical scale, thereby obtaining the velocity field VT at time T. S53: During the camera movement process, the instantaneous surface flow field of different areas of the water surface is obtained. The flow field is required to be integrated to obtain the long-distance surface flow field in the direction of the flume. Taking the camera starting point as the initial starting point and the camera movement direction as the positive direction, the total length of the camera movement up to time i is calculated as: (6); Subsequently, a grid is established. According to the positioning of each frame of the image, the instantaneous flow fields Fi generated at n-1 different times and positions are comprehensively counted, and then a grid is established for the entire flow field area; the flow direction and spanwise velocity scatter data at all the above different coordinates at different times are interpolated, and finally an accurate long-distance time-averaged flow field on the open channel surface is obtained.

[0010] Preferably, light and thin wood chips are selected as tracer particles, and the tracer particles are evenly sprinkled above the water surface in a direction perpendicular to the water flow.

[0011] Preferably, the tracer particles are 7 m away from the test measurement section in front of the water tank, 0.1 m above the water surface, 8 checkerboard control points are evenly arranged, and the slide rail is fixed above the water tank via a bracket.

[0012] Compared with the prior art, the present invention has the following beneficial effects: This paper proposes a high-precision method for measuring surface flow fields over long areas under control point conditions. By constructing a uniform motion traction camera device, the mobile camera moves at a constant speed at a certain tilt angle, while simultaneously capturing images of the water flow in the flume. The surface flow field is calculated based on the captured images. The images captured at the tilt angle are then perspective-transformed to produce n consecutive orthographic images. Checkerboard control points are identified to locate each corrected image frame, and the tracer particle images are cropped using intelligent recognition of the coordinates of the wall control points. After extracting surface tracer particles using morphological methods, the instantaneous surface flow field is calculated using particle tracking velocimetry (PTV) and feature point recognition velocimetry (FMV). The instantaneous flow fields at different times are then spliced ​​to obtain a surface flow field over a larger area.

[0013] The mobile surface flow field measurement method used in the present invention can improve the accuracy and measurement range of the surface flow field. This method broadens the application conditions of surface flow field measurement, effectively solves the problems of low accuracy and small range of traditional fixed flow field measurement, and provides an effective technical means for hydraulic structure research and water body monitoring. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] Figure 1 This is a schematic diagram of a test device according to an embodiment of the present invention; Figure 2 A schematic diagram of chessboard control points according to an embodiment of the present invention; Figure 3 Schematic diagram of the perspective transformation principle of the present invention; Figure 4 This is a schematic diagram of the cutting and alignment flow chart of the present invention; Figure 5 Schematic diagram of feature point matching diagram of the present invention; Figure 6 This is a schematic diagram of the flow field splicing diagram of the present invention; Figure 7 This is the average flow field result diagram of the test of the present invention; In the figure: 10-water tank, 110-gravel layer, 120-water inlet pipe, 130-drain pipe, 20-vertical support rod, 30-slide rail, 410-tooth surface, 40-high-speed camera, 50-slide seat, 60-storage tank, 610-tracer particle. DETAILED DESCRIPTION

[0015] The present invention will be described in detail below with reference to the accompanying drawings and specific embodiments.

[0016] See also Figure 1-7 As shown, the mobile surface flow field measuring device and method are further described in detail below in conjunction with specific implementation methods.

[0017] In a specific implementation, a mobile surface flow field measurement device and method for a long water tank is provided. In this method, a camera is moved to shoot the water flow in the water tank at a certain tilt angle and the surface flow field of the water flow is obtained by calculation based on the shooting results. The invention comprises a local scour test model of a river channel, wherein the local scour test model of the river channel comprises a water trough 10, the bottom of the water trough is paved with gravel to form a gravel layer 110 for simulating the riverbed morphology, and a vertical support rod 20 simulating a building component is fixed in the middle of the water trough; a slide rail 30 is arranged above the water trough and arranged along the length direction of the water trough, and a slide seat 50 is slidably fitted on the slide rail, and a high-speed camera 40 is fixed on the slide seat; the slide seat 50 is driven by a driving mechanism to move the slide rail so that the high-speed camera moves at a uniform speed along the direction of water flow, and the high-speed camera is tilted toward the water trough to facilitate shooting of the water flow in the water trough, and the high-speed camera is connected to a control computer, one end of the water trough is a water inlet and the other end has a water inlet, and one end of the water trough has a water outlet; In the patent of the present invention, the water outlet and water inlet of the sink are connected through a pipe. The pipe is provided with a pump body to realize the circulation of water flow. In addition, a filter is provided in the pipe to filter gravel. A flow meter is designed at the water inlet to control the speed of the water flow.

[0018] A storage tank 610 is fixed above the water inlet of the water tank. The storage tank 610 is used to store the tracer particles. The lower end of the storage tube has an opening, and a control valve is installed at the opening to control the release of the tracer particles 610. Of course, other methods such as manual operation can also be used to release the tracer particles in actual operation.

[0019] In one embodiment of the present invention, the driving mechanism may use an electric cylinder (i.e., an electric slide) to drive the slide 50 to move the high-speed camera at a constant speed.

[0020] The specific steps are as follows: a. Set fixed rails on both sides of the water tank to fix the camera and move it along the water flow at a certain angle. Set the shooting process parameters on the control device and then shoot according to the settings; b. Eight control points (GCPs) are attached perpendicular to the glass wall on the outside of the water tank along the direction of the slide rail for image position calibration; c. Light and thin sawdust is selected as tracer particles 610, and a high-speed camera is driven along the flume to continuously acquire tracer particle images of the local surface water flow in the open channel; d. Perform perspective transformation on the image taken at an oblique angle. The four corner coordinates (x0, y0), (x1, y1), (x2, y2), and (x3, y3) in the source image are transformed from one vector space into another vector space through a linear transformation and a translation. This transforms the original image plane into the new target image plane. The corresponding four corner coordinates in the target image are (Y'0, Y'0), (Y'1, Y'1), (Y'2, Y'2), and (Y'3, Y'3). This results in n consecutive orthographic images. e) After identifying control points frame by frame in n consecutive horizontal images and calculating the movement distance Di between two adjacent frames, the original images are cropped to obtain the overlapping area between the two adjacent moving frames. f) Tracer particles are extracted and identified in the two consecutive frames. The instantaneous surface flow field is calculated using particle tracking velocimetry (PTV) and feature point velocimetry (FMV). The continuous flow field is then gridded and interpolated to obtain accurate flow field information for the entire captured area.

[0021] When implemented, in step a, Figure 1 The schematic diagram of the device shows rails arranged on both sides of the flume, along the direction of the water flow, to facilitate the movement of the camera at a certain angle. The rails are 2.0m long and 1.0m high, and the actual distance the camera can move is approximately 1.8m. An adjustable-speed motor is installed on the outside of the flume, set to a fixed frequency to control the camera's movement speed, allowing the tilted camera to shoot at a uniform speed along the rails. The entire camera movement process takes approximately 40 seconds. The camera's save path and shooting parameters are set in the computer control software according to research needs. The camera's focal length and aperture are used to adjust the clarity and brightness of the camera's shooting, respectively. The camera moves on the rails to capture continuous flow field images.

[0022] During implementation, in step b, make Figure 2 The black and white checkerboard grid shown here serves as control points (GCPs). The number and layout of the checkerboard grid can be flexibly and evenly distributed according to the test site. Evenly distributing eight checkerboard control points can enhance camera recognition. The eight control points are named AH and their coordinates are set as A (-0.03, 0, 0.255), B (-0.03, 0, 0.255), C (0.03, 0, 0.255), D (-0.03, 0.03, 0.255), E (0, 0, 0.255), F (0.03, 0.03, 0.255), G (-0.03, 0.06, 0.255), and H (0, 0.06, 0.255). The unit is meter.

[0023] During implementation, control point calibration requires no specific tools or modules. A simple black and white checkerboard allows for quick and easy detection of calibration points on images captured by a high-speed camera, facilitating subsequent image processing. This application requires only one calibration, and the same control points can be used in all subsequent measurements.

[0024] During implementation, in step c, thin sawdust was used as tracer particles. After the water flow stabilized, tracer particles were evenly sprinkled in a reciprocating pattern across the flume, approximately 7 m from the test measurement section and 0.1 m above the water surface. Areas with less tracer coverage were promptly replenished to ensure even particle distribution. A high-speed camera was then driven along the flume to continuously capture images of the tracer particles on the local surface of the open channel.

[0025] During implementation, in step d, the image captured at an oblique angle is subjected to perspective transformation. The four corner coordinates (x0, y0), (x1, y1), (x2, y2), and (x3, y3) in the source image are transformed from one vector space into another vector space through a linear transformation and a translation. This transforms the original image plane to the new target image plane. The corresponding four corner coordinates in the target image are (Y'0, Y'0), (Y'1, Y'1), (Y'2, Y'2), and (Y'3, Y'3). This results in n consecutive orthographic images. During the processing, the projection mapping matrix is ​​calculated based on the four reference coordinate pairs in the source and target images. The parameters within the mapping matrix are used to solve a set of two-variable linear equations to obtain the source image coordinates. The transformation of the perspective transformation matrix is ​​shown in Formula (1).

[0026] (1); in Represents the target point to move to, represents the perspective transformation matrix, Represents the source and target points; Since the image is converted from two-dimensional space to three-dimensional space, and the image is in a two-dimensional plane, it is divided by Z. Represents a point on the image, as shown in formula (2): (2); To get X' and Y', let a33 = 1, and expand formula (2) to get a point as shown in formula (3); (3); According to the four reference coordinate pairs in the source image and the target image, eight equations can be obtained to solve the perspective transformation matrix A as shown in formula (4); (4); The schematic diagram of perspective transformation principle is as follows Figure 3shown.

[0027] During implementation, in step e, to determine the specific image positioning, control points in n consecutive rectified images are identified frame by frame. The checkerboard grid center coordinates are identified using a checkerboard detection method based on changes in image grayscale curvature. To obtain clear center coordinates, the original image is binarized and smaller areas are removed. The checkerboard grid detection method obtains the pixel coordinates of the control point checkerboard center in the image. The obtained coordinates of the GCPs Pi and Pi+1 for the i-th and i+1-th frames are then calculated repeatedly. The camera moves the distance over time Δt, i.e., the pixel distance Di between the calibration points in two adjacent frames = Pi+1-Pi. After determining the distance between the two adjacent frames, the images are cropped to obtain the overlapping area between the two adjacent frames of the moving image. Assuming the image width is B, the B×Di pixel range at the front of the i-th frame Mi and the B×Di pixel range at the end of the i+1-th frame Mi+1 are truncated to obtain a new pair of images with the same pixel size: the aligned images Ai,i and Ai,i+1. N consecutive frames of images can be obtained through cyclic operation (n-1) pairs. The image cropping and alignment calculation is shown in formula (5): (5) ; The image cropping and alignment process is as follows Figure 4 shown.

[0028] During implementation, the f step specifically includes the following steps: 1) Flow field correlation operations require the extraction and identification of tracer particles in two consecutive rectified image frames. Morphological methods are used to process the rectified images. First, a top-hat transform is used to correct the uneven background color of the image, followed by binarization. Erosion and dilation are then used to remove noise interference, resulting in independent images of each tracer particle. Tracer particle 610 still contains a small amount of fine impurities. A uniform threshold is applied to remove small areas of the image to obtain a clear tracer particle image.

[0029] 2) Select two consecutive frames of local images at time T and time T+1 after image processing, and use the SURF operator in the PTV algorithm and the FMV algorithm to match the feature points of the tracer particles. The latter has a higher recognition rate for the feature points of the area covered by the tracer particles. The feature point matching process is as follows: Figure 5 As shown in the figure, the matching accuracy is determined by calculating the Euclidean distance between two feature points and introducing the Hessian matrix trace. The smaller the Euclidean distance, the higher the matching accuracy. Feature point matching is performed based on the Euler distance operator. The displacement of the feature points in the image from time T to T+1 is calculated, and the pixel scale is converted to the physical scale to obtain the velocity field VT at time T.

[0030] 3) During the camera movement process, the instantaneous surface flow field of different areas on the water surface is obtained. The flow field needs to be integrated to obtain the long-distance surface flow field in the direction of the flume. Taking the camera starting point as the initial starting point and the camera movement direction as the positive direction, the total length of the camera movement at time i is calculated as shown in formula (6): (6); After calculating the total length of camera movement, a grid is established. According to the positioning of each frame, the instantaneous flow fields generated at different times and positions are comprehensively counted, and a grid is established for the entire flow field area. The flow direction and spanwise velocity scattered data at all the above different coordinates at different times are interpolated to finally obtain the accurate long-distance time-averaged flow field on the open channel surface. The flow field splicing flow chart is shown below. Figure 6 shown.

[0031] In one embodiment of the present invention, a uniform dropping mechanism for evenly sprinkling tracer particles is provided above the water tank. The uniform dropping mechanism includes a storage tank for storing tracer particles. A drop opening is provided below the storage tank. The drop opening is long and oblate. A vibration motor is provided on the side wall of the storage tank.

[0032] Through the above-mentioned surface flow field shooting equipment and flow field calculation principle, the mobile surface flow field measurement technology is applied to the surface flow field measurement in the water tank test. The surface flow field results are as follows Figure 7 As shown. Using the self-identifying checkerboard control point method to locate the flow field, mobile large-area flow field calculations are performed to obtain a large-scale surface flow field with good calculation results. Mobile shooting overcomes the shortcomings of traditional overall measurement with low accuracy and provides new ideas for surface flow field measurement research. The present invention achieves high-precision measurement of surface flow fields, broadens the application conditions of surface flow field measurement, effectively solves the problems of low accuracy and small range of traditional fixed flow field measurements, and provides an effective technical means for hydraulic structure research and water body monitoring.

[0033] The above descriptions are merely embodiments of the present invention and are not intended to limit the patent scope of the present invention. Any equivalent structure or equivalent process transformation made using the contents of the present invention's description and drawings, or directly or indirectly applied in other related technical fields, are also included in the patent protection scope of the present invention.

Claims

1. A method for measuring the surface flow field of a long water tank, characterized in that: The invention comprises a local scour test model of a river channel, wherein the local scour test model of the river channel comprises a water trough, the bottom of the water trough is paved with gravel, and a vertical support rod simulating a building component is fixed in the middle of the water trough; a slide rail is arranged above the water trough along the length direction of the water trough, and a slide seat is slidably fitted on the slide rail, and a high-speed camera is fixed on the slide seat; the slide seat is driven by a driving mechanism to move the slide rail so that the high-speed camera moves at a uniform speed along the direction of water flow, and the high-speed camera is tilted toward the water trough to facilitate shooting of the water flow in the water trough, and the high-speed camera is connected to a control computer, one end of the water trough is a water inlet, the other end has a water inlet, and one end of the water trough has a water outlet; The following steps are involved: S10: Paste several control points along the outside of the tank perpendicular to the glass wall for image position calibration; S20: Tracer particles are placed in the water tank, and a high-speed camera is driven to move at a constant speed along the direction of the water flow in the water tank. The images of the tracer particles on the local surface of the water tank are obtained by continuously shooting at an inclined angle; S30: Performing perspective transformation processing on the image obtained by shooting at an oblique angle, performing a linear transformation and a translation on the four corner coordinate points (x0, y0), (x1, y1), (x2, y2), and (x3, y3) in the source image from one vector space to another vector space, so that the original image plane is transformed to the new target image plane. The corresponding four corner coordinate points in the target image are (X'0, Y'0), (X'1, Y'1), (X'2, Y'2), and (X'3, Y'3), thereby obtaining n consecutive orthographic images; S40: After identifying the control points in the consecutive n corrected images frame by frame and calculating the moving distance Di between two adjacent frames of images, the original image is cropped to obtain the overlapping area between the two adjacent frames of motion images; S50: extracting and identifying tracer particles in two consecutive corrected image frames, using image feature point recognition to measure velocity and calculate the instantaneous surface flow field; and performing gridding and interpolation splicing processing on the continuous flow field to obtain accurate flow field information of the entire shooting area; Based on the above flow field measurement method, it is used to analyze the uniform movement of the high-speed camera at an inclined angle along the positive direction of the water tank and obtain the surface flow field results of the water flow in the experiment.

2. The long water tank mobile surface flow field measurement method according to claim 1, characterized in that: The black and white checkerboard squares serve as control points.

3. The long water tank mobile surface flow field measurement method according to claim 2, characterized in that: Step S30: Performing perspective transformation on the image captured at an oblique angle to obtain n consecutive orthographic images; during the processing, a projection mapping matrix is ​​calculated based on the four corner coordinates of the source image (x0, y0), (x1, y1), (x2, y2), and (x3, y3) and the corresponding four corner coordinates of the target image (X'0, Y'0), (X'1, Y'1), (X'2, Y'2), and (X'3, Y'3); and the parameters within the mapping matrix are used to solve a system of two-variable linear equations to obtain the source image coordinates; The transformation of the perspective transformation matrix is ​​shown in formula (1): （1）； in Represents the target point to move to, represents the perspective transformation matrix, Represents the source and target points; The image is converted from two-dimensional space to three-dimensional space, and the image is in the two-dimensional plane, so it is divided by Z. Represents a point on the image, as shown in formula (2): (2)； In order to obtain X' and Y', let a 33 =1, expanding formula (2) yields a point as shown in formula (3): (3)； Based on the four coordinate points on the source image and the target image, eight equations can be listed to solve the perspective transformation matrix A as shown in formula (4): (4)。 4. The long water tank mobile surface flow field measurement method according to claim 2, characterized in that: Step S40: Identify the control points in n consecutive corrected images frame by frame, and use a checkerboard detection method based on the change of image grayscale curvature to identify the center coordinates of the control points; to obtain clear center coordinates, binarize the original image and delete smaller areas; The pixel coordinates of the center of the control point chessboard in the image can be obtained by the checkerboard detection method. The coordinates of the calibration points GCP Pi and Pi+1 of the i-th frame and i+1-th frame images are obtained by cyclic calculation. The distance the camera moves in the Δt time is Di = Pi+1-Pi, that is, the pixel distance between the calibration points of two adjacent frames of images. After determining the moving distance between two adjacent frames of images, the original image is cropped to obtain the overlapping area between the two adjacent frames of motion images; The image width is set to B. The B×Di range at the front of the i-th frame image Mi and the B×Di pixel area at the end of the i+1-th frame image Mi+1 need to be truncated to obtain a new pair of images with the same pixel size, namely the aligned image Ai,i and image Ai,i+1; N consecutive frames of images can be obtained through cyclic operation (N-1) pairs. The image cropping and alignment calculation is shown in formula (5): （5）。 5. The long water tank mobile surface flow field measurement method according to claim 2, characterized in that: In step S50: The specific steps include: S51: Flow field correlation calculations require extraction and identification of tracer particles in two consecutive frames of rectified images. The original images are processed using morphological methods: first, the uneven background color of the image is processed using top-hat transformation, and then the image is binarized. On this basis, erosion and dilation are used to remove noise interference to obtain independent images of each tracer particle. The tracer particles still contain a small amount of fine impurities. A uniform threshold is given to remove small areas of the image to obtain clear tracer particle images. S52: Select two consecutive frames of tracer particle images processed by S51, i.e., at time T and time T+1, and use the SURF operator in the PTV algorithm and the FMV algorithm to perform feature point matching on the tracer particles. The matching accuracy is determined by calculating the Euclidean distance between the two feature points and introducing the Hessian matrix trace. Feature point matching is performed based on the Euler distance of the feature point operator. After calculating the displacement of the feature points in the images from time T to time T+1, the pixel scale is converted to the physical scale, thereby obtaining the velocity field VT at time T. S53: The camera movement process obtains the instantaneous surface flow field of different areas on the water surface. It is required to integrate the flow field to obtain the long-distance surface flow field in the direction of the water tank. Taking the camera starting point as the initial starting point and the camera movement direction as the positive direction, the total length of the camera movement at time i is calculated, as shown in formula (6): （6）； Subsequently, a grid is established. According to the positioning of each frame of the image, the instantaneous flow fields Fi generated at n-1 different times and positions are comprehensively counted, and then a grid is established for the entire flow field area; the flow direction and spanwise velocity scatter data at all the above different coordinates at different times are interpolated, and finally an accurate long-distance time-averaged flow field on the open channel surface is obtained.

6. The long water tank movable surface flow field measurement method according to claim 2, characterized in that: It is required to use light and thin wood chips as tracer particles, and the tracer particles are evenly sprinkled above the water surface in the direction perpendicular to the water flow. A uniform dropping mechanism for evenly sprinkling the tracer particles is provided above the water tank. The uniform dropping mechanism includes a storage tank for storing the tracer particles. A drop opening is provided below the storage tank, and the drop opening is long and flat. A vibration motor is provided on the side wall of the storage tank.

7. The long water tank movable surface flow field measurement method according to claim 1, characterized in that: The tracer particles are located in front of the water tank at a distance of 7 m from the test measurement section and 0.1 m above the water surface. Eight checkerboard control points are evenly arranged, and the slide rail is fixed above the water tank via a bracket.

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