A river flow measurement method and system based on a UAV

Abstract

The present application relates to river flow measurement technical field, specifically at a kind of river flow measurement method and system based on unmanned aerial vehicle, the method includes: at target river section, through unmanned aerial vehicle synchronous collection water surface radar point surface flow velocity and video surface flow velocity field;According to radar point surface flow velocity, video surface flow velocity field is fused and calibrated, and correction surface flow velocity field is obtained;According to the real-time water level data of target river section, water surface gradient data and section width-depth ratio, adaptive conversion coefficient field is determined, and correction surface flow velocity field is converted into perpendicular line average flow velocity field by adaptive conversion coefficient field;According to perpendicular line average flow velocity field, real-time water level data and riverbed topography data, the instantaneous flow of target river section is determined.The present application improves the measurement accuracy and its reliability of river flow measurement by non-contact means.

Description

Technical Field

[0001] This invention relates to the field of river flow measurement technology, specifically to a method and system for measuring river flow based on unmanned aerial vehicles (UAVs). Background Technology

[0002] River flow velocity and flow measurement is a core component of hydrological work, providing crucial basic data support for water resource management, flood control and disaster reduction, water project safety, and water environment protection.

[0003] Traditional flow measurement methods mainly rely on contact instruments (such as rotor flow meters) or non-contact devices that require fixed installation (such as bridge-based radar flow measurement systems). These methods are often limited by complex terrain, severe weather, or lack of infrastructure in emergency scenarios, making it difficult to achieve rapid, flexible, and high-precision measurements across a wide range of situations.

[0004] With the development of drone technology, non-contact flow measurement using drones equipped with sensors has become an emerging trend. However, the overall measurement effect and reliability of existing drone flow measurement methods in complex or emergency hydrological scenarios are still not ideal, making it difficult to meet the business needs of speed, convenience and high accuracy. Summary of the Invention

[0005] To address the limitations of current UAV-based river flow measurement methods and their unreliability, this invention aims to provide a UAV-based river flow measurement method and system. The specific technical solution adopted is as follows:

[0006] Firstly, a method for measuring river flow based on unmanned aerial vehicles (UAVs) is provided. This method includes: simultaneously collecting radar point surface velocity and video surface velocity field data of the water surface at a target river cross-section using a UAV. The radar point surface velocity includes water surface velocity data from multiple discrete measurement points, while the video surface velocity field is continuously spatially distributed water surface velocity data acquired from video images. The video surface velocity field is then fused and calibrated based on the radar point surface velocity to obtain a corrected surface velocity field. An adaptive conversion coefficient field is determined based on real-time water level data, water surface gradient data, and the width-to-depth ratio of the target river cross-section, and the corrected surface velocity field is converted into a vertically averaged velocity field using the adaptive conversion coefficient field. Finally, the instantaneous flow rate of the target river cross-section is determined based on the vertically averaged velocity field, real-time water level data, and riverbed topography data.

[0007] In one possible design, the surface velocity field of the video is fused and calibrated based on the surface velocity of the radar points to obtain a corrected surface velocity field. This includes: spatiotemporally registering the radar measurement point data of each radar point with the video surface velocity field to obtain multiple fused data pairs. Each fused data pair includes a radar measurement point data and reference velocity data of the matching region corresponding to the radar measurement point data in the video surface velocity field; fitting a spatial correction function along the river width direction of the target river channel cross-section based on the multiple fused data pairs; and correcting the velocity data of each grid point in the video surface velocity field using the spatial correction function to generate the corrected surface velocity field.

[0008] In one possible design, the surface velocity data of each radar measurement point in the radar point is spatiotemporally registered with the surface velocity field in the video to obtain multiple fused data pairs. This includes: mapping each radar measurement point data to the image coordinate system corresponding to the video surface velocity field based on the UAV's pose data and radar beam angle; for each radar measurement point data, determining the corresponding matching region in the video surface velocity field with the mapped coordinate position of the radar measurement point data as the center; determining the mean of the video surface velocity data of all grid points in the matching region as the reference velocity data corresponding to the radar measurement point data; and forming a fused data pair based on the radar measurement point data and the reference velocity data.

[0009] In one possible design, a spatial correction function is fitted along the river width direction of the target river channel cross-section based on multiple fused data pairs. This includes: for each fused data pair, determining the velocity difference parameter corresponding to the fused data pair based on the radar measurement point data and reference velocity data included in the fused data pair; the velocity difference parameter is used to quantify the velocity deviation between the fused data pairs; determining the velocity gradient constraint parameter corresponding to the fused data pair based on the distribution characteristics of the surface velocity along the river width direction of the radar points and the distribution characteristics of the surface velocity field along the river width direction of the video; and using a preset fitting algorithm, a continuous spatial correction function is fitted along the river width direction based on the velocity difference parameter and velocity gradient constraint parameter corresponding to all fused data pairs.

[0010] In one possible design, an adaptive conversion coefficient field is determined based on real-time water level data, water surface gradient data, and cross-sectional width-to-depth ratio of the target river cross-section. This includes: determining hydraulic characteristic factors based on real-time water level and water surface gradient data, whereby the hydraulic characteristic factors characterize the comprehensive influence of the flow conditions of the target river cross-section on the uniformity of vertical velocity distribution; determining basic conversion coefficient values ​​based on the hydraulic characteristic factors and the cross-sectional width-to-depth ratio, in conjunction with a reference width-to-depth ratio; and determining the corresponding conversion coefficient for each grid point on the corrected surface velocity field based on the basic conversion coefficient values, the lateral distance of the grid point relative to the centerline of the target river cross-section, and the velocity of the grid point. The conversion coefficients of all grid points constitute the adaptive conversion coefficient field.

[0011] In one possible design, the conversion coefficient corresponding to a grid point is determined based on the base value of the conversion coefficient, the lateral distance of the grid point relative to the centerline of the target river channel cross-section, and the flow velocity of the grid point. This includes: determining a first influence factor based on the lateral distance of the grid point relative to the centerline of the target river channel cross-section and the width of the target river channel cross-section; the first influence factor characterizes the influence of the lateral position of the grid point on the conversion coefficient; determining a second influence factor based on the flow velocity of the grid point and the maximum flow velocity in the corrected surface velocity field; the second influence factor characterizes the influence of the flow velocity magnitude of the grid point on the conversion coefficient; and determining the conversion coefficient corresponding to the grid point based on the first influence factor, the second influence factor, and the base value of the conversion coefficient. The conversion coefficient corresponding to the grid point increases with the increase of the first influence factor and the increase of the second influence factor.

[0012] In one possible design, the instantaneous flow rate of the target river section is determined based on the vertical average velocity field, real-time water level data, and riverbed topography data. This includes: determining the boundary of the cross-section corresponding to the target river section based on the real-time water level data and riverbed topography data; dividing the cross-section into multiple continuous sub-regions along the river width direction based on the boundary of the cross-section, and obtaining the cross-sectional area of ​​each sub-region; obtaining the vertical average velocity corresponding to each sub-region from the vertical average velocity field; determining the instantaneous flow rate of the sub-region based on the vertical average velocity and the cross-sectional area; and determining the instantaneous flow rate of the target river section based on the instantaneous flow rates of all sub-regions.

[0013] In one possible design, determining the velocity gradient constraint parameters corresponding to the fused data pairs includes: fitting the radar point surface velocity and the video surface velocity field to standard single-peak curves along the river width direction, and determining the difference in the tangent slope of the two curves at the corresponding positions of each fused data pair; obtaining the local fitting residual of fitting the video surface velocity field to the standard single-peak curve at the corresponding positions of each fused data pair; and determining the velocity gradient constraint parameters corresponding to each fused data pair based on the difference in tangent slope and the residual at the corresponding positions of each fused data pair.

[0014] In one possible design, the target river section is a straight river segment that meets preset conditions, including that the water flow streamline is parallel to the shoreline, the cross-sectional shape is regular, and the riverbed is stable and free of obvious obstacles.

[0015] Secondly, a UAV-based river flow measurement system is provided, comprising: a data acquisition unit for simultaneously acquiring radar point surface velocity and video surface velocity field data of the water surface at a target river cross-section using a UAV; the radar point surface velocity includes water surface velocity data from multiple discrete measurement points, and the video surface velocity field is continuously spatially distributed water surface velocity data acquired from video images; a velocity field calibration unit for performing fusion calibration processing on the video surface velocity field based on the radar point surface velocity to obtain a corrected surface velocity field; a velocity field conversion unit for determining an adaptive conversion coefficient field based on real-time water level data, water surface gradient data, and cross-sectional width-to-depth ratio of the target river cross-section, and converting the corrected surface velocity field into a vertically averaged velocity field using the adaptive conversion coefficient field; and a flow determination unit for determining the instantaneous flow rate of the target river cross-section based on the vertically averaged velocity field, real-time water level data, and riverbed topography data.

[0016] The present invention has the following beneficial effects:

[0017] In the UAV-based river flow measurement method provided by this invention, the synchronous acquisition and fusion calibration of radar and video velocity data not only leverages the high precision of discrete radar measurement points to compensate for the systematic bias of the video velocity field, but also utilizes the continuous spatial distribution advantage of the video velocity field to achieve comprehensive coverage of cross-sectional velocity, effectively balancing the measurement accuracy and coverage of velocity data. Simultaneously, an adaptive conversion coefficient field is determined based on real-time water level, water surface gradient, and cross-sectional width-to-depth ratio, accurately converting the corrected surface velocity field into a vertical average velocity field. This overcomes the bottleneck of traditional fixed conversion coefficients being unable to adapt to the spatial heterogeneity of cross-sections and dynamic changes in water flow, fully conforming to the vertical velocity distribution patterns under different river morphologies and flow conditions, and significantly reducing velocity conversion errors. Ultimately, by combining the vertical average velocity field, real-time water level, and riverbed topography data, instantaneous flow rate is calculated, achieving precise matching of dynamic and geometric parameters. This comprehensively improves the automation level, data reliability, and environmental adaptability of river flow measurement, effectively addressing complex hydrological and topographical conditions and providing high-precision flow data support for water resource management, flood control and disaster relief, and water project operation and maintenance. Attached Figure Description

[0018] To more clearly illustrate the technical solutions and advantages in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0019] Figure 1 A schematic diagram of a river flow measurement system based on an unmanned aerial vehicle (UAV) provided in one embodiment of the present invention;

[0020] Figure 2 This is a flowchart illustrating a method for measuring river flow based on unmanned aerial vehicles (UAVs) according to an embodiment of the present invention. Detailed Implementation

[0021] To further illustrate the technical means and effects adopted by the present invention to achieve its intended purpose, the following, in conjunction with the accompanying drawings and preferred embodiments, details the specific implementation, structure, features, and effects of a river flow measurement method and system based on unmanned aerial vehicles (UAVs) proposed according to the present invention. In the following description, different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. Furthermore, specific features, structures, or characteristics in one or more embodiments can be combined in any suitable form.

[0022] In embodiments of the present invention, the terms "exemplary" or "for example" are used to indicate that something is an example, illustration, or description. Any embodiment or design described as "exemplary" or "for example" in embodiments of the present invention should not be construed as being more preferred or advantageous than other embodiments or designs. Specifically, the use of the terms "exemplary" or "for example" is intended to present the relevant concepts in a specific manner.

[0023] In the description of this invention, unless otherwise stated, " / " means "or". For example, A / B can mean A or B. The term "and / or" in this document is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A alone, A and B simultaneously, and B alone. Furthermore, "at least one" and "more than one" refer to two or more. The terms "first," "second," etc., do not limit the quantity or order of execution, and "first," "second," etc., do not necessarily imply differences.

[0024] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains.

[0025] The following description, in conjunction with the accompanying drawings, details a specific scheme for a method and system for measuring river flow based on unmanned aerial vehicles (UAVs) provided by the present invention.

[0026] Please see Figure 1 The diagram illustrates a structural schematic of a river flow measurement system based on an unmanned aerial vehicle (UAV) according to an embodiment of the present invention. Figure 1 As shown, the UAV-based river flow measurement system 10 includes a data acquisition unit 11, a flow velocity field calibration unit 12, a flow velocity field conversion unit 13, and a flow determination unit 14.

[0027] The data acquisition unit 11 is used to simultaneously collect radar point surface velocity and video surface velocity field of the water surface at the target river cross section by means of a drone. The radar point surface velocity includes water surface velocity data from multiple discrete measurement points, and the video surface velocity field is water surface velocity data with continuous spatial distribution obtained based on video images.

[0028] The data acquisition unit 11 includes a UAV sensor acquisition module, a water level data acquisition module, and a terrain data acquisition module.

[0029] The UAV sensor acquisition module includes a radar flow measurement device and a video acquisition device. The UAV flies along a preset route (the preset route is adapted to the straightness characteristics of the target river cross-section). The radar flow measurement device outputs the radar surface velocity (including the coordinates of the measurement points) at multiple discrete measurement points. The video acquisition device continuously captures images of the river surface and then extracts the continuously spatially distributed video surface velocity field through a preset image processing algorithm. During the acquisition process, the UAV sensor acquisition module monitors the data quality in real time and removes abnormal data that exceeds the preset velocity range (e.g., an empirical value of 0.01-10 m / s), has excessive positioning deviation (e.g., ±0.5 m), or does not meet the data continuity requirements (e.g., velocity fluctuations of more than 20% across three consecutive grid points), ensuring the reliability of the original velocity data.

[0030] The water level data acquisition module is used to collect real-time water level data of the target river section by deploying a water level gauge near the section.

[0031] The terrain data acquisition module is used to retrieve pre-stored riverbed terrain data corresponding to the target river channel cross-section from the database.

[0032] The velocity field calibration unit 12 is used to perform fusion calibration processing on the video surface velocity field based on the surface velocity of the radar point, and output the corrected surface velocity field to provide a reliable surface velocity basis for subsequent velocity conversion.

[0033] The velocity field calibration unit 12 includes a spatiotemporal registration module, a correction function construction module, and a grid correction module.

[0034] The spatiotemporal registration module first maps each radar measurement point data in the radar point surface velocity to the image coordinate system corresponding to the video surface velocity field based on the UAV's pose data (including position data and attitude data) and radar beam angle. At the same time, the time synchronization module aligns the timestamps of the radar data and video data to ensure their consistency in the spatiotemporal dimension. Then, for each mapped radar measurement point, a matching area with the ground coverage size of the radar beam is determined in the video surface velocity field with its coordinate position as the center. The mean value of the video surface velocity data of all grid points in this area is extracted as the reference velocity data corresponding to the radar measurement point data, and finally multiple fused data pairs are formed.

[0035] The correction function construction module is used to determine the velocity difference parameter for each fused data pair, based on the radar measurement point data and the reference velocity data, to quantify the velocity deviation between the two. Simultaneously, combining the single-peak distribution characteristics of the radar point surface velocity and the video surface velocity field along the river width direction ("faster in the middle, slower on both sides"), the module fits both to standard single-peak curves, calculates the difference in tangent slope between the two curves at the same location, and obtains the residual after fitting the video surface velocity field. The velocity gradient constraint parameter is determined by combining the slope difference and the residual. Finally, using the reciprocal of the velocity gradient constraint parameter as the confidence weight, a preset fitting algorithm (aiming at minimizing the overall curvature) is used to perform a weighted smooth fitting of all discrete velocity difference parameters along the river width direction, resulting in a continuous spatial correction function.

[0036] The grid correction module is used to traverse each grid point in the video surface velocity field. Based on the specific location of each grid point in the target river section, it queries the spatial correction function to obtain the corresponding correction coefficient. The original video surface velocity data of the grid point is multiplied by the correction coefficient to complete the velocity correction of each grid point, and finally generates a corrected surface velocity field covering the entire section.

[0037] The velocity field conversion unit 13 is used to determine the adaptive conversion coefficient field based on the real-time water level data, water surface gradient data and cross-sectional width-to-depth ratio of the target river section, and convert the corrected surface velocity field into the vertical average velocity field through the adaptive conversion coefficient field.

[0038] The velocity field conversion unit 13 includes an adaptive conversion coefficient field construction module and a vertical average velocity field generation module.

[0039] The adaptive conversion coefficient field construction module is used to receive real-time water level data and water surface gradient data from the data acquisition unit 11. After processing both into dimensionless ratios, the hydraulic characteristic factor is determined by multiplying the two. This factor is used to characterize the comprehensive influence of the flow conditions of the target river section on the uniformity of vertical velocity distribution.

[0040] The adaptive conversion coefficient field construction module is also used to combine the target channel cross-section width-to-depth ratio and the reference width-to-depth ratio obtained based on human experience, multiply the hydraulic characteristic factor by the ratio of the cross-section width-to-depth ratio and the reference width-to-depth ratio, and determine the basic value of the conversion coefficient. This basic value reflects the overall velocity conversion benchmark level of the cross-section.

[0041] The adaptive conversion coefficient field construction module is also used to determine, for each grid point on the calibration surface velocity field, a first influence factor to characterize the influence of the grid point's lateral position based on the lateral distance of the grid point relative to the centerline of the target channel section and the channel width; simultaneously, a second influence factor to characterize the influence of the grid point's velocity magnitude based on the grid point's velocity and the maximum velocity in the calibration surface velocity field; finally, combining the basic value of the conversion coefficient, the first influence factor, and the second influence factor, the conversion coefficient corresponding to each grid point is determined (the conversion coefficient increases with the increase of the first influence factor and with the increase of the second influence factor), and the conversion coefficients of all grid points together constitute the adaptive conversion coefficient field.

[0042] The vertical average velocity field generation module is used to traverse all grid points of the corrected surface velocity field, multiply the corrected surface velocity data of each grid point by the corresponding conversion coefficient in the adaptive conversion coefficient field, and obtain the vertical average velocity of each grid point. Through grid-by-grid calculation, a vertical average velocity field covering the entire target river cross section is finally formed.

[0043] The flow determination unit 14 is used to determine the instantaneous flow of the target river section based on the vertical average velocity field, real-time water level data and riverbed topography data.

[0044] The flow determination unit 14 includes a sub-region division module and a flow calculation module.

[0045] The sub-region division module receives real-time water level data and riverbed topography data from the data acquisition unit 11. Based on the intersection of the water level line corresponding to the real-time water level and the riverbed topography, it determines the actual boundary of the cross-section corresponding to the target river channel, clarifying the spatial range for flow calculation. Based on the boundary of the cross-section, it divides the cross-section into multiple continuous sub-regions along the river width direction, ensuring that each sub-region completely contains several grid points of the vertical average velocity field. Simultaneously, based on the riverbed topography data and real-time water level, it calculates the flow area of ​​each sub-region; this area is the core geometric parameter for calculating the instantaneous flow rate of the sub-region.

[0046] The flow calculation module extracts the vertical average velocity data of all grid points within each sub-region, calculates the statistical values ​​of these data (such as the mean and median), and uses them as the representative vertical average velocity for that sub-region, ensuring accurate matching between the velocity data and the spatial range of the sub-region. Then, the representative vertical average velocity of each sub-region is multiplied by the corresponding flow area to obtain the instantaneous flow rate of each sub-region. Finally, the instantaneous flows of all sub-regions are summed to obtain the total instantaneous flow rate through the target river cross-section, completing the entire flow measurement process.

[0047] Please see Figure 2The diagram illustrates a flowchart of a method for measuring river flow based on unmanned aerial vehicles (UAVs) according to an embodiment of the present invention, including the following steps S201-S204.

[0048] S201. At the target river section, the radar point surface velocity and video surface velocity field of the water surface are collected simultaneously by drone.

[0049] Among them, the radar point surface velocity includes water surface velocity data from multiple discrete measurement points, while the video surface velocity field is water surface velocity data with a continuous spatial distribution obtained from video images.

[0050] As one possible approach, firstly, a straight river section meeting preset conditions is selected as the target river cross-section. These preset conditions include: the water flow lines being parallel to the shoreline; a regular cross-sectional shape (such as approximately rectangular, trapezoidal, or parabolic); a stable riverbed without significant obstacles; and the absence of large eddies or turbulent flows, ensuring that the data collection environment meets the reliability requirements for flow velocity data. The UAV, equipped with integrated radar flow measurement and video acquisition equipment, flies along a preset route. This route is pre-planned to fit the spatial range of the target river cross-section, ensuring that the radar flow measurement and video acquisition equipment can completely cover the entire water surface area of ​​the target river cross-section.

[0051] The drone's synchronous data acquisition function was further activated, with the radar flow measurement equipment and video acquisition equipment starting work simultaneously: the radar flow measurement equipment detected water surface velocity by emitting radar beams, outputting radar point surface velocity containing multiple discrete measurement points. Each radar measurement point's data was associated with a unique measurement point coordinate, and the velocity value of each radar point surface velocity was used to characterize the actual water surface flow velocity at the corresponding discrete measurement point; the video acquisition equipment continuously captured water surface images of the target river cross-section, and analyzed the continuous images using a preset image processing algorithm (e.g., based on particle image velocimetry or optical flow velocity technology). This algorithm calculated the motion vector of the water surface point represented by each pixel or pixel block on the image plane by tracking the movement of natural water surface textures, foam, or artificial tracers in the video sequence. Using camera calibration parameters and the drone's current pose data, these image motion vectors were converted into two-dimensional water surface velocity vectors in the real-world coordinate system. Finally, a flow velocity data grid that covers the entire observation section and is spatially continuous is generated. This grid data is the video surface flow velocity field. Each basic unit in the field is called a grid point, and each grid point contains its planar position coordinates and its corresponding water surface velocity.

[0052] During the data acquisition process, the timestamps of the radar flow measurement equipment and the video acquisition equipment are aligned using the time synchronization module on the UAV. This ensures that the flow velocity value at each measurement point on the radar surface and the flow velocity value at each grid point in the video surface flow velocity field are the flow velocity observation data at the same moment. At the same time, the UAV's pose data (including position data and attitude data) and radar beam angle are recorded synchronously. The UAV's pose data is used to establish the spatial mapping relationship between the radar measurement point and the video image coordinate system, and the radar beam angle is used to determine the size of the subsequent matching area.

[0053] In some embodiments, the data quality of the radar point surface velocity and video surface velocity field is monitored in real time, and abnormal data that exceeds the preset velocity range, has excessive positioning deviation, or does not meet the data continuity requirements are removed to ensure the validity of the original velocity data. After the acquisition is completed, the radar point surface velocity set (including the velocity value of each measuring point and the corresponding position coordinates), the video surface velocity field (including the velocity value of each grid point and the corresponding spatial position), and the synchronously recorded UAV pose data and radar beam angle are obtained.

[0054] S202. Perform fusion calibration on the video surface velocity field based on the radar point surface velocity to obtain the corrected surface velocity field.

[0055] As one possible implementation, the radar measurement point data of each radar point surface velocity is spatiotemporally registered with the video surface velocity field to obtain multiple fused data pairs. Each fused data pair includes a radar measurement point data and reference velocity data of the matching region corresponding to the radar measurement point data in the video surface velocity field.

[0056] In some embodiments, based on the pose data (including position and attitude data) and radar beam angle synchronously recorded during UAV acquisition, a spatial mapping algorithm is used to accurately project the radar measurement point data with position coordinates for each radar point onto the image coordinate system corresponding to the video surface velocity field. Simultaneously, the aforementioned time synchronization mechanism ensures that each set of radar measurement point data and video velocity data represents the observation results at the same moment, achieving precise spatiotemporal matching. For each mapped radar measurement point, a matching region is delineated in the video surface velocity field, centered on its coordinate position in the image coordinate system. The size of the matching region is consistent with the ground coverage size of the radar beam, ensuring that the region corresponds to the same hydrological unit as the detection range of the radar measurement point. The video surface velocity data of all grid points within the matching region are extracted, and their arithmetic mean is calculated. This mean is used as the reference velocity data corresponding to the radar measurement point data. Each radar measurement point data and its corresponding reference velocity data together constitute a fused data pair.

[0057] Furthermore, based on multiple fused data pairs, a spatial correction function is obtained by fitting along the river width direction of the target river channel cross-section.

[0058] In some embodiments, firstly, for each fused data pair, based on the flow velocity values ​​of the radar measurement points and the reference flow velocity data, the corresponding flow velocity difference parameter for the fused data pair is determined, and its formula is expressed as follows: In the formula, The flow velocity difference parameter corresponds to the fused data pair. This parameter quantifies the degree of flow velocity deviation between the radar-measured flow velocity and the video reference flow velocity in a single fused data pair. For discrete water surface velocities collected by radar flow measurement equipment, This represents the mean velocity of all grid points in the matching region corresponding to the radar measurement point in the video surface velocity field. It is a very small positive number, and an empirical value of 0.001 can be taken to prevent the denominator from being zero.

[0059] Secondly, based on the distribution characteristics of the surface velocity at radar points along the river width and the distribution characteristics of the surface velocity field in the video along the river width, the corresponding velocity gradient constraint parameters for the fused data pairs are determined. Specifically, taking into account the natural single-peak distribution characteristic of river flow along the river width—faster in the middle and slower on both sides—the surface velocity at radar points and the surface velocity field in the video are fitted to standard single-peak curves along the river width of the target river cross-section. The difference in the tangent slope of the two curves at corresponding positions in each fused data pair is calculated and denoted as . Simultaneously, at the corresponding location of each fused data pair, the local fitting residual of the video surface velocity field to a standard single-peak curve is obtained. Furthermore, based on the difference in tangent slope at corresponding locations and the local fitting residuals for each fused data pair, the velocity gradient constraint parameters for each fused data pair are determined, expressed by the following formula: In the formula, To fuse the data, corresponding velocity gradient constraint parameters are used to characterize the degree of spatial distribution deviation of the video surface velocity field at corresponding locations. This represents the difference in the slope of the tangent line between the two fitted curves at the corresponding position in the fused data pair. To fit the local fitting residual of the standard single-peak curve to the corresponding location of the fused data pair, To reference the difference in tangent slope, the average of the differences in tangent slope corresponding to multiple fused data pairs is used to eliminate the dimensions of the difference in tangent slope. The reference local fitting residual is the average of the local fitting residuals corresponding to multiple fused data pairs, in order to eliminate the dimensions of the local fitting residuals and make the final velocity gradient constraint parameter a dimensionless parameter. Wherein, when The larger the value, the greater the difference in the velocity change trends of the two curves at the same position, indicating that there may be local distortion in the video data; the larger the local fitting residual, the greater the degree of velocity correction should be during correction; if the slope difference is large and the local fitting residual is large, it indicates that there is a systematic bias in the video data at that point, which needs to be corrected.

[0060] In a preferred embodiment of the present invention, the reference tangent slope difference can also be the maximum value of the tangent slope differences corresponding to multiple fused data pairs; the reference local fitting residual can also be the maximum value of the local fitting residuals corresponding to multiple fused data pairs.

[0061] Subsequently, based on the corresponding velocity difference parameters and velocity gradient constraint parameters of all fused data, a pre-defined fitting algorithm (such as a smooth spline fitting algorithm aiming to minimize overall curvature) is used to fit a continuous spatial correction function along the river width direction. Specifically, the velocity gradient constraint parameters for each fused data point are... The reciprocal of the value is used as the confidence weight (the greater the deviation of the fused data, the lower the weight, to avoid outlier data interfering with the fitting results). A preset fitting algorithm is used to apply the confidence weight to all discrete velocity difference parameters along the river width direction of the target river section. A weighted fitting process is performed, with the goal of minimizing the overall curvature of the function, ultimately yielding a value related to the river width. Continuous and smooth spatial correction function The function It expresses the continuous variation of the correction coefficient required for the surface velocity field of the video along the river width direction.

[0062] Finally, the velocity data of each grid point in the video surface velocity field is corrected using a spatial correction function to generate a corrected surface velocity field.

[0063] In some embodiments, each grid point in the video surface velocity field is traversed, and the location of each grid point in the target river cross-section is considered. Through spatial correction function The correction coefficient corresponding to each grid point is obtained. The original video surface velocity data for each grid point is multiplied by the corresponding correction coefficient to obtain the corrected surface velocity data for that grid point. After completing the correction calculation for all grid points, the corrected velocity values ​​for all grid points are recombined according to the original spatial structure to form a corrected surface velocity field covering the entire target river cross-section. This velocity field retains the continuous spatial distribution characteristics of the video surface velocity field and achieves deviation calibration through the high-precision characteristics of the radar point surface velocity, ensuring the reliability of the velocity data.

[0064] Understandably, in this embodiment of the invention, by spatiotemporally registering discrete high-precision radar point surface velocity with a continuous, wide-coverage video surface velocity field to generate a fused data pair, and fitting a spatial correction function along the river width direction based on this, the video velocity field is then corrected grid-by-grid. This effectively transfers and diffuses the high precision and high reliability characteristics of radar flow measurement technology to the entire two-dimensional velocity field obtained by video flow measurement technology. This fundamentally overcomes the spatial systematic deviation problem introduced by single video flow measurement methods due to optical distortion, algorithm errors, or environmental interference, while avoiding the loss of spatial details in the velocity field caused by point-based discrete observations in single radar flow measurement. The generated spatial correction function can adaptively reflect and correct local distortions in the natural gradient distribution of velocity along the river width, making the final corrected surface velocity field not only more accurate and reliable in absolute velocity values, but also more realistic and reasonable in spatial distribution structure. This provides a crucial and consistent quality input data foundation for subsequently converting surface velocity into vertical average velocity and implementing accurate flow integrals, significantly improving the accuracy and robustness of UAV river flow measurement results.

[0065] S203. Based on the real-time water level data, water surface gradient data, and cross-sectional width-to-depth ratio of the target river section, determine the adaptive conversion coefficient field, and convert the corrected surface velocity field into a vertical average velocity field through the adaptive conversion coefficient field.

[0066] As one possible approach, firstly, based on real-time water level data and water surface gradient data, hydraulic characteristic factors are determined. These hydraulic characteristic factors are used to characterize the comprehensive influence of the flow conditions of the target river section on the uniformity of vertical velocity distribution.

[0067] In some embodiments, the formula for determining the hydraulic characteristic factor is as follows:

[0068]

[0069] In the formula, For hydraulic characteristic factors, The dimensionless relative water level is obtained by dividing the real-time water level data by the reference water level data (e.g., the upper limit of the water level at the current river cross-section). The relative gradient is dimensionless and is obtained by dividing the water surface gradient data by the reference gradient.

[0070] Among these factors, the higher the relative water level and the deeper the water, the smaller the impact of riverbed friction on surface flow, and the more uniform the vertical velocity distribution tends to be. A higher relative level also means greater potential energy driving the flow, increased turbulence, and further contribute to a more uniform vertical velocity distribution. Therefore, a higher hydraulic characteristic factor indicates that current flow conditions are more likely to result in a uniform vertical velocity distribution, and theoretically, a smaller difference between surface velocity and the average vertical velocity.

[0071] It should be noted that a drone can briefly hover or fly at low speed over the target river section and an adjacent upstream section. Using a lidar mounted on the drone, water surface elevation data at the two sections can be measured synchronously or quasi-synchronously. The absolute difference in water surface elevation data is then divided by the known distance between the two sections along the river's centerline to obtain the water surface gradient data. The reference gradient can be determined using the multi-year average gradient of the watershed where the target river section is located, the average gradient during the mid-water season, etc., for example, values ​​of 1‰ or 0.1‰. This embodiment of the invention does not specifically limit this value.

[0072] Secondly, based on the hydraulic characteristic factors and the width-to-depth ratio of the cross section, and in conjunction with the reference width-to-depth ratio, the basic value of the conversion coefficient is determined.

[0073] In some embodiments, the formula for determining the basic value of the conversion factor is as follows:

[0074]

[0075] In the formula, The base value for the conversion factor. For hydraulic characteristic factors, The width-to-depth ratio is the ratio of the width of the target river channel to the average water depth. The reference width-to-depth ratio is determined based on hydraulic experience or standard river types.

[0076] in, This reflects the degree to which the target river channel cross-section is "wide and shallow" or "narrow and deep" relative to the standard shape. This indicates that the target river section is located in a wide and shallow channel, where the influence of sidewall friction is relatively weakened, and vertical mixing of the water flow may be more complete, resulting in a lower conversion coefficient base value. Should be in On the basis of positive adjustment and increase, conversely, when This indicates that the target channel cross-section is located in a narrow and deep channel, where the influence of the sidewalls is enhanced, potentially causing the point of maximum velocity to shift downstream, thus affecting the base value of the conversion coefficient. Should be in Based on the negative phase adjustment, the conversion coefficient base value is reduced. Integrating hydrodynamics With cross-sectional morphology The impact.

[0077] It should be noted that the typical width-to-depth ratio of the target river section or similar river segments during characteristic hydrological periods (such as the floodplain period or the multi-year average flow period) can be extracted from long-term hydrological observation data of the target river channel or its basin as a reference width-to-depth ratio, or the standard cross-sectional width-to-depth ratio used in water conservancy project planning and design can be adopted. For example, an empirical value of 20 can be taken for straight sandy riverbeds in alluvial plain areas, and an empirical value of 10 can be taken for pebble riverbeds in mountainous and hilly areas.

[0078] Furthermore, for each grid point on the corrected surface velocity field, the conversion coefficient corresponding to the grid point is determined based on the basic value of the conversion coefficient, the lateral distance of the grid point relative to the center line of the target channel section, and the velocity of the grid point. The conversion coefficients of all grid points constitute an adaptive conversion coefficient field.

[0079] In some embodiments, for each grid point on the corrected surface velocity field, a first influence factor is first determined based on the lateral distance of the grid point relative to the centerline of the target channel cross-section and the width of the target channel cross-section. The formula is expressed as follows: In the formula, For the first The first influence factor for each grid point For the first The lateral distance of each grid point relative to the centerline of the target river channel cross-section The width of the target river channel cross section is not zero. The first influencing factor is used to characterize the influence of the lateral position of the grid point on the conversion coefficient. The larger the value, the closer the grid point is to the riverbank and the stronger the influence of the sidewall friction.

[0080] Next, based on the flow velocity at the grid points and the maximum flow velocity in the corrected surface velocity field, the second influencing factor is determined, and its formula is expressed as follows: In the formula, For the first The second influence factor for each grid point For the first The flow velocity value at each grid point To correct for the maximum velocity value among all grid points in the surface velocity field, a second influence factor is used to characterize the effect of the velocity magnitude at the grid point on the conversion coefficient. The larger the value, the more complete the turbulence at that location.

[0081] Subsequently, based on the first impact factor, the second impact factor, and the baseline values ​​of the conversion coefficients, the conversion coefficients corresponding to the grid points are determined, and the formula is expressed as follows: In the formula, For the first The transformation coefficients corresponding to each grid point The base value for the conversion factor. As the number one impact factor, The conversion coefficient is the second influencing factor and is positively correlated with the first influencing factor (the farther the grid point is from the center of the river channel, the larger the first influencing factor, and the larger the conversion coefficient of the grid point, which conforms to the law that the influence of the sidewall increases with increasing distance in hydraulics). It is also positively correlated with the second influencing factor (the larger the flow velocity value of the grid point, the larger the second influencing factor, and the larger the conversion coefficient of the grid point. In the high-speed zone, the water flow is sufficiently turbulent and the vertical mixing is uniform, so the conversion coefficient can be appropriately increased).

[0082] Finally, the corrected surface velocity field is converted into a vertical average velocity field using an adaptive conversion coefficient field.

[0083] In some embodiments, each grid point in the corrected surface velocity field is traversed, and the corrected surface velocity data of that grid point is compared with the corresponding data in the adaptive transformation coefficient field. Multiplying these values ​​yields the average velocity value along the vertical direction at that grid point. By traversing the grid point by point, the continuously distributed corrected surface velocity field is completely converted into a vertical average velocity field. This velocity field not only eliminates the limitation that surface velocity only reflects the surface water flow state, but also compensates for the differences in vertical velocity distribution at different locations and under different water flow conditions through an adaptive conversion coefficient.

[0084] S204. Determine the instantaneous flow rate of the target river section based on the vertical average velocity field, real-time water level data, and riverbed topography data.

[0085] The riverbed topographic data includes the riverbed elevation data at various points along the width of the target river channel.

[0086] As one possible approach, firstly, based on real-time water level data and riverbed topography data, the boundary of the cross-section corresponding to the target river channel is determined.

[0087] In some embodiments, based on real-time water level data (typically referring to water surface elevation data relative to a certain absolute elevation datum) Locate the water level line on the current riverbed topographic profile. The intersections with the riverbeds or bank slopes on both sides determine the width boundary of the cross-section at the water surface; within the cross-section, the upper boundary is the horizontal water surface line. The lower boundary is the known riverbed topographic line ( (i) Riverbed elevation data, which is the lateral position of the riverbed in any river width direction. At this location, the local water depth is For points without riverbed elevation data, the elevation can be obtained through linear interpolation using adjacent known riverbed elevation data.

[0088] Secondly, based on the boundary of the cross-section, the cross-section is divided into multiple continuous sub-regions along the width of the river, and the cross-sectional area of ​​each sub-region is obtained.

[0089] In some embodiments, based on the defined boundaries of the cross-section, the cross-section is divided along the river width direction into sections. The system divides the river into continuous, non-overlapping sub-regions, which can be divided at equal intervals or adaptively based on topographic changes. The principle of division is that each sub-region completely includes several grid points in the vertically averaged velocity field. Based on this, for each sub-region, its width (the lateral distance between adjacent dividing lines) and the water depth of adjacent dividing lines on the riverbed topography can be obtained. Then, the trapezoidal approximation method is used to calculate the flow area of ​​a single sub-region; or, based on the elevation data of all riverbed points within the coverage area of ​​the sub-region, the average depth of the region can be calculated, and the flow area of ​​a single sub-region can be obtained based on the product of the width and the average depth.

[0090] Next, for each sub-region, the vertical average velocity corresponding to the sub-region is obtained from the vertical average velocity field.

[0091] In some embodiments, for the first Sub-regions ( The process involves identifying all grid points falling within the sub-region and extracting the corresponding vertical average velocity from the vertical average velocity field. Then, statistical values ​​of these velocity values ​​(such as the average, weighted average based on grid point location, median, etc.) are used to determine the vertical average velocity corresponding to the sub-region, denoted as . .

[0092] Finally, the instantaneous flow rate of the sub-region is determined based on the average vertical velocity and the cross-sectional area; and the instantaneous flow rate of the target river section is determined based on the instantaneous flow rates of all sub-regions.

[0093] In some embodiments, the formula for calculating the instantaneous flow rate of the target river section is as follows:

[0094]

[0095] In the formula, The instantaneous flow rate at the target river cross-section For the first The vertical average flow velocity corresponding to each sub-region For the first The water flow area of ​​each sub-region.

[0096] Understandably, in the UAV-based river flow measurement method provided in this embodiment of the invention, the synchronous acquisition and fusion calibration of radar and video velocity data not only leverages the high precision of discrete radar measurement points to compensate for the systematic bias of the video velocity field, but also utilizes the continuous spatial distribution advantage of the video velocity field to achieve comprehensive coverage of cross-sectional velocity, effectively balancing the measurement accuracy and coverage of velocity data. Simultaneously, based on real-time water level, water surface gradient, and cross-sectional width-to-depth ratio, an adaptive conversion coefficient field is determined, accurately converting the corrected surface velocity field into a vertical average velocity field. This overcomes the bottleneck of traditional fixed conversion coefficients failing to adapt to the spatial heterogeneity of cross-sections and dynamic changes in water flow, fully conforming to the vertical velocity distribution patterns under different river morphologies and flow conditions, and significantly reducing velocity conversion errors. Ultimately, by combining the vertical average velocity field, real-time water level, and riverbed topography data, instantaneous flow rate is calculated, achieving precise matching of dynamic and geometric parameters. This comprehensively improves the automation level, data reliability, and environmental adaptability of river flow measurement, effectively addressing complex hydrological and topographical conditions and providing high-precision flow data support for water resource management, flood control and disaster relief, and water project operation and maintenance.

[0097] It should be noted that the order of the above embodiments of the present invention is merely for descriptive purposes and does not represent the superiority or inferiority of the embodiments. The processes depicted in the accompanying drawings do not necessarily require a specific or sequential order to achieve the desired result. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0098] The various embodiments in this specification are described in a progressive manner. The same or similar parts between the various embodiments can be referred to each other. Each embodiment focuses on describing the differences from other embodiments.

Claims

1. A method for measuring river flow based on unmanned aerial vehicles (UAVs), characterized in that, The method includes: At the target river section, radar point surface velocity and video surface velocity field are collected simultaneously by drone. The radar point surface velocity includes water surface velocity data from multiple discrete measurement points, and the video surface velocity field is water surface velocity data with continuous spatial distribution obtained based on video images. The video surface velocity field is fused and calibrated based on the surface velocity of the radar points to obtain a corrected surface velocity field. Based on the real-time water level and water surface gradient data of the target river section, hydraulic characteristic factors are determined. These hydraulic characteristic factors characterize the comprehensive influence of the flow conditions of the target river section on the uniformity of vertical velocity distribution. The hydraulic characteristic factors are determined using the formula... Sure, The hydraulic characteristic factor is... The ratio of the real-time water level data to the reference water level data. This is the ratio of the water surface gradient data to the reference gradient; Based on the hydraulic characteristic factors and the width-to-depth ratio of the target river channel section, combined with a reference width-to-depth ratio, the basic value of the conversion coefficient is determined; the basic value of the conversion coefficient is determined using the formula... Sure, The base value of the conversion coefficient, The aspect ratio of the cross section is... The reference aspect ratio; For each grid point on the corrected surface velocity field, a first influence factor is determined based on the lateral distance of the grid point relative to the centerline of the target channel cross-section and the width of the target channel cross-section. This first influence factor characterizes the impact of the lateral position of the grid point on the conversion coefficient. The first influence factor is determined using the formula... Sure, For the first The first influence factor of each grid point For the first The lateral distance of each grid point relative to the centerline of the target river channel cross-section The width of the target river channel cross-section is defined; and a second influence factor is determined based on the flow velocity at the grid points and the maximum flow velocity in the velocity field of the correction surface. This second influence factor characterizes the impact of the flow velocity at the grid points on the conversion coefficient. The second influence factor is determined using the formula... Sure, For the first The second influence factor for each grid point, For the first The flow rate at each grid point The maximum flow velocity in the corrected surface velocity field; Based on the first impact factor, the second impact factor, and the baseline value of the conversion coefficient, the conversion coefficient corresponding to the grid point is determined. The conversion coefficient corresponding to the grid point is obtained through the formula... Sure, For the first The conversion coefficients corresponding to each grid point increase with the increase of the first influence factor and the increase of the second influence factor. The conversion coefficients of all grid points constitute an adaptive conversion coefficient field. The corrected surface velocity field is converted into a vertical average velocity field through the adaptive conversion coefficient field. The value of each grid point in the vertical average velocity field is the product of the value of the corresponding grid point in the adaptive conversion coefficient field and the value of the corresponding grid point in the corrected surface velocity field. The instantaneous flow rate of the target river section is determined based on the vertical average velocity field, the real-time water level data, and the riverbed topography data.

2. The method for measuring river flow based on unmanned aerial vehicles according to claim 1, characterized in that, The video surface velocity field is fused and calibrated based on the surface velocity at the radar points to obtain a corrected surface velocity field, including: The radar measurement point data in the surface velocity of the radar point is spatiotemporally registered with the video surface velocity field to obtain multiple fused data pairs. Each fused data pair includes a radar measurement point data and a reference velocity data of the matching region corresponding to the radar measurement point data in the video surface velocity field. Based on the multiple fused data pairs, a spatial correction function is obtained by fitting along the river width direction of the target river channel cross section; The spatial correction function is used to correct the velocity data of each grid point in the video surface velocity field to generate the corrected surface velocity field.

3. The method for measuring river flow based on unmanned aerial vehicles according to claim 2, characterized in that, The data from each radar measurement point in the surface velocity at the radar point are spatiotemporally registered with the video surface velocity field to obtain multiple fused data pairs, including: Based on the pose data of the UAV and the radar beam angle, the data of each radar measurement point is mapped to the image coordinate system corresponding to the surface velocity field of the video. For each radar measurement point data, a corresponding matching region is determined in the video surface velocity field, with the coordinate position after the radar measurement point data is mapped as the center. The mean value of the video surface velocity data of all grid points in the matching area is determined as the reference velocity data corresponding to the radar measurement point data; A fused data pair is formed based on the radar measurement point data and the reference flow velocity data.

4. The method for measuring river flow based on unmanned aerial vehicles according to claim 2, characterized in that, Based on the multiple fused data pairs, a spatial correction function is fitted along the river width direction of the target river channel cross-section, including: For each fused data pair, based on the radar measurement point data and reference flow velocity data included in the fused data pair, a flow velocity difference parameter corresponding to the fused data pair is determined. The flow velocity difference parameter is used to quantify the flow velocity deviation between the fused data pairs. Based on the distribution characteristics of the surface velocity along the river width of the radar point and the distribution characteristics of the surface velocity field along the river width of the video, the corresponding velocity gradient constraint parameters of the fused data pair are determined. Based on the corresponding velocity difference parameters and velocity gradient constraint parameters of all fused data pairs, a preset fitting algorithm is used to fit a continuous spatial correction function along the river width direction.

5. The method for measuring river flow based on unmanned aerial vehicles according to claim 1, characterized in that, The instantaneous flow rate of the target river section is determined based on the vertical average velocity field, the real-time water level data, and the riverbed topography data, including: Based on the real-time water level data and the riverbed topography data, the boundary of the water passage section corresponding to the target river channel section is determined; Based on the boundary of the cross-section, the cross-section is divided into multiple continuous sub-regions along the river width direction, and the cross-sectional area of ​​each sub-region is obtained. For each sub-region, the vertical average velocity corresponding to the sub-region is obtained from the vertical average velocity field; The instantaneous flow rate of the sub-region is determined based on the average vertical flow velocity and the water flow area. The instantaneous flow rate of the target river section is determined based on the instantaneous flow rates of all sub-regions.

6. The method for measuring river flow based on unmanned aerial vehicles according to claim 4, characterized in that, Determining the flow velocity gradient constraint parameters corresponding to the fused data pair includes: The surface velocity at the radar point and the surface velocity field at the video are respectively fitted into standard single-peak curves along the river width direction, and the difference in the tangent slope of the two curves at the corresponding position of each fused data pair is determined. At the corresponding position of each fused data pair, obtain the local fitting residual of the video surface velocity field to fit the standard single-peak curve; Based on the difference in tangent slope at the corresponding position for each fused data pair and the residual, the velocity gradient constraint parameters corresponding to each fused data pair are determined.

7. The method for measuring river flow based on unmanned aerial vehicles according to claim 1, characterized in that, The target river section is a straight river segment that meets preset conditions, including that the water flow lines are parallel to the shoreline, the cross-sectional shape is regular, and the riverbed is stable and free of obvious obstacles.

8. A river flow measurement system based on unmanned aerial vehicles (UAVs), characterized in that, include: The data acquisition unit is used to simultaneously collect radar point surface velocity and video surface velocity field of the water surface at the target river cross section by means of a drone. The radar point surface velocity includes water surface velocity data from multiple discrete measurement points, and the video surface velocity field is water surface velocity data with continuous spatial distribution obtained based on video images. The velocity field calibration unit is used to perform fusion calibration processing on the video surface velocity field based on the surface velocity of the radar point to obtain a corrected surface velocity field. The velocity field conversion unit is used to determine hydraulic characteristic factors based on real-time water level data and water surface gradient data of the target river section. These hydraulic characteristic factors characterize the comprehensive influence of the flow conditions of the target river section on the uniformity of vertical velocity distribution. The hydraulic characteristic factors are determined using the formula... Sure, The hydraulic characteristic factor is... The ratio of the real-time water level data to the reference water level data. This is the ratio of the water surface gradient data to the reference gradient; Based on the hydraulic characteristic factors and the width-to-depth ratio of the target river channel section, and in conjunction with the reference width-to-depth ratio, the basic value of the conversion coefficient is determined; The basic value of the conversion coefficient is obtained through the formula. Sure, The base value of the conversion coefficient, The width-to-depth ratio of the cross section. The reference aspect ratio; For each grid point on the corrected surface velocity field, a first influence factor is determined based on the lateral distance of the grid point relative to the centerline of the target channel cross-section and the width of the target channel cross-section. This first influence factor characterizes the impact of the lateral position of the grid point on the conversion coefficient. The first influence factor is determined using the formula... Sure, For the first The first influence factor of each grid point For the first The lateral distance of each grid point relative to the centerline of the target river channel cross-section The width of the target river channel cross-section is defined; and a second influence factor is determined based on the flow velocity at the grid points and the maximum flow velocity in the velocity field of the correction surface. This second influence factor characterizes the impact of the flow velocity at the grid points on the conversion coefficient. The second influence factor is determined using the formula... Sure, For the first The second influence factor for each grid point, For the first The flow rate at each grid point The maximum flow velocity in the corrected surface velocity field; Based on the first impact factor, the second impact factor, and the baseline value of the conversion coefficient, the conversion coefficient corresponding to the grid point is determined. The conversion coefficient corresponding to the grid point is obtained through the formula... Sure, For the first The conversion coefficients corresponding to each grid point increase with the increase of the first influence factor and the increase of the second influence factor. The conversion coefficients of all grid points constitute an adaptive conversion coefficient field. The corrected surface velocity field is converted into a vertical average velocity field through the adaptive conversion coefficient field. The value of each grid point in the vertical average velocity field is the product of the value of the corresponding grid point in the adaptive conversion coefficient field and the value of the corresponding grid point in the corrected surface velocity field. The flow rate determination unit is used to determine the instantaneous flow rate of the target river section based on the vertical average velocity field, the real-time water level data, and the riverbed topography data.

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