Mountain area channel runoff monitoring method and system

Abstract

The invention relates to the technical field of mountain torrent monitoring, and provides a mountain flood channel runoff monitoring method and system, and the method comprises the steps: constructing a three-dimensional model of mountain torrent channel opening terrain, and obtaining monitoring section form data; calculating the channel gradient through the lowest point elevation of the upper and lower sections and the channel on-way length; synchronously shooting a plurality of groups of three-waveband mountain torrent photos, calculating a normalized water body index of the mountain torrent, and extracting a mountain torrent water surface line and a mountain torrent water surface width of a monitoring section; dividing the mountain torrent water surface line of the monitoring section into multiple sections based on the mountain torrent water surface width data, obtaining the height and the bottom edge length of each section based on the elevation of the water surface line and the elevation of the lowest point of the monitoring section, and calculating the mountain torrent water flow cross sectional area, the mountain torrent water flow wetted perimeter and the mountain torrent hydraulic radius; and calculating the runoff of the mountain torrents by using the channel gradient, the hydraulic radius of the mountain torrents and the cross-sectional area of the flow of the mountain torrents and adopting a Manning formula. According to the invention, non-contact type mountain torrent monitoring can be realized.

Description

Technical Field

[0001] The invention belongs to the technical field of mountain torrent monitoring, and in particular relates to a method and system for monitoring runoff in a mountain flood channel. Background Art

[0002] Monitoring of flash flood processes is the basis for carrying out management of flash floods, geological disasters, soil and water environment, etc. Monitoring of flash flood processes is the basic condition for downstream flood prevention, early warning, and forecasting.

[0003] The existing flash flood process monitoring system: For smaller flash flood ditches, simple water measuring structures are built. After measuring basic hydraulic parameters using pressure water level gauges, ultrasonic water level gauges, radar water level gauges, etc., the flash flood flow is calculated using the weir flow formula, Manning formula, Chebyshev formula, etc.

[0004] For small and medium-sized mountain torrents, relatively stable monitoring sections are generally selected, and the water level-flow relationship is used to calculate the flow process by measuring the water level.

[0005] For larger mountain torrents, flow process monitoring is carried out by establishing simplified hydrological monitoring stations.

[0006] In the existing monitoring system, the measurement of topographic parameters such as section width and channel gradient is generally replaced by large section data measured before or after the flood event; the measurement of hydrodynamic parameters such as water depth and water surface gradient is generally measured by water level gauges, etc., and then converted with topographic data. Finally, the flow is calculated using the water level-flow curve, Manning formula or specially fitted empirical formula.

[0007] According to the survey, the layout of water level gauges, a key equipment in flash flood monitoring facilities, has the following problems: because flash floods often have strong erosion and scouring capabilities, and they also carry a lot of sediment, gravel, etc., causing the flash flood ditch to have a more obvious swing. This makes it difficult to monitor water level gauges using contact measurements, and it is difficult to deploy water level gauges that use radar, microwaves, etc. to directly illuminate the water surface to obtain the water level. "Small water cannot reach it, and large water can easily destroy it." Summary of the invention

[0008] To solve the above problems, the present invention provides a method and system for measuring flood channel runoff in mountainous areas, which adopts binocular camera vision technology, including a series of operations such as camera setting and calibration, multi-angle shooting, image feature processing, and three-dimensional model construction and analysis. It can solve the problems of difficulty in laying out water level meters and inaccurate monitoring due to the characteristics of mountain torrents in mountain torrent monitoring.

[0009] The present invention is preferably implemented by the following scheme:

[0010] In a first aspect, the present invention provides a method for measuring runoff in a mountain torrent channel, comprising:

[0011] Use two cameras to obtain terrain images of the mountain torrent ditch mouth from different perspectives at the same time; use the terrain images of the mountain torrent ditch mouth to build a three-dimensional model of the mountain torrent ditch mouth scene;

[0012] The morphological data S1 of the monitoring section of the flash flood channel is obtained based on the three-dimensional model;

[0013] Select the channel containing the monitoring section from the three-dimensional model; extract the elevation of the lowest point of the upper section of the channel, the elevation of the lowest point of the lower section, and the length along the channel to calculate the channel gradient;

[0014] The two cameras are used to synchronously take multiple groups of three-band flash flood photos based on a set time interval; the morphological data S1 of the flash flood channel monitoring section is used to perform geometric correction on the data of the green band and the near-infrared band in the three-band flash flood photos, and the corrected green band and near-infrared band data are used to perform raster operations to obtain the normalized water body index of the flash flood;

[0015] The normalized water index of mountain torrents is used as the threshold for extracting mountain torrent bodies. The mountain torrent surface line of the monitoring section is extracted by the threshold segmentation method, and the mountain torrent surface width data is obtained based on the water surface line.

[0016] Based on the flash flood surface width data, the flash flood surface line of the monitoring section is divided into x sections, and the length of each section is d; based on the elevation of the flash flood surface line and the elevation of the lowest point of the monitoring section, the height of each section is obtained: h1, h2...h(x-1), the length of the bottom side of each section is obtained: Ld1, Ld2...Ldx; the flash flood flow cross-sectional area A is calculated as: A=d×(h1+h2+h3+h4......h(x-1)); the length P wetted by the flash flood flow in the monitoring section is calculated as: P=Ld1+Ld2+...Ldx;

[0017] The flash flood hydrodynamic radius is calculated based on the flash flood flow cross-sectional area A and the length of the monitoring section wetted by the flash flood flow;

[0018] The flash flood runoff is calculated using the Manning formula based on the channel gradient, flash flood water radius, and flash flood flow cross-sectional area A.

[0019] Further,

[0020] Two cameras are arranged on the same horizontal line, and the two cameras are used to obtain terrain pictures of the mountain torrent ditch mouth from different perspectives at the same time, so as to obtain a complete terrain of the mountain torrent ditch mouth.

[0021] Further,

[0022] Set up two cameras on the same horizontal line, including:

[0023] After calibrating the intrinsic parameters, extrinsic parameters and distortion coefficients of the two cameras respectively, the two cameras are subjected to stereo calibration and stereo correction;

[0024] Measure the camera's position and attitude data.

[0025] Further,

[0026] Use two cameras to obtain terrain images of the mountain torrent channel mouth from different perspectives at the same time, including:

[0027] Two cameras are used to shoot the same scene at the mouth of a flash flood ditch from different perspectives at the same location or at different locations, so as to obtain terrain pictures of the mouth of a flash flood ditch from different perspectives at the same time.

[0028] Further,

[0029] The method of constructing a three-dimensional model of a flash flood ditch mouth scene using a flash flood ditch mouth terrain picture comprises:

[0030] After performing feature detection and feature matching on two images of the mountain torrent gully mouth taken simultaneously at the gully mouth, the three-dimensional coordinate points of the scene are generated by triangulation method in combination with the camera position and attitude data;

[0031] Two synchronously taken terrain images of the mountain torrent ditch mouth are used to generate point cloud data using multiple three-dimensional coordinate points, and a three-dimensional model of the mountain torrent ditch mouth scene is constructed using the point cloud data.

[0032] Further,

[0033] The method uses the normalized water index of the mountain torrent as the mountain torrent water body extraction threshold, and extracts the mountain torrent surface line of the monitoring section by the threshold segmentation method, including:

[0034] Calculate the normalized difference water index of the flash flood object, obtain the NDWI histogram, take the NDWI value corresponding to the minimum value between the two peaks of the NDWI histogram as the flash flood water volume extraction threshold, and use the threshold segmentation method to extract the flash flood water volume range vector S2;

[0035] The morphological data S1 of the monitoring section and the range vector S2 of the flash flood are used to perform intersection operations to obtain the flash flood surface line S of the monitoring section.

[0036] Further,

[0037] The water surface line S of the monitoring section is:

[0038] S=S1(S1∩S2)

[0039] Where S1 is the monitoring section morphological data obtained based on the three-dimensional channel data; S2 is the range vector of the flash flood extracted by the bimodal threshold method; ∩ is the intersection operation; S1(S1∩S2) is the vector object in S1 that intersects with S2.

[0040] Further,

[0041] The hydraulic radius is calculated as:

[0042]

[0043] Where P is the wetted perimeter.

[0044] Further,

[0045] After performing feature detection and feature matching on the two photos taken simultaneously at the ditch entrance, three-dimensional coordinate points of the scene are generated by a triangulation method; including:

[0046] Extract feature points from two images and calculate descriptors of these feature points;

[0047] For each feature point descriptor, calculate the distance between it and all feature point descriptors in the other image; select the feature point pair with the smallest distance as the matching pair;

[0048] For the feature points in the matched left and right images, calculate their disparity;

[0049] According to the parallax, the three-dimensional coordinates of each feature point in the scene are calculated using the binocular geometry principle.

[0050] Further,

[0051] The Manning formula is used to calculate the mountain torrent runoff; the calculation formula is:

[0052]

[0053] Among them, Q is the flow rate; n is the Manning roughness coefficient, and the roughness n value of the flow monitoring section is selected according to the field river roughness table; A is the water flow cross-sectional area; R is the hydraulic radius; S is the channel gradient.

[0054] Further,

[0055] The calculation formula for calculating the normalized difference water index of the ground object is:

[0056]

[0057] Among them, Green and NIR are the remote sensing reflectances of the green band and near-infrared band respectively.

[0058] In a second aspect, the present invention provides a mountain torrent channel runoff measurement system, comprising:

[0059] The image acquisition module is used to use two cameras to acquire terrain images of the mountain torrent channel mouth from different perspectives at the same time;

[0060] The model building module uses the terrain pictures of the mountain torrent gully mouth to build a three-dimensional model of the mountain torrent gully mouth scene;

[0061] A channel gradient calculation module is used to obtain the morphological data S1 of the monitoring section based on the three-dimensional model, select the channel containing the monitoring section from the three-dimensional model, extract the elevation of the lowest point of the upper section of the channel, the elevation of the lowest point of the lower section, and the length along the channel to calculate the channel gradient;

[0062] A flash flood water index calculation module is used to synchronously take multiple groups of 3-band flash flood photos based on a set time interval using the two cameras; geometrically correct the data of the green band and the near-infrared band in the 3-band flash flood photos using the morphological data S1 of the flash flood channel monitoring section, and perform raster operations using the corrected green band and near-infrared band data to obtain a normalized water index of the flash flood;

[0063] A flash flood surface width calculation module is used to use the normalized water body index of the flash flood as the flash flood body extraction threshold, extract the flash flood surface line of the monitoring section through the threshold segmentation method, and obtain the flash flood surface width data based on the flash flood surface line;

[0064] The length calculation module of the flash flood flow in the monitoring section is used to divide the flash flood surface line of the monitoring section into x sections based on the flash flood surface width data, and the length of each section is d; based on the elevation of the flash flood surface line and the elevation of the lowest point of the monitoring section, the height of each section is obtained: h1, h2...h(x-1), the bottom length of each section is obtained: Ld1, Ld2...Ldx; the flash flood flow cross-sectional area A is calculated as: A=d×(h1+h2+h3+h4......h(x-1)); the length P of the flash flood flow in the monitoring section is calculated as: P=Ld1+Ld2+...Ldx;

[0065] The flash flood hydraulic radius calculation module is used to obtain the length of the monitoring section wetted by the flash flood flow, i.e., the wet perimeter, based on the morphological data of the monitoring section and the flash flood surface line; and calculate the flash flood hydraulic radius based on the flash flood flow cross-sectional area A and the wet perimeter of the monitoring section;

[0066] The flash flood runoff calculation module is used to calculate the flash flood runoff using the Manning formula based on the channel gradient, flash flood hydrodynamic radius, and flash flood flow cross-sectional area.

[0067] Compared with the prior art, the present invention has the following advantages:

[0068] Two cameras are set on the same horizontal line to shoot the same scene at the mouth of the flash flood ditch from different perspectives to obtain terrain pictures, and a three-dimensional model is constructed through feature detection, matching and triangulation principles to obtain the monitoring section morphological data and channel gradient. The camera is used to synchronously shoot multiple groups of 3-band photos and extract the band data to calculate the normalized water body index. The water surface line is extracted to obtain the water surface width and water depth data. The Manning formula is used to calculate the flash flood runoff after calculating the relevant parameters based on the monitoring section morphological data. The whole process does not rely on the water level meter to contact or illuminate the water surface to obtain water level information, avoiding the problem of the water level meter layout and accurate monitoring affected by the swing of the flash flood ditch and the silt and gravel, realizing reliable monitoring, and fundamentally solving the dilemma of traditional water level meters in flash flood monitoring.

[0069] Other features and advantages of the present invention will be described in the following description, and partly become apparent from the description, or understood by practicing the present invention. The purpose and other advantages of the present invention can be realized and obtained by the structures pointed out in the description, claims and drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0070] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0071] Figure 1 A schematic flow chart of the method of the present invention is shown;

[0072] Figure 2 A schematic diagram of a binocular camera image before stereo correction is shown;

[0073] Figure 3 A schematic diagram of a binocular camera image after stereo correction is shown;

[0074] Figure 4 A schematic diagram of the conversion between the world coordinate system and the camera coordinate system is shown;

[0075] Figure 5 A schematic diagram showing the conversion between the image pixel coordinate system and the image physical coordinate system is shown;

[0076] Figure 6 A schematic diagram of a parallel binocular vision model is shown;

[0077] Figure 7 A schematic diagram of calculating the cross-sectional area of ​​water flow according to the present invention is shown. DETAILED DESCRIPTION

[0078] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0079] Figure 1 A schematic block diagram of the method flow according to the present invention is shown, and the specific implementation details of the present invention will be explained below.

[0080] Preparation:

[0081] 1、 Install and debug the camera to ensure it is in optimal working condition.

[0082] Specific:

[0083] 1) When installing the camera:

[0084] Two high-resolution digital cameras are used, mounted on the same bracket and kept at a fixed distance.

[0085] 2) Camera placement:

[0086] According to the terrain characteristics, orientation, vegetation, etc. of the mountain torrent ditch mouth, combined with the changing characteristics of the solar altitude angle, 3S technology and field surveys were used to preliminarily select the location for the terrain and runoff information collection subsystem.

[0087] Combined with the requirements of photogrammetry and runoff measurement accuracy, select monitoring stations with no backlight, wide field of view, and appropriate distance (cross-section measurement accuracy error is at the cm level) for deployment. (Select monitoring station deployment points)

[0088] Get the coordinates of the monitoring point (x0, y0, z0), and based on the relative relationship between the bracket and the camera, get the coordinates of the center point of the camera photo (x1, y1, z1), (x2, y2, z2).

[0089] Select a section with smooth terrain transition and good observation conditions for monitoring station layout as the monitoring section for runoff measurement at the mouth of the mountain torrent channel. Set points toward the monitoring station at a location where the mountain torrent runoff water surface line is not suitable for flooding, set terrain correction marks (black and white chessboard squares, etc.) on both sides of the ditch mouth, and obtain the coordinates of the terrain correction mark points (X1, Y1, Z1), (X2, Y2, Z2), (X3, Y3, Z3), (X4, Y4, Z4), (X5, Y5, Z5), (X6, Y6, Z6), .... (Place terrain correction marks)

[0090] 3) Camera calibration:

[0091] When binocular stereo vision obtains the three-dimensional coordinates of a spatial point, the intrinsic parameters, extrinsic parameters and distortion coefficients of the binocular camera should be known. The intrinsic and extrinsic parameters and distortion coefficients of the binocular camera can be obtained through camera calibration. Therefore, camera calibration is an indispensable step in the binocular stereo vision system.

[0092] When calibrating a binocular camera, in order to ensure the accuracy of the calibration data, the left and right cameras are calibrated separately first. After obtaining the parameter information of the left and right cameras, the binocular camera is calibrated and corrected in stereo. This solution uses the Zhang Zhengyou calibration method to calibrate the camera.

[0093] Camera calibration principle:

[0094] Assume that there is an arbitrary point P in space, whose coordinates in the world coordinate system are (X w ,Y w ,Z w ). The projection point of point P on the camera is p, and the coordinates of the projection point p in the image pixel coordinate system are (u, v). According to the above pinhole imaging model, we can get:

[0095]

[0096] Where: s is the scale factor, A is the intrinsic matrix, R is the rotation matrix, and t is the translation matrix. For the convenience of subsequent calculations, it is assumed that the calibration plate plane is located in the world coordinate system Z w = 0. If the i-th row vector of the rotation matrix R is replaced by r i Expressed as follows, equation (1-1) can be transformed into:

[0097]

[0098] Let the homography matrix H = A[r 1 r 2 t], then the mapping relationship from the points on the calibration plate to the image is:

[0099] sp=HP (1-3)

[0100] Let H = [h 1 h 2 h 3 ], we can get:

[0101] [h 1 h 2 h 3 ]=λA[r 1 r 2 r 3 ] (1-4)

[0102] In the formula, λ is the scale factor, λ = 1 / s. Since the rotation matrix is ​​an orthogonal matrix, substituting formula (1-4) into it, we can get:

[0103]

[0104] Let B = A -T A -1 ,but:

[0105]

[0106] In the formula, B is a 3×3 matrix, which can be expressed as:

[0107] b=[B 11 B 12 B 13 B 22 B 23 B 33 ] T (1-8)

[0108] h i =[h i1 h i2 h i3 ] T (1-9)

[0109] In the formula, hi represents the i-th vector of H, which can be expressed as:

[0110]

[0111] v ij =[h i1 h j1 h i2 h j1 +h i1 h j2 h i2 h j2 h i3 h j1 +h i1 h j3 h i3 h j2 +h i2 h j3 h i3 h j3 ] T (1-11)

[0112] Arranging the two constraints of equations (1-5) and (1-6) yields:

[0113]

[0114] If the camera collects n calibration images (n≥3) during calibration, there will be n sets of (1-12) equations. By combining n sets of equations, the parameters can be obtained as follows:

[0115]

[0116] Using formula (2-13), the external parameters can be obtained as:

[0117]

[0118] Since the intrinsic and extrinsic parameters of the above cameras are derived without external interference, certain deviations may occur in actual situations. Therefore, maximum likelihood estimation is used to enhance the reliability of the camera calibration results. Assuming that there are n camera calibration images and each image has m corner points, the projection point of the jth corner point on the i-th image is The corresponding point M on the calibration plate i The j relationship can be expressed as:

[0119]

[0120] Where A is the camera internal parameter, R i and t i represents the rotation matrix and translation matrix of the i-th image. Then the probability density function can be expressed as:

[0121]

[0122] The likelihood function is constructed as:

[0123]

[0124] It can be seen from formula (2-17) that when formula (2-18) reaches the minimum value, L can reach the maximum value.

[0125]

[0126] When only radial distortion is considered, the distortion polynomial can be obtained as shown in formula (2-19).

[0127] x=x′+x′[k 1 (x′+y′) 2 +k 2 (x′+y′) 2 ]

[0128] y=y′+y′[k 1 (x′+y′) 2 +k 2 (x′+y′) 2 ] (1-19)

[0129] In the formula, (x′, y′) is the normalized image coordinate point in the ideal case, and (x, γ) is the normalized image coordinate point with distortion. According to the camera intrinsic parameter model, we can get:

[0130]

[0131] Assuming γ=0, combining equation (2-19) and equation (2-20) and transforming them into matrix form, we can get the following equation:

[0132]

[0133] In the formula, (u 0 ,ν 0 ) is the camera principal point, (u′, ν′) is the pixel coordinate under the ideal model, and (u, ν) is the pixel coordinate under the actual situation.

[0134] The radial distortion is then:

[0135] k=[k 1 k 2 ] T =(D T D) -1 D T d (1-22)

[0136] The optimal objective function obtained by using the maximum likelihood estimation optimization result and the Levenberg-Marquardt algorithm is as follows:

[0137]

[0138] Finally, the camera distortion coefficient k can be obtained 1 , k 2 .

[0139] Binocular camera stereo calibration:

[0140] After calibrating the left and right cameras separately using Zhang Zhengyou's calibration method, the external parameters of the left and right cameras can be combined for stereo calibration to obtain the relative position between the two cameras. The rotation matrix R and the translation vector T are used to represent the relative position between the cameras.

[0141] Assume that there is a point P in space, and its coordinates in the world coordinate system are P w , and projected onto the left and right cameras respectively l ,P r Two points. According to formula (5-1), we can get:

[0142]

[0143] Because P l and P r The following relationship exists:

[0144] P r =RP l +T (1-25)

[0145] Then the external parameters of the binocular camera are:

[0146]

[0147] So far, the intrinsic parameters, extrinsic parameters, distortion coefficients of the left and right cameras and the transformation relationship between the binocular cameras have been successfully calibrated, and the calibration of the binocular camera has been completed.

[0148] Binocular camera stereo calibration:

[0149] The parallel structure model is an ideal model, but in reality, it is difficult for binocular cameras to remain completely parallel. In order to facilitate subsequent calculations, the image needs to be stereo rectified so that the left and right images are in complete line alignment.

[0150] exist Figure 2 In the figure, the binocular non-parallel structure model is corrected to a binocular parallel structure model. The image planes of the left and right cameras are parallel to the baseline (the line between the optical centers of the two cameras), the optical axes of the two cameras are parallel, and the poles are at infinity. Stereo correction is achieved using the rotation matrix and translation matrix of the binocular camera. First, decompose the rotation matrix R obtained by camera calibration in the previous section into R l With R r In order to minimize the reprojection distortion after stereo correction, the left and right cameras are rotated half each. At this time, R l With R r for:

[0151]

[0152] Construct the transformation matrix R based on the translation vector r ect, let the pole of the left view transform to infinity, at which point the pole line is parallel to the image plane. It can be concluded that the direction of the left pole is the translation vector of the projection center. Construct e l As shown below:

[0153]

[0154] Build 2 , due to e 2 The direction is perpendicular to the main optical axis, and e 2 With e l Orthogonal, so:

[0155]

[0156] Definition 3 Respectively with e l 、e 2 Orthogonal, as shown below:

[0157] e 3 =e 1 ×e 2 (1-30)

[0158] Finally, the transformation matrix R is obtained rect for:

[0159]

[0160] The overall rotation matrix of the left and right cameras is the product of the composite rotation matrix and the transformation matrix, as shown in formula (1-32).

[0161] R′ l =R rect R l

[0162] R′ r =R rect R r (1-32)

[0163] The left and right cameras adjust the rotation matrix according to formula (1-32) to complete the stereo correction and obtain an ideal binocular stereo image pair.

[0164] Practical operation method of camera calibration:

[0165] The camera calibration toolbox of Matlab is a set of tools based on Zhang Zhengyou's calibration method. It provides a user interface that allows users to calibrate cameras through a graphical interface. This toolbox includes functions such as calibration image acquisition, image preprocessing, and calibration parameter calculation. Users can use this toolbox to automatically complete the camera calibration process, including feature point detection, estimation of internal and external parameters, and calculation of distortion parameters. A 9×7 black and white checkerboard calibration board is used, and the size of the checkerboard is 28mm×28mm.

[0166] By changing the camera position, multiple sets of calibration images are collected. The Camera Calibrator toolbox in Matlab is used to calibrate the left and right cameras respectively. After obtaining the calibration results of the two cameras, the left and right cameras are calibrated in stereo, and the image pairs with large errors are deleted, and corner detection is performed. The relative relationship between the position and posture of the binocular camera and the calibration plate is determined, and the binocular camera is calibrated in stereo.

[0167] Finally, the result of binocular camera calibration is obtained, that is, the specific internal and external parameters of the binocular camera are obtained, and the calibration of the binocular camera is completed.

[0168] Camera parameters include: intrinsic parameters (focal length, optical center position, etc.) and extrinsic parameters (position, attitude, etc.). Calibration can be performed using a checkerboard pattern or other standard calibration plate with known coordinates.

[0169] The camera matrix (Intrinsic Matrix) describes the internal parameters of the camera, including focal length, principal point (image center), and pixel scaling factor. The camera matrix is ​​usually represented as a 3x3 matrix:

[0170]

[0171] Where: f x and f y is the focal length of the image (in pixels), corresponding to the horizontal and vertical focal lengths of the camera, usually f x and f y can be assumed to be equal. c x and c y is the principal point of the image (the coordinates of the center of the image), usually the center of the image. Usually, pixels are not always square, so f x and f y May be different.

[0172] The camera mounting system includes:

[0173] Binocular camera system: Two high-resolution digital cameras mounted on the same bracket and kept at a fixed distance (baseline).

[0174] Bracket: Used to fix the camera, ensure its stability, and adjust the angle of the camera.

[0175] Measurement tools: Equipment such as total stations or RTK GPS are used to accurately measure the position and attitude of the camera.

[0176] 2、 Measure camera position and attitude:

[0177] Use equipment such as total stations or RTK GPS to accurately measure the position and attitude of the camera.

[0178] Use a total station to measure camera position and attitude, including:

[0179] A total station is a high-precision measuring device that can measure the three-dimensional coordinates and angles of a target. The following are the general steps for measuring the position and attitude of a camera using a total station:

[0180] (1) Install and calibrate the total station: Install the total station on a stable base and calibrate it to ensure measurement accuracy.

[0181] (2) Install the camera and calibration plate: Install the calibration plate or reflecting prism used as the measurement reference on the camera.

[0182] (3) Measuring camera coordinates: Use a total station to measure the physical position of the camera, that is, the three-dimensional coordinates (X, Y, Z) of the camera's principal point.

[0183] (4) Measuring attitude angles: Use a total station to measure the camera attitude angles, including pitch, yaw, and roll. These angles are usually used to describe the rotation state of the camera relative to the total station.

[0184] (5) Record data: Record the measured three-dimensional coordinates and posture angles for use in subsequent binocular vision calculations.

[0185] Use RTK GPS to measure camera position and attitude, including:

[0186] RTK (Real-Time Kinematic) GPS technology can provide centimeter-level positioning accuracy, which is particularly suitable for high-precision position measurement in outdoor environments. The following are the general steps for measuring the position and attitude of a camera using RTK GPS:

[0187] (1) Install the RTK GPS receiver: Install the RTK GPS receiver near the camera and ensure that the receiver can accurately reflect the camera's position.

[0188] (2) Base station setup: An RTK GPS base station is set up in the survey area to ensure the transmission and calculation of differential GPS data.

[0189] (3) Measure camera coordinates: Use RTK GPS to measure the physical position of the camera, that is, the three-dimensional coordinates (X, Y, Z) of the camera's principal point.

[0190] (4) Measuring attitude angles: If precise attitude measurement is required, an RTKGPS device with attitude measurement function can be used, or sensors such as an inertial measurement unit (IMU) can be used to measure the pitch, yaw, and roll angles of the camera.

[0191] (5) Record data: Record the measured three-dimensional coordinates and posture angles for use in subsequent binocular vision calculations.

[0192] 3、 Arrange verification points:

[0193] At the monitoring section, several verification points are arranged to ensure that the distribution of these points is both uniform and reasonable, and the location of each point is quite obvious, in order to facilitate the subsequent rapid identification and accurate measurement. These verification points can be natural markers that use existing features on site, such as significant terrain features, trees, rocks, etc., or they can be artificial markers set up specifically for monitoring purposes, such as specially made measurement signs, eye-catching reflective patches, or easily identifiable vertical markers.

[0194] In order to ensure the accuracy and reliability of the measurement results, we use advanced total stations or high-precision RTK GPS systems to accurately measure all the arranged verification points. Through this series of professional measurement steps, we can accurately obtain the three-dimensional coordinate data of each verification point. The measurement method is the same as the camera position measurement.

[0195] During the measurement process, the 3D coordinate values ​​of each verification point are recorded in detail. These precise 3D coordinate data will play an important role in the subsequent image matching work, helping us to match and calibrate the field measurement data with the image-reconstructed terrain with high precision. At the same time, they are also indispensable basic data for terrain verification, which can strongly support our scientific analysis and evaluation of terrain changes in the monitoring area.

[0196] Step 1 of this method:

[0197] Two multispectral cameras at fixed positions are used to shoot the same scene at the mouth of a flash flood ditch from different perspectives at the same position or different positions (make sure the two cameras shoot at the same time so that the feature points can be correctly matched during subsequent processing) to obtain terrain images and multispectral images of the mouth of a flash flood ditch from different perspectives at the same time.

[0198] The multispectral camera is a three-spectral camera with panchromatic band + green band + near-infrared band.

[0199] Step 2 of this method:

[0200] Constructing a 3D model, including:

[0201] 1、 Preprocess the image (such as denoising, contrast enhancement, etc.).

[0202] The feature matching algorithm (BFMather algorithm is proposed) is used to find the corresponding points between two images. BFMatcher (Brute-Force Matcher) is a simple and effective feature matching algorithm, which is often used in image matching tasks in computer vision. Its basic principle is to compare each feature point in one image with all feature points in another image through a brute force search method to find the most similar matching pair.

[0203] How BFMatcher works:

[0204] ① Feature descriptors: First, feature points are extracted from the two images and the descriptors of these feature points are calculated.

[0205] ② Distance calculation: For each feature point descriptor, calculate the distance between it and all feature point descriptors in the other image. Commonly used distance metrics include Euclidean distance (L2 norm) and Hamming distance.

[0206] ③ Matching selection: Select the feature point pair with the smallest distance as the matching pair. In order to improve the accuracy of matching, methods such as cross matching (crossCheck) and K nearest neighbor matching (knnMatch) can be used.

[0207] ④ Triangulation: Based on the corresponding feature points and their disparity in the two images, the world coordinates of each feature point are calculated using binocular geometry principles. The world coordinate system uses world coordinates to represent the relative position of the target object, and its origin is set at any position in space; the camera coordinate system uses camera coordinates to represent the relative position of the target object, and its origin is set at the optical center of the camera. The relative relationship between the world coordinate system and the camera coordinate system is as follows: Figure 4 shown.

[0208] Figure 4 In, O w -X w Y w Z w Represents the world coordinate system; O c -X c Y c Z c Represents the camera coordinate system. In the camera coordinate system, the origin O c is the camera optical center; Z c The X axis is the camera optical axis, which is perpendicular to the image plane; c The axis is parallel to the x-axis of the image's physical coordinate system; c The axis is parallel to the Y axis of the image physical coordinate system; Z c The intersection point O of the axis and the image plane 1 is the main point of the image; O c O 1is the focal length of the camera.

[0209] Assume that the coordinates of a point P on the surface of the target object in the world coordinate system are (X w , Y w , Z w ), the coordinates in the camera coordinate system are (X c , Y c , Z c ), then (X w , Y w , Z w ) and (X c , Y c , Z c ) is shown in formula (2).

[0210]

[0211] In formula (2-1), the matrix R is a 3×3 orthogonal unit rotation matrix; the matrix T is a 3×1 translation matrix; It is the transformation matrix between the world coordinate system and the camera coordinate system, also called the external parameter matrix, denoted by M.

[0212] The coordinate transformation of a point P in the image between the image physical coordinate system and the image pixel coordinate system is as follows: Figure 5 shown.

[0213] 2、 3D Reconstruction:

[0214] 3D reconstruction uses binocular cameras to simultaneously capture images of the target object from different angles, uses a stereo matching algorithm to obtain a disparity map of the reference image, and then calculates the 3D coordinates of the object, and uses the 3D coordinates to restore the depth information of the target object. 3D reconstruction is actually the inverse process of object imaging, which simulates the principle of human binocular observation. Parallel binocular vision models such as Figure 6 shown.

[0215] The three-dimensional model is constructed by triangulating a series of point cloud data. The three-dimensional coordinate points of the object or scene are generated by combining the depth information with the plane coordinates of the image. These three-dimensional points can be used to generate a point cloud, which can further construct a three-dimensional model of the entire scene.

[0216] Figure 4 In, O c1 -X c1 Y c1 Z c1 and O c2 -X c2 Y c2 Z c2 are the left and right camera coordinate systems respectively; Ow -X w Y w Z w is the world coordinate system; O 1 -x 1 y 1 and O 2 -x 2 y 2 are the physical coordinate systems of the left and right images respectively, b is the baseline, which is the distance between the two cameras. P is a point on the surface of the spatial object, p 1 is the projection of point P in the left camera image coordinate system, p 2 is the projection of point P in the right camera image coordinate system. The coordinates of point P are obtained by combining the camera calibration parameters and the properties of similar triangles.

[0217]

[0218] To maintain the right-hand coordinate system, Z must be negative; due to the inversion phenomenon of the image formation process, Y must be negative. (X, Y, Z) is the spatial coordinate of point P calculated from the image. After using formula (2-2) to calculate the spatial coordinates of each pixel in the reference image, all the three-dimensional coordinates of the photographed object are formed. These coordinates and the corresponding reference image pixel color values ​​are stored in the point cloud file for three-dimensional reconstruction. The three-dimensional points can be calculated using the triangulation method through Python's OpenCV library.

[0219] 3、 3D reconstruction verification:

[0220] The accuracy of the 3D model can be compared with field measurement data. The changing trend of cross-sectional data can be analyzed to assess the changes in the topography at the mouth of the mountain torrent channel.

[0221] Specifically, they include:

[0222] The precise three-dimensional coordinates of the monitoring section measured by a total station or RTK GPS equipment are compared with the corresponding data in the three-dimensional model. The accuracy of the three-dimensional model in terms of shape, size and spatial position is evaluated by calculating the error range and analyzing the deviation distribution.

[0223] By calculating the error between the binocular reconstruction point cloud and the total station measurement point cloud, the Euclidean distance between the corresponding points can be calculated to obtain the error of each point. The mean square error (MSE) of all point errors as well as the maximum error and minimum error can also be calculated to identify the error range. According to the calculated error index, the accuracy of the three-dimensional model reconstructed by binocular stereo vision is judged. If the error is within an acceptable range, the reconstructed model is considered to have high accuracy; otherwise, further optimization is required. The camera parameters can be adjusted, that is, the internal and external parameters of the camera can be recalibrated to ensure that the parameters are accurate. More landmarks can also be added in key areas to improve the reconstruction accuracy. The algorithm can also be improved to optimize the stereo matching and triangulation algorithms to improve the accuracy of the point cloud model.

[0224] Step 3 of this method:

[0225] Using two photos taken simultaneously at the gully mouth, with the selected runoff measurement monitoring section as the focus, the full-color band images were used to measure the gully mouth terrain and solve the monitoring section terrain, and the terrain correction mark points were used for accuracy correction and verification. The channel gradient of the monitoring section was obtained using the terrain measurement results.

[0226] Specifically: based on the three-dimensional model, the morphological data S1 of the monitoring section is obtained; the river section including the monitoring section is selected from the three-dimensional model; the elevation of the lowest point of the upper section of the river section, the elevation of the lowest point of the lower section, and the length along the river section between the lowest points are extracted to calculate the channel gradient;

[0227] The channel gradient is: the difference between the elevation of the lowest point of the upper section and the elevation of the lowest point of the lower section / the length of the river section.

[0228] Step 4 of this method:

[0229] Set the time interval for synchronous acquisition of photos. Depending on the rainfall conditions and the shape of the catchment area, the monitoring frequency can generally be 1min, 5min, 15min, 30min, etc. Use two cameras to synchronously take multiple sets of 3-band flash flood photos based on the set time interval, use the morphological data S1 of the flash flood channel monitoring section to perform geometric correction on the green band and near-infrared band data, use the corrected green and near-infrared bands to perform raster operations, and calculate the normalized water index (NDWI). The normalized water index is:

[0230]

[0231] Among them, Green and NIR are the remote sensing reflectances of the green band and near-infrared band respectively.

[0232] Step 5 of this method:

[0233] The normalized water index of flash floods is used as the threshold for flash flood body extraction. The flash flood surface line of the monitoring section is extracted through the threshold segmentation method, and the flash flood surface width data is obtained based on the water surface line.

[0234] Specifically, the following is an embodiment of water extraction of the present invention:

[0235] (1) Calculate the normalized difference water index of the ground object, obtain the NDWI histogram, take the NDWI value corresponding to the minimum value between the two peaks of the NDWI histogram as the water extraction threshold, and use the threshold segmentation method to extract the flash flood water range vector S2;

[0236] (2) The morphological data S1 of the monitoring section and the range vector S2 of the mountain flood are used to perform an intersection operation to obtain the water surface line S of the monitoring section.

[0237] The water surface line S of the monitoring section is:

[0238] S=S1(S1∩S2)

[0239] Where S1 is the monitoring section morphological data obtained based on the three-dimensional channel data; S2 is the range vector of the flash flood extracted by the bimodal threshold method; ∩ is the intersection operation; S1(S1∩S2) is the vector object in S1 that intersects with S2.

[0240] Specifically, this patent uses three-dimensional model data to perform geometric correction on green band and near-infrared band data to correct the pixel spatial position errors in the image caused by sensor distortion, platform posture changes and terrain undulations, thereby ensuring that the green band and near-infrared band data are accurately aligned with the three-dimensional geographic space coordinates.

[0241] The correction process is based on a strict geometric transformation model to project the image pixel position into a three-dimensional geographic space coordinate system. Mathematically, the terrain effect is corrected by combining the three-dimensional model data and introducing the elevation information of the ground objects. The principle is described as follows:

[0242] x′=f(x,y,z;P),

[0243] y′=g(x,y,z;P),

[0244] Among them, (x, y) is the original pixel position in the image, z is the elevation information of the ground object provided by the 3D model, (x′, y′) is the corrected pixel position, and P is the geometric parameters of the sensor (such as focal length, field of view, and attitude parameters). Functions f and g describe the mapping relationship from image pixel coordinates to the actual position on the ground, which is determined by the sensor model and the 3D model.

[0245] Step 6 of this method:

[0246] Based on the water surface width data, the water surface line of the monitoring section is divided into x equal sections, and the length of each section is d. Based on the elevation of the water surface line and the elevation of the lowest point of the monitoring section, the height of each section (h1, h2...h(x-1)) and the length of the bottom edge of each section (Ld1, Ld2...Ldx) are obtained; the cross-sectional area of ​​the water flow A is calculated as: A=d×(h1+h2+h3+h4......h(x-1)); the wetted perimeter of the water flow (the length of the monitoring section wetted by the mountain torrent flow) P is calculated as: P=Ld1+Ld2+...Ldx. Figure 7 shown.

[0247] Step 7 of this method:

[0248] Based on the morphological data and water surface line of the monitoring section, the length of the monitoring section wetted by water flow, namely the wetted perimeter, is obtained; based on the cross-sectional area A of the water flow and the wetted perimeter of the monitoring section, the hydraulic radius is calculated.

[0249] Step 8 of this method:

[0250] The flow rate of mountain torrents is calculated using the Manning formula, using the channel gradient and hydraulic radius.

[0251] The calculation formula is:

[0252]

[0253] in:

[0254] Q is the flow rate; n is the Manning roughness coefficient. The roughness n value of the flow monitoring section is selected according to the field river roughness table; A is the cross-sectional area of ​​the water flow; R is the hydraulic radius; S is the channel gradient.

[0255] Based on the method of the present invention, an embodiment of the present invention further provides a system corresponding to the above method, which includes:

[0256] The image acquisition module is used to use two cameras to acquire terrain images of the mountain torrent channel mouth from different perspectives at the same time;

[0257] The model building module uses the terrain pictures of the mountain torrent gully mouth to build a three-dimensional model of the mountain torrent gully mouth scene;

[0258] A channel gradient calculation module is used to obtain the morphological data S1 of the monitoring section based on the three-dimensional model, select the channel containing the monitoring section from the three-dimensional model, extract the elevation of the lowest point of the upper section of the channel, the elevation of the lowest point of the lower section, and the length along the channel to calculate the channel gradient;

[0259] A flash flood water index calculation module is used to synchronously take multiple groups of 3-band flash flood photos based on a set time interval using the two cameras; geometrically correct the data of the green band and the near-infrared band in the 3-band flash flood photos using the morphological data S1 of the flash flood channel monitoring section, and perform raster operations using the corrected green band and near-infrared band data to obtain a normalized water index of the flash flood;

[0260] A flash flood surface width calculation module is used to use the normalized water body index of the flash flood as the flash flood body extraction threshold, extract the flash flood surface line of the monitoring section through the threshold segmentation method, and obtain the flash flood surface width data based on the flash flood surface line;

[0261] The length calculation module wetted by the mountain torrent flow is used to divide the water surface line of the monitoring section into x segments based on the water surface width data, and the length of each segment is d; based on the elevation of the water surface line and the elevation of the lowest point of the monitoring section, the height of each segment is obtained: h1, h2...h(x-1), the bottom length of each segment is obtained: Ld1, Ld2...Ldx; the cross-sectional area of ​​the water flow A is calculated as: A=d×(h1+h2+h3+h4......h(x-1)); the length P wetted by the mountain torrent flow in the monitoring section is calculated as: P=Ld1+Ld2+...Ldx;

[0262] A flash flood hydraulic radius calculation module is used to calculate the flash flood hydraulic radius based on the flash flood flow cross-sectional area A and the length of the monitoring section wetted by the flash flood flow;

[0263] The flash flood runoff calculation module uses the Manning formula to calculate the flash flood runoff based on the channel gradient, flash flood hydraulic radius, and flash flood flow cross-sectional area A.

[0264] Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent substitutions for some of the technical features therein; and these modifications or substitutions do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for measuring runoff in a mountain torrent channel, characterized in that: include: Use two cameras to obtain terrain images of the mountain torrent ditch mouth from different perspectives at the same time; use the terrain images of the mountain torrent ditch mouth to build a three-dimensional model of the mountain torrent ditch mouth scene; The morphological data S1 of the monitoring section of the flash flood channel is obtained based on the three-dimensional model; Select the channel containing the monitoring section from the three-dimensional model; extract the elevation of the lowest point of the upper section of the channel, the elevation of the lowest point of the lower section, and the length along the channel to calculate the channel gradient; The two cameras are used to synchronously take multiple groups of three-band flash flood photos based on a set time interval; the morphological data S1 of the flash flood channel monitoring section is used to perform geometric correction on the data of the green band and the near-infrared band in the three-band flash flood photos, and the corrected green band and near-infrared band data are used to perform raster operations to obtain the normalized water body index of the flash flood; The normalized water index of mountain torrents is used as the threshold for extracting mountain torrent bodies. The mountain torrent surface line of the monitoring section is extracted by the threshold segmentation method, and the mountain torrent surface width data is obtained based on the water surface line. Based on the flash flood surface width data, the flash flood surface line of the monitoring section is divided into x sections, and the length of each section is d; based on the elevation of the flash flood surface line and the elevation of the lowest point of the monitoring section, the height of each section is obtained: h1, h2...h(x-1), the length of the bottom side of each section is obtained: Ld1, Ld2...Ldx; the flash flood flow cross-sectional area A is calculated as: A=d×(h1+h2+h3+h4......h(x-1)); the length P wetted by the flash flood flow in the monitoring section is calculated as: P=Ld1+Ld2+...Ldx; The flash flood hydrodynamic radius is calculated based on the flash flood flow cross-sectional area A and the length of the monitoring section wetted by the flash flood flow; The flash flood runoff is calculated using the Manning formula based on the channel gradient, flash flood water radius, and flash flood flow cross-sectional area A.

2. A method for measuring runoff in a mountain torrent channel according to claim 1, characterized in that: Two cameras are arranged on the same horizontal line, and the two cameras are used to obtain terrain pictures of the mountain torrent ditch mouth from different perspectives at the same time, so as to obtain a complete terrain of the mountain torrent ditch mouth.

3. A method for measuring runoff in a mountain torrent channel according to claim 1, characterized in that: Set up two cameras on the same horizontal line, including: After calibrating the intrinsic parameters, extrinsic parameters and distortion coefficients of the two cameras respectively, the two cameras are subjected to stereo calibration and stereo correction; Measure the camera's position and attitude data.

4. A method for measuring runoff in a mountain torrent channel according to claim 1, characterized in that: Use two cameras to obtain terrain images of the mountain torrent channel mouth from different perspectives at the same time, including: Two cameras are used to shoot the same scene at the mouth of a flash flood ditch from different perspectives at the same location or at different locations, so as to obtain terrain pictures of the mouth of a flash flood ditch from different perspectives at the same time.

5. A method for measuring runoff in a mountain torrent channel according to claim 1, characterized in that: The method of constructing a three-dimensional model of a flash flood ditch mouth scene using a flash flood ditch mouth terrain picture comprises: After performing feature detection and feature matching on two images of the mountain torrent gully mouth taken simultaneously at the gully mouth, the three-dimensional coordinate points of the scene are generated by triangulation method in combination with the camera position and attitude data; Two synchronously taken terrain images of the mountain torrent ditch mouth are used to generate point cloud data using multiple three-dimensional coordinate points, and a three-dimensional model of the mountain torrent ditch mouth scene is constructed using the point cloud data.

6. A method for measuring runoff in a mountain torrent channel according to claim 1, characterized in that: The method uses the normalized water index of the mountain torrent as the mountain torrent water body extraction threshold, and extracts the mountain torrent surface line of the monitoring section by the threshold segmentation method, including: Calculate the normalized difference water index of the flash flood object, obtain the NDWI histogram, take the NDWI value corresponding to the minimum value between the two peaks of the NDWI histogram as the flash flood water volume extraction threshold, and use the threshold segmentation method to extract the flash flood water volume range vector S2; The morphological data S1 of the monitoring section and the range vector S2 of the flash flood body are used to perform an intersection operation to obtain the flash flood surface line S of the monitoring section.

7. A method for measuring runoff in a mountain torrent channel according to claim, characterized in that: The monitoring section mountain flood surface line S is: S=S1(S1∩S2) Where S1 is the monitoring section morphological data obtained based on the three-dimensional data of the channel; S2 is the range vector of the flash flood in the channel extracted by the bimodal threshold method; ∩ is the intersection operation; S1(S1∩S2) is the vector object in S1 that intersects with S2.

8. A method for measuring runoff in a mountain torrent channel according to claim 5, characterized in that: After performing feature detection and feature matching on two images of the mountain torrent gully mouth taken simultaneously at the gully mouth, the three-dimensional coordinate points of the scene are generated by triangulation method; including: Extract feature points from two images and calculate descriptors of these feature points; For each feature point descriptor, calculate the distance between it and all feature point descriptors in the other image; select the feature point pair with the smallest distance as the matching pair; For the feature points in the matched left and right images, calculate their disparity; According to the parallax, the three-dimensional coordinates of each feature point in the scene are calculated using the binocular geometry principle.

9. A method for measuring runoff in a mountain torrent channel according to claim 1, characterized in that: The Manning formula is used to calculate the mountain torrent runoff; the calculation formula is: Among them, Q is the flow rate; n is the Manning roughness coefficient, and the roughness n value of the flow monitoring section is selected according to the field river roughness table; A is the cross-sectional area of ​​the mountain torrent flow; R is the mountain torrent hydrodynamic radius; S is the channel gradient.

10. A mountain torrent channel runoff measurement system, characterized in that: include: The image acquisition module is used to use two cameras to acquire terrain images of the mountain torrent channel mouth from different perspectives at the same time; The model building module uses the terrain pictures of the mountain torrent gully mouth to build a three-dimensional model of the mountain torrent gully mouth scene; A channel gradient calculation module is used to obtain the morphological data S1 of the monitoring section based on the three-dimensional model, select the channel containing the monitoring section from the three-dimensional model, extract the elevation of the lowest point of the upper section of the channel, the elevation of the lowest point of the lower section, and the length along the channel to calculate the channel gradient; A flash flood water index calculation module is used to synchronously take multiple groups of 3-band flash flood photos based on a set time interval using the two cameras; geometrically correct the data of the green band and the near-infrared band in the 3-band flash flood photos using the morphological data S1 of the flash flood channel monitoring section, and perform raster operations using the corrected green band and near-infrared band data to obtain a normalized water index of the flash flood; A flash flood surface width calculation module is used to use the normalized water body index of the flash flood as the flash flood body extraction threshold, extract the flash flood surface line of the monitoring section through the threshold segmentation method, and obtain the flash flood surface width data based on the flash flood surface line; The length calculation module of the flash flood flow in the monitoring section is used to divide the flash flood surface line of the monitoring section into x sections based on the flash flood surface width data, and the length of each section is d; based on the elevation of the flash flood surface line and the elevation of the lowest point of the monitoring section, the height of each section is obtained: h1, h2...h(x-1), the bottom length of each section is obtained: Ld1, Ld2...Ldx; the flash flood flow cross-sectional area A is calculated as: A=d×(h1+h2+h3+h4......h(x-1)); the length P of the flash flood flow in the monitoring section is calculated as: P=Ld1+Ld2+...Ldx; The flash flood hydraulic radius calculation module is used to obtain the length of the monitoring section wetted by the flash flood flow, i.e., the wet perimeter, based on the morphological data of the monitoring section and the flash flood surface line; and calculate the flash flood hydraulic radius based on the flash flood flow cross-sectional area A and the wet perimeter of the monitoring section; The flash flood runoff calculation module is used to calculate the flash flood runoff using the Manning formula based on the channel gradient, flash flood hydrodynamic radius, and flash flood flow cross-sectional area.

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