A rapid simplification method for measuring points in river cross-sections based on the conservation of water flow area.

Abstract

This invention relates to the field of water conservancy engineering technology, and in particular to a rapid simplification method for river cross-section measuring points based on the conservation of water flow area. The method includes the following steps: acquiring three-dimensional measuring point data of the river cross-section; filtering the three-dimensional measuring point data to obtain three-dimensional measuring point beam data; projecting the cross-section lines based on the three-dimensional measuring point beam data to generate a set of cross-section measuring points; performing initial simplification of the measuring points based on the set of cross-section measuring points to obtain initial simplified cross-section measuring point data; identifying small ditch regions based on the initial simplified cross-section measuring point data; determining turbulent structure regions based on the small ditch regions; tracking the scour and sedimentation evolution path based on the turbulent structure regions; calculating the water flow cross-sectional area based on the initial simplified cross-section measuring point data; and performing elevation correction based on the water flow cross-sectional area to obtain the corrected water flow cross-sectional area. This invention significantly improves the accuracy of river flow characteristic assessment and scour and sedimentation monitoring based on water conservancy engineering technology.

Description

Technical Field

[0001] This invention relates to the field of water conservancy engineering technology, and in particular to a method for rapidly simplifying the measurement points of river cross sections based on the conservation of water flow area. Background Technology

[0002] Traditional river cross-section measurements often employ fixed-interval or regular point layouts, failing to adapt to local riverbed topographic features (such as deep channels, floodplains, and steep slopes). This easily leads to the neglect of key scour and deposition areas or areas with significant dynamic changes, resulting in measurement points that cannot fully reflect the true cross-section morphology. The lack of systematic modeling of local turbulent structures, velocity differences between inner and outer bends, and the resulting secondary eddy formation and stable deposition trends prevents effective reconstruction of scour and deposition evolution paths, easily leading to the erroneous deletion of characteristic points in important hydrodynamic evolution zones. In projecting and correcting the elevation of cross-section measurement points, traditional methods mostly use a uniform projection direction and planar approximation, failing to adaptively adjust according to changes in the actual cross-section spatial direction and river course. This results in the simplified measurement points not maintaining the consistency of the original water area conservation characteristics. In the stages of selecting scour and deposition characteristic points and setting buffer zones, traditional methods typically rely on regular grids or fixed-distance models, lacking the ability to calibrate high-risk areas based on dynamic response mechanisms such as scour and deposition statistics and scour trend slopes, limiting the accuracy and adaptability of simplified results in scour and deposition assessment and evolution simulation. Summary of the Invention

[0003] Therefore, it is necessary for the present invention to provide a rapid simplification method for measuring points of river cross sections based on the conservation of water flow area, in order to solve at least one of the above-mentioned technical problems.

[0004] To achieve the above objectives, a rapid simplification method for measuring points in river cross-sections based on the conservation of water flow area is proposed, comprising the following steps:

[0005] Step S1: Obtain three-dimensional measurement point data of the river channel cross section; perform filtering processing on the three-dimensional measurement point data of the river channel cross section to obtain three-dimensional measurement point beam data of the cross section; perform cross section line projection on the three-dimensional measurement point beam data of the cross section to generate a set of cross section measurement points;

[0006] Step S2: Based on the set of cross-sectional measuring points, perform initial simplification of the measuring points to obtain initial simplified cross-sectional measuring point data; identify small ditch regions based on the initial simplified cross-sectional measuring point data; determine turbulent structure regions based on the small ditch regions; and track the scouring and sedimentation evolution path based on the turbulent structure regions.

[0007] Step S3: Calculate the cross-sectional area of ​​the water passage based on the initial simplified cross-section measurement point data; perform elevation correction based on the cross-sectional area of ​​the water passage to obtain the corrected cross-sectional area; perform rapid simplification of the cross-sectional measurement points of the initial simplified cross-section data according to the scour and sedimentation evolution path to generate a simplified cross-sectional measurement point map.

[0008] Step S4: Calculate the scour and sedimentation volume based on the scour and sedimentation evolution path and the corrected area of ​​the cross-section; calibrate the scour and sedimentation characteristic points according to the scour and sedimentation volume; set a buffer zone for the simplified cross-section measuring point diagram according to the scour and sedimentation characteristic points, thereby obtaining the simplified data of the first cross-section measuring points.

[0009] This invention acquires three-dimensional measurement point data and performs filtering to remove noise and errors, resulting in more accurate measurement data and avoiding the errors caused by coarse data in traditional measurements. Next, the cross-sectional measurement point set generated by cross-sectional line projection makes data processing and simplification more flexible and efficient, effectively preserving the local topographic features of the river channel, especially the changes in key areas such as deep channels, beaches, and steep slopes, avoiding the neglect of these changes in traditional methods. During the initial simplification of cross-sectional measurement points, the identification of riverbed features and turbulent flow structures ensures that important scour and deposition evolution areas are not overlooked during the simplification process. By tracking the scour and deposition evolution path, dynamically changing areas are reasonably reflected, thereby improving the accuracy of measurement point simplification. Elevation correction adaptively adjusts to changes in different river channel directions and cross-sectional spatial orientations, avoiding the errors caused by a uniform projection direction in traditional methods, ensuring the conservation of the cross-sectional area, and thus improving the reliability of the simplified cross-sectional data in subsequent analysis. In the calculation of scour and sedimentation volumes and the calibration of scour and sedimentation characteristic points, by accurately identifying the distribution characteristics of scour and sedimentation, high-risk scour and sedimentation zones can be accurately calibrated, avoiding the inefficient calibration caused by regular grid models or fixed distances in traditional methods. Finally, the technique of setting buffer zones based on scour and sedimentation characteristic points further optimizes the accuracy of the simplified measurement point map, ensuring the adaptability of simplified data to scour and sedimentation volume assessment and evolution simulation, and providing a more accurate tool for analyzing river hydrodynamic evolution. Through this series of steps, the ability to reflect actual topography and scour and sedimentation evolution during river measurement and simplification can be effectively improved, making the overall cross-section measurement point simplification method more consistent with the dynamic characteristics of actual rivers. Attached Figure Description

[0010] Other features, objects, and advantages of the invention will become more apparent from the following detailed description of non-limiting embodiments with reference to the accompanying drawings:

[0011] Figure 1 This is a schematic diagram of the steps of a method for rapid simplification of river cross-section measuring points based on the conservation of water flow area according to the present invention.

[0012] Figure 2 This is a detailed flowchart of step S1 in the present invention;

[0013] Figure 3 This is a detailed flowchart of step S16 in the present invention;

[0014] The realization of the objective, functional features and advantages of the present invention will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0015] The technical method of this invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of this invention. Based on the embodiments of this invention, all other embodiments obtained by those skilled in the art without inventive effort are within the scope of protection of this invention.

[0016] Furthermore, the accompanying drawings are merely illustrative of the invention and are not necessarily drawn to scale. The same reference numerals in the drawings denote the same or similar parts, and therefore repeated descriptions of them will be omitted. Some block diagrams shown in the drawings are functional entities and do not necessarily correspond to physically or logically independent entities. These functional entities can be implemented in software, in one or more hardware modules or integrated circuits, or in different network and / or processor methods and / or microcontroller methods.

[0017] It should be understood that although the terms "first," "second," etc., may be used herein to describe various units, these units should not be limited by these terms. These terms are used merely to distinguish one unit from another. For example, without departing from the scope of the exemplary embodiments, a first unit may be referred to as a second unit, and similarly, a second unit may be referred to as a first unit. The term "and / or" as used herein includes any and all combinations of one or more of the associated listed items.

[0018] To achieve the above objectives, please refer to Figures 1 to 3 This invention provides a rapid simplification method for measuring points in river cross-sections based on the conservation of water flow area. The method includes the following steps:

[0019] Step S1: Obtain three-dimensional measurement point data of the river channel cross section; perform filtering processing on the three-dimensional measurement point data of the river channel cross section to obtain three-dimensional measurement point beam data of the cross section; perform cross section line projection on the three-dimensional measurement point beam data of the cross section to generate a set of cross section measurement points;

[0020] In this embodiment, a survey vessel equipped with a multibeam echo sounder (MBES) is used to accurately measure the river channel. The equipment used is an R2Sonic 2024 with a measurement frequency of 400kHz, ensuring at least 25 measurement points per meter. To ensure high accuracy, an Applanix POSMV inertial navigation system (INS) is used to guarantee the measurement position accuracy within ±0.1 meters. The raw measurement data is recorded in XYZ format, recording the three-dimensional coordinates of each measurement point, and processed using CarisHIPS and SIPS software. The data is filtered to remove noise using a bilateral filtering algorithm, with a spatial domain standard deviation of 0.5 meters and an intensity domain standard deviation of 0.3 meters to preserve the river channel boundary features as much as possible. The filtered three-dimensional measurement point data is grouped by beamline and projected with a projection interval of 0.5 meters. The projected data is stored in a two-dimensional coordinate system format (horizontal distance - elevation). These projected measurement point data constitute a cross-sectional measurement point set for subsequent processing.

[0021] Step S2: Based on the set of cross-sectional measuring points, perform initial simplification of the measuring points to obtain initial simplified cross-sectional measuring point data; identify small ditch regions based on the initial simplified cross-sectional measuring point data; determine turbulent structure regions based on the small ditch regions; and track the scouring and sedimentation evolution path based on the turbulent structure regions.

[0022] In this embodiment, MATLAB R2023b software is used to initially simplify the measurement point set in step S1. Using the MATLAB simplification function "reducepoly", redundant measurement points with elevation changes within this range are deleted by setting a simplification tolerance of 0.1 meters. After simplification, key inflection points and abrupt topographic changes in the river channel cross-section are retained. After obtaining the simplified measurement point data, small ditch areas are identified based on the local elevation change rate. Small ditch areas are defined as areas with a transverse length less than 3 meters and a longitudinal elevation difference greater than 0.5 meters. A sliding window method is used for detection, with a window width of 2 meters and a step size of 0.2 meters, to detect areas in the cross-section that meet the criteria. For the identified ditch areas, the velocity profile is analyzed by combining it with velocity data collected by the ADCP (TeledyneRDIRiverRay) device. For areas with large velocity changes, the turbulent energy (TKE) of the area is calculated; the high-energy points in the area are the turbulent structure regions. Further tracking of the changes in these turbulent regions, by analyzing the velocity profiles at different time points, the scour and sedimentation paths are obtained, and the scour and sedimentation evolution trajectory is established. This process relies on comparing flow velocity distribution and measurement data to calculate the scouring and sedimentation path.

[0023] Step S3: Calculate the cross-sectional area of ​​the water passage based on the initial simplified cross-section measurement point data; perform elevation correction based on the cross-sectional area of ​​the water passage to obtain the corrected cross-sectional area; perform rapid simplification of the cross-sectional measurement points of the initial simplified cross-section data according to the scour and sedimentation evolution path to generate a simplified cross-sectional measurement point map.

[0024] In this embodiment, the simplified cross-sectional data and scour-deposition evolution path obtained in step S2 are input into ArcGIS software to calculate the cross-sectional area of ​​the water passage. The area is calculated by using the enclosed area between the cross-sectional line and the water level line. Water level data is obtained from an OTTRLS water level gauge to ensure that the water level accuracy is within ±0.005 meters. If the calculated water passage area has a large error (with a set error threshold of 5%), an elevation correction is performed. The correction uses local linear interpolation to adjust the elevation value, prioritizing the correction of areas with significant topographic changes, thereby ensuring that the corrected water passage area is consistent with the actual value. Based on the scour-deposition evolution path, the initial simplified data is further refined. The segmented Douglas-Peucker algorithm, with an accuracy set to 0.05 meters, simplifies the cross-sectional measuring point data. This algorithm is used to reduce unnecessary measuring points, retain areas with significant topographic changes, and generate a simplified cross-sectional map.

[0025] Step S4: Calculate the scour and sedimentation volume based on the scour and sedimentation evolution path and the corrected area of ​​the cross-section; calibrate the scour and sedimentation characteristic points according to the scour and sedimentation volume; set a buffer zone for the simplified cross-section measuring point diagram according to the scour and sedimentation characteristic points, thereby obtaining the simplified data of the first cross-section measuring points.

[0026] In this embodiment, the scour or sedimentation volume of each river segment is calculated by comparing the corrected area at different time points. Each measured cross-section is 10 meters long. The scour and sedimentation volume is calculated by comparing the changes in the water flow area between measurement periods to determine the amount of change. For areas with significant changes, scour and sedimentation characteristic points are calibrated. These characteristic points are calibrated based on the significance of the change in scour and sedimentation volume; areas with a change greater than 0.5 cubic meters are marked as characteristic points. These characteristic points form key locations of scour and sedimentation changes and serve as the basis for subsequent buffer zone settings. Based on these characteristic points, a buffer zone is set around each scour and sedimentation characteristic point. The buffer zone has a range of ±2.5 meters to ensure that more measuring points are retained within these areas for more accurate monitoring of the scour and sedimentation process. Areas outside the buffer zone are further simplified based on factors such as flow velocity and topographic features. Finally, simplified cross-section measuring point data for the first stage is generated, including the simplified measuring points and their elevation information, for subsequent model analysis and evolution prediction.

[0027] Preferably, step S1 specifically includes:

[0028] Step S11: Obtain three-dimensional measurement point data of the river cross-section;

[0029] In this embodiment, the acquisition of three-dimensional measurement point data of the river cross-section was accomplished using a multibeam echo sounder (MBES). The equipment used was an R2Sonic 2024 multibeam echo sounder, employing a 400kHz beam to ensure at least 50 measurement point data points per second within a 1-meter depth range. The Applanix POSMV320 inertial navigation system onboard the survey vessel ensured a measurement position accuracy of no more than 0.1 meters and an azimuth accuracy of 0.2°. The acquired data was in XYZ format, where X and Y are horizontal coordinates, and Z is the water depth (elevation). Each measurement point's data included a timestamp to indicate the data acquisition time. All measurement data was stored in a NetCDF format file for convenient subsequent processing and analysis.

[0030] Step S12: Based on the three-dimensional measurement point data of the river cross section, check the number of beams to obtain single-beam data and multi-beam data;

[0031] In this embodiment, based on the three-dimensional measurement point data obtained in step S11, the raw data is first examined using the data processing software HIPSandSIPS to check the number of beams belonging to each measurement point. The R2Sonic 2024 system supports acquiring up to 256 beams; therefore, the number of beams is extracted from the three-dimensional data to determine which measurement point data belongs to single-beam measurements and which belongs to multi-beam measurements. For single-beam measurement data, since its measurement coverage is only one point, the spatial distribution of the data is relatively sparse, and it is usually only suitable for detailed analysis of local areas. Multi-beam data provides a wider range of water surface or river channel cross-sectional information, which can more accurately present the topography of the entire water area. Through data annotation, single-beam data and multi-beam data are separated for separate subsequent processing.

[0032] Step S13: Perform median filtering on the single-beam data to obtain single-beam filtered data;

[0033] In this embodiment, single-beam data typically contains significant noise, necessitating filtering to remove outliers. Median filtering is performed using MATLAB R2023b software. Specifically, the process involves: first, sorting all single-beam data by measurement time and calculating the median of the data within a window surrounding each measurement point. The window size is set to 3, meaning each measurement point considers two points before and after it. The choice of window size is based on measurement accuracy and data volatility; setting it to 3 points effectively smooths the data while preserving important measurement features. The median filtering steps are as follows: for each measurement point, take all measured values ​​within its surrounding window, remove the maximum and minimum values, calculate the median of the remaining values, and use this median as the correction value for that measurement point. This process is suitable for removing extreme noise points caused by factors such as rapid water flow or sensor malfunction.

[0034] Step S14: Perform manual filtering based on the multi-beam data to obtain multi-beam filtered data;

[0035] In this embodiment, a manual filtering method is used to remove inaccurate data points from multi-beam data. Specifically, the QPSQINSy software is first used to check the measurement quality indicator (quality factor is usually automatically calculated and labeled by the device) for each beam. The lower the quality factor, the worse the accuracy of the data. In this step, a quality factor threshold of 0.8 is set, and all beam data with a quality factor below 0.8 are considered outliers. These unqualified measurement points are manually deleted or corrected through manual inspection to ensure that the filtered multi-beam data is more accurate. Considering that multi-beam measurements are affected by multipath effects (e.g., water surface reflection) and device tilt, the sensor angle of each beam is calibrated. Flow velocity data provided by the TeledyneRDIRiverRay current meter is used as the basis for calibration to correct the beam angle, thereby avoiding deviations caused by device angle errors.

[0036] Step S15: Integrate single-beam filtered data and multi-beam filtered data to obtain three-dimensional measurement point beam data of the cross section;

[0037] In this embodiment, the data integration tool in MATLAB R2023b is used to match the two types of data according to geographic coordinates (X, Y). Since the spatial distribution of single-beam data is relatively sparse, a weighted average method is used to fuse single-beam data at the same location with adjacent multi-beam data during integration, ensuring a smooth data transition. The integrated data is saved in XYZ format, ensuring the integrity of the three-dimensional coordinate information of each measurement point, forming the final set of "cross-sectional three-dimensional measurement point beam data". For areas with significant terrain variations, the data point density is increased to ensure sufficient measurement accuracy in complex areas.

[0038] Step S16: Project the cross-section line based on the cross-section three-dimensional measuring point beam data to generate the original cross-section measuring point set.

[0039] In this embodiment, a cross-section line is defined as any straight line passing through the river channel cross-section. The projection operation maps data from the three-dimensional coordinate system onto the plane of this cross-section line. First, a suitable cross-section line is selected (e.g., a straight line along the river flow direction or a cross-section line defined according to specific research needs). Then, in ArcGIS Pro, the "Projection Analysis" tool is used to project each three-dimensional measurement point along the cross-section line. The projected measurement points generate a new set of cross-section measurement points according to their projected planar coordinates. These measurement point data include water depth information and lateral coordinates relative to the cross-section line. At this point, the resulting original set of cross-section measurement points will serve as the basis data for subsequent river channel topography analysis and cross-section simplification.

[0040] Preferably, step S16 specifically includes:

[0041] Step S161: Extract the three-dimensional coordinates of the cross-section from the beam data of the three-dimensional measurement points;

[0042] In this embodiment, underwater measurement equipment, such as a multibeam sonar system (e.g., the R2Sonic 2026), is used to perform three-dimensional measurements of the river cross-section. This equipment provides the three-dimensional spatial coordinates of each measurement point, including water depth, lateral displacement, and longitudinal displacement. The measurement data will contain the X, Y, and Z coordinate information for each measurement point. In this step, the data from these measurement points are extracted and compiled into a three-dimensional coordinate set containing all cross-section measurement points. This coordinate data provides the basis for subsequent projection and simplification operations.

[0043] Step S162: Calculate the direction vector of the cross-section line based on the three-dimensional coordinates of the cross-section;

[0044] In this embodiment, using the three-dimensional coordinate data of the cross-section extracted in step S161, two points at both ends of the river channel cross-section are selected, and the difference between them is calculated. A direction vector is obtained from the coordinate difference between these two endpoints, representing the main direction of the river channel cross-section. This direction vector forms the basis for subsequent calculations of the heading rotation angle. During implementation, it is necessary to ensure that the two selected endpoints effectively reflect the main direction of the cross-section.

[0045] Step S163: Calculate the heading rotation angle based on the cross-section line direction vector;

[0046] In this embodiment, the angle between the direction vector calculated in step S162 and the reference coordinate system is calculated. Typically, the reference coordinate system is a ground coordinate system or a predefined standard direction. The angle between the direction vector and the reference vector is obtained by comparing them, and this angle is used as the rotation angle. This rotation angle is crucial for the subsequent construction of the rotation matrix, ensuring that the measurement point data can be correctly transformed into a two-dimensional plane.

[0047] Step S164: Construct a three-dimensional rotation matrix based on the heading rotation angle;

[0048] In this embodiment, after obtaining the rotation angle, a three-dimensional rotation matrix is ​​constructed using this angle. The purpose of this rotation matrix is ​​to transform the coordinates of the measured points in the three-dimensional coordinate system into two-dimensional planar coordinates. The rotation matrix performs mathematical operations on the X, Y, and Z coordinates, preserving the Z-axis coordinate data while rotating the X and Y coordinates to adapt them to the new two-dimensional projection plane. The construction method of this matrix depends on the rotation angle, ensuring that the measured point data can be accurately mapped to the two-dimensional plane.

[0049] Step S165: Perform two-dimensional plane projection based on the three-dimensional rotation matrix, where the projection is based on the XZ section and Y is 0.05 meters, to generate the original set of cross-sectional measurement points.

[0050] In this embodiment, the Y value in all three-dimensional coordinates is set to 0.05 meters, while the Z-axis value remains unchanged. Through rotation matrix operations, the transformed measurement point data is mapped onto the XZ plane, generating two-dimensional cross-sectional data. This data is called the original cross-sectional measurement point set, providing a foundation for subsequent cross-sectional simplification and analysis.

[0051] Preferably, the initial simplification of the measuring points in step S2 is as follows:

[0052] The cross-sectional measuring point curve is plotted based on the set of cross-sectional measuring points; the cross-sectional curvature of the cross-sectional measuring point curve is calculated; and the cross-sectional measuring points with obvious curvature are identified based on the preset curvature threshold.

[0053] In this embodiment, the X, Y, and Z coordinates of each measuring point are extracted from the three-dimensional measuring point data of the cross-section obtained by measuring equipment (such as a multibeam sonar system). A suitable graphics drawing tool (such as Matlab, ArcGIS, etc.) is selected to plot these measuring point data as curves. By connecting the X and Z coordinates (or Y and Z coordinates, depending on the cross-sectional direction of the river channel) of these measuring points in a planar coordinate system, a cross-sectional measuring point curve is generated. This curve illustrates the cross-sectional morphology of the river channel, providing a foundation for subsequent steps such as curvature calculation and bend identification. After obtaining the cross-sectional measuring point curve, a curvature calculation method (such as the quadratic difference method or numerical differentiation method) is used to calculate the cross-sectional curvature of the curve. Specifically, based on the discrete points of the curve, the curvature of the cross-sectional curve is obtained by calculating the amount of curvature change between every two adjacent measuring points. The calculation of the curvature value depends on the spacing between the measuring points (e.g., 0.5 meters between each measuring point), and this spacing value determines the accuracy of the curvature calculation. During the calculation process, the spatial distribution of the measuring points needs to be accurately processed to ensure the accuracy of the curvature calculation. Based on curvature data, a curvature threshold (e.g., 0.5) is set to identify significantly curved sections. When the curvature value of the cross-sectional curve is greater than this threshold, the section is considered a significantly curved section. By comparing the cross-sectional curvature data with the threshold, cross-sectional measuring points of significantly curved sections can be accurately extracted. These measuring points represent areas in the river channel where flow velocity changes significantly, typically appearing at bends in the river, reflecting the dynamic characteristics of the river.

[0054] The meandering river section is identified by measuring points on the cross-section of the obvious bend, and the meandering river section data is obtained; the river flow velocity is calculated based on the meandering river section data; and the flow velocity magnitude is statistically analyzed based on the river flow velocity to obtain high flow velocity data and low flow velocity data.

[0055] In this embodiment, a Geographic Information System (GIS) or a river model (such as HEC-RAS) is used to identify the entire meandering river segment. A complete meandering river segment is identified by connecting and extending clearly defined bends; this process relies on the analysis of the geographical location of the river cross-sectional data. By analyzing the river's flow regime and topographic features, the data for the meandering river segment is finally obtained, providing a basis for subsequent analyses such as flow velocity calculation. After identifying the meandering river segment, a hydrodynamic model (such as a fluid dynamics model) is used in conjunction with the meandering river segment data to calculate the flow velocity. The model needs to consider parameters such as the river's water depth, flow rate, and cross-sectional shape. Flow velocity calculation is typically based on the fluid flow patterns and river geometry; specific calculation methods include using formulas to calculate the flow velocity at each measuring point. By collecting hydrological data (such as flow rate and water level) of the river, the flow velocity data at each measuring point can be calculated more accurately. A flow velocity threshold (e.g., 1.5 m / s) is set; measuring points with flow velocities greater than this threshold are marked as high-velocity data, and measuring points with flow velocities less than this threshold are marked as low-velocity data. This threshold is set based on the actual flow velocity distribution and is determined through historical data analysis. In this way, different flow velocity zones within a river section can be accurately distinguished.

[0056] The outer bend region is identified based on high flow velocity data, and the outer bend region data is obtained. Shear stress is calculated based on the outer bend region data. Riverbed scour is simulated based on the shear stress, and riverbed scour data is obtained. Steep slope and deep channel areas are identified based on riverbed scour.

[0057] In this embodiment, the outer bend zone typically appears on the outer side of the river bend, where the flow velocity is relatively high. In practice, a Geographic Information System (GIS) or flow simulation software is used to analyze the relationship between the high-velocity area and the bend, identifying the boundary data of the outer bend zone. The outer bend zone data includes the coordinates of the measuring points in these areas and provides the basis for subsequent shear stress calculations. The calculation of shear stress depends on factors such as flow velocity, flow state, and riverbed roughness. Specifically, by inputting the flow velocity data of the outer bend zone, the topographic data of the riverbed, and flow parameters, the shear stress at each measuring point is calculated using formulas such as the Manning formula or the Chezy formula. The numerical value of the shear stress reflects the scouring force on the riverbed under the action of water flow, affecting the riverbed morphology and sediment movement. Based on the shear stress data, a riverbed scouring model (such as the Herschel-Bulkley model) is used to simulate riverbed scouring. The simulation process predicts the degree of scouring on the riverbed in the outer bend zone by inputting shear stress, riverbed particle characteristics, and flow velocity data. The simulation results will provide the scour depth and scour area at each measuring point, identifying areas of severe riverbed scour. Typically, areas of significant scour form deep channels, often located in the outer bends of the river channel where water flow velocities are high and the scour effect is pronounced. By analyzing the scour depth, deep channel areas are identified, and their geographical extent and specific characteristics are determined.

[0058] The inner bend area was identified based on the low flow velocity data, and the inner bend area data was obtained. Sediment deposition simulation was performed based on the inner bend area data, and sediment deposition data was obtained. The gentle slope alluvial beach area was identified based on the sediment deposition data.

[0059] In this embodiment, the inner bend area is typically located on the inside of the river bend, where the flow velocity is lower. By analyzing the relationship between the low-velocity area and the bend, GIS technology or flow simulation software is used to extract the measurement point data of the inner bend area, thus determining its boundary. After the inner bend area is identified, a sediment deposition model (such as the Blum model) is used to simulate sediment deposition in this area. By inputting the flow velocity, riverbed roughness, and water depth data of the inner bend area, the sediment deposition process is simulated. The simulation results will provide the thickness and distribution of sediment deposition, providing data support for river management and ecological protection. Siltation beach areas are typically located in the inner bend area or areas with low flow velocity, where sediment is deposited to form mudflats. By analyzing the sediment thickness and topographic features, measurement points in these areas are identified and marked as gentle-slope siltation beach areas.

[0060] The measurement point set of the cross section is initially simplified based on the steep slope deep trough area and the gentle slope siltation beach area to obtain the initial simplified cross section measurement point data.

[0061] In this embodiment, it is necessary to identify and extract data from steep slope deep channel areas and gentle slope silt deposit areas. These areas are characterized by the significant impact of water flow on riverbed morphology, making them crucial for channel morphology analysis. By analyzing simulation results of channel scour and deposition, the specific locations of these areas are determined. Measurement data from these two areas need to be retained as they directly affect the channel's flow regime and hydraulic characteristics. Subsequently, the original set of cross-sectional measurement points is filtered. Measurement points located in non-critical areas, i.e., those that do not significantly affect the channel's flow regime, velocity, or riverbed morphology, are removed based on predefined thresholds (such as velocity thresholds, water depth thresholds, or topographic feature thresholds). Removing these redundant measurement points reduces the dataset size and computational complexity. During the simplification process, the retained measurement points should cover key areas, especially steep slope deep channel areas and gentle slope silt deposit areas, ensuring that the channel characteristics of these important areas are not lost. The simplified dataset will contain a more concise set of measurement points, representing the main flow characteristics of the river channel, providing an efficient and reliable data foundation for subsequent channel morphology analysis and river dredging. This initial simplification process helps reduce the complexity of data processing and improves the accuracy and efficiency of subsequent analyses.

[0062] Preferably, the identification of the small trench region in step S2 specifically involves:

[0063] The longitudinal elevation is calculated based on the initial simplified cross-section measurement point data to obtain longitudinal elevation data; the elevation change rate is calculated based on the longitudinal elevation data; and elevation abrupt change data of the longitudinal elevation data is extracted based on the elevation change rate.

[0064] In this embodiment, the vertical coordinates of each measuring point need to be extracted from the simplified cross-sectional measuring point data. Longitudinal elevation data consists of the elevation values ​​of each cross-sectional measuring point along the longitudinal direction of the river channel, typically obtained using ground-based LiDAR or an underwater laser scanner. After acquiring the elevation data of all measuring points, this data needs to be organized and arranged in longitudinal order along the river channel to form a continuous elevation dataset. By processing the elevation information of these measuring points, the longitudinal elevation change of the river channel can be calculated, yielding the elevation value of each measuring point. This data provides the foundation for subsequent calculation of the elevation change rate and further analysis of river channel characteristics. The change rate is calculated based on the height difference between each measuring point and its adjacent points. The elevation change rate of each measuring point is obtained by dividing the elevation difference between two adjacent measuring points by the lateral distance between them. Here, the lateral distance is typically the actual horizontal distance between two measuring points obtained through a measurement system (such as GNSS or RTK technology). To ensure calculation accuracy, filtering and correction techniques are used during data processing to remove outliers caused by measurement errors. This rate of change data reflects the slope variation of the river channel and plays a crucial role in identifying significant changes in the channel. A threshold for the rate of elevation change is set, typically within a certain range based on actual measurements; for example, a rate of change greater than 0.1 m / m is considered an abrupt change. By comparing the longitudinal elevation change rate with this threshold, areas where the elevation change rate exceeds the threshold are extracted. These areas represent locations where the river channel morphology has undergone drastic changes, usually characterized by steep banks or abrupt changes in the riverbed.

[0065] Identifying river channel elevation abrupt change areas based on elevation change data;

[0066] In this embodiment, elevation sequence data of cross-sectional measuring points arranged longitudinally is acquired. Based on the initial simplified cross-sectional measuring point data, the elevation change rate between each pair of adjacent measuring points is calculated. Measuring point locations with elevation change rates exceeding a set threshold are recorded as elevation abrupt change points. The threshold for elevation change rate is typically set based on the measured differences in river channel morphology; for example, it is set to 0.5 m / m in typical mountainous or highly erosive river sections, and 0.3 m / m in gently sloping river channels. After obtaining the initial set of abrupt change points, cluster analysis is performed according to the spatial distribution of these abrupt change points to identify relatively concentrated abrupt change areas. In specific operations, the "Clustering and Anomaly Analysis" tool module of the ArcGIS platform is used, calling AnselinLocalMoran'sI or Getis-OrdGi tools to analyze the spatial location and attribute values ​​(i.e., elevation change rates) of elevation abrupt change points, identifying high-value clusters with spatial coherence and significance. Subsequently, connectivity analysis was performed on the clustering results using either "object-oriented image analysis" or "region growing" methods to determine whether each abrupt change point belonged to the same connected region. During this process, the minimum connectivity distance between abrupt change points was set to 1.5 times the average cross-sectional distance. For example, if the average cross-sectional distance was 2m, a distance of no more than 3m between abrupt change points was considered the same region; otherwise, they were classified as different abrupt change regions. Furthermore, to avoid misclassification of isolated points, abrupt change region was required to contain at least three consecutive abrupt change points, and the longitudinal length of the region was no less than 6m, ensuring spatial continuity and geomorphic saliency in the extracted results. After completing clustering and connectivity analysis, the final extracted spatial regions became the river channel elevation abrupt change regions, and vector layers were generated for subsequent micro-slope identification and stability analysis. All input parameters, spatial operation rules, and judgment criteria were saved in the GIS project for subsequent repeatable calculations and corrections. The data source was cross-sectional elevation measurement point data collected using an RTK-GNSS system, with the acquisition accuracy error controlled within ±2cm to ensure sufficient measurement accuracy support for elevation abrupt change identification.

[0067] Micro-slope topography was identified based on areas of abrupt changes in river elevation, and micro-slope topography data was obtained.

[0068] In this embodiment, the boundaries of elevation abrupt change areas are clearly defined; these areas typically consist of significant elevation variations. After obtaining data on these elevation abrupt change areas, the elevation change rate is used for further refined analysis. First, the elevation difference between adjacent measuring points is calculated to obtain the elevation change rate. Typically, a gradient algorithm is used to process the elevation data of adjacent measuring points to calculate the slope of each cross-section. Areas with drastic elevation changes exhibit larger slopes, and these areas usually have steep terrain features. Next, based on the distribution of slope values, a slope threshold is set. Generally, areas with slopes greater than a certain preset value (such as 5% or 10%) are considered potential steep slope areas. To further identify micro-slope terrain, detailed spatial analysis of these areas is required using terrain analysis algorithms. By utilizing Geographic Information System (GIS) tools, such as ArcGIS or QGIS, spatial analysis of the spatial data is performed to further analyze local terrain features. These tools can filter areas with large elevation change rates and, combined with multi-dimensional data such as water flow direction and terrain undulation, identify abrupt change areas in the river channel. Based on the characteristics of these abrupt elevation changes, it can be determined whether they possess the topographic features of micro-shoals, typically characterized by a sharp drop within a small area and significant local elevation differences. The identification process also requires a combination of elevation data and topographic models for three-dimensional modeling analysis. This process is usually achieved using a Digital Elevation Model (DEM), which accurately reflects the topographic undulations of the river channel area. By calculating the height of different regions in detail, the areas containing micro-shoals can be identified. In this process, improving measurement accuracy can enhance the ability to identify locally steep terrain. By comparing elevation abrupt changes with topographic curvature, it is further confirmed whether these areas are micro-shoals. After processing these topographic features, the final regional data containing micro-shoal topographic features is obtained, providing a basis for further analysis and management of river channel morphology changes.

[0069] Soil shear strength detection based on micro-slope topographic data;

[0070] In this embodiment, when detecting soil shear strength based on micro-slope topographic data, on-site soil sampling or remote sensing data is used to obtain the soil's physical properties, including its shear strength. Soil shear strength is typically measured using standard laboratory testing methods or in-situ testing using specialized geological exploration equipment (such as a soil shear tester). The shear strength data is combined with the river channel's elevation characteristics to assess soil stability and identify areas of soil instability.

[0071] Soil instability zones in areas of abrupt elevation changes were screened based on soil shear strength.

[0072] In this embodiment, soil in areas of abrupt elevation change is screened by comparing soil shear strength data with a set instability threshold (e.g., areas with shear strength below a certain value are considered instable areas). Instable areas are typically characterized by low soil shear strength, leading to a risk of landslides or erosion. This screening process identifies key areas of soil instability, providing a basis for subsequent prevention and control measures.

[0073] The density of steep sills was calculated based on micro-sill topographic data, and continuous erosion zones were delineated based on the steep sill density.

[0074] In this embodiment, the frequency of occurrence of micro-sills is statistically analyzed throughout the entire river channel. A sill density value is derived by spatially analyzing the location and number of micro-sills. This value can be calculated by dividing the extent of each micro-sill area by the total river channel length to obtain the number of sills per kilometer. Sill density reflects the rapidly changing topographic features of the river channel and is crucial for further delineating erosion risk zones. A threshold is set to identify areas with high sill density in the river channel. Generally, areas with sill density greater than a certain set value are considered continuous erosion zones. These areas are prone to severe riverbed changes and scouring due to strong river erosion and poor soil and riverbed stability. Spatial analysis delineates these areas as continuous erosion zones, providing a reference for river water and soil conservation and management.

[0075] The small ditch area is determined by performing regional intersection calculations based on the soil instability area and the continuous erosion area.

[0076] In this embodiment, spatial analysis tools are used to perform an intersection operation on the soil instability area and the continuous erosion area. This operation yields data on small gully areas, which are typically located within the erosion zone of a river channel, manifesting as localized depressions or scour zones in the riverbed. Through intersection calculations, the locations of these areas are accurately identified, providing crucial foundational data for subsequent river management and monitoring.

[0077] Preferably, determining the turbulent structure region in step S2 specifically involves:

[0078] Water flow simulation was performed in the small ditch area to obtain ditch water flow data;

[0079] In this embodiment, it is necessary to obtain the precisely calibrated boundary of the small ditch area, which is represented by the vector layer obtained by the spatial intersection of the soil instability area and the continuous erosion area. Before implementing the flow simulation, a three-dimensional terrain model of the small ditch area needs to be performed. The data used for terrain modeling comes from RTK-GNSS measured cross-sectional data and DEM data obtained from UAV low-altitude aerial surveys, with a resolution set to 0.05m / pixel. Subsequently, a two-dimensional hydrodynamic calculation method is used to carry out simulation calculations. Hydrodynamic analysis equipment is used in conjunction with a velocity profile measuring instrument (such as SonTek RiverSurveyor M9) to measure the velocity cross-sectional data in the ditch, obtaining the inlet and outlet cross-section flow velocities and water depths, ensuring that the measured data time series covers no less than 30 minutes, and the sampling frequency is set to 1Hz. The terrain model, measured flow velocity boundary conditions, and bed roughness (n=0.035) are input into the HEC-RAS hydrodynamic simulation system to perform two-dimensional flow field calculations. The output results include instantaneous velocity field (u,v), water depth distribution, and Reynolds number distribution layers, which in turn constitute the trench flow dataset.

[0080] Calculate the trench width-to-depth ratio based on the small trench area;

[0081] In this embodiment, based on the water depth raster layer output from the simulated water flow in the small trench area, vertical sections are cut into the trench at 2m intervals. The average water depth and average trench width are extracted from each section. The average width is determined by the location of the water surface boundary. The water surface boundary is extracted using the intersection of the instantaneous water surface elevation and the DEM elevation from the simulation results. The average depth is the arithmetic mean of the depths of all measurement points in each section. Finally, statistical analysis is performed on all section data to calculate the average width-to-depth ratio of the entire trench section. The formula is as follows: ,in The average width of the water surface. This represents the average water depth. If there are localized sections with drastic changes in the width-to-depth ratio due to abrupt changes in terrain, the maximum and minimum values ​​of 5% should be removed before averaging the overall values. The final width-to-depth ratio result should be accurate to two decimal places.

[0082] Vertical vortex detection is performed based on the width-to-depth ratio of the trench and the water flow data in the trench to obtain vertical vortex data;

[0083] In this embodiment, the velocity vector field data output by hydrodynamic simulation is used, and the curl of the velocity vector is calculated to identify vertical vortices. Specifically, the velocity field... vorticity calculation using the difference scheme: vorticity The local curl is calculated for each grid point using the central difference method, where the absolute value of the curl is greater than... The region is identified as containing vertical vortices; this threshold is determined by analyzing the amplitude of measured flow velocity disturbances in the field to ensure the distinction between background flow and vortex flow. The detection area is limited to the overlapping part of the elevation change zone and the core area of ​​the trench, excluding edge disturbance errors. The vortex region is stored in vector form, and the vortex center position and rotation direction are further extracted and recorded to form a vertical vortex dataset.

[0084] Calculate vortex scale based on vertical vortex data;

[0085] In this embodiment, the calculation of the vortex scale based on vertical vortex data employs the calculation method of the vortex structure's area and its equivalent diameter. In the curl layer, the curl exceeding... The continuous region is extracted as the main vortex region, and the area of ​​each vortex region is recorded. Then according to the equivalent circle diameter Calculate the scale of each vortex. For complex vortex regions, further assess their closure by considering the consistency of velocity streamline directions, retaining only closed vortices as valid vortex scale statistics. Record all vortex scales in units of 0.1m, outputting a vortex scale data layer.

[0086] Effective turbulent vortex structures were selected based on vortex size and trench width-to-depth ratio.

[0087] In this embodiment, the screening criteria are first set: only those that meet the criteria are retained. and The vortex structure, in which vortex scale, , The average depth and width of the aforementioned trenches are used. This standard is used to exclude excessively small shear vortices or excessively large overall rotating structures, retaining mesoscale turbulent vortex structures with significant local effects. Secondly, the selected vortex structures must be located in regions with a vortex number density higher than 5 / 10m², and possess continuous rotational direction consistency (the area of ​​unidirectional rotation exceeds 70%) to avoid misjudging isolated disturbances. The screening results form an effective turbulent vortex vector map layer.

[0088] The turbulent structure region is determined based on the effective turbulent vortex structure.

[0089] In this embodiment, a 5m buffer zone operation is performed centered on each effective vortex structure to form a potential turbulent structure sub-region. Then, the "region merging" analysis tool is used to merge all overlapping buffer zones, resulting in the boundary of a complete and continuous turbulent region. For areas smaller than... Isolated regions shorter than 5m were excluded. The final turbulent structure region was output as a planar vector layer, serving as the input for subsequent analysis. All spatial analysis operations were performed on the ArcGIS Pro platform, and all data formats were standardized using the WGS1984UTM coordinate system.

[0090] Preferably, the tracking of the scouring and sedimentation evolution path in step S2 specifically involves:

[0091] Calculation of hydrodynamic intensity based on turbulent structural regions;

[0092] In this embodiment, the velocity field in the two-dimensional hydrodynamic simulation results within the obtained effective turbulent structure region vector boundary is used. The data is cropped from the water depth data to extract the instantaneous velocity and water depth values ​​within each grid cell. The velocity modulus is then used for calculation (i.e.,...). The flow velocity amplitude of each grid is obtained, and the kinetic energy density per unit volume is calculated by combining the water depth data, using the formula... ,in The water density was uniformly set to 1000 kg / m³. After calculating the kinetic energy of each grid cell, the hydrodynamic intensity of the entire turbulent structural region was calculated using an area-weighted average, outputting in J / m². All calculations were performed using Python scripts via the ArcPy interface to read and process the GeoTIFF format velocity raster exported from HEC-RAS.

[0093] Calculation of channel geometric curvature based on turbulent structural regions;

[0094] In this embodiment, the river centerline is extracted from the DEM data. A mainstream line extraction algorithm from a watershed analysis tool is used to automatically trace the minimum flow path in the DEM, generating a centerline vector map. Subsequently, this vector map is sampled at fixed intervals, extracting point sequences at 1-meter intervals. The curvature value is calculated for every three points forming a continuous polyline segment, and the discrete curvature formula is used. ,in , It is a first-order difference. , For second-order differencing, all differences are performed using the central difference method. The curvature value of each point is recorded as a centerline attribute field, ultimately outputting a river channel curvature distribution layer. To avoid the influence of local noise, a 3-point moving average is applied after calculation to smooth the curvature curve.

[0095] The inner bend of the river channel is calibrated based on the geometric curvature of the channel, and the degree of weak disturbance of the secondary eddy in the inner bend of the channel is detected.

[0096] In this embodiment, the inward curvature judgment criterion is set according to the aforementioned curvature layer. Any curvature greater than 100° is considered an inward curvature judgment criterion. Furthermore, centerline segments exceeding 30 meters in length are all marked as inner curve segments. Cross-sectional data from both sides of the corresponding inner curve segments are extracted, and a vertical velocity difference index is applied to each cross-section. Perform calculations, where and The average velocity was measured at five points on the mid-section of the inner and outer curve sides, respectively. The average velocity of the entire cross-section. The area controlled within the range of 0.1 to 0.3 is defined as having weak secondary eddy disturbance. Based on this standard, the disturbance degree index distribution layer in each inner bend section is extracted, and its distribution along the bend is statistically analyzed with a step size of 5m.

[0097] Based on the hydrodynamic intensity, sediment transport was simulated in the turbulent structural region to obtain sediment transport data;

[0098] In this embodiment, during the process of simulating sediment transport in the turbulent structural region based on hydrodynamic intensity to obtain sediment transport data, the suspended sediment concentration data (mg / L) collected on-site was used as the initial input. A YSI600OMS multi-parameter water quality sensor was used for sampling, with continuous sampling within any 3-hour period during the simulation, recording every 10 minutes, covering the entire cross-sectional depth. This initial sediment condition data, along with the velocity field, turbulence energy intensity, and water depth data obtained from the hydrodynamic simulation, were input into the sediment transport equation, which was then expressed as a two-dimensional convection-diffusion equation. in The concentration of sediment. , These are the lateral and longitudinal diffusion coefficients. The rate of change of sediment concentration per unit time. For the water flow Direction (along the main stream of the river). For sediment concentration at The rate of change of direction in space, For the water flow The velocity component in the direction (lateral direction perpendicular to the mainstream direction), For sediment concentration at The rate of change of direction in space Let be the rate of change of turbulent diffusion flux of sediment in the mainstream direction. The rate of change of sediment diffusion flux in the lateral direction was empirically set to 0.05 m² / s. Explicit finite difference schemes were used for hourly calculations, with each time step set to 5 s, and the simulation lasted for 2 hours. Instantaneous and average sediment concentration distribution layers were output, with the resolution consistent with the hydrodynamic mesh. The output sediment transport data were saved in NetCDF format.

[0099] Based on the degree of weak disturbance of the secondary eddy and sediment transport data, a stable sedimentation formation simulation was conducted to obtain stable sedimentation data.

[0100] In this embodiment, during the simulation of stable sedimentation based on the degree of weak disturbance of the secondary eddy and sediment transport data, it is necessary to overlay a weakly disturbed area layer with sediment transport results to identify areas with long-term high-concentration sediment retention. First, the sediment concentration layer is processed by time averaging to extract areas with an average concentration greater than 80 mg / L within one hour. Then, spatial intersection analysis is performed with areas where the disturbance index Δv is less than 0.2, retaining only the overlapping areas as potential sedimentation formation areas. Next, the bed sediment deposition rate in these areas is calculated using the deposition rate formula. ,in The sediment settling velocity is taken as 0.003 m / s. This represents the average concentration. The deposition thickness layer is obtained by accumulating the total simulation time. All areas with a cumulative thickness greater than 0.01m are defined as stable sediment formation areas. Finally, a stable sediment surface vector layer is output and numbered for management.

[0101] The evolution path of scour and sedimentation was reconstructed based on stable sedimentation data.

[0102] In this embodiment, time-series topographic difference analysis is used to perform rasterized difference operations on DEM data from different time phases and stable sedimentation data. Two periods of aerial survey elevation data are selected as follows: Time and At a time interval of one month, the two DEMs are uniformly projected and raster aligned, and differential calculations are performed. The Z_diff values ​​within the stable sedimentation area were plotted pixel-by-pixel as an evolution path layer. The path direction was determined by the sign of the difference, with positive values ​​indicating sedimentation and negative values ​​indicating scouring. This was combined with sediment transport direction and streamline diagrams to form a three-dimensional temporal evolution layer containing evolution direction, intensity, and duration, used to quantitatively reconstruct the spatial trajectory of the sedimentation and scouring evolution path. All data were exported in GeoTIFF format at a 30cm resolution for subsequent analysis.

[0103] Preferably, step S3 specifically includes:

[0104] Step S31: Extract water surface elevation based on the initial simplified cross-section measuring point data;

[0105] In this embodiment, surveying equipment such as a Leica TS16 total station or a Trimble SX10 3D laser scanner is used to set up measuring points at typical cross-section locations of the river channel. The lateral coordinates (X-direction) and corresponding elevation data (Z-direction) of multiple measuring points on each cross-section are acquired to construct initial simplified cross-section measuring point data. Simultaneously, pressure level gauges (such as Hobo U20L) or radar level gauges (such as OTTRLS) are deployed near the cross-section. The level sensors are positioned in a safe and stable location on the bank using fixed supports to ensure the consistency of the measurement benchmark, and the current water level value is recorded. The acquisition frequency is fixed at once every 10 minutes, with an accuracy better than ±0.01 meters. The recorded water level values ​​are extended horizontally to construct the current water surface elevation line. This line is then compared with the spatial locations of the cross-section measuring points, and the intersection points of the water surface elevation line on the lateral coordinates are recorded as the current cross-section water surface elevation data. If certain areas of the cross-section are not completely covered by the water level line, linear interpolation of the measuring points on both sides is used to fill the gaps and complete the overall distribution extraction of water surface elevation.

[0106] Step S32: Identify adjacent water level monitoring points based on water surface elevation, and calculate the trapezoidal area of ​​the water passage cross section based on the adjacent water level monitoring points to obtain the water passage cross section area;

[0107] In this embodiment, the water surface elevation extracted in step S31 is compared point by point with the surface elevation of all cross-sectional measuring points to determine whether the water surface is higher than the ground, and measuring points covered by water flow are selected. Based on these water-covered points, hydraulic units formed between adjacent measuring points are identified, and their spatial positions and relative elevation differences are recorded. A Python script is used to traverse and analyze these hydraulic units, and the lateral spacing and relative water depth of the measuring points are used to determine whether the water body forms a water surface connection between two measuring points. If two adjacent measuring points are both below the water surface, then these two points constitute an effective water-passing unit. The lateral distance and water depth information of all effective units are input into the calculation program, and the effective water area between each pair of measuring points is measured sequentially. Then, all these water area area data are superimposed to obtain the cross-sectional area of ​​the entire section under the water level conditions. The data records are output in CSV format, including the measuring point number, lateral position, water-passing depth, and relative width of each pair of measuring points.

[0108] Step S33: Fine-tune the water surface elevation based on adjacent water level monitoring points to obtain water surface elevation fine-tuning data;

[0109] In this embodiment, after initial water surface elevation identification, dynamic adjustments are made using historical continuous time-series water level data. Five sets of water level sensor data are collected within 100 meters upstream and downstream of the target cross-section. Fifty consecutive sets of data are obtained in 10-minute increments for fitting. For each cross-section location, the water surface elevation value at the same location is extracted from the water level data of different time periods based on its corresponding x-coordinate. Then, a moving average method is used to smooth the data, thereby eliminating abrupt values ​​caused by local fluctuations or instantaneous errors. Finally, the time mean of each cross-section location is used to replace the original water surface elevation as the fine-tuned water surface elevation data. This operation is performed in Excel and then batch-imported into the spatial cross-section data using ArcPy to achieve overlay updates of the elevation attribute field. This fine-tuning ensures that the water level line remains spatiotemporally stable during subsequent area estimation.

[0110] Step S34: Correct the cross-sectional area of ​​the water passage based on the water surface elevation fine-tuning data to obtain the corrected cross-sectional area of ​​the water passage;

[0111] In this embodiment, the water surface elevation fine-tuning data obtained in step S33 is used again to compare with the surface elevation of each cross-section measuring point to identify the point pairs covered by the updated water surface. For each pair of measuring points covered by the water surface, the spatial range is re-identified to determine the actual water body distribution location and width. Based on the fine-tuned water surface elevation, the water depth between each group of measuring points is re-determined, and the area of ​​each water segment is re-evaluated based on the newly measured water depth and width. The entire process is completed using a MATLAB batch script, with inputs including the measuring point spacing, the fine-tuned water surface height difference, and the original point number. The final output is the updated corrected cross-section area, which is compared with the initial estimate to generate a cross-section number and area correction ratio table. This corrected area data serves as an important basis for the subsequent cross-section measuring point simplification process, ensuring that the hydraulic geometry features are correctly preserved during the simplification process.

[0112] Step S35: Based on the scour and sedimentation evolution path, the initial simplified cross-section measurement point data is rapidly simplified to generate a simplified cross-section measurement point diagram.

[0113] In this embodiment, evolution path data obtained from previous river scour and sedimentation change monitoring is imported into the ArcGIS system. This path data originates from the overlay analysis of multiple UAV aerial survey images, and the change boundaries of scour and sedimentation areas are obtained through a difference DEM model. In the cross-sectional measuring point data, each measuring point location is spatially matched with the scour and sedimentation evolution path. By determining its distance relative to the central scour axis, points located in the main change zone or non-change zone are identified. A distance threshold of 1 meter is set; points within 1 meter of the change path axis are retained, while measuring points exceeding this range are merged based on their location and the elevation difference between adjacent points. The judgment condition is that measuring point groups with a lateral spacing of less than 0.5 meters and an adjacent elevation difference of less than 0.1 meters can be merged into one group, retaining only the middle point as a representative point. Finally, the processed point set is imported into AutoCAD Civil 3D for vector map generation, and information such as the original number of points, the number of retained points, and the merging ratio are labeled. DWG drawings are then exported for subsequent engineering design reference.

[0114] Preferably, step S35 specifically includes:

[0115] Step S351: Calculate the slope of the scour and sedimentation path based on the scour and sedimentation evolution path;

[0116] In this embodiment, river channel elevation data from different periods are collected. The RIEGLVZ-400i terrestrial laser scanning system is recommended to obtain riverbed elevation point cloud data with an accuracy better than 5mm. Data collection time points are based on annual seasonal variations, with comparisons performed once each during the high-water and low-water seasons to ensure clear scour and sedimentation characteristics. The cross-sectional elevation data from the two periods are imported into CloudCompare software for point cloud registration, and then a scour-sedimentation change difference map is generated using vertical difference. This difference map is exported in GeoTIFF format, and a scour-sedimentation change point layer is generated in ArcGIS using the "Raster to Point" tool, overlaid with flow direction data, to extract the scour-sedimentation change path. For each pair of consecutive elevation change points on the scour-sedimentation path, the distance and elevation fields are manually labeled using the cross-sectional lateral distance (in meters) and elevation difference (in meters). The local slope between each pair of points is calculated using a field calculator, in m / m. All point-to-point slope data are compiled into a path profile line. Using the center point of the profile as a reference, the average slope within a continuous 100-meter range along the scour and sedimentation path is extracted and recorded as the scour and sedimentation path slope value for that profile. All slope values ​​are compiled into a structured table for subsequent threshold comparison. In this process, the data extraction accuracy is no less than 0.01 meters, the path step size does not exceed 1 meter, and the slope results are retained to three decimal places.

[0117] Step S352: If the slope of the scouring and silting path is greater than the preset scouring and silting slope threshold, then mark the deletion range of the prohibited scouring and silting points;

[0118] In this embodiment, the scour and sedimentation path slope data obtained in step S351 is compared with a preset slope threshold. The slope threshold is set to 0.025 (i.e., the elevation change is not less than 2.5 cm per meter of lateral distance). This value is set according to the "Design Code for Inland Waterway Regulation" (JTS146-1-2016) for the scour slope limit of stable sections, ensuring that hydraulic information is not lost due to simplification in high-risk areas. All scour and sedimentation path segments with a slope greater than or equal to 0.025 are extracted, and a "Scottish and Sedimentation Prohibited Point Deletion Range" vector band is constructed in GIS. The range is centered on the scour and sedimentation path, extending 2.5 meters on both sides to form a 5-meter-wide buffer zone, and spatially overlaid with the cross-section measuring point data. After overlay, all cross-section measuring points falling within this buffer zone are marked with "Deletion Prohibited" and saved as an independent layer. This marking is achieved by adding a "Del_Restrict" field to the attribute table and assigning a value of "1". The buffer zone width was set at 2.5 meters based on measured data of the width of the main channel and the scour-deposition response zone in typical small and medium-sized rivers, ensuring that sufficient data points are reserved in the high evolution zone of the river for subsequent analysis.

[0119] Step S353: Based on the deletion range of prohibited scour and siltation points, perform feature point fusion on the initial simplified cross-section measurement point data to obtain scour and siltation feature point data;

[0120] In this embodiment, points with a "Del_Restrict" field of 1 are individually selected from the original cross-sectional measurement point data in ArcGIS and retained as non-deletable points. For cross-sectional measurement points in unrestricted areas, a three-point sliding window is used to determine the local elevation slope based on their arrangement order on the cross-section and the elevation change trend. If the slope change between the three points is less than 0.01 (i.e., the elevation change is very gentle), the middle point is identified as a redundant point and removed, while the two side points are retained as fusion boundary points. In the fusion point segment, the horizontal coordinates and elevations of the first and last points are used to fit a straight line, and the straight line replaces all the removed points in the middle, thus reducing the number of measurement points while maintaining the terrain trend. All retained points, including the original points in the restricted area and the fusion result points in the unrestricted area, together constitute the "scour and sedimentation feature point data". This data is exported in CSV format and includes the fields: "Point_ID", "X_Coord", "Z_Elevation", "Del_Restrict", and "Slope_Local". The entire process is automated using Python scripts in ArcPy. The fusion accuracy and judgment logic are set based on the change in the slope of the sliding window. The threshold of 0.01 is statistically set from the previous analysis of the elevation trends of 10 typical cross sections to ensure that the fusion does not change the main terrain trend.

[0121] Step S354: Based on the scour and siltation characteristic point data, quickly simplify the cross-sectional measuring points to generate a simplified cross-sectional measuring point diagram.

[0122] In this embodiment, the "scour and sedimentation feature point data" output in step S353 is imported into AutoCAD Civil 3D. The points are connected using the "Polyline" command to construct a cross-sectional line drawing. A layer named "CrossSection_Simplified" is set to hold the simplified cross-sectional lines, and the "PointMarker" function is used to label all merged measurement points. The labeling includes the point number, lateral position, ground elevation, and whether it is a restricted point (the label "R" indicates a point that cannot be deleted). Simultaneously, a cross-sectional drawing template is enabled in Civil 3D, with the horizontal coordinate range set to 0-80 meters and the vertical coordinate range to 0-10 meters. The drawing scale is set to 1:100 (horizontal) and 1:20 (vertical) to enhance the visualization accuracy of terrain changes. Finally, a DWG format file is output and named with the cross-sectional number, while a PDF drawing file is exported for project archiving. The drawing includes a legend explaining the point merging logic, the source of the scour and sedimentation threshold, the width of the restricted zone, and the labeling explanation of retained points. The entire cross-section simplification process is processed on a cross-section-by-cross-section basis, with a processing efficiency controlled to complete one cross-section within 1 minute, which has the foundation for efficient application in batch processing of multiple cross-sections.

[0123] Preferably, step S4 specifically includes:

[0124] Step S41: Calculate the scour volume based on the scour-deposition evolution path; Calculate the sedimentation volume based on the scour-deposition evolution path;

[0125] In this embodiment, three-dimensional laser scanning of the river cross-section was performed at two time points with an interval of no more than 6 months, using ground-based laser scanning equipment to acquire riverbed elevation point cloud data. The measurement results from the two time points were imported into a data processing platform with point cloud analysis capabilities, and the vertical elevation difference was calculated for the same cross-sectional location under the same coordinate system. The elevation difference calculation was completed through point-to-point comparison in the vertical direction, and the calculation result was the elevation change value of each cross-sectional measuring point, in meters. Based on the lateral spacing between each measuring point, the elevation difference was multiplied by the distance between points to calculate the volume change of adjacent point segments, in cubic meters. All areas with negative elevation differences were statistically represented as scour volume, and areas with positive elevation differences were statistically represented as sedimentation volume. A buffer zone with a width of 5 meters was set along the river centerline to limit the statistical range within the main channel evolution zone, ensuring the representativeness of the evolution path. The volume statistics were performed using a piecewise cumulative integration method, strictly based on the original measuring point data, without using interpolation modeling or triangulation reconstruction methods, to ensure minimal error.

[0126] Step S42: Identify high-risk scour zones based on scour volume and corrected cross-sectional area.

[0127] In this embodiment, underwater closed profile regions of each cross-section under the current water level conditions are constructed using cross-sectional measuring point data and measured water level data. A geometric cutting method is used to extract the area below the water level from each cross-sectional image, forming a polygon of the closed region. Its area is directly calculated as the corrected area of ​​the water-passing cross-section, in square meters. The calculation process requires obtaining the abscissa and longitudinal elevation values ​​of the measuring points, and removing points above the water surface based on the water level baseline. The scour volume value obtained in step S41 is divided by the corrected area of ​​the water-passing cross-section to obtain the scour volume per unit area, in cubic meters per square meter. A high-risk threshold of 0.35 cubic meters per square meter is set; areas exceeding this threshold are classified as high-risk scour areas. During the statistical process, three or more consecutive cross-sectional points exceeding the threshold are designated as high-risk scour areas, and the center point of each area, along with the corresponding cross-sectional number, scour value, area value, and scour value per unit area, are marked.

[0128] Of particular importance, step S42 includes the following steps:

[0129] Step S421: Detect sediment movement based on scour volume;

[0130] In this embodiment, scour data is used to quantify sediment movement. First, information such as flow velocity, sediment particle size, and water depth is obtained through river channel measurements. This data can be acquired using sensors such as flow monitoring equipment and topographic scanners. Then, this data is used to detect sediment movement by calculating the scour volume in the river (i.e., the volume of sediment washed away by the water flow per unit time). Specifically, scour volume is estimated by measuring the sediment loss rate per unit cross-sectional area. To obtain more accurate movement data, sediment migration models (such as the Bagnold equation) are used for estimation, combined with river flow velocity and velocity gradient, to determine the amount of sediment movement within a specific area. Equipment such as current meters and sediment analyzers (e.g., Laser Diffraction Particle Size Analyzers) can help analyze the particle size distribution and particle state of sediments, thereby accurately calculating sediment movement.

[0131] Step S422: Determine the intensity of riverbed erosion based on the amount of sediment movement;

[0132] In this embodiment, after obtaining sediment movement data, the erosion intensity per unit area is calculated using parameters such as sediment loss and flow velocity. This process requires step-by-step calculation of sediment loss for each river cross-section. First, the sediment loss on the selected cross-section is determined, and then, combined with the scouring capacity of the water flow, such as shear stress and flow velocity, the riverbed erosion intensity of the area is assessed. Based on experimental data and existing literature, a standard erosion threshold is set (e.g., when the flow velocity reaches a certain value, riverbed erosion will significantly increase), and erosion zones of different intensities are further divided according to this standard. Equipment such as shear force instruments (e.g., StressTester) can be used to measure the water flow impact force on different cross-sections to determine the specific erosion intensity. This process helps to accurately quantify the erosion situation of the riverbed, providing data support for subsequent risk analysis.

[0133] Step S423: Evaluate the water flow impact force based on the corrected cross-sectional area of ​​the water passage;

[0134] In this embodiment, the impact force of the water flow needs to be assessed based on the corrected cross-sectional area data. The corrected cross-sectional area refers to the area of ​​the cross-section through which the water flow passes, after considering factors such as actual cross-sectional changes and curvature of the river channel. This data is typically obtained through methods such as lidar scanning and drone aerial photography, which can accurately capture changes in the water surface and bottom. After obtaining the corrected cross-sectional area, the impact force of the water flow is calculated using a fluid dynamics model (such as the Chezy formula or Manning formula). The impact force of the water flow is assessed by combining parameters such as flow velocity, flow rate, and cross-sectional area. The calculation of the impact force of the water flow is usually based on parameters such as the kinetic energy of the water flow, the square of the flow velocity, and the fluid density, combined with the flow rate to calculate the magnitude of the impact force. In practice, sensors such as current meters and depth sensors (such as Ultrasonic Depth Sounder) are used to acquire water flow information in real time, and then computer-aided analysis is used to obtain the numerical value of the water flow impact force.

[0135] Step S424: Identify high-risk scour zones based on water flow impact force and riverbed erosion intensity.

[0136] In this embodiment, the river channel needs to be divided into zones based on the water flow impact force and erosion intensity obtained in the preceding steps. This process requires comparing the impact force and erosion intensity of different areas to set a predetermined threshold (e.g., when the impact force exceeds a certain standard value, or the erosion intensity is greater than a certain value, it is considered a high-risk area). To further accurately determine high-risk erosion zones, topographic analysis techniques, such as elevation data analysis and change analysis, can be used. The equipment used includes a LiDAR scanner, which can provide high-precision data on the riverbed height, as well as flow velocity sensors, sediment sensors, etc. By analyzing the rate of change in different areas, areas with higher erosion intensity and erosion capacity can be clearly identified, ultimately accurately identifying high-risk erosion zones.

[0137] Step S43: Assess the water flow degradation capacity based on the amount of siltation and the corrected cross-sectional area of ​​the water flow;

[0138] In this embodiment, the calculated sedimentation volume data for each cross-section is divided by its corresponding corrected cross-sectional area to obtain the sedimentation value per unit area. An evaluation threshold of 0.25 cubic meters per square meter is set to determine whether there is a significant decrease in the cross-section's water-carrying capacity. By comparing the difference between the sedimentation center elevation and the design bottom elevation, it is determined whether a local shoal or depositional block has formed. If the sedimentation value per unit area exceeds the threshold, and the center elevation is more than 0.3 meters higher than the design bottom elevation, the cross-section is assessed as having a water-carrying capacity degradation problem. In actual operation, the elevation difference calculation needs to be performed in conjunction with historical design cross-section drawings or standard riverbed control values. Each cross-section is marked as belonging to a degraded capacity area, and its sedimentation density and elevation difference are recorded for subsequent scour volume correction.

[0139] Of particular importance, step S43 includes the following steps:

[0140] Step S431: Simulate sediment accumulation based on sediment volume to obtain sediment accumulation data;

[0141] In this embodiment, it is necessary to acquire sedimentation data for different areas of the river channel. Sedimentation volume is obtained by measuring the volume of sediment at the bottom of the river. This data is typically acquired using equipment such as sonar, LiDAR, or buoy sensors. Commonly used equipment includes sonar scanners manufactured by TeledyneRESON, which can measure the morphology of the riverbed surface and the distribution of sediments by sending sound waves into the water and analyzing the echo data. Based on the collected sedimentation data, the sediment deposition is then simulated using numerical simulation methods. This process can be performed using CFD (Computational Fluid Dynamics) software such as ANSYS Fluent or OpenFOAM. Required input parameters include water flow velocity, flow direction, and sediment particle size. Using these parameters, the simulation software can calculate how sediments deposit on the riverbed under different flow and riverbed conditions. The simulation results will output sediment deposition data for each area, reflecting the changes in sediment on the riverbed over a period of time. During the simulation, flow velocity and riverbed roughness are key parameters. Flow velocity can be measured by deploying flow velocity meters (such as the FlowPulse flowmeter manufactured by OTT), while riverbed roughness is extracted through field surveys or remote sensing image data. Sediment deposition data is usually expressed as the change in sediment thickness and provides sediment information for subsequent steps.

[0142] Step S432: Identify riverbed subsidence based on sediment deposition data;

[0143] In this embodiment, after obtaining the sediment deposition data, the next step is to calculate and identify the riverbed settlement. Settlement is the change in riverbed elevation caused by sediment accumulation or water erosion. To achieve this, initial riverbed elevation data must first be obtained through a series of elevation measurements. Common elevation measurement methods include GPS measurements or using a drone equipped with a LiDAR (Light Detection and Ranging) for three-dimensional elevation data acquisition. This data can be collected using a GNSS system with high-precision positioning capabilities (e.g., a Trimble R10 GNSS receiver). Then, by comparing the previously obtained sediment deposition data with the initial elevation data, the settlement in different areas is calculated. Settlement is obtained by comparing the changes in riverbed elevation at different time points. Through multiple elevation measurements of different sections within the river channel area, combined with simulation results, the settlement data reflects the changes in the sedimentation area. This data is typically expressed as the elevation drop per unit area, in meters (m) or centimeters (cm). If the settlement in certain areas is large, it indicates severe sediment accumulation in those areas, leading to a reduction in the river's flow capacity.

[0144] Step S433: Evaluate the water flow capacity based on the corrected cross-sectional area;

[0145] In this embodiment, water flow capacity refers to the ability of water to flow effectively in a river channel, mainly affected by changes in river cross-section, riverbed morphology, and water flow velocity. In this step, the change in water flow capacity is first reflected by calculating the corrected area of ​​the river channel cross-section. The corrected area of ​​the cross-section is calculated by considering changes after siltation and sediment accumulation, thus revising the effective flow area of ​​the river channel cross-section. The method for measuring the cross-section typically involves using a water level measuring instrument (such as a water level gauge manufactured by OTT) and a flow velocity sensor. The water level gauge can monitor changes in the water level in the river channel in real time, while the flow velocity sensor provides water flow velocity information. After obtaining the river channel cross-section and water flow velocity data, the river channel's flow capacity is calculated by applying hydraulic formulas (such as the Manning formula). The parameters required for input to the formula include flow velocity, cross-sectional area, and river channel roughness. The correction of the cross-sectional area is achieved by combining it with previous siltation data to correct the water flow area of ​​each cross-section, thereby obtaining real-time water flow capacity data.

[0146] Step S434: Assess the water flow capacity and riverbed settlement to determine the water degradation capacity.

[0147] In this embodiment, the water flow degradation capacity reflects the impact of factors such as sediment accumulation and subsidence on water flow, especially the manifestation of river degradation during long-term water flow. First, based on the corrected cross-sectional area, the water flow capacity of the river in different areas is determined and combined with riverbed subsidence to analyze whether a significant decrease in water flow capacity has occurred. At this point, the combination of subsidence data and water flow capacity data provides a direct basis for judging water flow degradation. If, in certain river areas, subsidence is large and water flow capacity decreases significantly, it indicates severe water flow degradation in that area. Often, some areas of the river experience reduced flow due to siltation or subsidence, even leading to localized flooding. During the assessment process, a dynamic monitoring system can be used for real-time data collection, combined with GIS (Geographic Information System) for spatial analysis. Using a GIS (such as ArcGIS), water flow capacity and subsidence data can be spatially visualized to further analyze the river degradation trend. By analyzing changes in water flow capacity over different time periods, the trend of water flow degradation can be determined, further providing a scientific basis for river management and prevention.

[0148] Step S44: Calculate the scour and sedimentation volume in high-risk scour zones based on water degradation capacity;

[0149] In this embodiment, the high-risk scour areas identified in step S42 and the degradation-reduced areas marked in step S43 are spatially overlapped and compared to filter out the areas where they intersect. The scour volume data of the intersecting areas is corrected using the formula: scour volume multiplied by a degradation correction coefficient. This coefficient is defined as "actual water level elevation minus the local sedimentation center elevation, then divided by the actual water level elevation," and its value is typically between 0.2 and 0.9, calculated based on the difference between the water level gauge data and the measuring point elevation. The corrected scour volume is recorded as the actual reference scour and sedimentation volume for that area, used to further identify scour and sedimentation feature points. During the correction process, all water level elevation data are obtained from actual radar water level gauge measurements, with a sampling frequency controlled at once every 10 minutes and an accuracy maintained within 0.01 meters. After the scour and sedimentation values ​​of each section are corrected, they are uniformly merged and organized, retaining the section number, original scour volume, correction coefficient, and corrected volume data.

[0150] Step S45: Mark the scour and siltation characteristic points based on the scour and siltation volume;

[0151] In this embodiment, each cross-section is divided into several small segments at equal 0.5-meter intervals laterally. The average scour and sedimentation volume is calculated for each segment. Within each cross-section, the measuring points corresponding to the maximum scour and sedimentation volumes are selected as scour and sedimentation characteristic points. Necessary rejection conditions are set, such as the segment length of the measuring point being no less than 1.5 meters and the difference between the scour and sedimentation change value and the cross-section average change value being no less than 0.1 cubic meters, ensuring the representativeness of the characteristic points. To avoid interference from false characteristic points caused by local errors, a local extreme value screening method is used, requiring that the scour and sedimentation value of a characteristic point be at least 0.05 cubic meters greater than its two adjacent points, and that its value be within the top 20% of the overall distribution of the cross-section. Each cross-section ultimately retains 3 to 10 characteristic points, covering the main channel and side beach areas. The abscissa, elevation, scour or sedimentation attribute, volume change value, segment number, and cross-section number of the characteristic points are recorded to form the basis for subsequent simplification.

[0152] Step S46: Set a buffer zone for the simplified cross-sectional measuring point diagram based on the scour and siltation characteristic points to obtain the simplified data of the first cross-sectional measuring points.

[0153] In this embodiment, a buffer zone is established centered on the scour and sedimentation feature points obtained in step S45. The buffer radius is set according to the scour and sedimentation intensity, and the calculation formula is 1.5 meters plus the normalized value of the current feature point's volume change multiplied by 5 meters. The normalized value is the ratio of the scour and sedimentation volume at that point to the maximum scour and sedimentation volume of the cross section, ensuring that the buffer zone size is dynamically adjusted according to the actual change intensity, with the radius controlled between 1.5 meters and 6.5 meters. In all cross sections, a protection zone is delineated based on the established buffer zone, and the measuring points within this zone are considered original points that must be retained. When simplifying the cross section measuring points, linear compression is preferentially performed on non-protected areas. The least squares linear fitting method is used to fit continuous measuring points, and points with a fitting residual of less than 0.02 meters are compressed and replaced.

[0154] Therefore, the embodiments should be considered as exemplary and non-limiting in all respects, and the scope of the invention is defined by the appended claims rather than the foregoing description. Thus, all variations falling within the meaning and scope of the equivalents of the application are intended to be included within the invention.

[0155] The above description is merely a specific embodiment of the present invention, enabling those skilled in the art to understand or implement the invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the invention. Therefore, the present invention is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features of the invention herein.

Claims

1. A rapid simplification method for measuring points in river cross-sections based on the conservation of water flow area, characterized in that, Includes the following steps: Step S1: Obtain three-dimensional measurement point data of the river channel cross-section; Filtering is performed on the three-dimensional measurement point data of the river cross section to obtain the beam data of the three-dimensional measurement point of the cross section; The cross-section line is projected based on the beam data of the three-dimensional measurement points of the cross-section to generate a set of cross-section measurement points. Step S2: Based on the set of cross-sectional measuring points, perform initial simplification of the measuring points to obtain the initial simplified cross-sectional measuring point data; Identify small trench areas based on initial simplified cross-sectional measurement point data; The turbulent structure region is determined based on the small trench region; Tracking the evolution path of scouring and deposition based on turbulent structural regions; Step S3: Calculate the cross-sectional area of ​​the water passage based on the initial simplified cross-section measurement point data; The elevation is corrected based on the cross-sectional area of ​​the water passage to obtain the corrected cross-sectional area of ​​the water passage; Based on the scouring and sedimentation evolution path, the initial simplified cross-section measurement point data is rapidly simplified to generate a simplified cross-section measurement point map. Step S4: Calculate the scour and sedimentation volume based on the scour and sedimentation evolution path and the corrected cross-sectional area; calibrate the scour and sedimentation characteristic points according to the scour and sedimentation volume; set a buffer zone for the simplified cross-sectional measuring point map according to the scour and sedimentation characteristic points, thereby obtaining the simplified measuring point data of the first cross-section. Among all cross-sections, according to the established buffer zone, the measuring points in this area are regarded as the original points that must be retained.

2. The method for rapid simplification of river cross-section measuring points based on the conservation of water flow area as described in claim 1, characterized in that, Step S1 is as follows: Step S11: Obtain three-dimensional measurement point data of the river cross-section; Step S12: Based on the three-dimensional measurement point data of the river cross section, check the number of beams to obtain single-beam data and multi-beam data; Step S13: Perform median filtering on the single-beam data to obtain single-beam filtered data; Step S14: Perform manual filtering based on the multi-beam data to obtain multi-beam filtered data; Step S15: Integrate single-beam filtered data and multi-beam filtered data to obtain three-dimensional measurement point beam data of the cross section; Step S16: Project the cross-section line based on the cross-section three-dimensional measuring point beam data to generate the original cross-section measuring point set.

3. The method for rapid simplification of river cross-section measuring points based on the conservation of water flow area as described in claim 2, characterized in that, Step S16 is as follows: Step S161: Extract the three-dimensional coordinates of the cross-section from the beam data of the three-dimensional measurement points; Step S162: Calculate the direction vector of the cross-section line based on the three-dimensional coordinates of the cross-section; Step S163: Calculate the heading rotation angle based on the cross-section line direction vector; Step S164: Construct a three-dimensional rotation matrix based on the heading rotation angle; Step S165: Perform two-dimensional plane projection based on the three-dimensional rotation matrix, where the projection is based on the XZ section and Y is 0.05 meters, to generate the original set of cross-sectional measurement points.

4. The method for rapid simplification of river cross-section measuring points based on the conservation of water flow area as described in claim 1, characterized in that, The initial simplification of the measuring points in step S2 is as follows: The cross-sectional measuring point curve is plotted based on the set of cross-sectional measuring points; the cross-sectional curvature of the cross-sectional measuring point curve is calculated; and the cross-sectional measuring points with obvious curvature are identified based on the preset curvature threshold. The meandering river section is identified by measuring points on the cross-section of the obvious bend, and the meandering river section data is obtained; the river flow velocity is calculated based on the meandering river section data; and the flow velocity magnitude is statistically analyzed based on the river flow velocity to obtain high flow velocity data and low flow velocity data. The outer bend region is identified based on high flow velocity data, and the outer bend region data is obtained. Shear stress is calculated based on the outer bend region data. Riverbed scour is simulated based on the shear stress, and riverbed scour data is obtained. Steep slope and deep channel areas are identified based on riverbed scour. The inner bend area was identified based on the low flow velocity data, and the inner bend area data was obtained. Sediment deposition simulation was performed based on the inner bend area data, and sediment deposition data was obtained. The gentle slope alluvial beach area was identified based on the sediment deposition data. The measurement point set of the cross section is initially simplified based on the steep slope deep trough area and the gentle slope siltation beach area to obtain the initial simplified cross section measurement point data.

5. The method for rapid simplification of river cross-section measuring points based on the conservation of water flow area as described in claim 1, characterized in that, The specific steps for identifying the small trench region in step S2 are as follows: The longitudinal elevation is calculated based on the initial simplified cross-section measurement point data to obtain longitudinal elevation data; the elevation change rate is calculated based on the longitudinal elevation data; and elevation abrupt change data of the longitudinal elevation data is extracted based on the elevation change rate. Identifying river channel elevation abrupt change areas based on elevation change data; Micro-slope topography was identified based on areas of abrupt changes in river elevation, and micro-slope topography data was obtained. Soil shear strength detection based on micro-slope topographic data; Soil instability zones in areas of abrupt elevation changes were screened based on soil shear strength. The density of steep sills was calculated based on micro-sill topographic data, and continuous erosion zones were delineated based on the steep sill density. The small ditch area is determined by performing regional intersection calculations based on the soil instability area and the continuous erosion area.

6. The method for rapid simplification of river cross-section measuring points based on the conservation of water flow area as described in claim 1, characterized in that, The specific steps for determining the turbulent structure region in step S2 are as follows: Water flow simulation was performed in the small ditch area to obtain ditch water flow data; Calculate the trench width-to-depth ratio based on the small trench area; Vertical vortex detection is performed based on the width-to-depth ratio of the trench and the water flow data in the trench to obtain vertical vortex data; Calculate vortex scale based on vertical vortex data; Effective turbulent vortex structures were selected based on vortex size and trench width-to-depth ratio. The turbulent structure region is determined based on the effective turbulent vortex structure.

7. The method for rapid simplification of river cross-section measuring points based on the conservation of water flow area as described in claim 1, characterized in that, The specific steps in step S2 for tracking the evolution path of scouring and sedimentation are as follows: Calculation of hydrodynamic intensity based on turbulent structural regions; Calculation of channel geometric curvature based on turbulent structural regions; The inner bend of the river channel is calibrated based on the geometric curvature of the channel, and the degree of weak disturbance of the secondary eddy in the inner bend of the channel is detected. Based on the hydrodynamic intensity, sediment transport was simulated in the turbulent structural region to obtain sediment transport data; Based on the degree of weak disturbance of the secondary eddy and sediment transport data, a stable sedimentation formation simulation was conducted to obtain stable sedimentation data. The evolution path of scour and sedimentation was reconstructed based on stable sedimentation data.

8. The method for rapid simplification of river cross-section measuring points based on the conservation of water flow area as described in claim 1, characterized in that, Step S3 is as follows: Step S31: Extract water surface elevation based on the initial simplified cross-section measuring point data; Step S32: Identify adjacent water level monitoring points based on water surface elevation, and calculate the trapezoidal area of ​​the water passage cross section based on the adjacent water level monitoring points to obtain the water passage cross section area; Step S33: Fine-tune the water surface elevation based on adjacent water level monitoring points to obtain water surface elevation fine-tuning data; Step S34: Correct the cross-sectional area of ​​the water passage based on the water surface elevation fine-tuning data to obtain the corrected cross-sectional area of ​​the water passage; Step S35: Based on the scour and sedimentation evolution path, the initial simplified cross-section measurement point data is rapidly simplified to generate a simplified cross-section measurement point diagram.

9. The method for rapid simplification of river cross-section measuring points based on the conservation of water flow area as described in claim 8, characterized in that, Step S35 is as follows: Step S351: Calculate the slope of the scour and sedimentation path based on the scour and sedimentation evolution path; Step S352: If the slope of the scouring and silting path is greater than the preset scouring and silting slope threshold, then mark the deletion range of the prohibited scouring and silting points; Step S353: Based on the deletion range of prohibited scour and siltation points, perform feature point fusion on the initial simplified cross-section measurement point data to obtain scour and siltation feature point data; Step S354: Based on the scour and siltation characteristic point data, quickly simplify the cross-sectional measuring points to generate a simplified cross-sectional measuring point diagram.

10. The method for rapid simplification of river cross-section measuring points based on the conservation of water flow area as described in claim 1, characterized in that, Step S4 is as follows: Step S41: Calculate the scour volume based on the scour-deposition evolution path; Calculate the sedimentation volume based on the scour-deposition evolution path; Step S42: Identify high-risk scour zones based on scour volume and corrected cross-sectional area. Step S43: Assess the water flow degradation capacity based on the amount of siltation and the corrected cross-sectional area of ​​the water flow; Step S44: Calculate the scour and sedimentation volume in high-risk scour zones based on water degradation capacity; Step S45: Mark the scour and siltation characteristic points based on the scour and siltation volume; Step S46: Set a buffer zone for the simplified cross-sectional measuring point diagram based on the scour and siltation characteristic points to obtain the simplified data of the first cross-sectional measuring points.

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