River three-dimensional terrain generation method and device

Abstract

The present application relates to river course topography modeling technical field, specifically to a kind of river course three-dimensional topography generation method and device, comprising: obtaining river course point cloud data, river course elevation data and the historical river data of several monitoring points, determine several local points in river course point cloud data and combine river course elevation data to respectively on plane and vertical direction to river course point cloud data carry out morphological analysis, obtain the morphological parameters at each local point;With the river parameters of different local points in river course point cloud data in different time obtained by combining historical river data, the resolution coefficient at corresponding position is obtained by the morphological parameters and river parameters at different positions, river course three-dimensional topography modeling is carried out by combining river course point cloud data and the resolution of model is adjusted using resolution coefficient, to generate river course three-dimensional topography model.The present application optimizes the calculation efficiency while ensuring high precision, realizes dynamic, adaptive river course three-dimensional topography generation.

Description

Technical Field

[0001] This invention relates to the field of river topography modeling technology, specifically to a method and apparatus for generating three-dimensional river topography. Background Technology

[0002] The generation of 3D river topography has significant applications in modern water conservancy engineering, particularly in flood simulation, ecological restoration, navigation safety, and water resource management. These applications all require consideration of river evolution. Furthermore, with the increasing frequency of extreme hydrological events caused by climate change, the rate of river evolution is accelerating, making accurate 3D river topography models a crucial technology for early warning systems, emergency response, and long-term water conservancy planning. However, existing river topography acquisition and modeling technologies still face several technical bottlenecks in terms of accuracy, efficiency, and applicability, which urgently require breakthroughs.

[0003] Currently, mainstream methods for 3D river modeling include triangular network of irregularities (TIN) modeling and digital elevation model (DEM) interpolation. While these methods have solved the problem of terrain reconstruction to some extent, the production of high-precision 3D river terrain models and the efficiency of river evolution are mutually restrictive. Therefore, it is necessary to optimize the generation process of 3D river terrain models and adjust the balance between model accuracy and generation efficiency. Summary of the Invention

[0004] This invention provides a method and apparatus for generating three-dimensional river topography to solve existing problems.

[0005] The present invention provides a method and apparatus for generating three-dimensional river topography, which adopts the following technical solution:

[0006] In a first aspect, one embodiment of the present invention provides a method for generating three-dimensional river channel topography, the method comprising the following steps:

[0007] Acquire river point cloud data, river elevation data, and historical river data from several monitoring points. The historical river data includes water velocity and sediment concentration data from several monitoring points along the river in history.

[0008] Several local points in the river point cloud data were identified and the river elevation data were combined to perform morphological analysis on the river point cloud data in both the plane and vertical directions to obtain the morphological parameters of each local point.

[0009] By combining historical river data, river parameters are obtained from different local points in the river point cloud data at different times. These river parameters are used to describe the degree to which local points are affected by erosion or siltation caused by river water.

[0010] The resolution coefficients at different locations are obtained by using morphological and river parameters at different locations. The river channel point cloud data is then combined to create a 3D terrain model of the river channel. During the modeling process, the resolution coefficients are used to adjust the model resolution.

[0011] Obtain and visualize the 3D terrain model of the river channel after resolution adjustment.

[0012] Furthermore, the specific method for determining several local points in the river channel point cloud data and performing morphological analysis on the river channel point cloud data in both the planar and vertical directions in conjunction with river channel elevation data to obtain the morphological parameters at each local point includes:

[0013] Using river elevation data, several equally spaced riverbed curves are obtained from the river point cloud data;

[0014] Obtain several liquid surface curves that are parallel to the horizontal plane and equally spaced from the river point cloud data;

[0015] The intersection points formed by the riverbed curve and the liquid surface curve are used as local points in the river point cloud data. The contour morphology features of the local area where the local points are located are analyzed to obtain the morphological parameters of the local points.

[0016] Furthermore, the specific method for obtaining the riverbed curve is as follows:

[0017] A digital elevation model is established using river elevation data. The centerline of the river is obtained from the river point cloud data. Starting from the upstream starting point of the river, river cross sections perpendicular to the river centerline are generated at equal intervals of distance d based on the digital elevation model. The curve formed by the river cross sections in the river point cloud data is used as the riverbed curve, where d is a preset distance parameter.

[0018] Furthermore, the specific method for obtaining the liquid level curve is as follows:

[0019] Curve fitting is performed on the river point cloud data using the least squares method to obtain several liquid surface curves on both sides of the river point cloud data, such that the surface on which the liquid surface curves are located is parallel to the horizontal plane, and the liquid surface curves are evenly distributed with an interval of d.

[0020] Furthermore, the specific method for obtaining the morphological parameters of the local points is as follows:

[0021] For any local point in the river point cloud data, all data points within a preset neighborhood of the local point are obtained as neighborhood points of the local point, resulting in a local region formed by the local point and its corresponding neighborhood points. The local region is then fitted with a surface using the least squares method to obtain the local surface corresponding to the local point. The elevation corresponding to each data point in the river point cloud data is obtained. The difference between the average elevation of all data points on the local surface of the local point and the elevation value corresponding to the liquid surface curve with the largest elevation is taken as the local elevation difference of the local point. The average curvature of the local point on the corresponding local surface and the standard deviation of the elevations corresponding to all data points on the local surface are obtained. Based on the local elevation difference, the average curvature, and the standard deviation, the morphological parameters of the corresponding local point are obtained, wherein the average curvature and the local elevation difference are positively correlated with the morphological parameters, and the standard deviation is negatively correlated with the morphological parameters.

[0022] Furthermore, the specific method for obtaining the historical river data of the aforementioned monitoring points is as follows:

[0023] Using one year as a monitoring cycle, multiple monitoring points are evenly set up on the river channel. Within year A, a number of fixed monitoring times are set to obtain the water flow velocity data at each monitoring point at each monitoring time. Simultaneously, water samples are collected at each monitoring point, and the suspended sediment concentration is determined using the standard drying and weighing method as sediment content data. Each monitoring cycle corresponds to one flow velocity data and one sediment content data, where A is the preset first parameter.

[0024] Furthermore, the specific method for obtaining river parameters at different locations in the river point cloud data at different times by combining historical river data includes:

[0025] The river point cloud data is divided into several monitoring segments in the watershed using historical river data from all monitoring points;

[0026] For any monitoring segment, the average flow velocity value of the monitoring segment at the same monitoring time in all monitoring cycles is taken as the flow velocity level value of the monitoring segment at the corresponding monitoring time, and the sediment concentration level value of the monitoring segment at the corresponding monitoring time is obtained using the same method.

[0027] Based on the magnitude of the flow velocity and sediment concentration levels corresponding to different monitoring sections in the river point cloud data, as well as their spatial and temporal variations in the river channel, the river parameters at any local point in the river point cloud data at various monitoring times are obtained.

[0028] Furthermore, the specific method for obtaining the river parameters is as follows:

[0029] For any given monitoring time, the spatial river factor of the local point at that monitoring time is obtained based on the difference in flow velocity level between the monitoring segment where the local point is located and the previous monitoring segment, as well as the difference in sediment concentration level between the current monitoring segment and the next monitoring segment. The forward difference values ​​of the flow velocity level and sediment concentration level of the monitoring segment where the local point is located at that monitoring time are obtained over time, and the average values ​​of the flow velocity level and sediment concentration level of the monitoring segment where the local point is located at all monitoring times are obtained. Based on the forward difference values ​​of the flow velocity level and sediment concentration level over time and the differences between the corresponding flow velocity level and sediment concentration level values, the temporal river factor of the local point at that monitoring time is obtained. Combining the spatial river factor, temporal river factor, flow velocity level, and sediment concentration level of the local point at that monitoring time, the river parameters of the local point at that monitoring time are obtained.

[0030] Furthermore, the resolution coefficients at different locations are obtained by using morphological parameters and river parameters, and then combined with river point cloud data to perform 3D terrain modeling of the river channel. During the modeling process, the resolution coefficients are used to adjust the model resolution. Specific methods include:

[0031] For any monitoring time, the resolution coefficient of the local point at the monitoring time is obtained based on the morphological parameters of the local point and the river parameters of the local point at the monitoring time, wherein the morphological parameters and the river parameters are both positively correlated with the resolution coefficient.

[0032] For any monitoring time, the Delaunay subdivision method is used to model the irregular triangular network of the river point cloud data, and the resolution coefficient is used to adjust the local resolution of the river 3D terrain model during the 3D modeling process.

[0033] Secondly, another embodiment of the present invention provides a three-dimensional river terrain generation device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of any one of the three-dimensional river terrain generation methods described above.

[0034] Thirdly, another embodiment of the present invention also provides a three-dimensional river channel terrain generation system, the system comprising:

[0035] Data acquisition module: used to acquire river point cloud data, river elevation data and historical river data from several monitoring points. Historical river data includes water flow velocity data and sediment content data from several monitoring points on the river in history.

[0036] Morphological analysis module: used to identify several local points in the river point cloud data and combine them with river elevation data to perform morphological analysis on the river point cloud data in both the plane and vertical directions, and obtain the morphological parameters at each local point;

[0037] River Analysis Module: Used to combine historical river data to obtain river parameters of different local points in river point cloud data at different times. The river parameters are used to describe the degree to which local points are affected by erosion or siltation caused by river water.

[0038] Resolution module: Used to obtain the resolution coefficients at different locations by using morphological parameters and river parameters at different locations, and to perform 3D terrain modeling of the river channel by combining river point cloud data. The resolution coefficients are used to adjust the model resolution during the modeling process.

[0039] Model generation module: Used to obtain and visualize the 3D terrain model of the river channel after resolution adjustment.

[0040] The beneficial effects of the technical solution of this invention are as follows: By combining planar and vertical morphological analysis of local points with historical river parameters (quantifying the effects of erosion and sediment deposition), the model can realistically reflect the dynamic process of river channel evolution over time, avoiding the limitations of traditional static methods, and is particularly suitable for detailed depiction of complex river sections. Furthermore, by introducing a resolution coefficient, the model resolution is adaptively adjusted based on morphological and river parameters. In critical areas with severe erosion or frequent sedimentation (such as high-velocity zones), the resolution is automatically increased to preserve details, while the resolution is reduced in stable areas, significantly reducing computational resource consumption while ensuring the accuracy of key parts. Overall, this method optimizes computational efficiency while maintaining high accuracy, achieving dynamic and adaptive 3D terrain generation. Attached Figure Description

[0041] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0042] Figure 1 This is a flowchart illustrating the steps of a method for generating three-dimensional river terrain according to the present invention.

[0043] Figure 2 A schematic diagram of a riverbed curve provided in one embodiment of the present invention;

[0044] Figure 3 This is a schematic diagram of a three-dimensional river terrain generation device provided in an embodiment of the present invention.

[0045] Figure 4 This is a schematic diagram of the structure of a three-dimensional river terrain generation system provided in one embodiment of the present invention. Detailed Implementation

[0046] To further illustrate the technical means and effects adopted by the present invention to achieve its intended purpose, the following, in conjunction with the accompanying drawings and preferred embodiments, details the specific implementation, structure, features, and effects of a method and apparatus for generating three-dimensional river terrain according to the present invention. In the following description, different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. Furthermore, specific features, structures, or characteristics in one or more embodiments can be combined in any suitable form.

[0047] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains.

[0048] The following description, in conjunction with the accompanying drawings, details the specific scheme of the method and apparatus for generating three-dimensional river terrain provided by the present invention.

[0049] Please see Figure 1 The diagram illustrates a flowchart of a method for generating three-dimensional river terrain according to an embodiment of the present invention. The method includes the following steps:

[0050] Step S001: Obtain river point cloud data, river elevation data, and historical river data from several monitoring points.

[0051] It's important to note that generating 3D terrain for a river channel is a 3D digitization process, resulting in a corresponding digital, discrete model. To ensure the 3D terrain accurately reflects the actual physical state of the river, the resolution of this discrete digital model is typically adjusted. Higher resolution makes the 3D terrain model more realistic, but it also increases the amount of data. When using 3D terrain for river evolution analysis, different regions of the river may exhibit varying degrees of terrain complexity. For example, in the main channel, the water flow is concentrated, and the terrain changes are relatively gentle; while in tributaries, sharp bends, or areas where the flow is affected by obstacles, the terrain changes are more complex, involving more details and micro-variations. Therefore, higher resolution is needed in these high-variability areas to more accurately capture terrain changes and ensure model accuracy. Furthermore, a complete river model may cover a large area, with some areas exhibiting relatively stable terrain changes while others show significant variations. To conserve computational resources, lower resolution is typically used in areas with minimal changes, while higher resolution is used in areas with dramatic changes. This approach improves computational efficiency while maintaining accuracy. River channel surveying is the foundation for three-dimensional terrain modeling of rivers. In this embodiment of the invention, three-dimensional laser scanning technology is used to collect data on the river channel, obtain point cloud data of the river channel, and preprocess the river channel point cloud data to facilitate subsequent model generation.

[0052] Specifically, in order to implement the three-dimensional terrain generation method for river channels proposed in this embodiment, it is first necessary to collect river channel point cloud data. The specific process is as follows:

[0053] Step S110: Data is collected from the river channel using a multi-station 3D laser scanner. The multi-station laser scan data obtained from the multi-station laser scanner is then stitched together, undergoes coordinate transformation, and preprocessed to obtain river channel point cloud data. The river channel point cloud data contains several data points, each corresponding to coordinates in 3D space. The specific process for acquiring the river channel point cloud data is as follows:

[0054] First, for the 3D data obtained from scanning at different sites and angles, the target stitching function in RISCAN PRO software is used to stitch the 3D data from all sites and angles into point clouds.

[0055] Then, for non-ground points in the point cloud that contain errors, noise, and vegetation, we first remove excessively low points, high points, and isolated points caused by the specular reflection of water bodies.

[0056] Step S120: Collect historical data on water velocity and sediment concentration at different locations along the river channel as historical river data. The specific steps are as follows: First, using one year as a monitoring cycle, multiple monitoring points are evenly set up along the river channel. Within year A, several fixed monitoring times are set to obtain water velocity data at each monitoring point at each monitoring time. Simultaneously, water samples are collected at each monitoring point, and the suspended sediment concentration is determined using the standard drying and weighing method as sediment concentration data. Each monitoring cycle corresponds to one flow velocity data point and one sediment concentration data point, where A is a preset first parameter.

[0057] It should be noted that the first parameter A is preset to 10 based on experience, and can be adjusted according to the actual situation. This embodiment of the invention does not impose specific limitations. In addition, when using the standard drying and weighing method to determine the concentration of suspended sediment, the "GB / T50159-2015 Standard for Testing Suspended Sediment in Rivers" can be referred to.

[0058] Step S122: Denoise and standardize the flow velocity and sediment content data of the water body.

[0059] Step S130: Obtain river elevation data using a total station.

[0060] Thus, river point cloud data and historical river data were obtained through the above methods.

[0061] Step S002: Determine several local points in the river point cloud data and perform morphological analysis on the river point cloud data in both the plane and vertical directions in combination with the river elevation data to obtain the morphological parameters at each local point.

[0062] It should be noted that due to erosion and siltation at different locations along the river channel, river course changes gradually occur. To facilitate river management and for monitoring or predicting river course changes, it is usually necessary to build a 3D model based on point cloud data of the river channel and combine this model with the current state of the river channel to achieve the desired objectives. During this process, dynamic analysis of the 3D model of the river channel is required. In this dynamic analysis, the resolution of the 3D model of the river channel topography affects the efficiency and accuracy of the analysis. Higher resolution requires more computation and is less efficient, but more accurate. Conversely, lower resolution requires less computation but is less accurate. Therefore, to ensure the accuracy of the analysis results, the resolution of the 3D model of the river channel topography should be adaptively adjusted to balance the efficiency and accuracy of the analysis.

[0063] It should be noted that the morphology of different locations along a river channel affects the erosion and sedimentation effects at those locations. Furthermore, the specific changes that occur in the river channel are inextricably linked to the water flow. Therefore, in this embodiment of the invention, the morphological characteristics of local locations within the point cloud data corresponding to the river channel, along with historical river data, are used for erosion and sedimentation analysis. The specific steps are as follows:

[0064] It should be noted that, specifically, the planar morphology of a river channel, such as its straightness, widening, narrowing, branching, and bends, directly affects the distribution of water flow velocity and the circulation structure, thus influencing the evolution of shoals. For example, straight rivers have a more uniform water flow, while bends create circulation at the bends, leading to erosion on the concave bank and deposition on the convex bank, forming meanders. In addition, the vertical morphology of the riverbed, including the height of the side banks, has a significant impact on the scouring and deposition of shoals. When the side banks are high, the water flows into the channel earlier, the scouring period is longer, and the scouring of shoals is more extensive. When the side banks are low, the water flows into the channel later, the scouring period is shorter, and the scouring of shoals is less extensive. If the side banks are particularly low, the riverbed channel is not distinct, the water flow is uncontrolled, and the water depth of the shoals is shallow and highly variable.

[0065] First, riverbed curves with equal intervals are obtained from the river point cloud data using river elevation data.

[0066] As an optional embodiment, the method for obtaining the riverbed curve is as follows: a digital elevation model (DEM) is established using river elevation data. The centerline of the river is obtained from the river point cloud data. Starting from the upstream starting point of the river, river cross sections perpendicular to the river centerline are generated at equal intervals based on the digital elevation model. The curve formed by the river cross sections in the river point cloud data is used as the riverbed curve, where d is a preset distance parameter.

[0067] Then, several liquid surface curves that are parallel to the horizontal plane and equally spaced are obtained from the river point cloud data.

[0068] As an optional embodiment, the method for obtaining the liquid surface curve is as follows: curve fitting is performed from the river point cloud data using the least squares method to obtain several liquid surface curves contained on both sides of the river point cloud data, such that the surface where the liquid surface curve is located is parallel to the horizontal plane, and the liquid surface curves are equally spaced, with an interval distance of d.

[0069] It should be noted that, as Figure 2 The diagram shows a riverbed curve. Based on experience, the preset distance parameter d is 2m, which can be adjusted according to the actual situation. This embodiment of the invention does not impose a specific limitation. In addition, the interval between the liquid surface curves can be adjusted to other values ​​according to the actual situation, and it is not necessary to make the interval between the liquid surface curve and the riverbed curve completely equal.

[0070] Finally, the intersection of the riverbed curve and the liquid surface curve is taken as a local point in the river point cloud data. The contour morphology features of the local area where the local point is located are analyzed to obtain the morphological parameters of the local point.

[0071] As a preferred embodiment, the method for obtaining the morphological parameters of the local point is as follows: For any local point in the river point cloud data, all data points within a preset neighborhood of the local point are obtained as neighborhood points of the local point, resulting in a local region formed by the local point and its corresponding neighborhood points. The local region is then fitted with a surface using the least squares method to obtain the local surface corresponding to the local point. The elevation corresponding to each data point in the river point cloud data is obtained. The difference between the average elevation of all data points on the local surface of the local point and the elevation value corresponding to the liquid surface curve with the largest elevation is taken as the local elevation difference of the local point. The average curvature of the local point on the corresponding local surface and the standard deviation of the elevations corresponding to all data points on the local surface are obtained. Based on the local elevation difference, the average curvature, and the standard deviation, the morphological parameters of the corresponding local point are obtained, wherein the average curvature and the local elevation difference are positively correlated with the morphological parameters, and the standard deviation is negatively correlated with the morphological parameters.

[0072] It should be noted that, in the embodiments of the present invention, the preset neighborhood range of a local point is a spherical region with a radius of 0.5m, which can be adjusted according to the actual situation. The embodiments of the present invention do not impose specific limitations.

[0073] As an optional embodiment, the specific calculation method for the morphological parameters is as follows:

[0074] ;

[0075] Among them, Xt k r represents the morphological parameters of the k-th local point in the river point cloud data. k Δh represents the average curvature of the k-th local point in the river point cloud data on the local surface. k σz represents the local elevation difference of the k-th local point in the river point cloud data. k This represents the standard deviation of the elevations corresponding to all data points on the local surface corresponding to the k-th local point in the river point cloud data. This represents the average elevation of all data points on the local surface corresponding to the k-th local point in the river point cloud data. represents the elevation value corresponding to the liquid surface curve with the highest elevation; || represents the absolute value function; norm() represents the linear normalization function.

[0076] It should be noted that the morphological parameters of a local point describe the degree to which the corresponding local surface is susceptible to river erosion or siltation. A larger value for the morphological parameter indicates a higher degree of river erosion or siltation at that location. k This reflects the channel curvature at the corresponding local point, and the channel curvature determines the intensity of the circulation generated during river flow. The intensity of the circulation directly affects the distribution of scour or sedimentation at that location; the greater the curvature, the higher the probability of channel scour or sedimentation occurring at that location. Additionally, the elevation difference Δh... k This reflects the relative height of the beach, Δh k The larger the diameter, the earlier the water flows back into the channel, and the longer the scouring duration; the standard deviation σz of the elevations corresponding to all data points on the local curved surface. k The smaller the standard deviation, the more significant the difference in elevation between the shoals and the channel, meaning the clearer the boundary between the shoals and the deep channel, the steeper the topographic outline of the river channel, and the higher the degree or possibility of topographic changes when river erosion and siltation occur. On the other hand, the larger the standard deviation, the flatter the topography, the smoother the flow of the river, and the less likely it is to cause erosion and siltation.

[0077] Thus, the morphological parameters of each local point in the river point cloud data are obtained through the above method.

[0078] Step S003: Combine historical river data to obtain river parameters for different local points in the river point cloud data at different times.

[0079] It should be noted that the susceptibility of a riverbed to channel change is often influenced by the river's flow velocity and sediment content. Higher flow velocities result in greater erosion of the riverbed, making channel change more likely. Conversely, higher sediment content makes the water more viscous, slowing the flow and causing sediment to gradually settle on the riverbed, leading to siltation and further channel change. Therefore, this embodiment of the invention utilizes historical river data to analyze the degree of erosion or siltation at different locations within the river channel point cloud data. Specific steps include:

[0080] First, the river point cloud data is divided into several monitoring segments in the watershed using historical river data from all monitoring points.

[0081] As an optional embodiment, the method of dividing the river point cloud data into several monitoring segments in the watershed using historical river data from all monitoring points includes the following specific method: For the flow velocity and sediment concentration values ​​at any time in the flow velocity and sediment concentration data of all monitoring points, the flow velocity and sediment concentration values ​​of all monitoring points are sorted in order from upstream to downstream of the river point cloud data to form corresponding sequences, which serve as the river flow velocity sequence and the river sediment concentration sequence. Then, spline interpolation algorithms are used to interpolate the river flow velocity sequence and the river sediment concentration sequence to obtain the new river flow... The flow velocity sequence and the new river channel sediment concentration sequence are generated, where each element in the new river channel flow velocity sequence and the new river channel sediment concentration sequence corresponds to a new monitoring point. Based on the position of the original monitoring point on the river channel point cloud data, the new monitoring point corresponding to each element in the new river channel flow velocity sequence and the new river channel sediment concentration sequence is mapped onto the river channel point cloud data. According to the position of the new monitoring point on the river channel point cloud data, the river channel point cloud data is divided into several monitoring segments, so that each monitoring segment corresponds to a new monitoring point. The flow velocity value and sediment concentration value corresponding to each new monitoring point are used as the flow velocity value and sediment concentration value of the corresponding monitoring segment.

[0082] It should be noted that, since the monitoring points are relatively more dispersed during the collection of flow velocity and sediment concentration data, in order to improve the accuracy of subsequent analysis results using flow velocity and sediment concentration data, this embodiment of the invention selects to perform interpolation processing on the river flow velocity and sediment concentration in local areas of the river channel, thereby improving the resolution of flow velocity and sediment concentration monitoring results at different locations in the river channel.

[0083] Then, for any monitoring segment, the average flow velocity value of the monitoring segment at the same monitoring time in all monitoring cycles is taken as the flow velocity level value of the monitoring segment at the corresponding monitoring time, and the sediment concentration level value of the monitoring segment at the corresponding monitoring time is obtained using the same method.

[0084] It should be noted that, for example, for any monitoring segment, the flow velocity values ​​on June 1st of each monitoring period are 0.71, 0.73, 0.76, 0.68, 0.72, 0.66, 0.75, 0.64, 0.69, and 0.71, respectively. Then, the average value of the flow velocity values ​​on June 1st of the monitoring segment in all monitoring periods (i.e., 0.725) is taken as the flow velocity level value of the monitoring segment at the corresponding monitoring time. In addition, it should be noted that the flow velocity value in the embodiments of the present invention is not the actual river flow velocity, but the value after standardization in step S100.

[0085] Finally, based on the magnitude of the flow velocity and sediment concentration levels corresponding to different monitoring sections in the river point cloud data, as well as their spatial and temporal variations in the river channel, the river parameters at any local point in the river point cloud data at each monitoring time are obtained.

[0086] As a preferred embodiment, the method for obtaining the river parameters is as follows: For any monitoring time, based on the difference in flow velocity level between the monitoring segment where the local point is located and the previous monitoring segment of the monitoring segment at the monitoring time, and the difference in sediment concentration level between the monitoring segment where the local point is located and the next monitoring segment of the monitoring segment at the monitoring time, the spatial river factor of the local point at the monitoring time is obtained; the forward difference values ​​of the flow velocity level and sediment concentration level of the monitoring segment where the local point is located at the monitoring time are obtained in time, and the average values ​​of the flow velocity level and sediment concentration level of the monitoring segment where the local point is located at all monitoring times are obtained; based on the forward difference values ​​of the flow velocity level and sediment concentration level values ​​in time and the differences between the corresponding flow velocity level and sediment concentration level values, the temporal river factor of the local point at the monitoring time is obtained; combining the spatial river factor, temporal river factor, flow velocity level, and sediment concentration level of the local point at the monitoring time, the river parameters of the local point at the monitoring time are obtained.

[0087] As an optional embodiment, the specific calculation method for the river parameters is as follows:

[0088] ;

[0089] Among them, Hl kt KJ represents the river parameters of the k-th local point in the river point cloud data at the t-th monitoring time; kt SJ represents the spatial river factor of the k-th local point in the river point cloud data at the t-th monitoring time; kt This represents the time-river factor of the k-th local point in the river point cloud data at the t-th monitoring time; v k,t This represents the flow velocity level value of the monitoring segment containing the k-th local point in the river point cloud data at the t-th monitoring time; s k,t This represents the sediment concentration level of the monitoring segment containing the k-th local point in the river point cloud data at the t-th monitoring time. This represents the flow velocity level value at the t-th monitoring time of the previous monitoring segment of the monitoring segment containing the k-th local point in the river point cloud data. Δv represents the sediment concentration level of the monitoring segment preceding the k-th local point in the river channel point cloud data at the t-th monitoring time; k This represents the forward difference value of the flow velocity level of the monitoring segment containing the k-th local point in the river point cloud data at the t-th monitoring time. Δs represents the average flow velocity level of the k-th local point in the river point cloud data across all monitoring times. k This represents the forward difference value of the sediment concentration level of the k-th local point in the river point cloud data at the t-th monitoring time; represents the average sediment concentration level of the k-th local point in the river point cloud data at all monitoring times; exp() represents the exponential function with the natural constant as the base; tanh() represents the hyperbolic tangent function.

[0090] It should be noted that river parameters describe the degree to which a local point is affected by erosion or siltation caused by river water. The larger the value of the river parameter, the greater the degree of this influence on the corresponding local area, and the more easily the river channel topography in this local area will change. Therefore, it is necessary to ensure a high resolution to provide a model foundation for subsequent analysis of river channel topography evolution using the established 3D river channel topography model; KJ kt This reflects the spatial interaction between different monitoring sections in terms of flow velocity and sediment concentration. Specifically, high-velocity upstream water increases the risk of river erosion in the corresponding monitoring section, while high downstream sediment concentration indicates a decrease in sediment transport capacity within that monitoring section. Additionally, through SJ... kt This reflects the temporal trends of river flow velocity and sediment concentration, thereby quantifying the potential risk of continuous erosion or siltation in the corresponding monitoring section. Furthermore, higher flow velocity results in stronger shear forces and a higher risk of riverbed erosion, while higher sediment concentration increases the probability of sediment deposition. Simultaneously, the viscosity effect inhibits flow velocity; therefore, by using exp(-v... kt ×s kt At the same time, the competing effects of scouring and siltation are quantified to reflect the possibility of changes in river topography in the corresponding monitoring sections.

[0091] Thus, the river parameters of any local point in the river point cloud data at each monitoring time are obtained.

[0092] Step S004: Obtain the resolution coefficients at the corresponding locations by using the morphological parameters and river parameters at different locations, and combine them with the river point cloud data to perform three-dimensional terrain modeling of the river channel. During the modeling process, the resolution coefficients are used to adjust the model resolution.

[0093] It should be noted that after analyzing the three-dimensional morphology of data points at different locations in the river point cloud data and the impact of rivers at different locations on the river topography, in order to balance the efficiency and accuracy of the analysis results in subsequent tasks such as river evolution analysis using the three-dimensional river topography model, this embodiment of the invention chooses to further construct a resolution coefficient through morphological parameters and river parameters, thereby facilitating subsequent local adjustments to the resolution of the three-dimensional river topography model.

[0094] First, for any monitoring time, the resolution coefficient of the local point at that monitoring time is obtained based on the morphological parameters of the local point and the river parameters of the local point at that monitoring time, wherein the morphological parameters and the river parameters are both positively correlated with the resolution coefficient.

[0095] As an optional embodiment, the resolution coefficient of the k-th local point in the river point cloud data at the t-th monitoring time is calculated as follows:

[0096] ;

[0097] Among them, Xt k Hl represents the morphological parameters of the k-th local point in the river point cloud data. kt This represents the river parameters of the k-th local point in the river point cloud data at the t-th monitoring time.

[0098] It should be noted that the morphological parameters are based on the distribution of data points in the river point cloud data in a local area, reflecting the static topographic sensitivity of the local area. The river parameters are based on the performance of the monitoring segment to which different local points in the river point cloud data belong in terms of flow velocity and sediment content in historical river data, dynamically reflecting the intensity of the effect of water flow and river sediment on topographic evolution. Therefore, the embodiments of the present invention obtain the resolution coefficient by combining the morphological parameters and the river parameters. When the resolution coefficient is larger, the resolution at the corresponding location increases, and when it is smaller, the resolution is smaller.

[0099] Then, for any monitoring time, the Delaunay subdivision method is used to model the river channel point cloud data into an irregular triangular network (TIN), and the resolution coefficient is used to adjust the local resolution of the river channel three-dimensional terrain model during the three-dimensional modeling process.

[0100] The specific process is as follows: set the basic point spacing d0, and dynamically adjust the target point spacing of the grid where the data points are located in the river point cloud data according to the resolution coefficient to obtain the adjusted spacing.

[0101] As an optional embodiment, the specific calculation method for the adjusted spacing is as follows:

[0102] ;

[0103] in, Indicates; d min This is the preset minimum spacing.

[0104] Finally, during the triangulation growth process, for any triangle: if the average resolution coefficient F at its centroid is greater than 0.6, the strict Delaunay criterion is applied (i.e., the circumcircle contains no other points); if F < 0.4, the circumcircle is allowed to contain other points. The points are identified and the flat triangles are merged; otherwise, the Delaunay partitioning criterion is used.

[0105] It should be noted that, based on equipment accuracy or experience, the preset spacing between foundation points is 1m, with a minimum spacing d. min The value is 0.5m, which can be adjusted according to the actual situation. This embodiment of the invention does not impose specific limitations.

[0106] It should be noted that by adjusting the resolution coefficient in the process of modeling the three-dimensional topography of the river channel using point cloud data, the resolution of the triangular mesh in the resulting three-dimensional topography model is affected by the river channel topography. This results in higher resolution for local areas of the river channel topography that are prone to changes due to topographic contours, water erosion, and siltation, thereby improving the accuracy of subsequent river channel topography analysis using the model. Conversely, for local areas that are less affected, the corresponding resolution will be lower, thus reducing the computational load and improving analysis efficiency during river channel topography analysis.

[0107] Thus, the adjusted spacing is obtained through the above method.

[0108] Step S005: Obtain the 3D terrain model of the river channel after resolution adjustment and visualize it.

[0109] Specifically, firstly, a three-dimensional terrain model of the river channel is displayed using model simulation software (such as Blender or ParaView), and then gradient color bands are set according to elevation values ​​in local areas of the three-dimensional terrain model of the river channel.

[0110] Then, multi-angle views (top view, side view, perspective) are generated to display the three-dimensional terrain model of the river channel from multiple perspectives.

[0111] Through the above steps, the model construction and visualization of the three-dimensional terrain of the river channel are completed.

[0112] Another embodiment of the present invention provides a three-dimensional river terrain generation device; please refer to [link to relevant documentation]. Figure 3 The diagram illustrates a structural schematic of a three-dimensional river terrain generation device according to an embodiment of the present invention, including a memory 201, a processor 202, and a computer program 2011 stored in the memory 201 and executable on the processor 202. When the processor executes the computer program, it implements the steps of any one of the methods for generating three-dimensional river terrain.

[0113] The present invention can implement all or part of the processes in the methods of the above embodiments, or it can be implemented by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when the computer program is executed by a processor, it can implement the steps of the various method embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable file, or some intermediate form. The computer-readable medium can include: any entity or device capable of carrying computer program code, recording media, USB flash drive, portable hard drive, magnetic disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signals, telecommunication signals, and software distribution media, etc.

[0114] Furthermore, in an alternative embodiment, the memory described above may include read-only memory and random access memory, and provide instructions and data to the processor. The memory may also include non-volatile random access memory. For example, the memory may also store device type information.

[0115] The aforementioned processor can be a Central Processing Unit (CPU), or other general-purpose processors, Digital Signal Processors (DSPs), Application Specific Integrated Circuits (ASICs), Field-Programmable Gate Arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. General-purpose processors can be microprocessors or any conventional processor. It is worth noting that the processor can be a processor supporting Advanced Reduced Instruction Set Machines (ARM) architecture.

[0116] Please see Figure 4 Another embodiment of the present invention also provides a three-dimensional river terrain generation system, the system comprising:

[0117] Data acquisition module 301: used to acquire river point cloud data, river elevation data and historical river data of several monitoring points. The historical river data includes water flow velocity data and sediment content data of several monitoring points on the river in history.

[0118] Morphological analysis module 302: used to determine several local points in the river point cloud data and combine them with river elevation data to perform morphological analysis on the river point cloud data in both the plane and vertical directions, and obtain the morphological parameters at each local point.

[0119] River analysis module 303: used to combine historical river data to obtain river parameters of different local points in river point cloud data at different times. The river parameters are used to describe the degree to which local points are affected by erosion or siltation caused by river water.

[0120] Resolution module 304: used to obtain the resolution coefficients at corresponding locations by using morphological parameters and river parameters at different locations, and to perform three-dimensional terrain modeling of the river channel by combining river point cloud data. The resolution coefficients are used to adjust the model resolution during the modeling process.

[0121] Model generation module 305: Used to obtain and visualize the three-dimensional terrain model of the river channel after resolution adjustment.

[0122] It should be noted that the exp(-x) model used in this embodiment is only used to represent negative correlation and constrain the output of the model to be within the (0,1) interval. In specific implementation, it can be replaced by other models with the same purpose. This embodiment only uses the exp(-x) model as an example for description and does not make specific limitations on it, where x refers to the input of the model.

[0123] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for generating three-dimensional river channel terrain, characterized in that, The method includes the following steps: Acquire river point cloud data, river elevation data, and historical river data from several monitoring points. The historical river data includes water velocity and sediment concentration data from several monitoring points along the river in history. Several local points in the river point cloud data were identified and the river elevation data were combined to perform morphological analysis on the river point cloud data in both the plane and vertical directions to obtain the morphological parameters of each local point. By combining historical river data, river parameters are obtained from different local points in the river point cloud data at different times. These river parameters are used to describe the degree to which local points are affected by erosion or siltation caused by river water. The resolution coefficients at different locations are obtained by using morphological and river parameters at different locations. The river channel point cloud data is then combined to create a 3D terrain model of the river channel. During the modeling process, the resolution coefficients are used to adjust the model resolution. Obtain and visualize the 3D terrain model of the river channel after resolution adjustment; The specific method for determining several local points in the river channel point cloud data and combining them with river channel elevation data to perform morphological analysis of the river channel point cloud data in both the planar and vertical directions, and obtaining the morphological parameters at each local point, includes the following: Using river elevation data, several equally spaced riverbed curves are obtained from the river point cloud data; Obtain several liquid surface curves that are parallel to the horizontal plane and equally spaced from the river point cloud data; The intersection of the riverbed curve and the liquid surface curve is used as a local point in the river point cloud data. The contour morphology feature analysis of the local area where the local point is located is used to obtain the morphological parameters of the local point. The specific method for obtaining the morphological parameters of the local points is as follows: For any local point in the river point cloud data, all data points within a preset neighborhood of the local point are obtained as neighborhood points of the local point, resulting in a local region formed by the local point and its corresponding neighborhood points. The local region is then fitted with a surface using the least squares method to obtain the local surface corresponding to the local point. The elevation corresponding to each data point in the river point cloud data is obtained. The difference between the average elevation of all data points on the local surface of the local point and the elevation value corresponding to the liquid surface curve with the largest elevation is taken as the local elevation difference of the local point. The average curvature of the local point on the corresponding local surface and the standard deviation of the elevations corresponding to all data points on the local surface are obtained. Based on the local elevation difference, the average curvature, and the standard deviation, the morphological parameters of the corresponding local point are obtained, wherein the average curvature and the local elevation difference are positively correlated with the morphological parameters, and the standard deviation is negatively correlated with the morphological parameters. The specific method for obtaining river parameters at different local points in river point cloud data at different times by combining historical river data includes: The river point cloud data is divided into several monitoring segments in the watershed using historical river data from all monitoring points; For any monitoring segment, the average flow velocity value of the monitoring segment at the same monitoring time in all monitoring cycles is taken as the flow velocity level value of the monitoring segment at the corresponding monitoring time, and the sediment concentration level value of the monitoring segment at the corresponding monitoring time is obtained using the same method. Based on the magnitude of the flow velocity and sediment concentration levels corresponding to different monitoring sections in the river point cloud data, and their spatial and temporal variations in the river channel, the river parameters at any local point in the river point cloud data at various monitoring times are obtained; The specific method for obtaining the river parameters is as follows: For any given monitoring time, the spatial river factor of the local point at that monitoring time is obtained based on the difference in flow velocity level between the monitoring segment where the local point is located and the previous monitoring segment, and the difference in sediment concentration level between the current monitoring segment and the next monitoring segment. The forward differences in flow velocity and sediment concentration levels over time for the monitoring segment where the local point is located at that monitoring time are obtained, along with the average values ​​of flow velocity and sediment concentration levels for the monitoring segment at all monitoring times. Based on the forward differences in flow velocity and sediment concentration levels over time and the differences between these values ​​and the corresponding flow velocity and sediment concentration levels, the temporal river factor of the local point at that monitoring time is obtained. Combining the spatial river factor, temporal river factor, flow velocity level, and sediment concentration level of the local point at that monitoring time, the river parameters of the local point at that monitoring time are obtained. The method involves obtaining resolution coefficients at different locations using morphological and river parameters, combining this with river point cloud data to perform 3D terrain modeling of the river channel, and adjusting the model resolution using these resolution coefficients during the modeling process. Specific methods include: For any monitoring time, the resolution coefficient of the local point at the monitoring time is obtained based on the morphological parameters of the local point and the river parameters of the local point at the monitoring time, wherein the morphological parameters and the river parameters are both positively correlated with the resolution coefficient. For any monitoring time, the Delaunay subdivision method is used to model the irregular triangular network of the river point cloud data, and the resolution coefficient is used to adjust the local resolution of the river 3D terrain model during the 3D modeling process.

2. The method for generating three-dimensional river channel topography according to claim 1, characterized in that, The specific method for obtaining the riverbed curve is as follows: A digital elevation model is established using river elevation data. The centerline of the river is obtained from the river point cloud data. Starting from the upstream starting point of the river, river cross sections perpendicular to the river centerline are generated at equal intervals of distance d based on the digital elevation model. The curve formed by the river cross sections in the river point cloud data is used as the riverbed curve, where d is a preset distance parameter.

3. The method for generating three-dimensional river channel topography according to claim 2, characterized in that, The specific method for obtaining the liquid level curve is as follows: Curve fitting is performed on the river point cloud data using the least squares method to obtain several liquid surface curves on both sides of the river point cloud data, such that the surface on which the liquid surface curves are located is parallel to the horizontal plane, and the liquid surface curves are evenly distributed with an interval of d.

4. The method for generating three-dimensional river channel topography according to claim 1, characterized in that, The specific method for obtaining the historical river data of the aforementioned monitoring points is as follows: Using one year as a monitoring cycle, multiple monitoring points are evenly set up on the river channel. Within year A, a number of fixed monitoring times are set to obtain the water flow velocity data at each monitoring point at each monitoring time. Simultaneously, water samples are collected at each monitoring point, and the suspended sediment concentration is determined using the standard drying and weighing method as sediment content data. Each monitoring cycle corresponds to one flow velocity data and one sediment content data, where A is the preset first parameter.

5. A three-dimensional riverbed terrain generation device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the steps of a method for generating three-dimensional river terrain as described in any one of claims 1 to 4.

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