A Visual Simulation Design Method and System for Landscape Architecture Based on Digital Elevation Model

Abstract

This application relates to the field of digital model landscape design technology, specifically to a method and system for landscape visualization simulation design based on digital elevation models. The method includes: acquiring point cloud data of each grid cell in the landscape design area, obtaining time-series DEM stacks and time-series gradient field stacks at each acquisition time, and statistically analyzing pipe slope and pipe diameter data for each grid cell; acquiring the runoff accumulation and curvature values ​​of each grid cell at each acquisition time, determining the hydrological sensitivity index of the micro-topography for each grid cell at each acquisition time, and obtaining a hydrological sensitivity index matrix for each acquisition time; extracting the drainage demand density of each grid cell, and obtaining the runoff overload risk level of each grid cell at each acquisition time by combining the pipe slope and pipe diameter data with the degree of change of the hydrological sensitivity index in the hydrological sensitivity index matrix; and optimizing the design by zoning and strategy. This application can improve the effect of landscape visualization simulation design.

Description

Technical Field

[0001] This application relates to the field of digital model landscape design technology, specifically to a landscape visualization simulation design method and system based on digital elevation models. Background Technology

[0002] Digital elevation models (DEMs), as digital terrain models that accurately describe the Earth's surface morphology, have profoundly changed the field of landscape design through their development and application. Early DEMs were primarily used for basic topographic analysis. However, with the maturity of GIS technology, advancements in computer graphics, and the widespread availability of high-precision data acquisition methods, DEMs have been deeply integrated with plant libraries, material libraries, hydrological models, and lighting simulations, driving landscape visualization simulation from static display to the construction of highly realistic, interactive, and dynamic virtual environments.

[0003] In digital elevation model (DEM)-driven landscape visualization simulations, due to the uncertainty of ecological process simulations, existing landscape simulations often rely on empirical models such as the Manning formula. This makes it difficult to quantify the nonlinear impact of small DEM undulations on surface runoff paths, velocities, and local catchments, leading to drainage designs deviating from the actual hydrological response. Existing technologies, such as LISFLOOD-FP, employ hydrological models coupled with physical mechanisms to overcome the limitations of empirical formulas. By combining DEM grid cells with two-dimensional shallow water equations, they simulate gravity-driven water flow, dynamically calculating the water depth, velocity, and direction of each cell. Theoretically, this can more accurately reflect the impact of micro-topography on hydrological processes. However, this technology suffers from extreme computational resource dependence. With high-resolution DEMs, the physical model needs to iteratively solve fluid dynamics equations involving millions of grids, with a single simulation taking hours to days, far exceeding the design cycle tolerance. In practice, this often forces a reduction in resolution or simplification of boundary conditions, which weakens the micro-topographic effect and leads to uncertainties in the visualization simulation. Summary of the Invention

[0004] To address the aforementioned technical problems, the purpose of this application is to provide a method and system for landscape visualization simulation design based on digital elevation models. The specific technical solution adopted is as follows:

[0005] This application provides a garden visualization simulation design method based on a digital elevation model, including the following steps:

[0006] Point cloud data of each grid cell in the garden design area is acquired, and time-series DEM stacks are obtained by stacking. Time-series gradient field stacks are obtained by gradient field calculation, and pipe slope and pipe diameter data of each grid cell are statistically analyzed.

[0007] Based on the temporal DEM stack and temporal gradient field stack of all grid cells at each acquisition time, the runoff accumulation matrix and curvature matrix at each acquisition time are obtained. The runoff accumulation and curvature values ​​of each grid cell at each acquisition time are obtained, and then the hydrological sensitivity index of the micro-topography of each grid cell at each acquisition time is determined. Using the hydrological sensitivity index of the micro-topography of all grid cells at each acquisition time, the hydrological sensitivity index matrix at each acquisition time is obtained.

[0008] The drainage demand density of each grid cell is extracted by the hydrological sensitivity index matrix. By combining the drainage demand density, pipe slope and pipe diameter data of each grid cell in the hydrological sensitivity index matrix with the degree of change of the hydrological sensitivity index in the hydrological sensitivity index matrix, the runoff overload risk of each grid cell at each collection time is obtained.

[0009] Based on the runoff overload risk level, zoning is designed and strategies are optimized.

[0010] Preferably, the D8 algorithm is used to obtain the bus accumulation matrix corresponding to the time-series DEM stack of all grid cells at each acquisition time. Each element in the bus accumulation matrix at each acquisition time represents the bus accumulation of each grid cell at each acquisition time.

[0011] Preferably, the curvature matrix corresponding to the temporal gradient field stack of all grid cells at each acquisition time is obtained by using the terrain curvature field calculation method. Each element in the curvature matrix at each acquisition time represents the curvature value of each grid cell at each acquisition time.

[0012] Preferably, the method for obtaining the hydrological sensitivity index of the micro-topography for each grid cell at each acquisition time is as follows:

[0013] ;in, Let be the hydrological sensitivity index of the micro-topography of the i-th grid cell at acquisition time t. Let be the curvature value of the i-th grid cell at acquisition time t. The cumulative flow of the i-th grid cell at acquisition time t, lg() is a logarithmic function with base 10.

[0014] Preferably, the hydrological sensitivity indices of all grid cells at each acquisition time are used to construct a hydrological sensitivity index matrix for each acquisition time according to the coordinate position of the grid cells. The element in the x-th row and y-th column of the hydrological sensitivity index matrix at the current acquisition time represents the hydrological sensitivity index value of the micro-topography of the grid cell with coordinates (x,y) at the current acquisition time.

[0015] Preferably, the method for obtaining the drainage demand density of each grid unit is as follows:

[0016] The density estimation algorithm is used to estimate the density of the hydrological sensitivity index matrix at each acquisition time, so as to obtain the drainage demand density matrix at each acquisition time. Each element in the drainage demand density matrix is ​​the drainage demand density of the corresponding grid cell.

[0017] Preferably, the method for obtaining the runoff overload risk level of each grid cell in the landscape design at each acquisition time is as follows:

[0018] ;in, The runoff overload risk level of the i-th grid cell at time t in the landscape design. Let be the drainage demand density of the i-th grid cell at acquisition time t. The maximum value in the pipe flow capacity matrix at time t is the value of the pipe flow capacity matrix. It represents the maximum value of the spatial gradient corresponding to all grid cells in the hydrological sensitivity index matrix at acquisition time t.

[0019] Preferably, the kernel density, pipe slope, and pipe diameter data of each grid cell in the hydrological sensitivity index matrix at each acquisition time are used as inputs to the gravity drainage model to obtain the pipe flow capacity matrix at each acquisition time.

[0020] The gradient magnitude of each grid cell in the hydrological sensitivity index matrix at each acquisition time is obtained by using the Sobel operator, and the absolute value of the gradient magnitude is recorded as the spatial gradient of the hydrological sensitivity index of each grid cell.

[0021] Preferably, the step of designing zones and optimizing strategies based on the runoff overload risk level further includes:

[0022] Grid cells with a runoff overload risk level greater than 1 are designated as critical regions. Hydrological simulations are performed on critical regions using a two-dimensional shallow water equation physical model accelerated by GPU parallelism. Hydrological simulations are performed on non-critical regions using the Manning formula.

[0023] This application also provides a garden visualization simulation design system based on a digital elevation model, including a memory, a processor, and a computer program stored in the memory and running on the processor. When the processor executes the computer program, it implements the steps of any of the above-described garden visualization simulation design methods based on digital elevation models.

[0024] As can be seen from the above, the garden visualization simulation design method and system based on digital elevation model provided in this application have at least the following beneficial effects:

[0025] This application addresses the difficulty in quantifying the nonlinear hydrological response of micro-topography by using the D8 algorithm and curvature field calculation to comprehensively reflect the nonlinear control intensity of micro-topography on runoff path, velocity, and catchment, thus solving the problem that traditional empirical formulas cannot capture the hydrological deviations caused by minute topographic undulations.

[0026] Furthermore, to address the challenge of dynamically assessing the risk of local overload in drainage networks, a kernel density estimation (KDE) and gravity drainage model are employed, combined with a hydrological sensitivity index gradient to eliminate the neglect of micro-topographical abrupt changes in the simplified model, thereby accurately identifying high-risk nodes for urban flooding.

[0027] This application improves the efficiency of full-area simulation through an adaptive zoning calculation strategy. It dynamically divides critical and non-critical areas based on risk level B, and only enables the GPU-accelerated high-precision shallow water equation physical model in critical areas. The Manning formula empirical model is used in non-critical areas to achieve optimized allocation of computing resources, generate a three-dimensional ecological scene that integrates micro-topographic effects, support dynamic adjustment of landscape and drainage schemes, significantly improve design reliability and avoid local flood risks. Attached Figure Description

[0028] To more clearly illustrate the technical solutions and advantages in the embodiments of this application 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 this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0029] Figure 1 A flowchart illustrating the steps of the landscape visualization simulation design method based on digital elevation model provided in this application. Detailed Implementation

[0030] To further illustrate the technical means and effects adopted by this application to achieve the intended purpose of the invention, the following, in conjunction with the accompanying drawings and preferred embodiments, details the specific implementation, structure, features, and effects of the garden visualization simulation design method and system based on digital elevation models proposed in this application. 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.

[0031] Unless otherwise specified and limited, terms such as “comprising,” “including,” or any other variations thereof are intended to cover a non-exclusive inclusion, such that a circuit structure, article, or device that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such an article or device. Without further limitation, an element defined by the phrase “comprising one…” does not exclude the presence of other identical elements in the article or device that includes said element. Furthermore, the term “and / or” as used herein includes any and all combinations of one or more of the associated listed items. 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 application pertains.

[0032] The following, in conjunction with the accompanying drawings, details the specific scheme of the garden visualization simulation design method and system based on digital elevation model provided in this application.

[0033] Please see Figure 1 It illustrates a flowchart of the steps of a garden visualization simulation design method based on a digital elevation model according to an embodiment of this application, including the following steps:

[0034] Step 1: Obtain point cloud data for each grid cell in the garden design area, stack the data to obtain the temporal DEM stack at each acquisition time, obtain the temporal gradient field stack through gradient field calculation, and statistically analyze the pipe slope data and pipe diameter data for each grid cell.

[0035] In the digital elevation model-based landscape visualization simulation design system, a four-layer distributed architecture is adopted to achieve closed-loop management of the entire process: The data layer integrates multi-source heterogeneous data through a distributed storage cluster, including high-precision DEMs, multispectral vegetation indices, and soil property libraries collected by UAV LiDAR, providing the system with topographic and ecological foundations; The computing layer deploys a hydrological acceleration module and a micro-topographic feature extraction engine, and utilizes a CUDA-driven GPU parallel computing framework to perform hardware-level optimization of the shallow water equation solver and topographic curvature analysis algorithm, breaking through the computational bottleneck of traditional hydrological models; The simulation layer integrates a physical lighting model and a parametric plant growth algorithm based on the Unity engine to dynamically generate a three-dimensional ecological scene that incorporates micro-topographic and hydrological responses; The application layer relies on a VR interactive platform to support designers in real-time modification of topographic parameters and adjustment of plant configurations, and synchronous updates of visualization effects within 5 seconds.

[0036] This architecture unifies high-precision physical simulation and real-time visualization into a single workflow through a tightly coupled design of the computation layer and the simulation layer.

[0037] A drone-based LiDAR platform equipped with a centimeter-level RTK positioning system is deployed over the garden site to be designed, covering all ground grid units in the entire garden design area, and performing periodic time-series aerial surveys. In this embodiment, the design accuracy is 0.1m × 0.1m, dividing the garden area into regular square grids. Each grid unit generates a unique elevation value through LiDAR point cloud interpolation, which serves as the smallest spatial unit for hydrological analysis and visualization simulation.

[0038] High-density 3D point clouds are generated using LiDAR active pulse scanning. Ground points are separated using a cloth simulation filtering algorithm. The same garden area is continuously scanned by a drone at fixed time intervals of 5 minutes to capture dynamic changes in terrain or hydrological parameters. The point cloud data of each grid cell is then temporally aligned. In practical applications, other existing technologies can be used for temporal alignment of the point cloud data of each grid cell. In this embodiment, the preferred method is:

[0039] First, the data coordinate system of each grid cell is unified through the RTK positioning system;

[0040] Then, for each grid cell, at each acquisition time, the LiDAR system processes the ground point cloud of each grid cell in real time, generates elevation data slices for that time, and stacks them in chronological order according to the scanning order of the UAV to form a time-series DEM stack.

[0041] Furthermore, for the time-series DEM stack at each acquisition time, gradient field calculation is performed on it, and the time-series gradient field stack at each acquisition time is output. In this embodiment, zero-padding is performed on the left side of the output time-series gradient field stack to align it with the time-series DEM stack.

[0042] At this point, the temporal DEM stack and temporal gradient field stack for each grid cell at each acquisition time can be obtained.

[0043] Furthermore, for each grid cell, the elevation of the pipeline node is acquired in real time via GPS-RTK field measurements. The slope value is calculated for each pipe segment and spatialized to the corresponding grid cell to obtain the pipe slope for each grid cell. Pipe diameter parameters are extracted from the BIM / CAD design drawings and mapped to the grid cells covered by the pipeline network to obtain the pipe diameter for each grid cell.

[0044] At this point, the pipe slope data and pipe diameter data for each grid cell have been obtained.

[0045] Step 2: Based on the temporal DEM stack and temporal gradient field stack of all grid cells at each acquisition time, obtain the runoff accumulation matrix and curvature matrix at each acquisition time. This yields the runoff accumulation and curvature values ​​of each grid cell at each acquisition time, thereby determining the hydrological sensitivity index of the micro-topography for each grid cell at each acquisition time. Using the hydrological sensitivity indices of the micro-topography for all grid cells at each acquisition time, obtain the hydrological sensitivity index matrix for each acquisition time.

[0046] Because traditional landscape hydrological simulation relies on empirical formulas such as the Manning formula, it cannot quantify the nonlinear effects of minute topographic undulations in digital elevation models (DEMs) on surface runoff paths, velocities, and local catchments, resulting in significant deviations between drainage design and actual hydrological response.

[0047] Therefore, in this embodiment, taking the current acquisition time as an example, the time-series DEM stack of all grid cells at the current acquisition time is used as the input of the D8 algorithm. Due to the diversity of runoff directions in garden micro-topography, omnidirectional identification is required. An 8-neighborhood search mode is set, and a 0.1m elevation tolerance threshold is used to avoid flow direction oscillations caused by noise. The D8 algorithm is used to output the runoff accumulation matrix at the current acquisition time. Each element in the runoff accumulation matrix represents the runoff accumulation of each grid cell, and its value represents the overall runoff intensity of that grid cell. It should be noted that the specific process of extracting the runoff accumulation matrix through the D8 algorithm to obtain the runoff accumulation of each grid cell is a prior art known to those skilled in the art, and the specific process will not be described in detail in this embodiment.

[0048] Furthermore, using the time-series gradient field stack of all grid cells at the current acquisition time as input, the curvature matrix at the current acquisition time is extracted using the terrain curvature field calculation method. Preferably, in this embodiment, a 3×3 window quadratic surface fitting is set to balance micro-topography accuracy and noise resistance, and the curvature scaling factor is set to 0.01 / m to adapt to the terrain fluctuations at the garden scale. The curvature matrix at the current acquisition time can be output through terrain curvature field calculation. Positive values ​​in the curvature matrix represent convex ridges, and negative values ​​represent depressions. Each element in the curvature matrix represents the curvature value of each grid cell, and its value reveals the regulatory effect of micro-topography on water flow.

[0049] Specifically, based on the above process, the flow accumulation matrix and curvature matrix at each acquisition time can be obtained. For each element in the flow accumulation matrix and curvature matrix at the current acquisition time, it represents the flow accumulation and curvature value of each grid cell at the current acquisition time. Among them, the D8 algorithm and terrain curvature field calculation are well-known techniques and will not be elaborated here.

[0050] Based on the above analysis, a hydrological sensitivity index for the micro-topography of each grid cell at each acquisition time is constructed, and the specific calculation formula is as follows:

[0051] ;in, Let be the hydrological sensitivity index of the micro-topography of the i-th grid cell at acquisition time t, and lg() be the logarithmic function with base 10. The curvature value of the i-th grid cell at acquisition time t quantifies the intensity of micro-topography's regulation of water flow. It represents the degree to which convex ridge topography enhances water flow dispersion. The larger the value, the stronger the regulation effect of micro-topography on water flow and the higher the uncertainty of hydrological response. The cumulative runoff amount of the i-th grid cell at time t is used to characterize the runoff intensity of the grid cell, reflecting the overall capacity of the area to receive and collect upstream water flow. The larger the value, the higher the runoff intensity, and the more likely the area is to experience water accumulation or runoff concentration.

[0052] Among them, the hydrological sensitivity index represents a micro-topographic sensitivity indicator, comprehensively quantifying the sensitivity of micro-topographic undulations to the nonlinear impact of surface hydrological processes. It highlights how minute micro-topographic undulations can cause the actual hydrological response to deviate from design expectations through nonlinear effects. The larger the value, the higher the sensitivity of micro-topography to hydrological processes, and the more likely small topographic errors in the design are to trigger significant drainage failures or local flood risks.

[0053] Furthermore, the hydrological sensitivity indices of all raster units within the garden design area at the same acquisition time are integrated into a spatially explicit hydrological sensitivity index matrix. This matrix dynamically characterizes the spatial distribution of sensitivity to the nonlinear influence of micro-topographic undulations on surface runoff paths, velocities, and catchments. Specifically, taking the current acquisition time as an example, each row in the hydrological sensitivity index matrix at the current acquisition time represents a north-south sequence in geographic space, and each column represents an east-west sequence in geographic space. The element in the x-th row and y-th column represents the hydrological sensitivity index value of the raster unit with coordinates (x, y) in the DEM at the current acquisition time.

[0054] Step 3: Extract the drainage demand density of each grid cell through the hydrological sensitivity index matrix. By combining the drainage demand density, pipe slope and pipe diameter data of each grid cell in the hydrological sensitivity index matrix with the degree of change of the hydrological sensitivity index in the hydrological sensitivity index matrix, obtain the runoff overload risk of each grid cell at each collection time.

[0055] Due to the uneven spatial distribution of surface runoff caused by the undulating micro-topography of the garden, traditional designs struggle to quantify the risk of local overload in the drainage network. For example, there may be excessive water accumulation in specific concave areas while the drainage capacity of steep slope areas is redundant. Therefore, in this embodiment, the hydrological sensitivity index matrix at each acquisition time is used as input, and a kernel density estimation algorithm is employed, with a bandwidth parameter h=20m set. The output is a drainage demand density matrix, where each element represents the drainage demand density of the corresponding grid cell, which is the runoff accumulation intensity per unit area in that grid cell. This is a probability density mapping of the spatial matching degree between the micro-topographic sensitive area and the pipe network.

[0056] Furthermore, using the kernel density, pipe slope, and pipe diameter data of each grid cell in the hydrological sensitivity index matrix at each acquisition time as input, a gravity drainage model is used, with the Manning roughness coefficient n=0.013 set. The pipe flow capacity matrix at each acquisition time is output, and the maximum value in the pipe flow capacity matrix is ​​extracted to characterize the maximum theoretical drainage efficiency of a single pipe section.

[0057] The construction and calculation of the gravity drainage model are well-known techniques and will not be elaborated upon here.

[0058] Furthermore, for the current acquisition time, the hydrological sensitivity index matrix at the current acquisition time is used as the input of the Sobel operator. The gradient magnitude of each grid cell in the hydrological sensitivity index matrix at the current acquisition time is calculated by the Sobel operator. The absolute value of the gradient magnitude is recorded as the spatial gradient of the hydrological sensitivity index of each grid cell. The specific process of the Sobel operator to obtain the gradient magnitude is a well-known technology and will not be described in detail in this embodiment.

[0059] Based on the above analysis, the runoff overload risk level of the landscape design for each grid cell at each data acquisition time is calculated:

[0060] ;in, The runoff overload risk level of the i-th grid cell at time t in the landscape design. Let be the drainage demand density of the i-th grid cell at acquisition time t. The water catchment concentration of the micro-topography sensitive area is quantified by kernel density estimation, which characterizes the probability density of runoff accumulation intensity per unit area. The larger the value, the stronger the local water catchment pressure and the higher the risk of overload of the drainage network. The maximum value in the pipeline flow capacity matrix at time t is calculated based on the Manning formula, taking into account the pipe diameter, slope, and roughness coefficient. It represents the maximum theoretical drainage efficiency of a single pipe section under gravity. The larger the value, the stronger the pipeline drainage capacity and the greater the margin for coping with the water catchment load. The maximum value of the spatial gradient corresponding to all grid cells in the hydrological sensitivity index matrix at acquisition time t reflects the maximum disturbance intensity of micro-topographical changes on the runoff path. The larger the value, the more severe the change in water flow direction caused by topographical undulations, and the higher the uncertainty of drainage design.

[0061] Among them, the runoff overload risk level B dynamically quantifies the mismatch risk between the drainage network system and the micro-topographic hydrological response. The larger the value, the higher the risk of local flooding or pipeline overflow in the selected grid unit during extreme hydrological events such as rainstorms, due to the coupling effect of micro-topographic disturbance and insufficient network capacity.

[0062] Step 4: Design zones and optimize strategies based on the runoff overload risk level.

[0063] In landscape visualization simulation design based on digital elevation models (DEMs), the high-precision DEM-driven physical hydrological model requires iteratively solving complex shallow water equations over the entire area at high resolution, resulting in extremely high computational resource consumption. A single simulation can take several hours or even days, far exceeding the tolerance range of the landscape design iteration cycle. In practice, to meet timeliness requirements, the resolution is often reduced or conditions are simplified, which weakens the key nonlinear effects of small undulations (micro-topography) in the DEM on surface runoff paths, flow velocities, and local catchments, leading to decreased simulation accuracy and drainage design deviating from the actual hydrological response.

[0064] Therefore, in this embodiment, the zoning and calculation strategies are designed based on the runoff overload risk level to achieve synergistic optimization of calculation accuracy and efficiency. The preferred zoning calculation strategy is designed as follows:

[0065] 1) Key area identification: First, the drainage pressure index obtained through the above process, namely the runoff overload risk level B, is used to perform a spatial scan of the entire garden design area. Grid cells with a runoff overload risk level >1 are identified as key areas with high drainage pressure, namely high-risk nodes where drainage pipe network overload or micro-topography leads to abnormal confluence.

[0066] 2) Refined Physical Model Focus: Furthermore, a high-precision two-dimensional shallow water equation physical model accelerated by GPU parallelism is used only in key areas where the identified runoff overload risk level is >1 to perform local refined hydrological simulation. This model can accurately characterize the dynamics of gravity-driven water flow (depth, velocity, and direction) and fully reflect the micro-topographic effects at key locations.

[0067] 3) Correction of empirical models for non-critical areas: For non-critical areas with a runoff overload risk level ≤1, the computationally efficient Manning formula is used for hydrological simulation to improve the rationality of the simulation of non-critical areas. In practical application scenarios, implementers can also select other empirical models to conduct hydrological simulation of non-critical areas.

[0068] 4) Results fusion and visualization: The high-precision calculation results of the physical model of the key area are seamlessly integrated with the hydrological simulation results of the non-key area to generate a real-time, high-fidelity hydrological response map of the entire garden site, and the data is fed back to the 3D visualization engine in real time to support designers in optimizing the drainage system and adjusting the landscape layout.

[0069] Based on the same inventive concept as the above method, this application also provides a garden visualization simulation design system based on a digital elevation model, including a memory, a processor, and a computer program stored in the memory and running on the processor. When the processor executes the computer program, it implements the steps of any of the above-described garden visualization simulation design methods based on a digital elevation model.

[0070] It is understood that the order of the embodiments described above is merely for descriptive purposes and does not represent the superiority or inferiority of the embodiments. Furthermore, the above description focuses on specific embodiments of this specification. Additionally, the processes depicted in the accompanying drawings do not necessarily require a specific or sequential order to achieve the desired results. In some implementations, multitasking and parallel processing are possible or may be advantageous.

[0071] The various embodiments in this specification are described in a progressive manner. The same or similar parts between the various embodiments can be referred to each other. Each embodiment focuses on describing the differences from other embodiments.

[0072] The above description is merely an embodiment of this application and is not intended to limit the scope of this application. Any equivalent structural or procedural transformations made based on the description and drawings of this application, or direct or indirect applications in other related technical fields, are similarly included within the protection scope of this application.

Claims

1. A garden visualization simulation design method based on digital elevation model, characterized in that, Includes the following steps: Point cloud data of each grid cell in the garden design area is acquired, and time-series DEM stacks are obtained by stacking. Time-series gradient field stacks are obtained by gradient field calculation, and pipe slope and pipe diameter data of each grid cell are statistically analyzed. Based on the temporal DEM stack and temporal gradient field stack of all grid cells at each acquisition time, the runoff accumulation matrix and curvature matrix at each acquisition time are obtained. The runoff accumulation and curvature values ​​of each grid cell at each acquisition time are obtained, and then the hydrological sensitivity index of the micro-topography of each grid cell at each acquisition time is determined. The hydrological sensitivity index of all grid cells at each acquisition time is constructed according to the coordinate position of the grid cells to form the hydrological sensitivity index matrix of each acquisition time. The element in the x-th row and y-th column of the hydrological sensitivity index matrix at the current acquisition time represents the hydrological sensitivity index value of the micro-topography of the grid cell with coordinates (x,y) at the current acquisition time. The drainage demand density of each grid cell is extracted by the hydrological sensitivity index matrix. By combining the drainage demand density, pipe slope and pipe diameter data of each grid cell in the hydrological sensitivity index matrix with the degree of change of the hydrological sensitivity index in the hydrological sensitivity index matrix, the runoff overload risk of each grid cell at each collection time is obtained. Based on the runoff overload risk level, the design zoning is carried out. Grid cells with a runoff overload risk level greater than 1 are designated as critical areas, and grid cells with a runoff overload risk level less than or equal to 1 are designated as non-critical areas. Hydrological simulation is performed on the critical areas using a two-dimensional shallow water equation physical model accelerated by GPU parallelism. Hydrological simulation is performed on the non-critical areas using the Manning formula for strategy optimization. The method for obtaining the hydrological sensitivity index of the micro-topography for each grid cell at each acquisition time is as follows: ;in, Let be the hydrological sensitivity index of the micro-topography of the i-th grid cell at acquisition time t. Let be the curvature value of the i-th grid cell at acquisition time t. The cumulative current of the i-th grid cell at acquisition time t, lg() is a logarithmic function with base 10; The method for obtaining the runoff overload risk level of the landscape design for each grid cell at each acquisition time is as follows: ;in, The runoff overload risk level of the i-th grid cell at time t in the landscape design. Let be the drainage demand density of the i-th grid cell at acquisition time t. The maximum value in the pipe flow capacity matrix at time t is the value of the pipe flow capacity matrix. It represents the maximum value of the spatial gradient corresponding to all grid cells in the hydrological sensitivity index matrix at acquisition time t.

2. The garden visualization simulation design method based on digital elevation model as described in claim 1, characterized in that, The D8 algorithm is used to obtain the bus accumulation matrix corresponding to the time-series DEM stack of all grid cells at each acquisition time. Each element in the bus accumulation matrix at each acquisition time represents the bus accumulation of each grid cell at each acquisition time.

3. The garden visualization simulation design method based on digital elevation model as described in claim 1, characterized in that, The curvature matrix corresponding to the temporal gradient field stack of all grid cells at each acquisition time is obtained by using the terrain curvature field calculation method. Each element in the curvature matrix at each acquisition time represents the curvature value of each grid cell at each acquisition time.

4. The garden visualization simulation design method based on digital elevation model as described in claim 1, characterized in that, The method for obtaining the drainage demand density of each grid cell is as follows: The density estimation algorithm is used to estimate the density of the hydrological sensitivity index matrix at each acquisition time, so as to obtain the drainage demand density matrix at each acquisition time. Each element in the drainage demand density matrix is ​​the drainage demand density of the corresponding grid cell.

5. The garden visualization simulation design method based on digital elevation model as described in claim 1, characterized in that, The kernel density, pipe slope, and pipe diameter data of each grid cell in the hydrological sensitivity index matrix at each acquisition time are used as inputs to the gravity drainage model to obtain the pipe flow capacity matrix at each acquisition time. The gradient magnitude of each grid cell in the hydrological sensitivity index matrix at each acquisition time is obtained by using the Sobel operator, and the absolute value of the gradient magnitude is recorded as the spatial gradient of the hydrological sensitivity index of each grid cell.

6. A garden visualization simulation design system based on a digital elevation model, comprising a memory, a processor, and a computer program stored in the memory and running on the processor, characterized in that, When the processor executes the computer program, it implements the steps of the garden visualization simulation design method based on the digital elevation model as described in any one of claims 1-5.

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