Garden visual simulation design method and system based on digital elevation model

Abstract

The invention relates to the technical field of digital model garden design, in particular to a garden visual simulation design method and system based on a digital elevation model, and the method comprises the steps: obtaining point cloud data of each grid unit of a garden design region, obtaining a time sequence DEM stack and a time sequence gradient field stack of each collection moment, and obtaining a digital elevation model (DEM) stack; performing statistics on pipeline slope data and pipe diameter data of each grid unit; acquiring a confluence cumulant and a curvature value of each grid unit at each acquisition moment, determining a hydrological sensitive index of each grid unit at each acquisition moment in the microtopography, and obtaining a hydrological sensitive index matrix at each acquisition moment; the drainage demand density of each grid unit is extracted, and the runoff overload risk degree of the garden design of each grid unit at each collection moment is obtained through pipeline gradient and pipe diameter data in combination with the change degree of hydrological sensitive indexes in the hydrological sensitive index matrix; and designing partitions and carrying out strategy optimization. According to the method, the garden visual simulation design effect can be improved.

Description

Technical Field

[0001] The present application relates to the technical field of digital model garden design, and in particular to a garden visualization simulation design method and system based on a digital elevation model. Background Art

[0002] The development and application of digital elevation models (DEMs), which accurately describe the earth's surface morphology, have profoundly transformed the field of landscape design. Early DEMs were primarily used for basic terrain analysis. With the maturity of GIS technology, advances 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. This has driven landscape visualization simulations from static displays 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 inherent in simulating ecological processes, existing landscape simulations often rely on empirical models such as the Manning equation. These models struggle to quantify the nonlinear effects of DEM micro-undulations on surface runoff paths, flow velocities, and local catchment, leading to deviations in drainage design from realistic hydrological responses. Existing hydrological models that couple physical mechanisms, such as LISFLOOD-FP, attempt to overcome the limitations of empirical formulas. By combining DEM grid cells with two-dimensional shallow water equations to simulate gravity-driven flow, they dynamically calculate the water depth, velocity, and direction for each cell. In theory, this technique can more accurately reflect the impact of microtopography on hydrological processes. However, this technique is extremely computationally resource-intensive. With high-resolution DEMs, the physical model requires iteratively solving fluid dynamics equations for millions of cells, resulting in simulation times that can range from hours to days, far exceeding the design cycle's tolerance. In practice, this often forces a reduction in resolution or simplification of boundary conditions, which in turn weakens the microtopography effect and leads to uncertainty in the visualization simulation. Summary of the Invention

[0004] In order to solve the above technical problems, the purpose of this application is to provide a garden visualization simulation design method and system based on a digital elevation model. The technical solutions adopted are as follows: The present invention provides a garden visualization simulation design method based on a digital elevation model, comprising the following steps: Obtain point cloud data for each grid cell in the garden design area, stack them to obtain a time-series DEM stack at each acquisition moment, calculate the gradient field to obtain a time-series gradient field stack, and calculate the pipe slope data and pipe diameter data for each grid cell. Based on the time series DEM stack and time series 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 value of each grid cell at each acquisition time are obtained, and then the hydrological sensitivity index of the microtopography of each grid cell at each acquisition time is determined. The hydrological sensitivity index matrix of each acquisition time is obtained using the hydrological sensitivity index of the microtopography of all grid cells at each acquisition time. The drainage demand density of each grid cell is extracted through the hydrological sensitivity index matrix. The runoff overload risk of the garden design of each grid cell at each sampling time is obtained 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. Zoning is designed and strategy optimization is performed based on the runoff overload risk.

[0005] Preferably, the D8 algorithm is used to obtain the runoff accumulation matrix corresponding to the time series DEM stack of all grid cells at each acquisition moment, and each element in the runoff accumulation matrix at each acquisition moment represents the runoff accumulation of each grid cell at each acquisition moment.

[0006] Preferably, a terrain curvature field calculation method is used to obtain the curvature matrix corresponding to the temporal gradient field stack of all grid units at each acquisition moment, and each element in the curvature matrix at each acquisition moment represents the curvature value of each grid unit at each acquisition moment.

[0007] Preferably, the method for obtaining the hydrological sensitivity index of the micro-topography of each grid unit at each acquisition moment is: ;in, is the hydrological sensitivity index of the micro-topography of the i-th grid cell at the acquisition time t, is the curvature value of the i-th grid cell at the acquisition time t, The cumulative flow of the i-th grid cell at the collection time t, lg( ) is a logarithmic function with base 10.

[0008] Preferably, the hydrological sensitivity indexes of all grid cells at each collection moment are formed into a hydrological sensitivity index matrix at each collection moment according to the coordinate positions of the grid cells, wherein the element in the xth row and yth column of the hydrological sensitivity index matrix at the current collection moment represents the hydrological sensitivity index value of the micro-topography of the grid cell with coordinates (x, y) at the current collection moment.

[0009] Preferably, the method for obtaining the drainage requirement density of each grid unit is: The kernel density estimation algorithm is used to estimate the density of the hydrological sensitivity index matrix at each collection moment, and the drainage demand density matrix at each collection moment is obtained. Each element in the drainage demand density matrix is ​​the drainage demand density of the corresponding grid cell.

[0010] Preferably, the method for obtaining the runoff overload risk of the garden design of each grid unit at each collection moment is: ;in, is the runoff overload risk of the garden design of the i-th grid unit at the collection time t, is the drainage demand density of the i-th grid cell at the collection time t, is the maximum value in the pipeline flow capacity matrix at the acquisition time t, It is the maximum value of the spatial gradient corresponding to all grid cells in the hydrological sensitivity index matrix at the acquisition time t.

[0011] Preferably, the kernel density, pipeline slope and pipe diameter data of each grid cell in the hydrological sensitivity index matrix at each collection time are used as inputs of the gravity drainage model to obtain the pipeline flow capacity matrix at each collection time; The gradient amplitude of each grid cell in the hydrological sensitivity index matrix at each acquisition moment is obtained by the Sobel operator, and the absolute value of the gradient amplitude is recorded as the spatial gradient of the hydrological sensitivity index of each grid cell.

[0012] Preferably, the designing of zoning and performing strategy optimization based on the runoff overload risk further includes: Grid cells with a runoff overload risk greater than 1 are regarded as key areas. Hydrological simulation is performed on key areas using a two-dimensional shallow water equation physical model accelerated by GPU parallelism; Manning's formula is used for hydrological simulation in non-critical areas.

[0013] An embodiment of the present application also provides 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. When the processor executes the computer program, it implements the steps of any one of the above-mentioned garden visualization simulation design methods based on a digital elevation model.

[0014] As can be seen from the above, the garden visualization simulation design method and system based on digital elevation model provided by this application have at least the following beneficial effects: This application addresses the difficulty in quantifying the nonlinear hydrological response of microtopography. It uses the D8 algorithm and curvature field calculation to comprehensively reflect the nonlinear regulation intensity of microtopography on runoff path, flow velocity, and catchment, solving the problem that traditional empirical formulas cannot capture hydrological deviations caused by small terrain fluctuations. Furthermore, to address the challenge of dynamic assessment of local overload risk in drainage networks, we used kernel density estimation (KDE) and a gravity drainage model, combined with a hydrological sensitivity index gradient to eliminate the effects of micro-topography mutations in simplified models, to accurately identify high-risk nodes for waterlogging. This application improves the simulation efficiency of the entire area through an adaptive zoning calculation strategy, dynamically divides critical areas and non-critical areas based on risk level B, enables GPU-accelerated high-precision shallow water equation physical model only in critical areas, and adopts Manning's formula empirical model in non-critical areas to achieve optimal allocation of computing resources, generate three-dimensional ecological scenes that integrate micro-topography effects, support dynamic adjustment of landscape and drainage plans, significantly improve design reliability and avoid local flood risks. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] In order to more clearly illustrate the technical solutions and advantages of the embodiments of the present application or the prior art, the following is a brief introduction to the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0016] Figure 1 This is a flowchart of the steps of the garden visualization simulation design method based on the digital elevation model provided in this application. DETAILED DESCRIPTION

[0017] To further illustrate the technical means and effects employed by this application to achieve the intended invention objectives, the following, in conjunction with the accompanying drawings and preferred embodiments, describes in detail the specific implementation, structure, features, and effects of the digital elevation model-based garden visualization simulation design method and system proposed in this application. In the following description, different references to "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. Furthermore, specific features, structures, or characteristics of one or more embodiments may be combined in any suitable manner.

[0018] Unless otherwise specified and limited, terms such as "comprises", "includes" or any other variants thereof are intended to cover non-exclusive inclusion, so that a circuit structure, article or device comprising a series of elements includes not only those elements, but also includes other elements not explicitly listed, or also includes elements inherent to such article or device. In the absence of further restrictions, an element defined by the sentence "comprises a ..." does not exclude the presence of other identical elements in the article or device comprising the element. In addition, the term "and\or" used herein includes any and all combinations of one or more related listed items. All technical and scientific terms used herein have the same meaning as those commonly understood by those skilled in the art to which this application belongs.

[0019] The specific scheme of the garden visualization simulation design method and system based on digital elevation model provided by this application is described in detail below with reference to the accompanying drawings.

[0020] See also Figure 1 , which shows a flowchart of a garden visualization simulation design method based on a digital elevation model provided by an embodiment of the present application, including the following steps: Step 1: Obtain the point cloud data of each grid cell in the garden design area, obtain the time series DEM stack at each acquisition time by stacking, obtain the time series gradient field stack by gradient field calculation, and count the pipeline slope data and pipe diameter data of each grid cell.

[0021] In the garden visualization simulation design system based on digital elevation models, 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 distributed storage clusters, including high-precision DEM, multispectral vegetation index and soil property library collected by drone LiDAR, providing the system with a terrain and ecological foundation; the computing layer deploys a hydrological acceleration module and a micro-topography feature extraction engine, and uses the CUDA-driven GPU parallel computing framework to optimize the shallow water equation solver and terrain curvature analysis algorithm at the hardware level, breaking through the computational bottleneck of traditional hydrological models; the simulation layer integrates physical lighting models and parametric plant growth algorithms based on the Unity engine to dynamically generate three-dimensional ecological scenes that integrate micro-topography and hydrological responses; the application layer relies on a VR interactive platform to support designers in real-time modification of terrain parameters and adjustment of plant configurations, and synchronously update visualization effects within 5 seconds.

[0022] This architecture integrates high-precision physical simulation and real-time visualization into the same workflow through a tightly coupled design of the computing layer and the simulation layer.

[0023] An unmanned aerial vehicle (UAV) laser radar (LiDAR) platform equipped with a centimeter-level RTK positioning system is deployed above the garden site to be designed, covering all ground grid cells in the entire garden design area and performing periodic time-series aerial surveys. In this embodiment, the design accuracy is 0.1m×0.1m, and the garden area is divided into a regular square grid. Each grid cell generates a unique elevation value through LiDAR point cloud interpolation, which serves as the minimum spatial unit for hydrological analysis and visualization simulation.

[0024] A high-density three-dimensional point cloud is generated through LiDAR active pulse scanning. Ground points are separated using a fabric simulation filtering algorithm. Continuous drone scanning of the same garden area is performed at a fixed time interval of once every 5 minutes to capture the dynamic changes in terrain or hydrological parameters. Time-series alignment processing is performed on the point cloud data of each grid unit. In actual application scenarios, implementers can use other existing technologies to perform time-series alignment processing on the point cloud data of each grid unit. In this embodiment, the following are preferred: First, the data coordinate system of each grid cell is unified through the RTK positioning system; Then, for each grid cell, at each acquisition moment, the LiDAR system processes the ground point cloud of each grid cell in real time, generates elevation data slices at that moment, and stacks them in chronological order according to the scanning order of the drone to form a time series DEM stack; Furthermore, for the temporal DEM stack at each acquisition moment, a gradient field calculation is performed on it, and the temporal gradient field stack at each acquisition moment is output. In this embodiment, a zero-padding operation is performed on the left side of the output temporal gradient field stack to align it with the temporal DEM stack.

[0025] At this point, the time series DEM stack and time series gradient field stack of each grid cell at each acquisition time can be obtained.

[0026] Furthermore, for each grid cell, the elevation of the pipeline network node is obtained in real time using GPS-RTK field measurements. The slope value is calculated for each pipe segment and spatialized to the corresponding grid, resulting in the pipe slope for each grid cell. Pipe diameter parameters are extracted from BIM / CAD design drawings and mapped to the grid cells covered by the pipeline network to obtain the pipe diameter for each grid cell.

[0027] At this point, the pipe slope data and pipe diameter data of each grid cell are obtained.

[0028] Step 2: Based on the time series DEM stack and time series 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 value of each grid cell at each acquisition time are obtained, and then the hydrological sensitivity index of the microtopography of each grid cell at each acquisition time is determined. The hydrological sensitivity index matrix at each acquisition time is obtained using the hydrological sensitivity index of the microtopography of all grid cells at each acquisition time.

[0029] Because traditional garden hydrological simulation relies on empirical formulas such as the Manning formula, it is unable to quantify the nonlinear effects of small terrain undulations in digital elevation models (DEMs) on surface runoff paths, flow rates, and local water collection, resulting in significant deviations between drainage designs and actual hydrological responses.

[0030] Therefore, in this embodiment, taking the current acquisition moment as an example, the time series DEM stack of all grid cells at the current acquisition moment 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 adopted to avoid flow direction oscillation caused by noise. The D8 algorithm is used to output the runoff accumulation matrix at the current acquisition moment. Each element in the runoff accumulation matrix represents the runoff accumulation of each grid cell, and its value represents the overall water collection intensity of the grid cell. It should be noted that the specific process of extracting the runoff accumulation matrix using the D8 algorithm to obtain the runoff accumulation of each grid cell is an existing technology well known to those skilled in the art, and the specific process will not be repeated in this embodiment.

[0031] Furthermore, the temporal gradient field stack of all grid cells at the current acquisition moment is used as input, and the curvature matrix of the current acquisition moment is extracted using the terrain curvature field calculation method. Preferably, in this embodiment, a quadratic surface fitting of a 3×3 window is set to balance the micro-topography accuracy and noise resistance, and the curvature scaling coefficient is set to 0.01 / m to adapt to the garden-scale terrain fluctuation. The curvature matrix of the current acquisition moment can be output through the terrain curvature field calculation. The positive value in the curvature matrix represents a ridge, and the negative value represents a depression. Each element in the curvature matrix represents the curvature value of each grid cell, and its value reveals the regulatory effect of the micro-topography on water flow.

[0032] Specifically, according to the above process, the runoff accumulation matrix and curvature matrix at each acquisition moment can be obtained. Each element in the runoff accumulation matrix and curvature matrix at the current acquisition moment represents the runoff accumulation and curvature value of each grid cell at the current acquisition moment, respectively. Among them, the D8 algorithm and terrain curvature field calculation are well-known technologies and will not be elaborated here.

[0033] Based on the above analysis, the hydrological sensitivity index of the micro-topography of each grid cell at each acquisition time is constructed. The specific calculation formula is: ;in, is the hydrological sensitivity index of the micro-topography of the i-th grid cell at the acquisition time t, lg( ) is the logarithmic function with base 10, is the curvature value of the i-th grid cell at the acquisition time t, which quantifies the intensity of micro-topography regulation on water flow and indicates the degree to which the 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 of the i-th grid cell at the collection time t is used to characterize the water collection intensity of the grid cell, reflecting the overall ability of the area to receive and collect upstream water. The larger the value, the higher the water collection intensity, and the more likely the area is to experience waterlogging or runoff concentration.

[0034] The hydrological sensitivity index (HSI) represents the sensitivity of microtopography to surface hydrological processes. It comprehensively quantifies the sensitivity of microtopography to the nonlinear effects of surface hydrological processes, highlighting how subtle microtopography fluctuations can cause actual hydrological responses to deviate from design expectations through nonlinear effects. A higher value indicates a greater sensitivity to microtopography, and a higher likelihood that even small topographic errors in the design will lead to significant drainage failures or localized flooding risks.

[0035] Furthermore, the hydrological sensitivity indices of all grid cells in the garden design area at the same acquisition time are integrated into a spatially explicit hydrological sensitivity index matrix. This matrix dynamically represents the spatial distribution of the sensitivity of the nonlinear effects of microtopography on surface runoff paths, flow rates, and catchment. 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 row x and column y represents the hydrological sensitivity index value of the microtopography at the current acquisition time for the grid cell with coordinates (x, y) in the DEM.

[0036] Step 3: Extract the drainage demand density of each grid cell through the hydrological sensitivity index matrix. Combined with the drainage demand density, pipe slope, and pipe diameter data of each grid cell in the hydrological sensitivity index matrix and the degree of change of the hydrological sensitivity index in the hydrological sensitivity index matrix, the runoff overload risk of the garden design of each grid cell at each collection time is obtained.

[0037] Due to the uneven spatial distribution of surface runoff caused by the undulating microtopography of the garden, traditional designs find it difficult to quantify the risk of local overload of the drainage network. For example, a specific depression may have excessive water collection while the steep slope area may have redundant drainage capacity. Therefore, in this embodiment, the hydrological sensitivity index matrix at each collection moment is used as input, and a kernel density estimation algorithm is used, in which the bandwidth parameter h = 20m is set to output a drainage demand density matrix. Each element in the drainage demand density matrix represents the drainage demand density of the grid cell corresponding to the element, which is the runoff concentration intensity per unit area in the grid cell. It is a probability density mapping of the spatial matching degree between the microtopography sensitive area and the drainage network.

[0038] Furthermore, the kernel density, pipeline slope, and pipe diameter data of each grid cell in the hydrological sensitivity index matrix at each collection moment were used as input. The gravity drainage model was used, and the Manning roughness coefficient n was set to 0.013. The pipeline flow capacity matrix at each collection moment was output, and the maximum value in the pipeline flow capacity matrix was extracted to represent the maximum theoretical drainage efficiency of a single pipeline section.

[0039] The construction and calculation of the gravity drainage model are well-known technologies and will not be elaborated here.

[0040] Furthermore, for the current collection moment, the hydrological sensitivity index matrix at the current collection moment is used as the input of the Sobel operator, and the gradient amplitude of each grid cell in the hydrological sensitivity index matrix at the current collection moment is calculated by the Sobel operator, and the absolute value of the gradient amplitude is recorded as the spatial gradient of the hydrological sensitivity index of each grid cell. The specific process of obtaining the gradient amplitude by the Sobel operator is a well-known technology and is not repeated in this embodiment.

[0041] Based on the above analysis, the runoff overload risk of the garden design of each grid unit at each collection time is calculated: ;in, is the runoff overload risk of the garden design of the i-th grid unit at the collection time t, is the drainage demand density of the i-th grid cell at the collection time t. The concentration of runoff in the micro-topography sensitive area is quantified by kernel density estimation, which represents the probability density of runoff concentration intensity per unit area. The larger the value, the stronger the runoff pressure in the local area and the higher the risk of overload of the drainage network. The maximum value in the pipeline flow capacity matrix at the sampling time t is calculated based on the Manning formula by combining the pipe diameter, slope, and roughness coefficient. It represents the maximum theoretical drainage efficiency of a single pipe section under the action of gravity. The larger the value, the stronger the pipeline drainage capacity and the greater the margin for handling the water load. It is the maximum value of the spatial gradient corresponding to all grid cells in the hydrological sensitivity index matrix at the acquisition time t, reflecting the maximum interference intensity of micro-topography mutation on the runoff path. The larger the value, the more severe the mutation of water flow direction caused by terrain undulation, and the higher the uncertainty of drainage design.

[0042] Among them, the runoff overload risk B dynamically quantifies the mismatch risk between the drainage network system and the micro-topography hydrological response. The larger the value, the higher the risk of local waterlogging or pipe overflow in extreme hydrological events such as heavy rain in the selected grid unit due to the coupling effect of micro-topography disturbance and insufficient pipe network capacity.

[0043] Step 4: Design zoning and perform strategy optimization based on the runoff overload risk.

[0044] In landscape visualization simulation design based on digital elevation models (DEMs), high-precision DEM-driven physical hydrological models require iteratively solving complex shallow-water equations across the entire region at high resolution. This consumes enormous computing resources, and a single simulation can take hours or even days, far exceeding the tolerance of the landscape design iteration cycle. In practice, to meet time constraints, resolution is often reduced or conditions are simplified. This, in turn, weakens the critical nonlinear effects of DEM micro-undulations (microtopography) on surface runoff paths, flow rates, and localized water collection. This results in reduced simulation accuracy and a deviation of drainage design from the true hydrological response.

[0045] Therefore, in this embodiment, the partitioning and calculation strategy are designed according to the runoff overload risk to achieve the coordinated optimization of calculation accuracy and efficiency. The preferred partitioning calculation strategy is designed as follows: 1) Critical area identification: First, the drainage pressure index (i.e., runoff overload risk B) obtained through the above process is used to perform a spatial scan of the entire garden design area. Grid cells with a runoff overload risk greater than 1 are identified as critical areas with high drainage pressure, i.e., high-risk nodes where the drainage network is overloaded or where runoff anomalies are caused by micro-topography.

[0046] 2) Refined Physical Model Focus: Furthermore, a high-precision, two-dimensional shallow water equation physical model, accelerated by GPU parallelism, is used to perform localized, refined hydrological simulations only in key areas identified where the runoff overload risk is greater than 1. This model accurately depicts gravity-driven flow dynamics (water depth, velocity, and direction), fully reflecting the microtopographic effects at key locations.

[0047] 3) Modification of the empirical model for non-critical areas: For non-critical areas with a runoff overload risk of ≤1, the computationally efficient Manning formula is used for hydrological simulation to improve the rationality of the simulation in non-critical areas. In actual application scenarios, implementers can also select other empirical models to perform hydrological simulation in non-critical areas.

[0048] 4) Result Fusion and Visualization: Seamlessly integrate the high-precision calculation results of the physical model in key areas with the hydrological simulation results in non-critical areas to generate a real-time, high-fidelity hydrological response map for the entire garden site. This map is then fed back to the 3D visualization engine in real time to support designers in optimizing drainage systems and adjusting landscape layouts.

[0049] Based on the same inventive concept as the above method, an embodiment of the present 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 one of the above-mentioned garden visualization simulation design methods based on a digital elevation model.

[0050] It should be understood that the order in which the embodiments of the present application are presented is for illustrative purposes only and does not necessarily represent the superiority or inferiority of the embodiments. Furthermore, the foregoing descriptions of specific embodiments of this specification are provided. Furthermore, the processes depicted in the accompanying drawings do not necessarily require the specific order or sequential sequence shown to achieve the desired results. In certain embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0051] The various embodiments in this specification are described in a progressive manner, and the same or similar parts between the various embodiments can be referred to each other. Each embodiment focuses on the differences from other embodiments.

[0052] The above content is only an implementation method of the present application and is not intended to limit the scope of the present application. Any equivalent structure or equivalent process transformation made using the contents of the present application specification and drawings, or directly or indirectly applied in other related technical fields, are also included in the scope of protection of the present application.

Claims

1. A garden visualization simulation design method based on a digital elevation model is characterized by: The following steps are involved: Obtain point cloud data for each grid cell in the garden design area, stack them to obtain a time-series DEM stack at each acquisition moment, calculate the gradient field to obtain a time-series gradient field stack, and calculate the pipe slope data and pipe diameter data for each grid cell. Based on the time series DEM stack and time series 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 value of each grid cell at each acquisition time are obtained, and then the hydrological sensitivity index of the microtopography of each grid cell at each acquisition time is determined. The hydrological sensitivity index matrix of each acquisition time is obtained using the hydrological sensitivity index of the microtopography of all grid cells at each acquisition time. The drainage demand density of each grid cell is extracted through the hydrological sensitivity index matrix. The runoff overload risk of the garden design of each grid cell at each sampling time is obtained 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. Zoning is designed and strategy optimization is performed based on the runoff overload risk.

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

3. The garden visualization simulation design method based on digital elevation model according to claim 1, characterized in that: The terrain curvature field calculation method is used to obtain the curvature matrix corresponding to the temporal gradient field stack of all grid cells at each acquisition moment. Each element in the curvature matrix at each acquisition moment represents the curvature value of each grid cell at each acquisition moment.

4. The garden visualization simulation design method based on digital elevation model according to claim 1, characterized in that: The method for obtaining the hydrological sensitivity index of the micro-topography of each grid unit at each acquisition time is: ;in, is the hydrological sensitivity index of the micro-topography of the i-th grid cell at the acquisition time t, is the curvature value of the i-th grid cell at the acquisition time t, The cumulative flow of the i-th grid cell at the collection time t, lg( ) is a logarithmic function with base 10.

5. The garden visualization simulation design method based on digital elevation model according to claim 1, characterized in that: The hydrological sensitivity indexes of all grid cells at each collection time are combined to form a hydrological sensitivity index matrix at each collection time according to the coordinate positions of the grid cells. The element in the xth row and yth column of the hydrological sensitivity index matrix at the current collection time represents the hydrological sensitivity index value of the micro-topography of the grid cell with coordinates (x, y) at the current collection time.

6. The garden visualization simulation design method based on digital elevation model according to claim 1, characterized in that: The method for obtaining the drainage requirement density of each grid cell is as follows: The kernel density estimation algorithm is used to estimate the density of the hydrological sensitivity index matrix at each collection moment, and the drainage demand density matrix at each collection moment is obtained. Each element in the drainage demand density matrix is ​​the drainage demand density of the corresponding grid cell.

7. The garden visualization simulation design method based on digital elevation model according to claim 1, characterized in that: The method for obtaining the runoff overload risk of the garden design of each grid unit at each collection time is as follows: ;in, is the runoff overload risk of the garden design of the i-th grid unit at the collection time t, is the drainage demand density of the i-th grid cell at the collection time t, is the maximum value in the pipeline flow capacity matrix at the acquisition time t, It is the maximum value of the spatial gradient corresponding to all grid cells in the hydrological sensitivity index matrix at the acquisition time t.

8. The garden visualization simulation design method based on digital elevation model according to claim 7 is characterized in that: The kernel density, pipe slope and pipe diameter data of each grid cell in the hydrological sensitivity index matrix at each sampling time are used as the input of the gravity drainage model to obtain the pipe flow capacity matrix at each sampling time. The gradient amplitude of each grid cell in the hydrological sensitivity index matrix at each acquisition moment is obtained by the Sobel operator, and the absolute value of the gradient amplitude is recorded as the spatial gradient of the hydrological sensitivity index of each grid cell.

9. The garden visualization simulation design method based on digital elevation model according to claim 1, characterized in that: The designing of zoning and performing strategy optimization based on the runoff overload risk further includes: Grid cells with a runoff overload risk greater than 1 are regarded as key areas. Hydrological simulation is performed on key areas using a two-dimensional shallow water equation physical model accelerated by GPU parallelism; Manning's formula is used for hydrological simulation in non-critical areas.

10. 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, the steps of the garden visualization simulation design method based on the digital elevation model are implemented as described in any one of claims 1 to 9.

Images