A garden landscape simulation method based on virtual reality technology and three-dimensional visualization

Abstract

The invention discloses a garden landscape simulation method based on virtual reality technology and three-dimensional visualization, which obtains three-dimensional point cloud data of garden terrain and generates a three-dimensional grid model; calculates the slope value of each block grid of the three-dimensional grid model, generates a slope matrix of the three-dimensional grid model, clusters the block grids belonging to the same slope value interval into similar sub-blocks; sorts the terrain slope interval corresponding to each similar sub-block from small to large, selects the adapted plant species according to the illumination condition of the sorted terrain, and predicts the photosynthetic rate value of the plant species based on the photosynthetic rate prediction model; calculates the number of plants planted in the terrain according to the area of ​​the terrain corresponding to each sub-block and the photosynthetic rate value of the plant in the terrain; simulates planting on the three-dimensional grid model, and performs garden landscape virtual reality display on the three-dimensional grid model based on virtual reality technology. The invention can carry out targeted design of different slope areas of garden landscape.

Description

Technical Field

[0001] The invention relates to a garden landscape simulation method based on virtual reality technology and three-dimensional visualization, and belongs to the technical field of wind field detection. Background Art

[0002] Landscape architecture is a comprehensive applied discipline that integrates natural sciences and humanities and arts. With the passage of time, the current construction of landscape architecture is gradually updated with the development of the times and the progress of science. The comprehensive science and art of landscape architecture have achieved breakthroughs based on traditional garden culture. Science and technology have played an important role in promoting the development of landscape architecture.

[0003] Virtual reality technology can effectively solve the problem of the connection between abstract thinking and the entities it produces. It has practical advantages such as safety, low cost, almost no constraints from environmental conditions, and the ability to change time or scale at will at any time. Designers can not only give full play to their artistic imagination, but also combine it with reality in the early design stage, shorten the design cycle, improve design quality, and save investment. Virtual reality technology can be applied to the information management of the entire life cycle of garden landscape projects. In the design process, designers use virtual reality technology to establish a comprehensive, intuitive, interactive, and truthfully reflect the design of the VR scene, which is convenient for a more intuitive and comprehensive understanding of the design, and also convenient for designers to make partial modifications to the design details, and analyze and compare with the scheme before the modification in real time, and strengthen the combination of VR technology and pan-planning design. In addition, in the virtual three-dimensional space of the garden landscape, different schemes can be switched in real time, and different people's different perspectives can be used to help compare the characteristics and shortcomings of different schemes, and accelerate effective communication and exchanges between designers, project managers, and decision makers.

[0004] However, the existing garden landscape simulation design method cannot carry out targeted design for different areas, and cannot improve the growth efficiency of plants and enhance the overall greening effect of the garden, thus leading to over-planting or under-planting. Summary of the invention

[0005] The technical problem to be solved by the present invention is that in the process of garden landscape simulation design, different areas need to be designed in a targeted manner to improve the growth efficiency of plants and enhance the overall greening effect of the garden.

[0006] In order to solve the above technical problems, the present invention provides a garden landscape simulation method based on virtual reality technology and three-dimensional visualization, comprising the following steps:

[0007] Step 1: Obtain 3D point cloud data of the garden terrain, divide the 3D point cloud data into multiple blocks, find the best block boundary, and generate a 3D grid model with multiple block grids;

[0008] Step 2: Calculate the slope value of each block grid of the three-dimensional grid model, generate the slope matrix of the three-dimensional grid model, and cluster the block grids belonging to the same slope value interval into similar sub-blocks;

[0009] Step 3: Sort each similar sub-block according to the corresponding slope value interval from small to large, so as to determine the lighting conditions of each similar sub-block after sorting, and select plant species suitable for planting based on different lighting conditions, and predict the photosynthetic rate value of the plant species on the terrain corresponding to the similar sub-block based on the photosynthetic rate prediction model;

[0010] Step 4: Calculate the number of plants planted on the terrain corresponding to each similar sub-block according to the area of ​​the terrain corresponding to each similar sub-block and the predicted photosynthetic rate value;

[0011] Step 5: simulate planting on the three-dimensional grid model according to the plant species in the terrain corresponding to each similar sub-block determined in step 3 and the number of plants planted calculated in step 4, and perform a virtual reality display of the garden landscape on the three-dimensional grid model based on virtual reality technology.

[0012] In the step 2: let the slope matrix of the three-dimensional grid model be W={w ij}, i represents the vertex, j represents the bottom edge corresponding to the vertex, i∈[1,n], n is the number of vertices in the three-dimensional mesh model, j∈[1,m], m is the number of bottom edges in the three-dimensional mesh model, w ij Represents the slope value from the i-th vertex to the corresponding bottom edge j;

[0013] The slope value w ij The block grids with values ​​in the same slope value range are classified into one category and defined as the same type of sub-blocks.

[0014] In the step three: the slope value interval data of the terrain corresponding to each type of sub-block is collected, the terrain is sorted from weak to strong according to the lighting conditions, and based on the lighting conditions of the sorted terrain, the plants suitable for planting in the terrain corresponding to each sub-block of the same type are determined.

[0015] A photosynthetic rate prediction model is established to predict the photosynthetic rate value of the terrain corresponding to each ordered sub-block of the same type in turn. The photosynthetic rate prediction model is:

[0016] ,

[0017] in, P is the predicted photosynthetic rate value, is the maximum photosynthetic rate of the plant, are model parameters, is the light intensity.

[0018] In the step 4: firstly, the reference planting density D is determined according to the empirical value; secondly, the adjustment coefficient K is set according to the predicted photosynthetic rate value;

[0019] Calculate the adjusted planting density Da: Da = D × K;

[0020] Among them, if the predicted photosynthetic rate value is high, the corresponding adjustment coefficient K is set to >1; if the predicted photosynthetic rate value is low, the corresponding adjustment coefficient K is set to <1.

[0021] Assume that the area of ​​the terrain corresponding to the same type of sub-block is A, and calculate the number m of plants planted in the terrain corresponding to the same type of sub-block as: m=Da×A.

[0022] Setting a plurality of appropriate adjustment coefficients K, calculating and obtaining a plurality of data sets M of plant quantities, and using distribution distances to remove deviating data from the plurality of data sets of plant quantities;

[0023] The data set M of multiple plant numbers m is transformed into a distribution distance D using its mean μ and the difference matrix V:

[0024] ,

[0025] For a data set M of multiple plant numbers, M=[m1,m2…,m b …,m n ] T , the difference matrix V is an n×n matrix, where each element is defined as follows:

[0026] ,

[0027] is the number of a-th and b-th plants m a and m b The covariance between

[0028] The distribution distance of all data in the data set M is calculated, and the data exceeding the threshold TH0 is removed from the data set M of multiple plant quantities to obtain an optimal planting quantity interval.

[0029] In step 3, the optimal plant configuration is confirmed by using the superior and inferior contribution algorithm, and the plant configuration value is calculated by the following formula:

[0030] Plant configuration value = high-quality contribution × first weight − low-quality contribution × second weight;

[0031] The plant with the highest configuration value is the plant species finally selected for planting.

[0032] The digital elevation model data is loaded into the three-dimensional network model, and the terrain analysis is performed using the digital elevation model to calculate the slope value of each block grid.

[0033] The inferior contributions include: inferior contribution of overgrowth, inferior contribution of invasiveness, and inferior contribution of maintenance cost; the superior contributions include: superior contribution of providing habitat, superior contribution of beautifying the environment, and superior contribution of ecology.

[0034] The beneficial technical effect achieved by the present invention is as follows: the method of the present invention obtains three-dimensional point cloud data of a garden terrain and generates a three-dimensional grid model of the garden terrain; by obtaining the three-dimensional point cloud data of the garden terrain, high-precision terrain information can be obtained, the slope value of each block grid of the three-dimensional grid model is calculated, and a slope matrix of the three-dimensional grid model is generated. Block grids belonging to the same slope value interval are clustered into similar sub-blocks, and the three-dimensional grid model is clustered into different sub-blocks according to the slope matrix, so as to realize targeted design of different slope areas of the garden landscape.

[0035] Based on the terrain slope interval corresponding to each similar sub-block from small to large, the optimal lighting terrain sorting is determined, and the adapted plant species are selected according to the lighting conditions of the sorted terrain, and the photosynthetic rate values ​​of the plant species are predicted based on the photosynthetic rate prediction model; sorting by slope interval and selecting adapted plant species according to lighting conditions can make plant configuration more scientific and reasonable, ensuring that plants can grow in the environment that is most suitable for their growth; selecting plant species based on lighting conditions and using the photosynthetic rate prediction model to predict the photosynthetic rate values ​​of plants can improve plant growth efficiency, thereby improving the overall greening effect of the garden.

[0036] At the same time, the present invention calculates the number of plants planted in the terrain according to the area of ​​the terrain corresponding to each sub-block and the photosynthetic rate value of the plants in the terrain; simulates the planting of the three-dimensional grid model according to the determined plant types in the terrain corresponding to each sub-block and the calculated number of plants planted, and performs a virtual reality display of the garden landscape on the three-dimensional grid model based on virtual reality technology, and determines the number of plants by calculating the terrain area corresponding to each sub-block and the photosynthetic rate value of the plants, which makes the estimation of the number of plants more accurate and avoids over-planting or under-planting.

[0037] Optimize planting layout: Simulate planting according to the determined plant species and quantity to optimize the layout of plants, ensuring that each plant can grow under its best growth conditions while avoiding competition between plants.

[0038] Three-dimensional visualization technology enables designers and clients to more intuitively understand garden design plans, including spatial layout, plant configuration, water body settings, etc., so as to better grasp the design details; the immersive experience provided by virtual reality technology allows users to feel as if they are in the designed garden, and experience the actual effect of the garden after it is built in advance, thereby increasing user participation and satisfaction. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] Figure 1 A schematic diagram of a flow chart of a garden landscape simulation method based on virtual reality technology and three-dimensional visualization according to Embodiment 1 of the present invention;

[0040] Figure 2 A schematic diagram of a three-dimensional point cloud image obtained by a laser radar;

[0041] Figure 3 A schematic diagram of marking plant species on a three-dimensional grid model according to the present invention;

[0042] Figure 4 This is a three-dimensional graph of the predicted photosynthetic rate value after taking into account the light quantum flux density and carbon dioxide concentration in the preferred embodiment of the present invention. DETAILED DESCRIPTION

[0043] In order to make the purpose, technical solution and advantages of the embodiments of the present application clearer, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0044] In the drawings of the specific embodiments of the present invention, in order to better and more clearly describe the working principles of the various components in the system, the connection relationship of the various parts in the device is shown, which only clearly distinguishes the relative position relationship between the various components, and cannot constitute a limitation on the signal transmission direction, connection sequence and size, dimensions and shape of the components or structures.

[0045] Example 1

[0046] As attached Figure 1 FIG. 1 is a flow chart of a garden landscape simulation method based on virtual reality technology and three-dimensional visualization according to Embodiment 1 of the present invention. The garden landscape simulation method includes the following steps:

[0047] Step 1: Obtain the 3D point cloud data of the garden terrain, divide the 3D point cloud data into multiple blocks, find the best block boundary, and generate a 3D grid model with multiple block grids.

[0048] First, three-dimensional point cloud data representing a garden terrain is received.

[0049] 3D point cloud data is a collection of a large number of 3D coordinate points, which comes from laser radar, 3D scanner or other 3D measurement equipment. These data provide the height information of the garden terrain surface, such as Figure 2Shown is a 3D point cloud image obtained by lidar.

[0050] Next, the received 3D point cloud data is segmented, divided into multiple blocks, and the optimal block boundary is found to generate a 3D mesh model with multiple block meshes; each block mesh is composed of vertices, edges and faces, and each block mesh includes additional information such as texture and color, making the final 3D mesh model more detailed.

[0051] The purpose of forming a 3D mesh model with multiple block meshes is to better manage and process data. Because the entire 3D point cloud data may be very large and complex, each block mesh after segmentation will contain less point cloud data, which is easier to process later.

[0052] Step 2: Calculate the slope value of each block grid of the three-dimensional grid model, generate the slope matrix of the three-dimensional grid model, and cluster the block grids belonging to the same slope value interval into similar sub-blocks.

[0053] First, define the slope value of each block grid in the 3D grid model.

[0054] Slope value: indicates the angle of the ground's inclination. The angle of inclination affects the intensity of light on the surface and determines the possibility of plant growth.

[0055] In this embodiment, it is assumed that the block mesh is a triangular shape, and all block meshes in the three-dimensional mesh model have vertices and corresponding bottom edges.

[0056] In a preferred embodiment, a digital elevation model is used to perform terrain analysis and calculate the slope value of each block grid. The following are specific steps and methods for calculating the slope value using the data of the digital elevation model:

[0057] Load digital elevation model data into the 3D network model:

[0058] First, you need to load DEM data containing terrain digital elevation information. These data are all in grid format, such as GeoTIFF, ASCII Grid, etc.

[0059] Select analysis tools: Use GIS software, such as ArcGIS, QGIS, etc., and use the built-in terrain analysis tools to perform slope analysis, and specify the input DEM data according to the required parameters.

[0060] Set parameters: Enter the block grid and select to load the DEM data corresponding to the block grid.

[0061] Output block grid, set the path and slope value of the output block grid.

[0062] Output Units of Measure: You can choose either DEGREE (degrees) or PERCENT_RISE (percent) to express the slope value.

[0063] Z factor: If the elevation unit of the DEM data is inconsistent with the spatial unit, you need to set the Z factor to adjust the scale.

[0064] Generate block grid slope values: After executing the above command, the system generates the slope value of each block grid based on the DEM data.

[0065] Symbolize and analyze results: The resulting block grid slope values ​​can be further symbolized for easier visualization.

[0066] In a preferred embodiment, based on the generated block grid slope values, deeper terrain analysis can be performed, such as aspect analysis, terrain relief analysis, etc. This information is also crucial for understanding and utilizing the terrain.

[0067] Specifically, based on the generated block grid slope values, a slope matrix of the three-dimensional grid model is formed.

[0068] Assume the slope matrix W of the three-dimensional grid model is W = {w ij}, i represents the vertex, j represents the bottom edge corresponding to the vertex, i∈[1,n], n is the number of vertices in the three-dimensional mesh model, j∈[1,m], m is the number of bottom edges in the three-dimensional mesh model, w ij Represents the slope value from the i-th vertex to the corresponding base j.

[0069] The slope value w in the matrix ij Different slope values ​​can be used to represent the slope attribute of the block grid. For the limit slope value w ij = 1, the slope of the block grid is 90 degrees; for the limit slope value w ij =0, it means the slope of the block grid is 0 degrees, w ij The value is in the range [0,1].

[0070] The slope value w ij The block grids with the same slope value range are classified into one category, which is defined as the same type of sub-block. The same type of sub-block is composed of multiple block grids, and the sub-block is a part of the original 3D grid model.

[0071] The same sub-blocks belonging to the same slope value range are marked with the same color. The larger the slope value, the darker the color. In this way, the slope attributes of the garden terrain can be classified in the three-dimensional grid model.

[0072] Step three: Sort each similar sub-block according to the corresponding slope value interval from small to large, so as to determine the lighting conditions of each similar sub-block after sorting, and select plant species suitable for planting based on different lighting conditions, and predict the photosynthetic rate value of the plant species on the terrain corresponding to the similar sub-block based on the photosynthetic rate prediction model.

[0073] First, each sub-block of the same type is sorted from small to large according to the corresponding slope value interval, so as to determine the lighting condition of each sub-block of the same type after sorting.

[0074] To determine the lighting conditions for each similar sub-block after sorting, it is necessary to consider the effect of the terrain slope on the lighting received by the surface. The intensity distribution of light on the surface will be affected by the terrain slope, and different slope values ​​will result in different light intensity effects. Places with gentler slopes are more likely to receive direct light, while places with larger slopes may produce shadows, affecting lighting.

[0075] In actual applications, the slope value interval data of the terrain corresponding to each type of sub-block is collected, and the slope value is divided into several intervals, such as flat terrain (0°-5°), slightly inclined terrain (6°-15°), moderately inclined terrain (16°-35°), steep terrain (greater than 35°), etc.

[0076] Evaluating the effect of light intensity:

[0077] Because changes in the sun's position will affect lighting conditions, the light intensity and time in different slope ranges are evaluated based on the sun's position (i.e., solar altitude and azimuth).

[0078] Prioritize:

[0079] Based on the light conditions, determine the terrain corresponding to the sub-block with the strongest light, and select suitable plants for planting according to the light conditions, such as light-loving plants; conversely, determine the terrain corresponding to the sub-block with the weakest light, and select suitable plants for planting according to the light conditions, such as shade-loving plants. Mark the plant species determined to be planted in each terrain on the three-dimensional grid model, such as Figure 3 shown.

[0080] Secondly, a photosynthetic rate prediction model is established, and the photosynthetic rate value of the plant species on the terrain corresponding to the sub-blocks of the same type is predicted based on the photosynthetic rate prediction model.

[0081] The plant species and terrain lighting conditions marked on the three-dimensional grid model are used as the basis for calculating the photosynthetic rate prediction.

[0082] When establishing a photosynthetic rate prediction model, it is necessary to obtain the plant growth information of the target species regularly through manual measurement methods in advance, and measure the photosynthetic rate of each plant species under the corresponding lighting conditions through a photosynthetic rate meter to form the training data of the photosynthetic rate prediction model. The training data is randomly divided into a photosynthetic rate prediction model training group and a test group, and the neural network parameters are set. The training group data is substituted into the photosynthetic rate prediction model for training until the photosynthetic rate prediction model training termination conditions are reached, thereby determining the model parameters in the photosynthetic rate prediction model. .

[0083] In the actual prediction process using the photosynthetic rate prediction model, light intensity and the maximum photosynthetic rate of the plant are used as input parameters of the photosynthetic rate prediction model, and the predicted photosynthetic rate value is used as the output parameter of the photosynthetic rate prediction model.

[0084] In a preferred embodiment, the input parameters of the photosynthetic rate prediction model need to be optimized to eliminate redundant data and reduce dimensionality.

[0085] The final trained photosynthetic rate prediction model is:

[0086] ,

[0087] Among them, P n is the predicted photosynthetic rate value, is the maximum photosynthetic rate of the plant, is a model parameter and I is the light intensity.

[0088] It should be noted that the maximum photosynthetic rate P of each type of plant max are different, so the use of the above-mentioned photosynthetic rate prediction model is targeted. When predicting for each specific terrain and its corresponding plant species, the input parameters in the model are different.

[0089] Of course, the input parameters of the photosynthetic rate prediction model can also take into account other environmental data in the terrain: not limited to light intensity and maximum photosynthetic rate, but also comprehensive temperature, humidity, CO2 concentration, etc., such as Figure 4 Shown is a three-dimensional graph of the predicted photosynthetic rate values ​​after taking into account the light quantum flux density and carbon dioxide concentration.

[0090] Step 4: Calculate the number of plants planted on the terrain corresponding to each similar sub-block according to the area of ​​the terrain corresponding to each similar sub-block and the predicted photosynthetic rate value.

[0091] First, the planting density of each plant is determined based on the predicted photosynthetic rate value.

[0092] Determine baseline planting density:

[0093] First, a benchmark density D is determined, which is a recommended value based on agricultural practice experience.

[0094] Adjustment factor:

[0095] The adjustment coefficient K is set according to the photosynthetic rate value. If the photosynthetic rate value is high, K is set to >1; if the photosynthetic rate value is low, K is set to <1.

[0096] Calculate the adjusted planting density:

[0097] Use the base density multiplied by the adjustment factor to get the adjusted planting density Da:

[0098] Da=D×K.

[0099] Secondly, the number of plants to be planted is calculated based on the planting density and the area of ​​the corresponding terrain.

[0100] Assuming the area of ​​the terrain corresponding to the sub-block is A, then calculate the number of plants m that can be planted in the sub-block:

[0101] m=Da×A.

[0102] According to the above steps, a plurality of appropriate adjustment coefficients K are set to calculate and obtain a plurality of data sets M of plant quantities, and the distribution distance is used to remove deviating data from the plurality of data sets of plant quantities.

[0103] Setting a plurality of appropriate adjustment coefficients K, calculating and obtaining a plurality of data sets M of plant quantities, and using distribution distances to remove deviating data from the plurality of data sets of plant quantities;

[0104] Specifically, a data set M of multiple plant numbers m is transformed into a distribution distance D using its average value μ and the difference matrix V;

[0105] ,

[0106] For a data set M with n number of plants, M=[m1,m2…,m b …,m n ] T , its difference matrix V is an n×n matrix, where each element is defined as follows:

[0107] ,

[0108] is the number of a-th and b-th plants m a and m b The covariance between

[0109] The difference matrix V is a matrix that describes the joint distribution characteristics of multiple plant numbers. The distribution distance of all data in the data set M is calculated, and the data exceeding the threshold TH0 (i.e., deviation data) is removed from the data set M of multiple plant numbers to obtain an optimal planting number interval. The specific planting number can be selected according to economic and environmental conditions.

[0110] Step 5: simulate planting on the three-dimensional grid model according to the plant species in the terrain corresponding to each similar sub-block determined in step 3 and the number of plants planted calculated in step 4, and perform a virtual reality display of the garden landscape on the three-dimensional grid model based on virtual reality technology.

[0111] In the three-dimensional grid model, according to the plant types in the terrain corresponding to each similar sub-block determined in the previous step three, the corresponding plant models are placed in the three-dimensional grid model.

[0112] Label each plant species in the 3D mesh model with its species name and number of plants planted; use color coding or other visual cues to distinguish different plant species.

[0113] Export the 3D mesh model to a format that supports VR and import it into the VR development platform. Set up interactive elements in the VR environment, such as clicking on a plant to display detailed information about the plant species.

[0114] Test the scene on the VR device to ensure that the location, species information and quantity markings of the plants are displayed correctly; optimize the garden scene to ensure smooth and uninterrupted operation in VR.

[0115] Design a user interface that allows users to freely explore the garden landscape through VR devices, and provide functions that allow users to change perspectives and even change seasons to view the landscape effects at different time periods.

[0116] Example 2

[0117] In step three, the best plant species are determined for the terrain corresponding to each sub-block of the same type, and the superior and inferior contribution algorithm is further used to confirm the optimal plant configuration.

[0118] The goal of the garden design of this embodiment is to maximize biodiversity and minimize resource consumption, and to define the competition intensity, light requirements, soil type preferences, etc. among different types of plants through high-quality contributions and low-quality contributions.

[0119] Premium contributions come from the positive impacts that plants have on the environment, such as:

[0120] Ecological quality contribution: such as purifying water quality, reducing soil erosion, improving air quality, etc. Certain points can be given according to the ecological benefits of plants. For example, plants with significant water purification functions can be given higher points.

[0121] Contribution to Habitat Quality: Assess the ability of plants to provide food or habitat for wildlife. If a plant attracts beneficial insects or birds, it can increase its positive score.

[0122] Good contribution to beautification: The aesthetic value of plants can also be counted towards positive points, especially those with seasonal flowers or unique forms.

[0123] Poor quality contributions come from the negative effects that plants may bring, such as:

[0124] Excessive growth and poor contribution: Some plants may become too widespread due to their rapid growth, posing a threat to the local ecosystem. Such plants should be assigned negative points.

[0125] Invasive poor contribution: Alien species should have their scores reduced if they become invasive plants, as they may crowd out native species and disrupt the ecological balance.

[0126] Maintenance Cost Poor Quality Contribution: Plants that require frequent pruning or special care may have increased maintenance costs, which should also be reflected in their score.

[0127] The plant configuration value of each plant is calculated by combining the high-quality contribution and the low-quality contribution through weighted average.

[0128] The pre-planted plant configuration value is further calculated by the following formula:

[0129] Plant configuration value = high-quality contribution × first weight − low-quality contribution × second weight;

[0130] Here, the weights are adjusted according to the specific circumstances. For example, if the garden attaches more importance to ecological contribution, a higher weight can be given to the high-quality contribution score; and vice versa.

[0131] For example, as shown in Table 1, suppose we have three candidate plants A, B, and C. Here are their scoring examples:

[0132] Table 1 The advantages and disadvantages of the three alternative planting plants A, B, and C

[0133]

[0134] Set the first weight and the second weight to 1, and calculate as follows:

[0135] A plant configuration value = (10 + 5 + 3) × 1- (2 + 3 + 1) × 1 = 12,

[0136] B plant configuration value = (8+3+4)×1-(1+2+0)×1=12,

[0137] C plant configuration value = (12 + 7 + 5) × 1- (3 + 4 + 2) × 1 = 15,

[0138] According to the evaluation of this simplified plant configuration model, plant C has the highest plant configuration value and is theoretically the most suitable choice.

[0139] In practical applications, the scoring criteria and corresponding weights for high-quality and low-quality contributions should be based on specific garden project goals and environmental conditions. The above examples are for reference only and do not include all possible influencing factors.

[0140] In one embodiment, a computer device is further provided, including a memory and a processor, wherein a computer program is stored in the memory, and the processor implements the steps in the above method embodiments when executing the computer program.

[0141] In one embodiment, a computer-readable storage medium is provided, storing a computer program, which implements the steps in the above method embodiments when executed by a processor.

[0142] In one embodiment, a computer program product or computer program is provided, the computer program product or computer program includes computer instructions, the computer instructions are stored in a computer-readable storage medium. A processor of a computer device reads the computer instructions from the computer-readable storage medium, and the processor executes the computer instructions, so that the computer device performs the steps in the above-mentioned method embodiments.

[0143] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiments can be implemented by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to a memory, a database or other medium used in the embodiments provided in this application can include at least one of a non-volatile and a volatile memory.

[0144] The database involved in each embodiment provided in this application may include at least one of a relational database and a non-relational database. The non-relational database may include a distributed database based on blockchain, etc., but is not limited thereto. The processor involved in each embodiment provided in this application may be a general-purpose processor, a central processing unit, a graphics processor, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, etc., but is not limited thereto.

[0145] The technical features of the above embodiments may be arbitrarily combined. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0146] The above-described embodiments only express several implementation methods of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the present application. It should be pointed out that, for a person of ordinary skill in the art, several variations and improvements can be made without departing from the concept of the present application, and these all belong to the protection scope of the present application. Therefore, the protection scope of the present application shall be subject to the attached claims.

Claims

1. A garden landscape simulation method based on virtual reality technology and three-dimensional visualization, characterized in that: The steps include: Step 1: Obtain 3D point cloud data of the garden terrain, divide the 3D point cloud data into multiple blocks, find the best block boundary, and generate a 3D grid model with multiple block grids; Step 2: Calculate the slope value of each block grid of the three-dimensional grid model, generate the slope matrix of the three-dimensional grid model, and cluster the block grids belonging to the same slope value interval into similar sub-blocks; Step 3: Sort each similar sub-block according to the corresponding slope value interval from small to large, so as to determine the lighting conditions of each similar sub-block after sorting, and select plant species suitable for planting based on different lighting conditions, and predict the photosynthetic rate value of the plant species on the terrain corresponding to the similar sub-block based on the photosynthetic rate prediction model; Step 4: Calculate the number of plants planted on the terrain corresponding to each similar sub-block according to the area of ​​the terrain corresponding to each similar sub-block and the predicted photosynthetic rate value: First, the benchmark planting density D is determined based on empirical values; second, the adjustment coefficient K is set based on the predicted photosynthetic rate value; Calculate the adjusted planting density Da: Da = D × K; Among them, if the predicted photosynthetic rate value is high, the corresponding adjustment coefficient K is set to >1; if the predicted photosynthetic rate value is low, the corresponding adjustment coefficient K is set to <1; Assume that the area of ​​the terrain corresponding to the same sub-block is A, and calculate the number of plants m planted in the terrain corresponding to the same sub-block as: m = Da × A; Step 5: simulate planting on the three-dimensional grid model according to the plant species in the terrain corresponding to each similar sub-block determined in step 3 and the number of plants planted calculated in step 4, and perform a virtual reality display of the garden landscape on the three-dimensional grid model based on virtual reality technology.

2. The garden landscape simulation method based on virtual reality technology and three-dimensional visualization according to claim 1 is characterized in that: In the step 2: let the slope matrix of the three-dimensional grid model be W = {w ij }, i represents the vertex, j represents the bottom edge corresponding to the vertex, i∈[1,n], n is the number of vertices in the three-dimensional mesh model, j∈[1,m], m is the number of bottom edges in the three-dimensional mesh model, w ij Represents the slope value from the i-th vertex to the corresponding bottom edge j; The slope value w ij The block grids with values ​​in the same slope value range are classified into one category and defined as the same type of sub-blocks.

3. The garden landscape simulation method based on virtual reality technology and three-dimensional visualization according to claim 1 is characterized in that: In the step three: the slope value interval data of the terrain corresponding to each type of sub-block is collected, the terrain is sorted from weak to strong according to the lighting conditions, and based on the lighting conditions of the sorted terrain, the plants suitable for planting in the terrain corresponding to each sub-block of the same type are determined.

4. The garden landscape simulation method based on virtual reality technology and three-dimensional visualization according to claim 3 is characterized in that: A photosynthetic rate prediction model is established to predict the photosynthetic rate value of the terrain corresponding to each ordered sub-block of the same type in turn. The photosynthetic rate prediction model is: P=P max ×(1-exp(-α×I)) Where P is the predicted photosynthetic rate value, P max is the maximum photosynthetic rate of the plant, α is a model parameter, and I is the light intensity.

5. The garden landscape simulation method based on virtual reality technology and three-dimensional visualization according to claim 1 is characterized in that: In step 4, a plurality of appropriate adjustment coefficients K are set, a plurality of data sets M of plant quantities are calculated, and the distribution distance is used to remove the deviating data from the data sets of the plurality of plant quantities; The data set M consisting of n plant numbers is transformed into a distribution distance D using its mean μ and the difference matrix V: D 2 (m b )=(m b -m) T V -1 (m b -m) For a data set M of multiple plant numbers, M = [m1, m2…, m b …,m n ] T , its difference matrix V is an n×n matrix, where each element is defined as follows: δ ab is the number of a-th and b-th plants m a and m b The covariance between The distribution distance of all data in the data set M is calculated, and the data exceeding the threshold TH0 is removed from the data set M of multiple plant quantities to obtain an optimal planting quantity interval.

6. The garden landscape simulation method based on virtual reality technology and three-dimensional visualization according to claim 1 is characterized in that: In step 3, the optimal plant configuration is confirmed by using the superior and inferior contribution algorithm, and the plant configuration value is calculated by the following formula: Plant configuration value = high-quality contribution × first weight - low-quality contribution × second weight; The plant with the highest configuration value is the plant species finally selected for planting.

7. The garden landscape simulation method based on virtual reality technology and three-dimensional visualization according to claim 1 is characterized in that: The digital elevation model data is loaded into the three-dimensional network model, and the terrain analysis is performed using the digital elevation model to calculate the slope value of each block grid.

8. The garden landscape simulation method based on virtual reality technology and three-dimensional visualization according to claim 6 is characterized in that: The inferior contributions include: inferior contribution of overgrowth, inferior contribution of invasiveness, and inferior contribution of maintenance cost; the superior contributions include: superior contribution of providing habitat, superior contribution of beautifying the environment, and superior contribution of ecology.

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