AI-Assisted Landscape Gardening Plan Generation Method and System

Abstract

The present invention provides a method and system for assisting in generating a landscape garden plan based on AI assistance. First, initial terrain data and landscape adjustment instructions including visual presentation requirements and ecological function requirements input by the user on the interaction interface are received. Then, the initial terrain data is processed by a terrain segmentation model to extract slope distribution, elevation range, and surface coverage characteristics. Next, the slope distribution characteristics are dynamically corrected into stepped terrain characteristics according to the visual requirements, and the vegetation distribution characteristics are obtained by matching the surface coverage characteristics according to the ecological requirements. After spatially superimposing the two, path connectivity analysis is performed in combination with tourist flow data to determine the tour path characteristics. Finally, a landscape garden plan including a three-dimensional visualization model and ecological evaluation parameters is rendered in multiple dimensions according to the tour path characteristics and sent to the user terminal to achieve interactive feedback, thereby efficiently integrating various requirements and improving the scientificity and intuitiveness of landscape garden design.

Description

Technical Field

[0001] The present invention relates to the field of artificial intelligence technology. Specifically, it relates to a method and system for assisting in generating landscape garden plans based on AI assistance. Background Art

[0002] In the field of landscape garden design, traditional design methods face many limitations. In the past, the generation of landscape garden plans mainly relied on the experience of designers and manual operations. When faced with the design requirements put forward by users, designers often needed to spend a lot of time and energy on on-site inspections of the terrain and manually draw various sketches and design plans. This method is not only inefficient, but also difficult to fully and accurately meet the diverse needs of users due to the limitations of personal experience and cognition. For example, for some complex terrains and special visual presentation requirements, designers may need to repeatedly modify the plan, resulting in a long design cycle and increased costs.

[0003] In terms of terrain analysis, traditional methods usually only perform simple terrain measurements and obtain initial data such as slopes and elevations, and cannot deeply explore various features of the terrain. The comprehensive analysis of slope distribution characteristics, elevation interval characteristics, and surface coverage characteristics is not comprehensive and detailed enough, making it difficult to fully integrate these features into landscape design, thus unable to give full play to the terrain advantages and achieve the perfect integration of the landscape and the terrain. In addition, in terms of tour path planning, traditional practices mainly rely on experience and general site layouts to determine, rarely conducting scientific analysis in combination with tourist flow data. As a result, the designed tour paths may have unreasonable places, such as overcrowding in some areas while few people visit other areas, affecting the overall tourist experience. Summary of the Invention

[0004] In view of the above-mentioned problems, in combination with the first aspect of the present invention, embodiments of the present invention provide a method for assisting in generating landscape garden plans based on AI assistance. The method includes:

[0005] Receiving initial terrain data and landscape adjustment instructions input by the user on the interaction interface, where the landscape adjustment instructions include visual presentation requirements and ecological function requirements for the initial terrain data;

[0006] Performing terrain segmentation processing on the initial terrain data through a terrain segmentation model, and extracting slope distribution characteristics, elevation interval characteristics, and surface coverage characteristics in the initial terrain data;

[0007] Dynamically correcting the slope distribution characteristics according to the visual presentation requirements to generate stepped terrain characteristics that meet the height difference threshold, and performing vegetation adaptability matching on the surface coverage characteristics based on the ecological function requirements to output vegetation distribution characteristics;

[0008] Perform spatial overlay processing on the stepped terrain feature and the vegetation distribution feature to generate a composite terrain-vegetation feature, and perform path connectivity analysis on the composite terrain-vegetation feature in combination with preset tourist flow data to determine the tour path feature;

[0009] Perform multi-dimensional rendering on the composite terrain-vegetation feature according to the tour path feature to generate a landscape garden plan including a three-dimensional visualization model and ecological evaluation parameters, and send the landscape garden plan to the user terminal for interactive feedback.

[0010] On the other hand, an embodiment of the present invention further provides a landscape garden plan auxiliary generation system based on AI assistance, including a processor and a machine-readable storage medium. The machine-readable storage medium is connected to the processor. The machine-readable storage medium is used to store programs, instructions or codes, and the processor is used to execute the programs, instructions or codes in the machine-readable storage medium to implement the above method.

[0011] Based on the above aspects, the embodiments of the present application achieve the full-process intelligence and personalized customization from terrain data input to the output of a complete landscape garden plan. First, by integrating the initial terrain data and landscape adjustment instructions input by the user, covering visual presentation requirements and ecological function requirements, it breaks the limitations of relying on manual processing and experience judgment in traditional landscape garden design, can quickly and accurately understand and respond to diverse design demands, and greatly improves design efficiency and user satisfaction. The use of the terrain segmentation model deeply mines the slope distribution feature, elevation range feature and surface coverage feature from the initial terrain data, dynamically corrects the slope distribution feature according to the visual presentation requirements to generate a stepped terrain feature that meets the height difference threshold, which not only meets the aesthetic requirements but also takes into account the engineering feasibility; performs vegetation adaptability matching on the surface coverage feature based on the ecological function requirements to output the vegetation distribution feature, ensuring the scientific nature and sustainability of the garden ecosystem. Moreover, perform spatial overlay of the stepped terrain feature and the vegetation distribution feature to generate a composite terrain-vegetation feature, and perform path connectivity analysis in combination with preset tourist flow data to determine the tour path feature, comprehensively optimizing the spatial layout and tour experience of the landscape garden, not only creating a reasonable and efficient tour route, but also enabling the landscape to be harmoniously integrated with the natural ecology, bringing unique and rich tour feelings to tourists. Finally, generate a landscape garden plan including a three-dimensional visualization model and ecological evaluation parameters, which can not only present the design effect in an intuitive and vivid way, facilitating the user to preview and adjust in advance, but also the ecological evaluation parameters provide a quantitative basis for the long-term development and ecological balance of the garden, realizing the transformation of landscape garden design from simple artistic creation to scientific decision-making and sustainable development. Send the plan to the user terminal through interactive feedback to form a closed-loop and continuously optimized design process. Description of the Drawings

[0012] Figure 1 It is a schematic execution flowchart of the AI - assisted landscape garden plan auxiliary generation method provided by an embodiment of the present invention.

[0013] Figure 2 It is a schematic hardware architecture diagram of the AI - assisted landscape garden plan auxiliary generation system provided by an embodiment of the present invention. Detailed implementation manners

[0014] The present invention will be specifically described below in conjunction with the accompanying drawings of the specification. Figure 1 It is a schematic flowchart of the AI - assisted landscape garden plan auxiliary generation method provided by an embodiment of the present invention. The AI - assisted landscape garden plan auxiliary generation method will be introduced in detail below.

[0015] Step S110: Receive the initial terrain data and landscape adjustment instructions input by the user on the interaction interface. The landscape adjustment instructions include visual presentation requirements and ecological function requirements for the initial terrain data.

[0016] Taking the landscape of a mountain scenic area with high altitude and large elevation difference as an example, the initial terrain data can be obtained through high - precision terrain measurement equipment. For example, a drone is used to carry a lidar to scan the entire mountain area, obtaining detailed terrain data including the height of the mountains, the undulation of the slopes, the depth of the valleys, etc. The above - mentioned terrain data is input into the data storage system in a digital form.

[0017] The visual presentation requirements in the landscape adjustment instructions are from the perspective of the tourist viewing experience. For example, in order to enable tourists to obtain an open and rich - level view at different viewing positions, it can be required to set up viewing platforms at certain high points, and there should be clear visual corridors between different altitude areas to avoid vegetation or terrain blocking the line of sight. At the same time, in order to match the grandeur of the mountain area, it may be required that the elevation difference change of the terrain has a strong visual impact.

[0018] In terms of ecological function requirements, since the ecological system in the mountain area is relatively fragile and needs to be protected. For example, it is required to maintain soil and water and reduce the scouring of the soil by surface runoff. At the same time, in order to maintain the biodiversity in the mountain area, it is necessary to provide suitable living environments for different types of animals and plants, such as providing forests for birds to perch and foraging and hiding places for small mammals.

[0019] Step S120: Perform terrain segmentation processing on the initial terrain data through a terrain segmentation model, and extract the slope distribution characteristics, elevation interval characteristics, and surface coverage characteristics in the initial terrain data.

[0020] For example, for mountainous areas with high altitudes and high drop, the initial terrain data is input into the pre-trained multi-scale terrain segmentation model. Among them, since there are often more exposed rock parts in high-altitude mountainous areas, the first convolution branch used to identify the exposed rock areas performs edge detection on the rock texture. For example, for rock surfaces with obvious ridges and rough textures, a rock distribution mask is generated. Based on the rock distribution mask, the geological stability parameters of each area can be calculated. For example, in steep cliff areas, the geological stability is poor and geological disasters such as rock slides are prone to occur.

[0021] The second convolution branch performs cluster analysis on the spectral features of vegetation in the initial terrain data. In high-altitude mountainous areas, vegetation distribution shows obvious vertical zoning. In lower altitude areas, there may be dense tree areas, such as pine trees, cypress trees and other tall trees grow luxuriantly; as the altitude increases, it transitions to the shrub transition zone, such as azalea and other shrubs are more common; in higher altitude areas, there are herbaceous plant areas, such as various cold-resistant herbaceous plants in alpine meadows. By counting the vegetation coverage density in each area, we can understand the distribution of vegetation.

[0022] The third convolution branch simulates the flow direction of the hydrological data in the initial terrain data. In mountainous areas, the flow direction of water is greatly affected by the terrain. The runoff collection area is usually located at the bottom of the valley, where streams or small lakes are easily formed. Potential erosion areas can be those with steep slopes and less vegetation cover, because water flow can easily carry away soil here. By marking the coordinates of the water area boundary, the scope of the water area can be clearly determined.

[0023] Finally, the geological stability parameters, vegetation coverage density and water boundary coordinates are integrated to generate a three-dimensional terrain grid. From this three-dimensional terrain grid, the continuous slope change curve can be extracted as the slope distribution feature, and different altitude zones can be divided as the altitude interval feature. For example, the area above 3,000 meters above sea level can be divided into high mountain areas, and 2,000-3,000 meters can be divided into medium mountain areas. At the same time, the spatial distribution of different surface types is marked as the surface coverage feature, such as clarifying which areas are rocks, which are vegetation, and which are water areas.

[0024] Step S130, dynamically modifying the slope distribution characteristics according to the visual presentation requirements, generating stepped terrain characteristics that meet the height difference threshold, and performing vegetation adaptability matching on the surface cover characteristics based on the ecological function requirements, and outputting vegetation distribution characteristics.

[0025] In the scenic garden landscape of high-altitude mountainous areas, analyze the viewpoint setting parameters and visual corridor parameters in the visual presentation requirements. Assume that a viewpoint is set at the top of a certain mountain. According to this requirement, determine the maximum allowable slope value of the target area. For example, to ensure the safety and comfort of tourists on the viewing platform, the slope within a certain range around the viewing platform should not exceed 30 degrees. The height difference threshold between adjacent steps is set to 1.5 meters to facilitate tourists' climbing.

[0026] Thus, in the slope distribution characteristics, identify the steep slope areas exceeding 30 degrees, and perform contour line densification on such steep slope areas. For example, the original relatively sparse contour line interval is 10 meters, and after densification, it becomes 5 meters, generating a densified contour line dataset. Then, based on the 1.5-meter height difference threshold, perform stepped interpolation on the densified contour line dataset. Insert transition platforms between adjacent contour lines, and adjust the width of the transition platform to 2 meters and the inclination angle to 5 degrees according to the viewpoint setting parameters to ensure that tourists can stand stably and view the scenery on the platform. Perform curvature smoothing on the interpolated contour lines to eliminate the sharp turns at the step edges and form a continuously transitional stepped surface. Finally, fuse the edge of this stepped surface with the original terrain data to generate stepped terrain features including anti-slip textures (such as anti-slip patterns engraved) and drainage grooves (groove depth is 0.1 meter and width is 0.2 meter) on the step surface.

[0027] Match the vegetation adaptability of the surface cover characteristics based on the ecological function requirements. Construct a set of vegetation types according to the soil and water conservation level parameters and biodiversity parameters. For example, in high-altitude mountainous areas, to meet the requirements of soil and water conservation, construct a set of vegetation types including a cold-resistant plant library (such as Rhododendron lapponicum, Saussurea involucrata, etc.), a slope-fixing plant library (such as plants with well-developed roots like Hippophae rhamnoides), and an ornamental plant library (such as alpine flowers blooming in different seasons).

[0028] Mark in the surface cover characteristics the shallow soil layer areas where the soil thickness is lower than the set thickness (such as 0.3 meter), and these areas may be located in the rock crevices on the mountain top. The easily eroded areas with a slope greater than the critical erosion angle (such as 45 degrees) are generally the steep slopes on the mountain slope. The sunny slope area is the slope facing the direction with sufficient sunlight.

[0029] Match plant species with well-developed roots and tolerance to infertility in the cold-resistant plant library for the shallow soil layer area, such as Rhododendron lapponicum, whose roots can penetrate into rock crevices to fix the soil. Match plant species with a reticulate root system structure in the slope-fixing plant library for the easily eroded area, like Hippophae rhamnoides, whose roots can interweave with each other to prevent the soil from being washed away by surface runoff on the slope. Match plant species with staggered flowering periods in the ornamental plant library for the sunny slope area, such as Primula malacoides that blooms in spring and Trollius chinensis that blooms in summer, so as to provide different ornamental flowers for tourists in different seasons. Generate a vegetation planting coordinate set according to the matching results of each area, and add plant growth simulation parameters to it. For example, the predicted shading range of Rhododendron lapponicum is a circular area with a radius of 1 meter, the root expansion speed is 0.2 meters per year, and the phenological change data is blooming in spring and having lush leaves in summer, forming the vegetation distribution characteristics.

[0030] Step S140, perform a spatial overlay process on the stepped terrain feature and the vegetation distribution feature to generate a composite terrain vegetation feature, and perform a path connectivity analysis on the composite terrain vegetation feature in combination with the preset tourist flow data to determine the tour path feature.

[0031] In the three-dimensional coordinate system of the high-altitude mountainous area, spatially align the surface vertex coordinates of the stepped terrain feature with the planting coordinates of the vegetation distribution feature. For example, the vertex coordinates of a certain stepped platform of the stepped terrain are (x1, y1, z1), and the coordinates of a certain plant in the vegetation planting coordinate set are (x2, y2, z2), and perform spatial matching on them. During this process, detect the conflict points in the coordinate overlapping area.

[0032] Suppose conflict points are found in the drainage groove area at the edge of a certain step. According to the priority judgment principle, give priority to retaining the structural parameters of the stepped terrain feature, and adjust the vertical height of the vegetation planting coordinates by 0.5 meters to avoid conflicts between the vegetation and the drainage groove structure. If conflict points are found in the viewing area of the stepped platform, give priority to retaining the phenological change data of the vegetation distribution feature, and modify the local curvature of the stepped platform, such as adjusting the original curvature radius from 5 meters to 4 meters to adapt to the growth needs of the vegetation.

[0033] Perform voxelization fusion on the adjusted stepped terrain feature and the vegetation distribution feature. Obtain the set of surface vertex coordinates in the adjusted stepped terrain feature and the updated vegetation planting coordinate set in the vegetation distribution feature. For example, extract the elevation gradient data from the set of surface vertex coordinates, such as the elevation of a certain slope ranges from 1000 meters to 1200 meters and the slope is 30 degrees; extract the vertical height offset from the vegetation planting coordinate set, such as a certain plant is 0.3 meters higher than the surrounding terrain.

[0034] Based on the spatial distribution relationship between elevation gradient data and vertical height offset, the set of surface vertex coordinates of the stepped terrain feature is aligned with the vegetation planting coordinate set in a three-dimensional space grid to generate a spatially aligned data set with a unified coordinate system reference. In the spatially aligned data set, identify the overlapping area of the drainage groove coordinates at the edge of the stepped platform and the coordinates of the vegetation root extension range, and detect the angle anomaly points between the direction of the terrain surface normal vector and the vegetation planting direction within the overlapping coordinate area. For example, it is found that the direction of the normal vector of the bottom surface of a certain drainage groove is vertically downward, while the growth direction of the vegetation roots is inclined downward by 30 degrees, forming an angle anomaly point.

[0035] According to the position distribution of the angle anomaly points, perform displacement compensation on the surface vertex coordinates of the stepped terrain feature so that the direction of the normal vector of the bottom surface of the drainage groove forms a perpendicular orthogonal relationship with the growth direction of the vegetation roots. Import the surface vertex coordinates after displacement compensation and the vegetation planting coordinate set into a preset voxel division engine. Read that the maximum elevation difference in the surface vertex coordinates after displacement compensation is 500 meters and the minimum curvature radius is 3 meters, and determine that the number of vertical layers of the voxel division engine is 50 layers based on the maximum elevation difference. Calculate the initial value of the voxel side length in the horizontal direction based on the minimum curvature radius as 1 meter, and start a dynamic encryption algorithm in the area where the curvature change rate exceeds a threshold (such as 0.5), and reduce the initial value of the voxel side length in proportion to the reciprocal of the curvature change rate. For example, in the area where the curvature change rate is 0.8, the voxel side length is reduced to 0.6 meters. Based on the number of vertical layers and the dynamically adjusted voxel side length, generate a non-uniform voxel grid with adaptive resolution, where the vertex coordinates of each voxel unit form a one-to-one mapping relationship with the surface vertex coordinates of the stepped terrain feature.

[0036] Traverse each voxel unit in the non-uniform voxel grid. If the voxel unit contains both the surface vertex coordinates of the stepped terrain feature and the vegetation planting coordinates, extract the corresponding rock texture roughness parameter, soil moisture content parameter, and vegetation shading range prediction value of the voxel unit. For example, the micro-surface uneven height data corresponding to the rock texture roughness parameter in a certain voxel unit is 0.02 meters, and the particle size distribution data in the soil diffuse reflection parameter of the adjacent voxel is 0.01 meters. Calculate the material mixing weight coefficient as 0.67 based on the proportional relationship between the micro-surface uneven height data and the particle size distribution data, and perform linear interpolation on the specular reflection intensity of the rock material and the diffuse reflection intensity of the soil material based on the weight coefficient. Map the interpolated reflection intensity data to all surface vertices of the voxel unit to generate a natural transition material effect from rock to soil.

[0037] Inject vegetation growth simulation parameters into the non-uniform voxel grid, label the voxel units corresponding to the vegetation planting coordinate set therein, and assign an initial root depth value and canopy coverage rate to each labeled voxel unit. For example, the initial root depth value of the voxel unit corresponding to a certain Rhododendron lapponicum is 0.1 meter, and the canopy coverage rate is 0.1. Dynamically calculate the daily growth increment of the root depth value according to the soil moisture content parameter in the voxel unit. For example, when the soil moisture content is 30%, the daily growth increment of the root depth is 0.001 meter. At the same time, calculate the seasonal expansion rate of the canopy coverage rate based on the light attenuation coefficient. For example, when the light is sufficient in summer, the seasonal expansion rate of the canopy coverage rate is 0.05 per month. When the canopy coverage rate of the adjacent voxel unit expands to the current voxel unit, trigger the canopy gap closure algorithm to automatically adjust the leaf density to eliminate the discontinuous shadow boundary in the light simulation.

[0038] Extract the cross-sectional geometric parameters of the drainage groove in the stepped terrain feature. For example, the bottom slope angle of the groove is 5 degrees and the roughness grade of the groove wall is 3. Calculate the component of the gravitational acceleration of the water flow as 0.87 m / s² according to the slope angle. Determine the viscous resistance coefficient of the water flow boundary layer as 0.05 based on the roughness grade of the groove wall, and solve the dynamic equilibrium equation of the surface tension in combination with the component of the gravitational acceleration. Discretize the solution result of the dynamic equilibrium equation into a water film thickness distribution map matching the voxel unit size. For example, the water film thickness is 0.01 meter at the bottom of the groove and 0.005 meter at the edge of the groove, and generate the intensity gradient data of the reflected light spot according to the water film thickness.

[0039] Verify the physical compatibility of the terrain material properties, vegetation growth properties, and light reflection properties in each voxel unit. Scan the light reflection property data of each voxel unit in the non-uniform voxel grid, and detect whether the brightness value of the specular reflection highlight area exceeds the photosynthesis saturation threshold of the vegetation leaves. If a highlight area exceeding the saturation threshold is detected, reduce the reflection intensity of this area proportionally according to the leaf density parameter in the vegetation shading range prediction value until it reaches the effective radiation range of photosynthesis. Synchronously update the reflectivity map in the terrain material properties of the voxel unit to ensure the visual continuity between the adjusted highlight area and the surrounding materials.

[0040] Encode each voxel unit in the non-uniform voxel grid into a multi-dimensional data block containing terrain material properties, vegetation growth properties, and light reflection properties. Construct an octree index structure according to the spatial arrangement order of the multi-dimensional data blocks, and label the voxel clustering identifier of the material transition area in the octree index structure. Perform LOD level division on the octree index structure, and dynamically call the voxel data blocks of the corresponding level according to different viewing distances to generate a composite voxel model supporting multi-resolution rendering as the composite terrain vegetation feature.

[0041] Perform path connectivity analysis on the composite terrain vegetation characteristics in combination with the preset tourist flow data. Obtain the peak-hour pedestrian flow density of 5 people per square meter and the average stay duration of 30 minutes in the historical tourist flow data. Combine with the viewing platform area of 100 square meters in the composite terrain vegetation characteristics to calculate the maximum carrying capacity of the viewing platform as 150 people.

[0042] Mark the candidate routes of the emergency evacuation channels in the composite terrain vegetation characteristics, such as the relatively gentle route along the valley bottom and the open route along the ridge. Calculate the cumulative climbing height and slope change rate of each candidate route based on the elevation difference data of the stepped terrain characteristics. For example, the cumulative climbing height of the valley bottom route is 100 meters, and the slope change rate is 0.1; the cumulative climbing height of the ridge route is 200 meters, and the slope change rate is 0.2.

[0043] Set diversion nodes for the candidate routes according to the maximum carrying capacity, and add virtual coordinates of the indication signs at the diversion nodes. For example, set a diversion node at the exit of the viewing platform, and indicate the directions of different routes and the expected pedestrian flow density at the node. Screen out the path set that meets the ergonomic comfort threshold (such as the cumulative climbing height does not exceed 200 meters and the slope change rate does not exceed 0.3) based on the cumulative climbing height and slope change rate.

[0044] Perform topological optimization on each path in the path set. Construct a node connection graph of each path in the path set, and identify the intersection nodes with degrees greater than the set degree (such as 3) in the node connection graph. Set direction guiding signs at the intersection nodes. For example, at a node where three paths meet, set a sign indicating the scenic spots that each path leads to. Adjust the connection priority of the intersection nodes according to the visibility parameter of the adjacent paths. For example, the path with a higher visibility has a higher connection priority. Enlarge the curvature radius of the paths with connection priority lower than the set priority to ensure the safety distance when two-way pedestrian flows meet. For example, enlarge the curvature radius from 2 meters to 3 meters, and add an anti-slip material sign at the change of the curvature radius. Generate a path topological structure according to the optimized node connection graph, and dynamically associate the path topological structure with the viewing platform coordinates in the composite terrain vegetation characteristics to update the branch guiding parameters in the tour path characteristics.

[0045] Step S150, perform multi-dimensional rendering on the composite terrain vegetation characteristics according to the tour path characteristics, generate a landscape garden plan including a three-dimensional visualization model and ecological evaluation parameters, and send the landscape garden plan to the user terminal for interactive feedback.

[0046] Specifically, data fusion is performed between the voxel model in the composite terrain vegetation characteristics and the topological structure in the tour path characteristics to generate a basic dataset for scene rendering. Based on the material properties in the basic dataset for scene rendering, rock texture maps are loaded. For example, a realistic granite texture map is loaded for the exposed rock surface; a dynamic vegetation growth animation is loaded. For example, the process of the flowers of Rhododendron lapponicum changing from budding to blooming has dynamic changes in different seasons; a water flow particle effect is loaded to simulate the flowing effect of water at the stream in the valley, and the shadow projection range is calculated according to the light angle parameter. For example, when the morning sun shines from the east, the shadow range on the west side of the mountain peak is calculated.

[0047] During the rendering process, the visual occlusion areas in the three-dimensional visualization model are detected in real time. Multiple preset observation viewpoints are set in the three-dimensional visualization model of the high-altitude mountainous area. For example, observation viewpoints are set at the bottom of the valley, on the mountainside, and at the mountain top respectively, and the visible range of each observation viewpoint is calculated. The visible range is compared with the key display area in the landscape adjustment instruction. For example, if the key display area is a rare plant community on a certain mountain peak, the visual blind area that does not cover the key display area is identified. A virtual observation point is inserted at the coordinate position corresponding to the visual blind area. For example, a virtual observation point is inserted in the area blocked by the mountain peak. A temporary connection path from the nearest tour path to the virtual observation point is generated based on the path optimization algorithm. The rendering level of the three-dimensional visualization model is adjusted, and a terrain profile with transparency processing is added at the end of the temporary connection path to expose the landscape details in the visual blind area, such as the distribution of the rare plant community.

[0048] The biodiversity parameters in the vegetation distribution characteristics and the soil and water conservation parameters in the stepped terrain characteristics are extracted to generate an ecological benefit evaluation matrix. For example, the biodiversity parameters can include the number of plant species, the number of animal habitats, etc.; the soil and water conservation parameters can include the reduction ratio of soil loss on the slope surface, etc. The ecological benefit evaluation matrix is associated and labeled with the three-dimensional visualization model.

[0049] The landscape garden plan containing the three-dimensional visualization model and ecological evaluation parameters is sent to the user terminal for interactive feedback. A rotatable thumbnail of the three-dimensional visualization model is displayed on the user terminal interface, and the user can view the landscape from different angles by rotating the thumbnail. At the same time, a comparison radar chart of the ecological evaluation parameters is displayed. For example, the ecological benefits of the current plan are compared with those of the original terrain to intuitively show the improvement in aspects such as biodiversity protection and soil and water conservation.

[0050] Receive the user's annotation modification instruction for the specified viewing area. For example, if the user is not satisfied with the vegetation types around a certain viewing platform and submits a vegetation type replacement request, hoping to replace the original Rhododendron lapponicum with Trollius chinensis. Locate the target data layer in the landscape garden plan according to the annotation modification instruction, such as the vegetation distribution feature data layer, and call the incremental update algorithm to perform local parameter replacement on the target data layer. Compare the updated landscape garden plan with the original plan to generate a modification impact report. The modification impact report includes the change amount of ecological parameters, such as the impact on biodiversity after vegetation replacement; the floating value of construction costs, such as the difference in the planting costs of Trollius chinensis and Rhododendron lapponicum; a comparison chart of visual effects, such as showing the visual effect differences of the two types of vegetation on the viewing platform, and superimpose and display the modification impact report on the user terminal interface so that the user can comprehensively understand the impact of the modification.

[0051] Based on the above steps, the embodiment of the present application realizes the full-process intelligence and personalized customization from terrain data input to the output of a complete landscape garden plan. First, by integrating the initial terrain data input by the user and the landscape adjustment instructions, covering visual presentation requirements and ecological function requirements, it breaks the limitations of relying on manual processing and empirical judgment in traditional landscape garden design, can quickly and accurately understand and respond to diverse design demands, and greatly improves the design efficiency and user satisfaction. The use of the terrain segmentation model deeply mines the slope distribution characteristics, elevation range characteristics, and surface coverage characteristics from the initial terrain data, dynamically corrects the slope distribution characteristics according to the visual presentation requirements to generate stepped terrain characteristics that meet the height difference threshold, which not only meets the aesthetic requirements but also takes into account the engineering feasibility; performs vegetation adaptability matching on the surface coverage characteristics based on the ecological function requirements to output vegetation distribution characteristics, ensuring the scientific nature and sustainability of the garden ecosystem. Moreover, superimpose the stepped terrain characteristics and the vegetation distribution characteristics in space to generate composite terrain-vegetation characteristics, and combine the preset tourist flow data to perform path connectivity analysis to determine the tour path characteristics, comprehensively optimizing the spatial layout and tour experience of the landscape garden, not only creating a reasonable and efficient tour route but also enabling the landscape to be harmoniously integrated with the natural ecology, bringing unique and rich tour experiences to tourists. Finally, generate a landscape garden plan including a three-dimensional visualization model and ecological evaluation parameters, which can not only present the design effect in an intuitive and vivid manner, facilitating the user to preview and adjust in advance, but also the ecological evaluation parameters provide a quantitative basis for the long-term development and ecological balance of the garden, realizing the transformation of landscape garden design from simple artistic creation to scientific decision-making and sustainable development, and sending the plan to the user terminal through interactive feedback to form a closed-loop and continuously optimized design process.

[0052] In a possible implementation manner, step S120 includes:

[0053] Step S121: Input the initial terrain data into a pre-trained multi-scale terrain segmentation model, which includes a first convolutional branch for identifying rock-exposed areas, a second convolutional branch for identifying vegetation-covered areas, and a third convolutional branch for identifying water distribution areas.

[0054] First, terrain data obtained by scanning the entire high-altitude mountainous area with a high-precision measurement device, such as a lidar carried by a drone, can be input into the pre-trained multi-scale terrain segmentation model. The first convolutional branch in this multi-scale terrain segmentation model is used to identify rock-exposed areas. In high-altitude mountainous areas, rock-exposed areas are relatively common. The first convolutional branch performs edge detection on the rock textures in the initial terrain data. For example, it precisely detects rock surfaces with obvious ridgelines, rough textures, and significant color differences from the surrounding environment.

[0055] Step S122: Perform edge detection on the rock textures in the initial terrain data through the first convolutional branch to generate a rock distribution mask, and calculate the geological stability parameters for each area based on the rock distribution mask.

[0056] After generating the rock distribution mask, the geological stability parameters for each area can be calculated based on the rock distribution mask. For example, near the mountaintop or in steep cliff areas, the rock distribution is relatively concentrated and the geological structure is complex. The geological stability parameters in these areas are relatively low, indicating a higher likelihood of geological disasters such as rock slides and mountain collapses.

[0057] Step S123: Perform clustering analysis on the vegetation spectral features in the initial terrain data through the second convolutional branch to divide into areas with dense trees, shrub transition areas, and herbaceous plant areas, and count the vegetation coverage density for each area.

[0058] In the vertical vegetation distribution zone of high-altitude mountainous areas, different types of vegetation have different spectral features. For tall trees in areas with dense trees (such as pine trees, fir trees, etc.), the spectral reflectance of their leaves has a specific numerical range in certain bands. By analyzing these spectral features, areas with dense trees can be accurately divided. In areas at slightly higher altitudes, the spectral features of shrub vegetation such as rhododendrons in shrub transition areas are different, and the second convolutional branch can accurately identify and divide this area. As the altitude further increases, the spectral features of alpine meadow vegetation (such as fescue) in herbaceous plant areas are also identified. At the same time, the vegetation coverage density for each area is counted. For example, the vegetation coverage density in areas with dense trees may reach 70%, in shrub transition areas it is 50%, and in herbaceous plant areas it is 30%. This helps to comprehensively understand the distribution of vegetation in the mountainous area.

[0059] Step S124: Perform a flow direction simulation on the hydrological data in the initial terrain data through the third integral branch, identify the runoff convergence areas and potential erosion areas, and mark the water area boundary coordinates.

[0060] In mountainous terrains, the flow direction of water is strongly influenced by the terrain. By analyzing information such as elevation and slope direction in the terrain data, the runoff convergence areas are identified. In high-altitude mountainous areas, the bottoms of valleys are often the runoff convergence areas because water flows along the slopes towards lower elevations, where streams or small water accumulation areas are formed. At the same time, those slopes with steeper gradients and less vegetation cover are identified as potential erosion areas because during rainfall, water is likely to carry away soil in these areas. Marking the water area boundary coordinates can clarify the exact scope of water areas such as lakes and streams. For example, the starting coordinates of a certain stream are (x1, y1, z1), and the ending coordinates are (x2, y2, z2).

[0061] Step S125: Generate a three-dimensional terrain grid by fusing the geological stability parameters, vegetation cover density, and water area boundary coordinates, extract the continuous slope change curve from the three-dimensional terrain grid as the slope distribution feature, divide the altitude bands as the altitude interval feature, and mark the spatial distribution of different surface types as the surface cover feature.

[0062] For example, from the generated three-dimensional terrain grid, the continuous slope change curve can be accurately extracted as the slope distribution feature. In high-altitude mountainous areas, the slope change curve may show a gradual increase from the foot of the mountain to the top, and then have varying degrees of undulation near the top. Divide the altitude bands as the altitude interval feature. For example, the area below 3000 meters above sea level can be divided into the low-altitude area, where the climate may be relatively mild and the vegetation and ecosystem are relatively rich; 3000 - 4000 meters is divided into the mid-altitude area, where the vegetation begins to become sparse and the climate conditions are more severe; above 4000 meters is the high-altitude area, mainly dominated by cold-resistant herbaceous plants and bare rocks. At the same time, mark the spatial distribution of different surface types (such as rocks, vegetation, water areas, etc.) as the surface cover feature to clarify the surface composition of each area, which is of great significance for subsequent landscape planning and ecological function design.

[0063] In a possible implementation manner, step S130 includes:

[0064] Step S131: Analyze the viewpoint setting parameters and visual corridor parameters in the visual presentation requirements, and determine the maximum allowable slope value of the target area and the height difference threshold between adjacent steps.

[0065] For example, assume that a main viewing point is set at the top of a certain main peak. Starting from this viewing point, the maximum allowable slope value of the target area is determined based on the viewing experience and safety factors of tourists. For example, to ensure the safety of tourists during the viewing process and a comfortable visual experience, within a range of 50 meters around the viewing platform, the maximum allowable slope value is set at 30 degrees. At the same time, the height difference threshold between adjacent steps is determined to be 1.5 meters. Such a height difference is convenient for tourists to climb and can also form an obvious stepped effect visually.

[0066] Step S132: Identify steep slope areas exceeding the maximum allowable slope value in the slope distribution feature, and perform contour line densification processing on the steep slope areas to generate a densified contour line dataset.

[0067] Step S133: Perform stepped interpolation on the densified contour line dataset based on the height difference threshold, insert transition platforms between adjacent contour lines, and adjust the width and inclination angle of the transition platforms according to the parameters set for the viewing point.

[0068] For example, steep slope areas exceeding 30 degrees can be identified in the slope distribution feature. These steep slope areas may be distributed on the sides of mountains or the steep slopes of valleys in high-altitude mountainous areas. Perform contour line densification processing on these steep slope areas. For example, the area where the original contour line interval was 10 meters is densified to 5 meters, thereby generating a densified contour line dataset. Perform stepped interpolation on the densified contour line dataset based on the 1.5-meter height difference threshold. Insert transition platforms between adjacent contour lines, and adjust the width and inclination angle of the transition platforms according to the parameters set for the viewing point. For example, in the area close to the viewing platform, the width of the transition platform is set at 2 meters and the inclination angle is 5 degrees, so that tourists can stand steadily on the platform and conveniently enjoy the surrounding scenery.

[0069] Step S134: Perform curvature smoothing processing on the interpolated contour lines to eliminate the sharp turns at the edges of the steps, form a continuously transitional stepped surface, and perform edge fusion on the stepped surface and the original terrain data to generate a stepped terrain feature including anti-slip textures and drainage grooves.

[0070] In this embodiment, curvature smoothing is performed on the interpolated contour lines to eliminate sharp turns at the stepped edges. Under the complex terrain of high-altitude mountainous areas, the smoothed contour lines can better blend with the original terrain to form a continuously transitional stepped surface. Finally, the stepped surface is edge-blended with the original terrain data to generate stepped terrain features on the stepped surface, including anti-slip textures (such as anti-slip grooves engraved with a groove depth of 0.02 m and a width of 0.03 m) and drainage grooves (with a groove depth of 0.1 m and a width of 0.2 m). The anti-slip texture can increase the friction when tourists walk, ensuring the safety of tourists, while the drainage grooves can drain rainwater in time to prevent rainwater from accumulating on the steps and avoid dangers such as landslides.

[0071] In a possible implementation manner, step S130 further includes:

[0072] Step S135, according to the soil and water conservation level parameter and biodiversity parameter in the ecological function requirements, construct a set of vegetation types including a cold-resistant plant library, a slope-fixing plant library, and an ornamental plant library.

[0073] For example, in high-altitude mountainous areas, due to their special ecological environment, the impact of vegetation on soil and water conservation and biodiversity needs to be particularly considered. The constructed cold-resistant plant library includes plants such as Saussurea involucrata and Rhododendron lapponicum that are adapted to the cold environment at high altitudes. The slope-fixing plant library includes plants such as Hippophae rhamnoides with strong root systems that can fix the soil. The ornamental plant library contains alpine flowers that bloom in different seasons, such as Primula malacoides that blooms in spring and Trollius chinensis that blooms in summer.

[0074] Step S136, mark in the surface cover features the shallow soil layer areas where the soil thickness is lower than the set thickness, the easily eroded areas where the slope is greater than the critical erosion angle, and the sunny slope areas.

[0075] For example, between the rock crevices on the mountaintop or in some areas with poor soil development, the soil thickness is lower than the set thickness (such as 0.3 m), and these areas are marked as shallow soil layer areas. The slopes with a slope greater than the critical erosion angle (such as 45 degrees), which are prone to soil erosion during rainfall, are marked as easily eroded areas. The sunny slope areas are the slopes facing the direction with sufficient sunlight, and these areas have better lighting conditions and relatively higher temperatures.

[0076] Step S137, match plant species with well-developed roots and tolerance to barrenness in the cold-resistant plant library for the shallow soil layer areas, match plant species with a reticulate root system structure in the slope-fixing plant library for the easily eroded areas, and match plant species with staggered flowering periods in the ornamental plant library for the sunny slope areas.

[0077] Match plant species with well-developed roots and tolerance to barrenness in the cold-resistant plant library for the shallow soil layer area. For example, Rhododendron lapponicum, whose roots can penetrate deep into rock crevices, can not only grow in barren soil but also play a role in fixing the soil. For the easily eroded area, match plant species with a reticulate root system structure in the slope-fixing plant library, such as Hippophae rhamnoides, whose roots interweave to form a network, which can effectively prevent the soil from being washed away by surface runoff on the slope. For the sunny slope area, match plant species with staggered flowering periods in the ornamental plant library, such as Primula malacoides and Trollius chinensis, so that tourists can enjoy different flower landscapes in different seasons.

[0078] Step S138, generate a vegetation planting coordinate set according to the matching results of each area, and add plant growth simulation parameters to the vegetation planting coordinate set. The plant growth simulation parameters include the predicted shading range value, root expansion speed, and phenological change data, so as to form the vegetation distribution characteristics.

[0079] For example, the planting coordinates of a certain Rhododendron lapponicum are (x3, y3, z3), and at the same time, plant growth simulation parameters are added to the vegetation planting coordinate set. The predicted shading range value of Rhododendron lapponicum is a circular area with a radius of 1 meter, which means that within a range of 1 meter around it, the surrounding area will be affected by the shading of its branches and leaves during its growth process. The root expansion speed is 0.2 meters per year. As time goes by, its roots will gradually expand around. The phenological change data is blooming in spring, lush leaves in summer, leaf color change in autumn, and dormancy in winter. These phenological change data can accurately reflect the growth state of the plant in different seasons, thus forming a complete vegetation distribution characteristic. This vegetation distribution characteristic provides an accurate vegetation layout basis for the landscape planning of high-altitude mountainous areas, which can not only meet the ecological function requirements but also enhance the ornamental value of the landscape.

[0080] In a possible implementation manner, step S140 further includes:

[0081] Step S141, spatially align the surface vertex coordinates of the stepped terrain feature with the planting coordinates of the vegetation distribution characteristic in the three-dimensional coordinate system, and detect the conflict points in the coordinate overlapping area.

[0082] For example, the surface vertex coordinates of a certain step in the stepped terrain feature are (x1, y1, z1), (x2, y2, z2), etc., and the planting coordinates of a certain plant in the vegetation distribution characteristic are (x3, y3, z3). Through precise calculation and positioning, these planting coordinates are matched in the three-dimensional space to ensure that the spatial relationship between the two is accurately reflected. During this process, it is necessary to detect the conflict points in the coordinate overlapping area.

[0083] Step S142: Determine the priority of the conflict point. When the conflict point is located in the drainage groove area at the edge of the step, the structural parameters of the stepped terrain feature are preferentially retained, and the vertical height of the vegetation planting coordinates is adjusted.

[0084] For the detected conflict point, the priority needs to be determined. For example, when the conflict point is located in the drainage groove area at the edge of the step, since the structure of the drainage groove is crucial for the drainage function of the entire stepped terrain, the structural parameters of the stepped terrain feature are preferentially retained. For example, the structural parameters such as the depth, width, and slope of the drainage groove must remain unchanged. At this time, the vertical height of the vegetation planting coordinates is adjusted. Suppose the original planting coordinates of a certain plant are (x4, y4, z4), and there is a conflict with the drainage groove area. Then, according to the structure of the stepped terrain feature and the surrounding spatial relationship, its vertical height is adjusted to (x4, y4, z5), where z5 is the new height value calculated to avoid conflict with the drainage groove, and at the same time, it can try to ensure that the plant grows at an appropriate altitude, meeting its requirements for environmental conditions such as light and temperature.

[0085] Step S143: When the conflict point is located in the viewing area of the step platform, the seasonal change data of the vegetation distribution feature are preferentially retained, and the local curvature of the step platform is modified.

[0086] When the conflict point is located in the viewing area of the step platform, the visual effect of the viewing area is mainly reflected by the seasonal changes of the vegetation. Therefore, the seasonal change data of the vegetation distribution feature are preferentially retained. For example, the alpine flowers with special seasonal changes are planted in this viewing area, and the seasonal changes such as blooming in spring, lush leaves in summer, and color change in autumn are important components of the landscape viewing value. In order to retain this viewing value, the local curvature of the step platform is modified. If the original local curvature radius of the step platform is r1, in order to adapt to the growth space requirements of the vegetation and not affect the viewing field of view, the curvature radius is adjusted to r2, and this adjustment is carried out on the premise of not destroying the stability of the entire step platform and the overall landscape coordination.

[0087] Step S144: Voxelize and fuse the adjusted stepped terrain feature and vegetation distribution feature to generate a composite voxel model including terrain material attributes, vegetation growth attributes, and light reflection attributes as the composite terrain vegetation feature.

[0088] After completing the processing of the conflict points, voxel fusion is performed on the adjusted stepped terrain features and vegetation distribution features. First, obtain the set of surface vertex coordinates in the adjusted stepped terrain features and the updated vegetation planting coordinate set in the vegetation distribution features. Extract information such as elevation gradient data from the set of surface vertex coordinates. Assume that the elevation of a certain slope changes from z6 to z7 from the bottom to the top, forming a certain elevation gradient. Extract data such as the vertical height offset from the vegetation planting coordinate set. For example, the vertical height offset of a certain plant relative to the surrounding terrain is Δz. Based on the spatial distribution relationship between these data, align the set of surface vertex coordinates of the stepped terrain features with the vegetation planting coordinate set in three-dimensional space to generate a spatially aligned dataset with a unified coordinate system reference.

[0089] In the spatially aligned dataset, identify the overlapping area between the coordinates of the drainage grooves at the edge of the stepped platform and the coordinates of the vegetation root expansion range, and detect the abnormal angle points between the direction of the terrain surface normal vector and the vegetation planting direction within the overlapping coordinate area. For example, the direction of the normal vector of the bottom surface of a certain drainage groove is vertically downward, while the growth direction of the vegetation roots is inclined downward at a certain angle, forming an abnormal angle point between the two. According to the position distribution of the abnormal angle points, perform displacement compensation on the surface vertex coordinates of the stepped terrain features. For example, make a slight adjustment to the surface vertex coordinates in the x, y, and z directions so that the direction of the normal vector of the bottom surface of the drainage groove forms a perpendicular orthogonal relationship with the growth direction of the vegetation roots, which helps to ensure the normal growth of the vegetation roots and the drainage function of the drainage grooves is not affected.

[0090] Import the surface vertex coordinates after displacement compensation and the vegetation planting coordinate set into a preset voxel division engine. Read parameters such as the maximum elevation difference and the minimum curvature radius in the surface vertex coordinates after displacement compensation. Assume that the maximum elevation difference is ΔZmax and the minimum curvature radius is rmin. Determine the number of vertical layers of the voxel division engine according to the maximum elevation difference. For example, set the number of vertical layers to n layers, where n is a reasonable number of layers calculated based on ΔZmax. Calculate the initial value of the voxel side length in the horizontal direction based on the minimum curvature radius as l0, and start the dynamic encryption algorithm in the area where the curvature change rate exceeds the threshold. If the curvature change rate of a certain area is k, when k exceeds the set threshold, reduce the initial value of the voxel side length in proportion to the reciprocal of the curvature change rate, such as becoming l1, thereby generating a non-uniform voxel grid with adaptive resolution, where the vertex coordinates of each voxel unit form a one-to-one mapping relationship with the surface vertex coordinates of the stepped terrain features.

[0091] Traverse each voxel cell in the non-uniform voxel grid. If the voxel cell contains both the surface vertex coordinates of the stepped terrain feature and the vegetation planting coordinates, extract the corresponding rock texture roughness parameters, soil moisture content parameters, predicted values of vegetation shading range, etc. For example, the micro-surface uneven height data corresponding to the rock texture roughness parameter in a certain voxel cell is h1, and the particle size distribution data in the soil diffuse reflection parameter of the adjacent voxel is d1. Further process these data to calculate terrain material properties, vegetation growth properties, light reflection properties, etc. By calculating the relationship between the rock texture roughness parameter and the soil moisture content parameter, etc., determine parameters such as the light attenuation coefficient and the osmotic pressure gradient of the vegetation root system in the voxel cell, so as to generate a composite voxel model containing terrain material properties (such as the hardness of the rock, the texture of the soil, etc.), vegetation growth properties (such as the root extension speed, canopy coverage rate, etc.), and light reflection properties (such as the reflectivity and scattering rate of light, etc.), as the composite terrain vegetation feature. This composite terrain vegetation feature synthesizes various characteristics of the terrain and vegetation, providing basic data for subsequent operations such as path connectivity analysis and multi-dimensional rendering.

[0092] In a possible implementation manner, step S144 includes:

[0093] Step S1441, obtain the set of surface vertex coordinates in the adjusted stepped terrain feature and the updated set of vegetation planting coordinates in the vegetation distribution feature, and extract the elevation gradient data in the set of surface vertex coordinates and the vertical height offset in the set of vegetation planting coordinates.

[0094] In this embodiment, in the high-altitude mountainous area, the set of surface vertex coordinates of the stepped terrain feature contains the coordinate information of each key point constituting the stepped terrain. For example, the vertex coordinates of a certain step may be (x1, y1, z1), and the vertex coordinates of the next adjacent step are (x2, y2, z2), etc. These coordinates constitute the shape of the entire stepped terrain in three-dimensional space. At the same time, the updated set of vegetation planting coordinates in the vegetation distribution feature determines the planting positions of various plants in this mountainous area. For example, the planting coordinates of a certain Rhododendron lapponicum are (x3, y3, z3). Then extract the elevation gradient data in the set of surface vertex coordinates. For example, on a certain slope from the foot of the mountain to the top of the mountain, the elevation gradually rises from 1000 meters to 1500 meters, forming a certain elevation gradient. At the same time, extract the vertical height offset from the set of vegetation planting coordinates. For example, a certain plant is 0.5 meters higher than the surrounding average ground height, and this is the vertical height offset.

[0095] Step S1442: Based on the spatial distribution relationship between the elevation gradient data and the vertical height offset, align the set of surface vertex coordinates of the stepped terrain feature with the vegetation planting coordinate set in a three-dimensional space grid to generate a spatially aligned data set with a unified coordinate system reference.

[0096] In this process, considering the complex terrain and vegetation distribution in high-altitude mountainous areas, it is necessary to accurately calculate the relative position relationship of each coordinate point in space. For example, determine the grid division method at different altitudes according to the elevation gradient data, and at the same time, combine the vertical height offset of the vegetation to accurately place the vegetation planting coordinates in the corresponding terrain grid, ensuring the accurate spatial relationship between the two in three-dimensional space, so as to generate a spatially aligned data set.

[0097] Step S1443: Identify the coordinate overlapping area between the drainage groove coordinates at the edge of the stepped platform and the coordinate range of the vegetation root system expansion in the spatially aligned data set, and detect the angle anomaly points between the terrain surface normal vector direction and the vegetation planting direction within the coordinate overlapping area.

[0098] In the stepped terrain of high-altitude mountainous areas, the drainage groove is used to drain rainwater and melted snow water, and its coordinate range is clear. For example, the starting coordinates of a certain drainage groove are (x4, y4, z4), and the ending coordinates are (x5, y5, z5). At the same time, the coordinate range of the vegetation root system expansion also exists. For different plants, the root system expansion range is different. When there is a coordinate overlapping area between the two, detect the angle anomaly points between the terrain surface normal vector direction and the vegetation planting direction within the coordinate overlapping area. For example, the normal vector direction of the bottom surface of a certain drainage groove is vertically downward, while the growth direction of the root system of a certain plant is inclined downward by 30 degrees, which forms an angle anomaly point.

[0099] Step S1444: According to the position distribution of the angle anomaly points, perform displacement compensation on the surface vertex coordinates of the stepped terrain feature so that the bottom surface normal vector direction of the drainage groove forms a perpendicular orthogonal relationship with the growth direction of the vegetation root system.

[0100] Under the terrain conditions of high-altitude mountainous areas, this displacement compensation is to ensure that the function of the drainage groove is not affected and the vegetation root system can grow normally. For example, for the area where the angle anomaly points are found, through precise calculation, move the surface vertex coordinates of the stepped terrain feature 0.1 meter in the x direction, 0.05 meter in the y direction, and 0.03 meter in the z direction, so that the bottom surface normal vector direction of the drainage groove forms a perpendicular orthogonal relationship with the growth direction of the vegetation root system, which not only ensures that the drainage groove can drain effectively but also provides a suitable growth space for the vegetation root system.

[0101] Step S1445: Import the surface vertex coordinates after displacement compensation and the vegetation planting coordinate set into a preset voxel division engine, dynamically adjust the voxel side length according to the curvature change rate of the stepped terrain feature, and generate a non-uniform voxel grid that matches the terrain undulation.

[0102] Step S1446: Traverse each voxel unit in the non-uniform voxel grid. If the voxel unit contains both the surface vertex coordinates of the stepped terrain feature and the vegetation planting coordinates, extract the corresponding rock texture roughness parameter, soil moisture content parameter, and vegetation shading range prediction value of the voxel unit.

[0103] In high-altitude mountainous areas, the rock texture roughness parameter reflects the roughness of the rock surface. For example, the micro-surface uneven height data corresponding to the rock texture roughness parameter in a certain voxel unit is 0.02 meters, which means there are certain undulations on the rock surface. The soil moisture content parameter is crucial for vegetation growth. Suppose the soil moisture content in a certain voxel unit is 30%. The vegetation shading range prediction value determines the shading effect of the vegetation on the surrounding environment. For example, the shading range prediction value of a certain plant is a circular area with a radius of 1 meter.

[0104] Step S1447: Match the reflectivity map of the anti-slip material according to the rock texture roughness parameter, calculate the osmotic pressure gradient of the vegetation root system according to the soil moisture content parameter, and determine the light attenuation coefficient in the voxel unit in combination with the shading range prediction value.

[0105] For example, on the stepped terrain in high-altitude mountainous areas, the reflectivity map of the anti-slip material has an important impact on the walking safety of tourists and the visual effect of the landscape. For example, for a rock surface with a higher roughness, the matched reflectivity map of the anti-slip material may have a lower reflectivity to reduce the visual interference caused by light reflection. Calculate the osmotic pressure gradient of the vegetation root system according to the soil moisture content parameter. Suppose when the soil moisture content is 30%, the osmotic pressure gradient of the vegetation root system is 0.2 obtained through a specific calculation formula. Determine the light attenuation coefficient in the voxel unit in combination with the shading range prediction value. For example, according to the shading range prediction value of a circular area with a radius of 1 meter and the surrounding light environment, the calculated light attenuation coefficient is 0.3.

[0106] Step S1448: Perform a material mixing operation on each voxel unit, and perform a weighted superposition of the reflectivity map of the anti-slip material and the soil diffuse reflection parameter of the adjacent voxel to generate a continuous transition effect of the terrain material attribute.

[0107] Step S1449: Inject vegetation growth simulation parameters into the non-uniform voxel grid, dynamically adjust the expansion speed of the vegetation root system according to the osmotic pressure gradient, and correct the pigment deposition rate of the vegetation leaves based on the light attenuation coefficient.

[0108] Step S14410: Extract the cross-sectional geometric parameters of the drainage grooves in the stepped terrain features, calculate the surface tension distribution when water flows through the grooves, and generate water flow reflection spot data matching the voxel unit size.

[0109] Step S14411: Perform optical path superposition on the water flow reflection spot data and the continuous transition effect of the terrain material attributes, and mark the specular reflection highlight area and the diffuse reflection shadow area in the non-uniform voxel grid.

[0110] Under the complex terrain and lighting conditions in high-altitude mountainous areas, optical path superposition is a complex process. Through precise ray propagation calculations, the water flow reflection spot data is combined with the continuous transition effect of the terrain material attributes. For example, in a certain voxel unit, the water flow reflection spot data causes a highlight reflection in a certain area, while the continuous transition effect of the terrain material determines the surrounding diffuse reflection situation, thereby accurately marking the specular reflection highlight area and the diffuse reflection shadow area to simulate the real lighting effect.

[0111] Step S14412: Verify the physical compatibility of the terrain material attributes, vegetation growth attributes, and light reflection attributes in each voxel unit. When it is detected that there is a spatial overlap between the specular reflection highlight area and the predicted value of the vegetation shading range, perform attenuation filtering on the reflection intensity of the highlight area.

[0112] Step S14413: Update the attribute parameters in the non-uniform voxel grid according to the verification results, and output a composite voxel model containing multi-attribute fusion data.

[0113] For example, in a possible implementation, step S1445 includes:

[0114] Read the maximum elevation difference and the minimum curvature radius in the vertex coordinates of the displacement-compensated surface, and determine the vertical stratification number of the voxel division engine according to the maximum elevation difference.

[0115] Based on the minimum curvature radius, calculate the initial value of the voxel side length in the horizontal direction, and start the dynamic encryption algorithm in the area where the curvature change rate exceeds the threshold, and reduce the initial value of the voxel side length according to the reciprocal ratio of the curvature change rate.

[0116] Based on the vertical stratification number and the dynamically adjusted voxel side length, generate a non-uniform voxel grid with adaptive resolution, where the vertex coordinates of each voxel unit form a one-to-one mapping relationship with the vertex coordinates of the stepped terrain features.

[0117] In this embodiment, in high-altitude mountainous areas, the maximum elevation difference may reach 500 meters, which reflects the undulation degree of the entire terrain; the minimum curvature radius may be 3 meters, which reflects the minimum radius of the terrain bend. Determine the number of vertical layers of the voxel division engine according to the maximum elevation difference. For example, according to the maximum elevation difference of 500 meters, set the number of vertical layers to 50 layers, so that each layer approximately corresponds to an elevation range of 10 meters. Calculate the initial value of the voxel side length in the horizontal direction based on the minimum curvature radius. Assuming the minimum curvature radius is 3 meters, calculate the initial value of the voxel side length in the horizontal direction to be 1 meter. And start the dynamic encryption algorithm in the area where the curvature change rate exceeds the threshold (such as 0.5). If the curvature change rate of a certain area is 0.8, reduce the initial value of the voxel side length according to the reciprocal ratio of the curvature change rate, that is, it becomes 0.625 meters. Based on the number of vertical layers and the dynamically adjusted voxel side length, generate a non-uniform voxel grid with adaptive resolution, where the vertex coordinates of each voxel unit form a one-to-one mapping relationship with the surface vertex coordinates of the stepped terrain features. In this way, in the complex terrain of high-altitude mountainous areas, a suitable voxel grid can be generated according to the actual situation of the terrain, which can not only accurately represent the undulation of the terrain but also reasonably distribute vegetation information.

[0118] For example, in a possible implementation manner, step S1448 includes:

[0119] Obtain the micro-surface uneven height data corresponding to the rock texture roughness parameter in the voxel unit, and the particle size distribution data in the diffuse reflection parameter of the adjacent voxel soil.

[0120] Calculate the material mixing weight coefficient according to the proportional relationship between the micro-surface uneven height data and the particle size distribution data, and perform linear interpolation on the specular reflection intensity of the rock material and the diffuse reflection intensity of the soil material based on the weight coefficient.

[0121] Map the interpolated reflection intensity data to all surface vertices of the voxel unit to generate a natural transition material effect from rock to soil.

[0122] In the voxel unit in the high-altitude mountainous area, obtain the micro-surface uneven height data corresponding to the rock texture roughness parameter (such as 0.02 meters) in the voxel unit, and the particle size distribution data (such as 0.01 meters) in the diffuse reflection parameter of the adjacent voxel soil. Calculate the material mixing weight coefficient according to the proportional relationship between the micro-surface uneven height data and the particle size distribution data. Assuming the proportional relationship is 2:1, the calculated material mixing weight coefficient is 0.67. Perform linear interpolation on the specular reflection intensity of the rock material and the diffuse reflection intensity of the soil material based on this weight coefficient. For example, the specular reflection intensity of the rock material is 0.4, and the diffuse reflection intensity of the soil material is 0.6. After linear interpolation, new reflection intensity data is obtained. Map the interpolated reflection intensity data to all surface vertices of the voxel unit to generate a natural transition material effect from rock to soil, making the transition between different materials in the terrain of the high-altitude mountainous area more natural and conforming to the actual visual and physical effects.

[0123] For example, in one possible implementation, step S1449 includes:

[0124] Mark the voxel units corresponding to the vegetation planting coordinate set in the non-uniform voxel grid, and assign an initial root depth value and a canopy coverage rate to each marked voxel unit.

[0125] Dynamically calculate the daily growth increment of the root depth value according to the soil moisture content parameter in the voxel unit, and calculate the seasonal expansion rate of the canopy coverage rate based on the light attenuation coefficient.

[0126] When the canopy coverage rate of the adjacent voxel unit expands to the current voxel unit, trigger the canopy gap closure algorithm to automatically adjust the leaf density to eliminate the discontinuous shadow boundary in the light simulation.

[0127] In this process, first, the voxel units corresponding to the vegetation planting coordinate set are marked in the non-uniform voxel grid. For example, the voxel unit corresponding to a certain Rhododendron lapponicum is accurately marked. Then, an initial root depth value and canopy coverage rate are assigned to each marked voxel unit. Suppose the initial root depth value of a certain plant is 0.1 meters and the canopy coverage rate is 0.1. The daily growth increment of the root depth value is dynamically calculated according to the soil moisture content parameter within the voxel unit. When the soil moisture content is 30%, the daily growth increment of the root depth value is calculated to be 0.001 meters through a specific growth model. At the same time, the seasonal expansion rate of the canopy coverage rate is calculated based on the light attenuation coefficient. For example, in summer when the light is sufficient and the light attenuation coefficient is 0.3, the seasonal expansion rate of the canopy coverage rate is calculated to be 0.05 per month. When the canopy coverage rate of the adjacent voxel unit expands to the current voxel unit, the canopy gap closure algorithm is triggered to automatically adjust the leaf density to eliminate the discontinuous shadow boundary in the light simulation. In high-altitude mountainous areas, the growth of vegetation is affected by various factors, and this simulation can accurately reflect the growth state of vegetation under different environmental conditions.

[0128] For example, in one possible implementation, step S14410 includes:

[0129] Extract the bottom slope angle and the groove wall roughness grade from the cross-sectional geometric parameters of the drainage groove, and calculate the component of the gravitational acceleration of the water flow according to the slope angle.

[0130] Determine the viscous resistance coefficient of the water flow boundary layer based on the groove wall roughness grade, and solve the dynamic equilibrium equation of the surface tension in combination with the component of the gravitational acceleration.

[0131] Discretize the solution result of the dynamic equilibrium equation into a water film thickness distribution map matching the voxel unit size, and generate the intensity gradient data of the reflected light spot according to the water film thickness.

[0132] In high-altitude mountainous areas, the cross-sectional geometric parameters of the drainage groove include the bottom slope angle and the groove wall roughness grade. For example, the bottom slope angle of a certain drainage groove is 5 degrees and the groove wall roughness grade is 3. The component of the gravitational acceleration of the water flow is calculated according to the slope angle, and the component of the gravitational acceleration is calculated to be 0.87 m / s² through physical formulas. The viscous resistance coefficient of the water flow boundary layer is determined to be 0.05 based on the groove wall roughness grade, and the dynamic equilibrium equation of the surface tension is solved in combination with the component of the gravitational acceleration. The solution result of the dynamic equilibrium equation is discretized into a water film thickness distribution map matching the voxel unit size. For example, in the case where the voxel unit size is 0.1 m × 0.1 m × 0.1 m, the water film thickness is 0.01 m at the bottom of the groove and 0.005 m at the edge of the groove, and the intensity gradient data of the reflected light spot is generated according to the water film thickness, so that the surface tension and light reflection effect of the water flow in the drainage groove in high-altitude mountainous areas can be accurately simulated.

[0133] For example, in one possible implementation, step S14412 includes:

[0134] Scan the light reflection property data of each voxel unit in the non-uniform voxel grid, and detect whether the brightness value of the specular highlight area exceeds the photosynthesis saturation threshold of the vegetation leaves.

[0135] If a highlight area exceeding the saturation threshold is detected, then according to the leaf density parameter in the predicted vegetation shading range, proportionally reduce the reflection intensity of this area until it reaches the effective radiation range of photosynthesis.

[0136] Synchronously update the reflectivity map in the terrain material properties of the voxel unit to ensure the visual continuity between the highlight area after the reflection intensity adjustment and the surrounding materials.

[0137] For example, the brightness value of the specular highlight area in a certain voxel unit is 1000 lux, while the photosynthesis saturation threshold of the vegetation leaves in this area is 800 lux. If a highlight area exceeding the saturation threshold is detected, then according to the leaf density parameter in the predicted vegetation shading range, proportionally reduce the reflection intensity of this area until it reaches the effective radiation range of photosynthesis. Suppose the leaf density parameter is 0.5, then reduce the reflection intensity to 0.8 times the original. Synchronously update the reflectivity map in the terrain material properties of the voxel unit to ensure the visual continuity between the highlight area after the reflection intensity adjustment and the surrounding materials, so that in the landscape of high-altitude mountainous areas, the lighting effect not only meets the needs of vegetation growth but also has a good visual effect.

[0138] For example, in one possible implementation, step S14413 includes:

[0139] Encode each voxel unit in the non-uniform voxel grid into a multi-dimensional data block containing terrain material properties, vegetation growth properties, and light reflection properties.

[0140] Construct an octree index structure according to the spatial arrangement order of the multi-dimensional data blocks, and label the voxel clustering identifiers of the material transition areas in the octree index structure.

[0141] Perform LOD level division on the octree index structure, and dynamically call the voxel data blocks of the corresponding levels according to different viewing distances to generate a composite voxel model that supports multi-resolution rendering.

[0142] For example, in the multi-dimensional data block of a voxel unit, the terrain material attributes include information such as rock hardness and soil texture, the vegetation growth attributes contain data such as root depth and canopy coverage rate, and the light reflection attributes have values such as reflectivity and scattering rate. An octree index structure is constructed according to the spatial arrangement order of the multi-dimensional data block, and the voxel clustering identification of the material transition region is marked in the octree index structure. The LOD level division is performed on the octree index structure, and the voxel data block of the corresponding level is dynamically called according to different observation distances. In the landscape browsing of high-altitude mountainous areas, when the observation distance is far, the voxel data block with low resolution is called to improve the rendering efficiency; when the observation distance is close, the voxel data block with high resolution is called to display more details, so as to generate a composite voxel model that supports multi-resolution rendering. This composite voxel model can accurately reflect the complex terrain, vegetation growth, light effects and other multi-faceted characteristics of the scenic garden landscape in high-altitude and high-drop mountainous areas.

[0143] In a possible implementation manner, step S140 further includes:

[0144] Step S145, obtaining the pedestrian flow density and average stay duration during peak hours in the historical tourist flow data, and calculating the maximum carrying capacity of each area in combination with the viewing platform area in the composite terrain and vegetation characteristics.

[0145] Suppose that through the statistical analysis of tourist data over the past years, the pedestrian flow density during peak hours is obtained as 5 people per square meter, and the average stay duration is 30 minutes. At the same time, the viewing platform area in the composite terrain and vegetation characteristics is accurately measured as 100 square meters. Calculate the maximum carrying capacity of the viewing platform according to these data. The calculation formula is: maximum carrying capacity = viewing platform area × pedestrian flow density during peak hours × (average stay duration ÷ total duration). Assuming that the total duration is 60 minutes, then the maximum carrying capacity of this viewing platform = 100×5×(30÷60) = 250 people. Calculate the maximum carrying capacity of different viewing platforms and other tourist gathering areas in the entire high-altitude mountainous area in this way. This data will provide an important basis for subsequent route planning and tourist diversion.

[0146] Step S146, marking the candidate routes of the emergency evacuation channels in the composite terrain and vegetation characteristics, and calculating the cumulative climbing height and slope change rate of each candidate route based on the elevation difference data of the stepped terrain characteristics.

[0147] Under the complex terrain of high-altitude mountainous areas, multiple factors need to be comprehensively considered when selecting candidate routes for emergency evacuation channels. For example, a relatively gentle route along the valley bottom may be marked as a candidate route because the valley bottom is at a lower altitude and is relatively flat, which is conducive to the rapid evacuation of personnel. At the same time, an open route along the ridge may also be a candidate route. The ridge has a wide view, which is convenient for guiding tourists to evacuate and is not easily affected by geological disasters such as landslides. For these candidate routes, relevant parameters are calculated based on the elevation difference data of the stepped terrain features. Suppose a candidate route along the valley bottom has an elevation difference data showing a cumulative climbing height of 100 meters from the starting point to the ending point. By measuring and calculating the slopes of different sections of the route, the slope change rate is obtained as 0.1. Another candidate route along the ridge has a cumulative climbing height of 200 meters and a slope change rate of 0.2. The accurate calculation of these data is crucial for evaluating the difficulty and safety of the candidate routes.

[0148] Step S147, set diversion nodes for the candidate routes according to the maximum carrying capacity, add virtual coordinates of indication signs at the diversion nodes, and screen out a set of paths that meet the ergonomic comfort threshold based on the cumulative climbing height and slope change rate.

[0149] Taking the previously calculated maximum carrying capacity of the viewing platform as 250 people as an example, set diversion nodes on each candidate route leading to the viewing platform. For example, set a diversion node at a certain distance (such as 50 meters) from the viewing platform, determine the position of the node according to the terrain and tourist flow in this area, and add virtual coordinates (x6, y6, z6) of indication signs for this diversion node. This virtual coordinate will play a role in subsequent navigation and sign setting. Screen the set of paths based on the cumulative climbing height and slope change rate, and set the ergonomic comfort threshold as the cumulative climbing height not exceeding 200 meters and the slope change rate not exceeding 0.3. Among the previously calculated candidate routes, the candidate route at the valley bottom has a cumulative climbing height of 100 meters and a slope change rate of 0.1, meeting this threshold requirement; the candidate route on the ridge has a cumulative climbing height of 200 meters and a slope change rate of 0.2, also meeting the requirement. These qualified routes are screened out to form a set of paths.

[0150] Step S148, perform topological optimization on each path in the set of paths, eliminate infinite loop sections and increase the insertion density of viewing viewpoints, and generate tour path features including path width parameters, handrail setting points, and rest area coordinates.

[0151] Among them, step S148 includes:

[0152] Step S1481, construct a node connection graph of each path in the set of paths, and identify cross nodes in the node connection graph with degrees greater than the set degree.

[0153] Step S1482, set a direction guiding sign at the intersection node, and adjust the connection priority of the intersection node according to the visibility parameter of adjacent paths.

[0154] In the path network of high-altitude mountainous areas, the intersection points, turning points, etc. of each path are regarded as nodes. For example, a certain path forms nodes at positions such as viewing platforms, rest areas, and fork roads. These nodes and the connection relationships between the nodes are constructed into a node connection graph. In this node connection graph, identify the intersection nodes with degrees greater than a set degree (such as 3). For example, at a place where three paths meet, the degree of this intersection node is 3 because it connects three different paths. Set a direction guiding sign at such an intersection node. The direction guiding sign can be a clear road sign or an electronic navigation indication, indicating the direction that tourists should go. At the same time, adjust the connection priority of the intersection node according to the visibility parameter of adjacent paths. In high-altitude mountainous areas, visibility is very important for tourists' tour experience and safety. For example, a path leads to an open valley, where distant mountains and streams can be seen, and its visibility is relatively high; while another path may be blocked by thick vegetation and its visibility is relatively low. For the path with high visibility, give a higher connection priority at the intersection node because tourists tend to choose the route with good visibility.

[0155] Step S1483, perform a curvature radius expansion process on the paths with connection priorities lower than the set priority, ensure the safety distance when two-way pedestrian flows meet, and add an anti-slip material sign at the curvature radius change.

[0156] Step S1484, generate a path topological structure according to the optimized node connection graph, dynamically associate the path topological structure with the viewing platform coordinates in the composite terrain vegetation features, and update the branch guiding parameters in the tour path features.

[0157] Specifically, if the curvature radius of a path with a low connection priority is small, for example, originally 2 meters, it is likely to cause congestion and danger during the convergence of the flow of people. Expand its curvature radius to 3 meters, which can increase the safety distance during the convergence of two-way flow of people. At the same time, add anti-slip material signs at the places where the curvature radius changes, such as the transition area from 2 meters to 3 meters. This is because in high-altitude mountainous areas, the weather is changeable, and there may be rainfall or snow accumulation. The anti-slip signs can remind tourists to pay attention to the safety under their feet and avoid slipping. Generate a path topology structure based on the optimized node connection diagram. This path topology structure clearly shows information such as the connection relationships between various paths, the positions of nodes, and the priorities of paths. Then dynamically associate this path topology structure with the viewing platform coordinates in the composite terrain vegetation characteristics. For example, when a tourist approaches the viewing platform, the path topology structure can accurately guide the tourist to the best viewing position of the viewing platform according to the coordinate information of the viewing platform. At the same time, update the branch guiding parameters in the tour path characteristics. These branch guiding parameters can include the guiding sign content, guiding frequency, etc. on different branch paths to better guide tourists during the tour process in high-altitude mountainous areas.

[0158] Based on the above steps, in the landscape of mountain scenic spots with high altitude and large elevation difference, perform path connectivity analysis on the composite terrain vegetation characteristics in combination with the preset tourist flow data, and finally determine the tour path characteristics. These tour path characteristics not only consider the tourist flow distribution, ergonomic comfort, but also take into account the complexity of the terrain and the viewing needs, providing a safe, comfortable and good viewing experience path planning for tourists in high-altitude mountainous areas.

[0159] In a possible implementation manner, step S150 may include:

[0160] Step S151, perform data fusion on the voxel model in the composite terrain vegetation characteristics and the topology structure in the tour path characteristics to generate a basic data set for scene rendering.

[0161] For example, the voxel model in the composite terrain vegetation features contains various information about the terrain, such as terrain elevation, slope, rock distribution, vegetation distribution, etc., and this information exists in the form of voxels. The topological structure in the tour path features details the connection relationships of the paths, node positions, as well as the path width, handrail setting points, and rest area coordinates, etc. During the data fusion process, each voxel in the voxel model needs to be associated with the corresponding elements in the topological structure. For example, the voxels located near the tour path in the voxel model need to be associated with the path elements in the topological structure to clarify their positional relationships around the path. At the same time, for the voxels located near the viewing platform, they need to be accurately associated with the viewing platform elements in the topological structure to determine their orientation and distance relative to the viewing platform. Through such association and fusion, a basic dataset for scene rendering is generated, and this dataset provides a comprehensive data foundation for subsequent rendering, containing information on terrain, vegetation, and tour paths, etc.

[0162] Step S152, load the rock texture map, vegetation dynamic growth animation, and water flow particle effect based on the material attributes in the basic dataset for scene rendering, and calculate the shadow projection range according to the light angle parameter.

[0163] For example, the material attributes in the basic dataset for scene rendering contain characteristic information about rocks, vegetation, and soil, etc. For rocks, load the rock texture map according to their material attributes. For example, if the rocks in a certain area are granite in texture, then load a texture map with granite characteristics, such as a map with a granular feel, a grayish-white color, and irregular textures, so that they present a real granite appearance in the rendered model. For vegetation, load the vegetation dynamic growth animation according to the type and growth stage of the vegetation. In high-altitude mountainous areas, different vegetation has different growth cycles and characteristics. For example, for Rhododendron lapponicum, flower buds gradually grow in spring, flowers bloom in summer, leaves change color in autumn, and it enters the dormant period in winter. By loading the vegetation dynamic growth animation, the growth changes of vegetation over time can be truly shown in the rendered 3D visualization model. For the water flow in the mountainous area, load the water flow particle effect according to the relevant data of the water flow. In high-altitude mountainous areas, the streams in the valleys or the surface runoff after rain can be simulated through the water flow particle effect to make the water flow look more real and natural. At the same time, calculate the shadow projection range according to the light angle parameter. In high-altitude mountainous areas, since the position and angle of the sun change at different times, the light angle parameter is set according to the local longitude, latitude, season, and time. For example, in the morning, the sun rises from the east, the light shines obliquely, and long shadows will be produced on the west side of the mountain peaks; while at noon, the sun shines directly, and the shadow range is relatively small. By accurately calculating the light angle parameter, the shadow projection range can be truly presented in the rendered model, enhancing the realism of the model.

[0164] Step S153, during the rendering process, the visual occlusion areas in the three-dimensional visualization model are detected in real time, and the viewing angles of the visual occlusion areas are adaptively adjusted to increase auxiliary observation viewpoints.

[0165] Step S154, extract the biodiversity parameters in the vegetation distribution characteristics and the soil and water conservation parameters in the stepped terrain characteristics, generate an ecological benefit evaluation matrix, and associate and label the ecological benefit evaluation matrix with the three-dimensional visualization model.

[0166] In this embodiment, the biodiversity parameters include information such as the number of plant species, the distribution ratio of different plants, and the structure of plant communities. For example, it is statistically found that there are various plants such as Rhododendron lapponicum, Abies fabri, and alpine meadows in the high-altitude mountainous area. Among them, the distribution area of Rhododendron lapponicum accounts for 20% of the total vegetation area, Abies fabri accounts for 30%, and alpine meadows account for 50%. And analyze the vertical distribution structure of these plant communities. For example, Rhododendron lapponicum is mainly distributed in the area with an altitude of 3000 - 3500 meters, Abies fabri is distributed in the area with an altitude of 2500 - 3000 meters, and alpine meadows are distributed in the area with an altitude of 3500 - 4000 meters, etc. The soil and water conservation parameters in the stepped terrain characteristics include the slope of the terrain, the impact of vegetation coverage on soil erosion, etc. For example, in areas with a gentle slope and high vegetation coverage, the degree of soil erosion is low; while in areas with a steep slope and low vegetation coverage, the degree of soil erosion is high. An ecological benefit evaluation matrix is generated based on the above biodiversity parameters and soil and water conservation parameters. The elements in the ecological benefit evaluation matrix include the contribution of plant species diversity to the stability of the ecosystem, the effect of vegetation coverage on soil and water conservation, etc. The ecological benefit evaluation matrix is associated and labeled with the three-dimensional visualization model. In the three-dimensional visualization model, for areas with rich plant species, a specific color or identifier can be used to represent its high contribution to the stability of the ecosystem; for areas with good soil and water conservation effects, corresponding identifiers can also be used to display. For example, in areas with high vegetation coverage and low soil erosion degree, a green identifier is used to represent good soil and water conservation effects, and at the same time, the biodiversity information such as the richness of plant species in this area is displayed in the model. In this way, when tourists browse the three-dimensional visualization model, they can intuitively understand the ecological benefit situation of the high-altitude mountainous area.

[0167] Among them, step S153 includes:

[0168] Step S1531, set multiple preset observation viewpoints in the three-dimensional visualization model, and calculate the visible range of each observation viewpoint.

[0169] Step S1532, compare the visible range with the key display area in the landscape adjustment instruction, and identify the visual blind areas that do not cover the key display area.

[0170] Step S1533: Insert a virtual observation point at the coordinate position corresponding to the visual blind area, and generate a temporary connection path from the nearest tour path to the virtual observation point based on a path optimization algorithm.

[0171] Step S1534: Adjust the rendering level of the 3D visualization model, add a terrain profile with transparency processing at the end of the temporary connection path, and expose the landscape details in the visual blind area.

[0172] Due to the undulation of the terrain and the distribution of vegetation, there may be visual occlusion areas. First, set multiple preset observation perspectives in the 3D visualization model. These preset observation perspectives are set according to the terrain characteristics of the high-altitude mountainous area and the key landscape areas. For example, set an observation perspective at the mountaintop to overlook the panoramic view of the entire mountainous area; set an observation perspective at the bottom of the valley to observe the streams and vegetation in the valley; set observation perspectives at the viewing platforms on the mountainside to enjoy the vegetation levels at different altitudes and the distant mountains. For each preset observation perspective, calculate its visible range. The calculation of the visible range needs to consider factors such as the undulation of the terrain, the height of the vegetation, and buildings (if any). For example, from the observation perspective of the viewing platform on the mountainside, due to a mountain peak blocking in front, the visible range may be the area between the valley on the left and the ridge on the right. Compare the visible range with the key display area in the landscape adjustment instruction to identify the visual blind areas that do not cover the key display area. Suppose the key display area in the landscape adjustment instruction is a valley where a certain rare plant community is located. However, due to the occlusion of the surrounding mountains and vegetation, the visible range from the existing observation perspectives cannot completely cover this valley, and this valley becomes a visual blind area. Insert a virtual observation point at the coordinate position corresponding to the visual blind area. For example, insert a virtual observation point above the valley where this rare plant community is located, and the coordinates (x7, y7, z7) of this virtual observation point are determined according to the position of the valley and the surrounding terrain. Then generate a temporary connection path from the nearest tour path to the virtual observation point based on a path optimization algorithm. The path optimization algorithm will consider the slope, distance of the terrain, and the existing tour path conditions to find the shortest and safest path from the nearest tour path to the virtual observation point. For example, along a relatively gentle path on the hillside, passing through several turning points, and finally reaching the virtual observation point. Finally, adjust the rendering level of the 3D visualization model, add a terrain profile with transparency processing at the end of the temporary connection path, and expose the landscape details in the visual blind area. In the high-altitude mountainous area, by adjusting the rendering level, the landscape details such as rare plant communities in the visual blind area can be displayed more clearly. For example, set the terrain profile at the end of the temporary connection path to a semi-transparent state, so that tourists can see the distribution, growth status, etc. of the rare plant community in the valley through the terrain profile.

[0173] Through the above steps, the composite terrain vegetation characteristics are rendered in multiple dimensions according to the tour path characteristics, and a landscape garden plan including a three-dimensional visualization model and ecological evaluation parameters is generated. Thus, it is possible to comprehensively and accurately display various information such as the terrain, vegetation, tour path, and ecological benefits of the scenic garden landscape in high-altitude and high-drop mountain areas, providing an important reference basis for the planning and construction of the scenic area and the tours of tourists.

[0174] In a possible implementation manner, step S150 further includes:

[0175] Step S155, displaying a rotatable thumbnail of the three-dimensional visualization model and a comparative radar chart of the ecological evaluation parameters in the user terminal interface.

[0176] In this embodiment, on the user terminal interface, the rotatable thumbnail of the three-dimensional visualization model enables the user to view the garden landscape in the high-altitude mountain area from different angles. For example, the user can rotate the thumbnail to the angle of overlooking the entire mountain area from the mountaintop through touch screen or mouse operation to view the stepped terrain, vegetation distribution, and the layout of the tour path. At the same time, the comparative radar chart of the ecological evaluation parameters intuitively presents the situation of multiple ecological evaluation indicators. For example, one axis of the radar chart may represent biodiversity, another axis may represent the soil and water conservation effect, and another axis may represent the vegetation coverage rate, etc. Through the comparative radar chart, the user can clearly see the comparison of the ecological indicators between the current landscape garden plan and other reference plans or the original state, and quickly understand the performance of this plan in terms of ecological benefits.

[0177] Step S156, receiving a marking modification instruction from the user for a specified viewing area, where the marking modification instruction includes a vegetation type replacement request, a path curvature adjustment request, or a terrain step number modification request.

[0178] For example, for the vegetation type replacement request, it may be because the currently planted Rhododendron lapponicum in a certain viewing area has poor viewing effects in a specific season, and the user hopes to replace it with Trollius chinensis with a longer flowering period and brighter colors. For the path curvature adjustment request, it may be because a certain section of the tour path is close to the cliff edge and has a large curvature, posing a safety hazard, and the user hopes to adjust the curvature of this section of the path to make it smoother. For the terrain step number modification request, it may be near a certain viewing platform, and the existing number of terrain steps is too large, making it difficult for tourists to climb, and the user hopes to reduce the number of steps.

[0179] Step S157, locating the target data layer in the landscape garden plan according to the marking modification instruction, and calling an incremental update algorithm to perform local parameter replacement on the target data layer.

[0180] For example, if it is a request to replace vegetation species, it is necessary to locate the vegetation distribution feature data layer. In this vegetation distribution feature data layer, relevant parameters of Rhododendron lapponicum in the specified viewing area are found, including its planting coordinates, growth simulation parameters, etc. Then, the incremental update algorithm is called to replace the relevant parameters of Rhododendron lapponicum with those of Trollius chinensis. For example, the planting coordinates of Trollius chinensis remain unchanged, but the predicted shading range, root expansion speed, and phenological change data of Rhododendron lapponicum are replaced with the corresponding parameters of Trollius chinensis. If it is a request to adjust the path curvature, locate the tour path feature data layer, find the curvature parameter of the target path segment, and adjust this parameter through the incremental update algorithm to change the path curvature. For a request to modify the number of terrain steps, locate the stepped terrain feature data layer and adjust the parameters related to the number of steps according to the modification instruction, such as the spacing of contour lines and the height difference of steps.

[0181] Step S158: Compare the updated landscape garden plan with the original plan to generate a modification impact report. The modification impact report includes the change amount of ecological parameters, the floating value of construction cost, and a visual effect comparison chart, and superimpose and display the modification impact report on the user terminal interface.

[0182] Specifically, during the comparison process, for the case of replacing vegetation species, calculate the change amount of ecological parameters. For example, the contributions of Trollius chinensis and Rhododendron lapponicum to biodiversity may be different. Trollius chinensis may attract different species of insects, thus affecting the biodiversity index. At the same time, in terms of the floating value of construction cost, there may be differences in the planting cost and maintenance cost of Trollius chinensis and Rhododendron lapponicum. For example, the seed cost of Trollius chinensis may be higher than that of Rhododendron lapponicum, but the maintenance difficulty is lower, so as to calculate the floating situation of the cost. The visual effect comparison chart intuitively shows the different visual effects of Rhododendron lapponicum and Trollius chinensis when viewed from the specified viewing area, such as the color contrast during the flowering period, the comparison of plant height and density, etc. For a request to adjust the path curvature, the change amount of ecological parameters may be reflected in the impact on the surrounding vegetation. If the path adjustment causes changes in earthwork, it may affect soil stability and thus affect vegetation growth. The floating value of construction cost is related to the engineering investment required for path adjustment, such as the cost change caused by the change in the workload of excavation or filling. The visual effect comparison chart shows the changes in tour convenience and aesthetics before and after the path adjustment. For a request to modify the number of terrain steps, the change amount of ecological parameters considers the impact on soil and water conservation. Reducing the number of steps may change the situation of surface runoff, thereby affecting the degree of soil erosion. The floating value of construction cost is related to the workload of step construction or demolition. The visual effect comparison chart shows the overall landscape effect change after the change in the number of terrain steps. Superimpose and display the modification impact report containing the change amount of ecological parameters, the floating value of construction cost, and the visual effect comparison chart on the user terminal interface, so that users can comprehensively understand the impact of the modification and make more reasonable decisions.

[0183] Figure 2 FIG. 2 shows a schematic diagram of exemplary hardware and software components of an AI-assisted landscape gardening plan auxiliary generation system 100 provided by some embodiments of the present application that can implement the idea of the present application. For example, the processor 120 can be used on the AI-assisted landscape gardening plan auxiliary generation system 100 and is used to execute the functions in the present application.

[0184] The AI-assisted landscape gardening plan auxiliary generation system 100 can be a general-purpose server or a special-purpose server, both of which can be used to implement the AI-assisted landscape gardening plan auxiliary generation method of the present application. Although only one server is shown in the present application, for convenience, the functions described in the present application can be implemented in a distributed manner on multiple similar platforms to balance the processing load.

[0185] For example, the AI-assisted landscape gardening plan auxiliary generation system 100 can include a network port 110 connected to a network, one or more processors 120 for executing program instructions, a communication bus 130, and different forms of storage media 140, such as disks, ROM, or RAM, or any combination thereof. Exemplarily, the AI-assisted landscape gardening plan auxiliary generation system 100 can also include program instructions stored in ROM, RAM, or other types of non-transitory storage media, or any combination thereof. The method of the present application can be implemented according to these program instructions. The AI-assisted landscape gardening plan auxiliary generation system 100 also includes an input / output (I / O) interface 150 between the computer and other input / output devices.

[0186] For ease of explanation, only one processor is described in the AI-assisted landscape gardening plan auxiliary generation system 100. However, it should be noted that the AI-assisted landscape gardening plan auxiliary generation system 100 in the present application can also include multiple processors. Therefore, the steps executed by one processor described in the present application can also be jointly executed or separately executed by multiple processors. For example, if the processor of the AI-assisted landscape gardening plan auxiliary generation system 100 executes step A and step B, it should be understood that step A and step B can also be jointly executed by two different processors or separately executed in one processor. For example, the first processor executes step A, the second processor executes step B, or the first processor and the second processor jointly execute steps A and B.

[0187] In addition, an embodiment of the present invention also provides a readable storage medium, in which computer-executable instructions are preset. When the processor executes the computer-executable instructions, the above-mentioned AI-assisted landscape gardening plan auxiliary generation method is implemented.

[0188] It should be noted that, in order to simplify the description of the disclosure of the present invention and thus help the understanding of one or more embodiments of the invention, in the foregoing description of the embodiments of the present invention, sometimes a plurality of features are merged into one embodiment, drawing or description thereof.

Claims

1. A method for generating landscape gardening schemes based on AI, characterized in that: The method comprises: Receiving initial terrain data and landscape adjustment instructions input by a user in an interactive interface, wherein the landscape adjustment instructions include visual presentation requirements and ecological function requirements for the initial terrain data; Performing terrain segmentation processing on the initial terrain data by using a terrain segmentation model to extract slope distribution characteristics, altitude interval characteristics and surface coverage characteristics in the initial terrain data; Dynamically modify the slope distribution characteristics according to the visual presentation requirements to generate stepped terrain characteristics that meet the height difference threshold, and perform vegetation adaptability matching on the surface cover characteristics based on the ecological function requirements to output vegetation distribution characteristics; The stepped terrain features and the vegetation distribution features are spatially superimposed to generate composite terrain vegetation features, and the composite terrain vegetation features are subjected to path connectivity analysis in combination with preset tourist flow data to determine the characteristics of the tour path; Performing multi-dimensional rendering of the composite terrain vegetation features according to the tour path features, generating a landscape gardening scheme including a three-dimensional visualization model and ecological assessment parameters, and sending the landscape gardening scheme to a user terminal for interactive feedback; The dynamically modifying the slope distribution characteristics according to the visual presentation requirements to generate stepped terrain characteristics that meet the height difference threshold includes: Analyze the viewing point setting parameters and visual corridor parameters in the visual presentation requirements, and determine the maximum allowable slope value of the target area and the height difference threshold between adjacent steps; Identifying a steep slope area exceeding the maximum allowable slope value in the slope distribution characteristics, and performing contour encryption processing on the steep slope area to generate an encrypted contour data set; Performing stepwise interpolation on the encrypted contour data set based on the height difference threshold, inserting a transition platform between adjacent contour lines, and adjusting the width and inclination angle of the transition platform according to the viewing point setting parameters; The interpolated contour lines are subjected to curvature smoothing to eliminate sharp turns at the step edges, thereby forming a continuously transitioned stepped surface. The stepped surface is then edge-fused with the original terrain data to generate stepped terrain features including anti-slip textures and drainage grooves.

2. The AI-assisted landscape gardening scheme generation method according to claim 1 is characterized in that: The performing terrain segmentation processing on the initial terrain data by using a terrain segmentation model to extract slope distribution characteristics, altitude interval characteristics and surface coverage characteristics in the initial terrain data includes: Inputting the initial terrain data into a pre-trained multi-scale terrain segmentation model, wherein the multi-scale terrain segmentation model comprises a first convolution branch for identifying rock exposed areas, a second convolution branch for identifying vegetation covered areas, and a third convolution branch for identifying water distribution areas; Performing edge detection on rock texture in the initial terrain data through the first convolution branch to generate a rock distribution mask, and calculating geological stability parameters of each region according to the rock distribution mask; Performing cluster analysis on the vegetation spectral features in the initial terrain data through the second convolution branch, dividing the tree-dense area, the shrub transition area and the herbaceous plant area, and counting the vegetation coverage density of each area; The third convolution branch is used to simulate the flow direction of the hydrological data in the initial terrain data, identify the runoff convergence area and the potential erosion area, and mark the coordinates of the water area boundary; The geological stability parameters, vegetation coverage density and water boundary coordinates are integrated to generate a three-dimensional terrain grid, a continuous slope change curve is extracted from the three-dimensional terrain grid as the slope distribution feature, altitude zones are divided as the altitude interval feature, and the spatial distribution of different surface types is marked as the surface cover feature.

3. The AI-assisted landscape gardening scheme generation method according to claim 1 is characterized in that: The performing vegetation adaptability matching on the surface cover characteristics based on the ecological function requirements and outputting vegetation distribution characteristics includes: According to the soil and water conservation grade parameters and biodiversity parameters in the ecological function requirements, a vegetation type collection including a cold-resistant plant library, a slope-fixing plant library and an ornamental plant library is constructed; In the surface cover features, shallow soil areas with soil thickness lower than a set thickness, erosion-prone areas with slopes greater than a critical erosion angle, and sunny slope areas are marked; The shallow soil layer area is matched with plant species with well-developed root systems and resistance to barrenness in the cold-resistant plant library, the easily eroded area is matched with plant species with a network root structure in the slope-fixing plant library, and the sunny slope area is matched with plant species with staggered flowering periods in the ornamental plant library; A vegetation planting coordinate set is generated according to the matching results of each area, and plant growth simulation parameters are added to the vegetation planting coordinate set. The plant growth simulation parameters include shading range prediction value, root expansion speed and seasonal phase change data to form the vegetation distribution characteristics.

4. The AI-assisted landscape gardening scheme generation method according to claim 1 is characterized in that: The step of spatially superimposing the stepped terrain features and the vegetation distribution features to generate composite terrain vegetation features includes: spatially aligning the surface vertex coordinates of the stepped terrain feature with the planting coordinates of the vegetation distribution feature in a three-dimensional coordinate system, and detecting conflicting points in the coordinate overlapping area; Prioritize the conflict point, and when the conflict point is located in the drainage ditch area at the edge of the step, give priority to retaining the structural parameters of the stepped terrain features and adjust the vertical height of the vegetation planting coordinates; When the conflict point is located in the viewing area of ​​the stepped platform, the seasonal variation data of the vegetation distribution characteristics is preferentially retained and the local curvature of the stepped platform is modified; The adjusted stepped terrain features and vegetation distribution features are voxel-fused to generate a composite voxel model including terrain material attributes, vegetation growth attributes, and light reflection attributes as the composite terrain vegetation features.

5. The AI-assisted landscape gardening scheme generation method according to claim 4 is characterized in that: The step of voxelizing the adjusted stepped terrain features and vegetation distribution features to generate a composite voxel model including terrain material attributes, vegetation growth attributes, and light reflection attributes includes: Acquire the surface vertex coordinate set in the adjusted stepped terrain feature and the updated vegetation planting coordinate set in the vegetation distribution feature, extract the elevation gradient data in the surface vertex coordinate set and the vertical height offset in the vegetation planting coordinate set; Based on the spatial distribution relationship between the elevation gradient data and the vertical height offset, the surface vertex coordinate set of the stepped terrain feature and the vegetation planting coordinate set are aligned in three-dimensional space grids to generate a spatial alignment data set with a unified coordinate system reference; Identify the overlapping area between the coordinates of the drainage ditch at the edge of the step platform and the coordinates of the extension range of the vegetation root system in the spatial alignment data set, and detect the abnormal angle between the normal vector direction of the terrain surface and the vegetation planting direction in the overlapping area of ​​the coordinates; According to the position distribution of the angle abnormal points, displacement compensation is performed on the coordinates of the curved surface vertices of the stepped terrain features, so that the bottom surface normal vector direction of the drainage ditch forms a vertical orthogonal relationship with the growth direction of the vegetation root system; The displacement-compensated surface vertex coordinates and the vegetation planting coordinate set are imported into a preset voxel partitioning engine, and the voxel side length is dynamically adjusted according to the curvature change rate of the stepped terrain feature to generate a non-uniform voxel grid that matches the terrain undulation; Traversing each voxel unit in the non-uniform voxel grid, if the voxel unit contains both the surface vertex coordinates of the stepped terrain feature and the vegetation planting coordinates, extracting the rock texture roughness parameter, soil moisture parameter and vegetation shading range prediction value corresponding to the voxel unit; Matching the reflectivity map of the anti-slip material according to the rock texture roughness parameter, calculating the osmotic pressure gradient of the vegetation root system according to the soil moisture content parameter, and determining the light attenuation coefficient within the voxel unit in combination with the shade range prediction value; Performing a material blending operation on each voxel unit, weightedly superimposing the reflectivity map of the anti-slip material with the soil diffuse reflection parameters of adjacent voxels, and generating a continuous transition effect of the terrain material attributes; Injecting vegetation growth simulation parameters into the non-uniform voxel grid, dynamically adjusting the expansion speed of vegetation roots according to the osmotic pressure gradient, and correcting the pigment deposition rate of vegetation leaves based on the light attenuation coefficient; Extracting cross-sectional geometric parameters of the drainage ditch in the stepped terrain feature, calculating the surface tension distribution of water flowing through the ditch, and generating water flow reflection spot data matching the voxel unit size; Superimposing the water flow reflection spot data and the continuous transition effect of the terrain material attribute on the optical path, marking the specular reflection highlight area and the diffuse reflection shadow area in the non-uniform voxel grid; Verify the physical compatibility of terrain material properties, vegetation growth properties, and light reflection properties within each voxel unit, and when it is detected that there is a spatial overlap between the specular reflection highlight area and the predicted value of the vegetation shading range, perform attenuation filtering on the reflection intensity of the highlight area; The attribute parameters in the non-uniform voxel grid are updated according to the verification result, and a composite voxel model containing multi-attribute fusion data is output.

6. The AI-assisted landscape gardening scheme generation method according to claim 1 is characterized in that: The method of performing path connectivity analysis on the composite terrain vegetation features in combination with preset tourist flow data to determine the characteristics of the tour path includes: Obtain the peak-hour passenger flow density and average length of stay in historical tourist flow data, and calculate the maximum carrying capacity of each area in combination with the viewing platform area in the composite terrain vegetation feature; Marking candidate routes of emergency evacuation passages in the composite terrain vegetation feature, and calculating the cumulative climbing height and slope change rate of each candidate route based on the height difference data of the stepped terrain feature; Setting diversion nodes for candidate routes according to the maximum carrying capacity, adding virtual coordinates of indicator marks at the diversion nodes, and selecting a set of paths that meet the ergonomic comfort threshold based on the cumulative climbing height and slope change rate; Topologically optimize each path in the path set, eliminate infinite loop sections and increase the density of interlaced viewing points, and generate a tour path feature including path width parameters, handrail setting points and rest area coordinates; The performing topology optimization on each path in the path set includes: Constructing a node connection graph of each path in the path set, and identifying cross nodes in the node connection graph whose degree is greater than a set degree; Setting a direction guidance mark at the intersection node, and adjusting the connection priority of the intersection node according to the field of view openness parameter of the adjacent path; For paths with a connection priority lower than the set priority, the curvature radius is enlarged to ensure a safe distance when two-way traffic flows meet, and anti-slip material markings are added where the curvature radius changes; A path topology structure is generated according to the optimized node connection graph, and the path topology structure is dynamically associated with the viewing platform coordinates in the composite terrain vegetation feature, and the branch guidance parameters in the tour path feature are updated.

7. The AI-assisted landscape gardening scheme generation method according to claim 1 is characterized in that: The multi-dimensional rendering of the composite terrain vegetation features according to the tour path features to generate a landscape gardening scheme including a three-dimensional visualization model and ecological assessment parameters includes: Performing data fusion on the voxel model in the composite terrain vegetation feature and the topological structure in the tour path feature to generate a scene rendering basic data set; Load rock texture maps, vegetation dynamic growth animations, and water flow particle effects based on the material properties in the scene rendering basic data set, and calculate the shadow projection range according to the illumination angle parameters; During the rendering process, the visual occlusion area in the three-dimensional visualization model is detected in real time, and the viewing angle of the visual occlusion area is adaptively adjusted to increase auxiliary observation viewpoints; Extracting biodiversity parameters from the vegetation distribution characteristics and soil and water conservation parameters from the stepped terrain characteristics, generating an ecological benefit evaluation matrix, and associating and annotating the ecological benefit evaluation matrix with the three-dimensional visualization model; The step of detecting the visually blocked area in the three-dimensional visualization model in real time during the rendering process and adaptively adjusting the viewing angle of the visually blocked area includes: Setting a plurality of preset observation angles in the three-dimensional visualization model, and calculating the visible range under each observation angle; Comparing the visual range with the key display area in the landscape adjustment instruction, and identifying a visual blind area that does not cover the key display area; Inserting a virtual observation point at the coordinate position corresponding to the visual blind spot, and generating a temporary connection path from the nearest tour path to the virtual observation point based on a path optimization algorithm; The rendering level of the three-dimensional visualization model is adjusted, and a transparent terrain section is added at the end point of the temporary connection path to expose the landscape details in the visual blind spot.

8. The AI-assisted landscape gardening scheme generation method according to claim 1 is characterized in that: The sending of the landscape gardening scheme to the user terminal for interactive feedback includes: Displaying a rotatable thumbnail of the three-dimensional visualization model and a comparative radar chart of the ecological assessment parameters in a user terminal interface; Receiving a user's instruction to modify a mark of a designated viewing area, wherein the instruction to modify a mark includes a request to replace a vegetation type, a request to adjust a path curvature, or a request to modify the number of terrain steps; Locating the target data layer in the landscape gardening scheme according to the annotation modification instruction, and calling the incremental update algorithm to replace local parameters of the target data layer; The updated landscape gardening plan is compared with the original plan to generate a modification impact report, which includes the change in ecological parameters, the floating value of construction costs and a visual effect comparison chart, and the modification impact report is superimposed and displayed on the user terminal interface.

9. An AI-assisted landscape gardening scheme generation system, characterized in that: The AI-assisted landscape gardening scheme assisted generation system includes a processor and a memory, the memory is connected to the processor, the memory is used to store programs, instructions or codes, and the processor is used to execute the programs, instructions or codes in the memory to implement the AI-assisted landscape gardening scheme assisted generation method according to any one of claims 1 to 8.

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