Garden landscape intelligent digital design method and system based on three-dimensional model

Abstract

The invention relates to the technical field of data processing, in particular to a garden landscape intelligent digital design method and system based on a three-dimensional model. The method comprises the following steps: obtaining garden landscape data; preprocessing the garden landscape data to obtain standard garden landscape data; performing multi-dimensional feature recognition on the standard garden landscape data to obtain garden multi-dimensional feature data; performing garden topographic feature extraction on the garden multi-dimensional feature data to obtain garden topographic features; and carrying out spatial layout measurement on the garden topographic features to obtain garden spatial layout data. According to the invention, through a data processing technology, a geographic information system technology, a mode identification technology and a three-dimensional mapping technology, association analysis of garden space layout features and plant landscape features is realized; and intelligent digital adjustment of the three-dimensional model of the landscape architecture is realized, so that the accuracy of presenting the landscape architecture is improved.

Description

Technical Field

[0001] The present invention relates to the field of data processing technology, and in particular to a method and system for intelligent digital design of garden landscape based on a three-dimensional model. Background Art

[0002] Early garden landscape design mainly relied on hand-drawn drawings and physical models. Garden designers conducted field surveys and recorded relevant garden information, drew garden landscape design drawings manually, and then constructed physical models based on the hand-drawn design drawings. As computer-aided design technology and modeling software are widely used in the field of garden landscape design, garden designers can use computer modeling software to create, modify and optimize garden landscape design models. This allows garden landscape design to present landscape detail features, including garden terrain, garden space structure, landscape vegetation and other detailed features. However, existing garden landscape design methods fail to correlate garden space layout features with plant landscape features. In addition, it is difficult to perform intelligent digital adjustments to garden landscape models for changes in garden landscapes, resulting in low accuracy in the presented garden landscape. Summary of the invention

[0003] Based on this, it is necessary to provide a three-dimensional model-based garden landscape intelligent digital design method and system to solve at least one of the above technical problems.

[0004] To achieve the above object, a garden landscape intelligent digital design method based on a three-dimensional model is provided, the method comprising the following steps: Step S1: obtaining garden landscape data; preprocessing the garden landscape data to obtain standard garden landscape data; performing multi-dimensional feature recognition on the standard garden landscape data to obtain garden multi-dimensional feature data; Step S2: extracting garden terrain features from the garden multidimensional feature data to obtain garden terrain features; measuring the spatial layout of the garden terrain features to obtain garden spatial layout data; extracting plant landscape features from the garden multidimensional feature data to obtain plant landscape features; and performing correlation analysis between the garden spatial layout data and the plant landscape features to generate garden landscape spatial correlation data; Step S3: performing three-dimensional data mapping on the garden landscape space association data to obtain the garden landscape three-dimensional data; using the garden landscape three-dimensional data to construct a garden landscape three-dimensional model on the garden multi-dimensional feature data to obtain the garden landscape three-dimensional model; Step S4: Perform lighting simulation on the garden landscape space mapping data to obtain landscape lighting simulation data; perform plant seasonal change analysis on the garden multidimensional feature data based on the landscape lighting simulation data to generate plant seasonal change data; perform landscape change dynamic adjustment on the garden landscape three-dimensional model based on the plant seasonal change data to generate landscape change adjustment data; perform intelligent digital visualization enhancement on the garden landscape three-dimensional model through the landscape change adjustment data to obtain a garden landscape three-dimensional digital enhancement model; perform garden landscape visualization report display based on the garden landscape three-dimensional digital enhancement model to generate a garden landscape visualization report.

[0005] The present invention obtains garden landscape data to ensure the basis for comprehensive understanding and analysis of garden landscape; then, the garden landscape data is preprocessed and converted into standard garden landscape data, which can eliminate noise and inconsistency in the data, thereby improving the quality and availability of the data; then, the standard garden landscape data is multi-dimensionally identified, which can reveal the deep-level characteristics of the garden landscape. Garden terrain features are extracted from the garden multi-dimensional feature data to clarify the terrain structure feature information of the garden; spatial layout measurement is performed on the garden terrain features, so that the spatial layout of the garden can be quantified, providing data basis for garden planning and design; plant landscape features are extracted from the garden multi-dimensional feature data to identify the distribution and types of plants in the garden; garden spatial layout data and plant landscape features are associated and analyzed to reveal the relationship between spatial layout and plant distribution. Three-dimensional data mapping is performed on the garden landscape spatial association data, so that the spatial information of the garden landscape can be intuitively displayed in three-dimensional space, enhancing the visualization effect of the garden landscape; the garden landscape three-dimensional model is constructed on the garden multi-dimensional feature data using the garden landscape three-dimensional data to obtain the garden landscape three-dimensional model, so that the multi-dimensional features of the garden landscape are converted into a three-dimensional model, providing a powerful tool for the display and analysis of the garden landscape. The illumination simulation of the garden landscape space mapping data can simulate the illumination effects under different time and weather conditions, and provide a scientific basis for the illumination design of the garden landscape; the seasonal change analysis of the garden multidimensional feature data according to the landscape illumination simulation data can evaluate the growth status and landscape effect of plants in different seasons; the dynamic adjustment of the landscape change of the garden landscape three-dimensional model based on the seasonal change data of the plant enables the garden landscape model to reflect the plant growth status that changes with time, and improves the timeliness and adaptability of the model; the intelligent digital visualization enhancement of the garden landscape three-dimensional model through the landscape change adjustment data can enhance the visual effect and information richness of the garden landscape model; the garden landscape visualization report is displayed based on the garden landscape three-dimensional digital enhancement model, and the garden landscape visualization report is generated, which provides an intuitive and detailed visual report for the display and communication of the garden landscape. Therefore, the present invention realizes the correlation analysis of the garden space layout characteristics and the plant landscape characteristics through data processing technology, geographic information system technology, pattern recognition technology and three-dimensional mapping technology; and realizes the intelligent digital adjustment of the garden landscape three-dimensional model, thereby improving the accuracy of the garden landscape presentation.

[0006] Preferably, step S2 comprises the following steps: Step S21: performing terrain feature recognition on the garden multidimensional feature data to generate terrain feature data; performing garden terrain feature marking on the garden multidimensional feature data according to the terrain feature data to obtain garden terrain marking data; performing garden terrain feature extraction on the garden multidimensional feature data through the garden terrain marking data to obtain garden terrain feature data; Step S22: performing geometric feature recognition on the garden terrain feature data to generate garden terrain geometric features; performing garden geometric relationship measurement on the garden terrain geometric features to obtain garden geometric relationship data; performing spatial structure recognition on the garden terrain features based on the garden geometric relationship data to generate garden spatial structure data; performing spatial layout measurement on the garden spatial structure data to obtain garden spatial layout data; Step S23: performing plant feature recognition on the garden multidimensional feature data to generate garden plant feature data; performing landscape feature marking on the garden multidimensional feature data based on the garden plant feature data to obtain garden landscape feature marking data; performing plant landscape feature extraction on the garden multidimensional feature data through the garden landscape feature marking data to obtain plant landscape features; Step S24: performing correlation analysis on the garden space layout data and the plant landscape characteristics to generate garden landscape space correlation data.

[0007] The present invention identifies terrain features of garden multidimensional feature data to provide a data basis for terrain analysis of gardens; marks garden terrain features of garden multidimensional feature data according to terrain feature data to help distinguish and identify different terrain areas in the garden; extracts garden terrain features from garden multidimensional feature data through garden terrain marking data to clarify the terrain features of the garden; identifies geometric features of garden terrain features to clarify the shape and structure of garden terrain; determines garden geometric relationships of garden terrain geometric features to reveal spatial relationships between garden terrains; identifies spatial structures of garden terrain features based on garden geometric relationship data to help analyze the overall spatial layout of the garden; and The spatial layout is calculated based on the data, which provides a scientific spatial layout basis for garden planning and design; plant characteristics are identified on the multi-dimensional feature data of the garden, and the landscape characteristics are marked on the multi-dimensional feature data of the garden based on the garden plant characteristic data, which can distinguish and identify different plant areas in the garden; plant landscape characteristics are extracted from the multi-dimensional feature data of the garden through the garden landscape characteristic marking data, so that the plant landscape characteristics of the garden can be clarified; the garden space layout data and plant landscape characteristics are correlated and analyzed, which can reveal the interaction and influence between the garden space layout and the plant landscape; the garden space layout data and plant landscape characteristics are correlated and analyzed, so as to fully understand the spatial correlation of the garden landscape.

[0008] Preferably, step S24 includes the following steps: Step S241: performing spatial structural element recognition on the garden space layout data to obtain spatial structural element data; performing garden boundary detection on the spatial structural element data to generate garden space boundary information; performing garden space range measurement on the garden space layout data based on the garden space boundary information to generate garden space range information; Step S242: performing garden area feature recognition on the garden space range information to obtain garden area feature data; performing area type classification on the garden area feature data to generate garden area type data; Step S243: dividing the garden space range information into garden space composition according to the garden area type data to obtain garden space composition data; identifying plant placement areas on the garden space composition data to generate plant placement area data; Step S244: calculating the plant landscape space coverage rate of the plant placement area data based on the plant landscape characteristics to obtain the plant landscape space coverage rate; performing garden space sight line detection on the garden space layout data according to the plant landscape space coverage rate to obtain garden space sight line information; Step S245: Associating the garden space layout data and the plant landscape features with the garden landscape space visual positions based on the garden space sight line information to generate garden landscape space associated data.

[0009] The present invention identifies spatial structural elements of garden space layout data, and can clarify the spatial composition of the garden; performs garden boundary detection on spatial structural element data, and clarifies the boundary range of the garden; performs garden space range measurement on garden space layout data based on garden space boundary information, and provides scope definition for garden space planning and management; performs garden area feature identification on garden space range information, and obtains garden area feature data, and can identify the unique features of different areas in the garden; performs area type classification on garden area feature data, and can systematically classify different areas of the garden; performs garden space composition division on garden space range information according to garden area type data, and is helpful to further refine the spatial structure of the garden; The spatial composition data is used to identify plant placement areas, which provides specific regional guidance for the rational layout of plants and optimizes the plant configuration of the garden; the plant landscape space coverage rate is calculated based on the plant placement area data based on the plant landscape characteristics, which is helpful to evaluate the distribution density of plants in the garden space; the garden space layout data is used to detect the garden space sight line according to the plant landscape space coverage rate, which is helpful to analyze and optimize the visual effect of the garden; the garden space layout data and the plant landscape characteristics are associated with the garden landscape space visual position based on the garden space sight line information, which integrates the spatial layout of the garden and the visual impact of the plant landscape, provides important data support for the visual design and optimization of the garden landscape, and enhances the overall functionality of the garden landscape.

[0010] Preferably, step S31 includes the following steps: Step S31: performing three-dimensional space coordinate conversion on the garden landscape space association data to obtain garden landscape three-dimensional coordinate data; performing geometric voxelization on the garden landscape three-dimensional coordinate data to obtain geometric voxelized three-dimensional data; performing spatial topological feature recognition on the geometric voxelized three-dimensional data to generate garden landscape spatial topological features; Step S32: determining the spatial topological relationship of the geometric voxelized three-dimensional data according to the spatial topological characteristics of the garden landscape to obtain spatial topological relationship data; performing garden landscape spatial dimension recognition on the spatial topological relationship data to obtain garden landscape spatial dimension data; performing spatial hierarchical division on the garden landscape spatial dimension data to generate garden landscape spatial hierarchical data; Step S33: performing entity object recognition on the garden landscape space associated data based on the garden landscape space hierarchical data to obtain landscape entity object data; performing entity object relationship extraction on the landscape entity object data to generate entity object relationship data; performing three-dimensional mapping on the garden landscape space associated data according to the entity object relationship data to obtain garden landscape three-dimensional data; Step S34: constructing a three-dimensional model of the garden landscape by using the three-dimensional garden landscape data and the multi-dimensional characteristic data of the garden to obtain the three-dimensional model of the garden landscape.

[0011] The present invention performs three-dimensional spatial coordinate conversion on spatial correlation data of garden landscape, so that the spatial information of garden landscape can be accurately represented in three-dimensional space; performs geometric voxelization on three-dimensional coordinate data of garden landscape, so as to convert continuous spatial data into discrete geometric bodies; performs spatial topological feature recognition on geometric voxelized three-dimensional data, so as to clarify the spatial structure information of garden landscape; performs spatial topological relationship measurement on geometric voxelized three-dimensional data according to the spatial topological feature of garden landscape, so as to clarify the mutual relationship between various geometric bodies in garden landscape; performs spatial dimension recognition on garden landscape for spatial topological relationship data, so as to help recognize the multi-dimensional characteristics of garden landscape; performs spatial hierarchical division on spatial dimension data of garden landscape, so as to provide a hierarchical perspective for the spatial structure of garden landscape, and to help in-depth understanding Understand the spatial organization of the garden; perform entity object recognition on the garden landscape space association data based on the garden landscape space hierarchical data, so as to extract specific entity objects from complex spatial data; perform entity object relationship extraction on the landscape entity object data, which can reveal the interaction between entity objects; perform three-dimensional mapping on the garden landscape space association data according to the entity object relationship data to obtain the garden landscape three-dimensional data, so as to provide a complete three-dimensional view of the garden landscape, which is convenient for further analysis and display; use the garden landscape three-dimensional data to construct a garden landscape three-dimensional model for the garden multi-dimensional feature data, so as to integrate the multi-dimensional characteristics and spatial information of the garden landscape into a three-dimensional model, providing an intuitive and accurate tool for the design, planning and display of the garden landscape.

[0012] Preferably, step S34 includes the following steps: Step S341: Based on the three-dimensional data of the garden landscape, the landscape granularity framework feature recognition is performed on the garden multidimensional feature data to generate landscape granularity framework feature data; based on the landscape granularity framework feature data, the terrain level granularity framework is constructed on the garden multidimensional feature data to obtain the terrain level granularity framework; Step S342: constructing a real-scene-level granularity framework for the garden multi-dimensional feature data through the landscape granularity framework feature data to obtain a real-scene-level granularity framework; merging the terrain-level granularity framework and the real-scene-level granularity framework into a multi-granularity framework to generate a multi-granularity garden landscape framework; Step S343: performing three-dimensional vector recognition on the three-dimensional garden landscape data based on the multi-granularity garden landscape framework to obtain the three-dimensional garden landscape vector data; performing vector feature processing on the three-dimensional vector features of the garden landscape to generate vectorized three-dimensional data; Step S344: constructing a preliminary three-dimensional garden landscape model for the vectorized three-dimensional data according to the multi-granularity garden landscape framework to obtain a preliminary three-dimensional garden landscape model; rendering the preliminary three-dimensional garden landscape model with landscape colors to generate a three-dimensional garden landscape model.

[0013] The present invention uses the three-dimensional data of the garden landscape to perform landscape granularity framework feature recognition on the garden multidimensional feature data, which is helpful to finely identify the key features in the garden landscape; constructs a terrain-level granularity framework for the garden multidimensional feature data according to the landscape granularity framework feature data, which provides a structured representation for the terrain features of the garden landscape; constructs a real-scene-level granularity framework for the garden multidimensional feature data through the landscape granularity framework feature data, which is helpful to capture the specific details in the garden landscape; merges the terrain-level granularity framework and the real-scene-level granularity framework into a multi-granularity framework, which provides a comprehensive framework for the comprehensive understanding and analysis of the garden landscape; based on the multi-granularity garden landscape framework The framework performs three-dimensional vector recognition on the three-dimensional data of the garden landscape, and converts the three-dimensional information of the garden landscape into vector form, which is convenient for graphic processing and analysis; the vector feature processing of the three-dimensional vector features of the garden landscape can further optimize the structure of the data, thereby improving the efficiency and accuracy of data processing; according to the multi-granularity garden landscape framework, the three-dimensional model of the garden landscape is preliminarily constructed based on the vectorized three-dimensional data, which provides a preliminary three-dimensional expression form for the garden landscape; the landscape color rendering is performed on the three-dimensional preliminary model of the garden landscape. This process gives the model color and texture, making the model more realistic and making the display of the three-dimensional model of the garden landscape more intuitive and accurate.

[0014] Preferably, step S4 comprises the following steps: Step S41: capturing the light and shadow environment reflection of the garden landscape space mapping data to obtain the light and shadow environment reflection data; tracing the garden landscape light and shadow path of the light and shadow environment reflection data to generate the garden landscape light and shadow path data; calculating the illumination angle of the garden landscape light and shadow path data to obtain the garden landscape illumination angle data; simulating the illumination of the garden landscape space mapping data based on the garden landscape illumination angle data to obtain the landscape illumination simulation data; Step S42: performing seasonal plant change analysis on the garden multi-dimensional feature data according to the landscape illumination simulation data to generate seasonal plant change data; Step S43: dynamically adjusting the landscape change of the garden landscape three-dimensional model based on the seasonal change data of the plants to generate landscape change adjustment data; Step S44: performing intelligent digital visualization enhancement on the garden landscape three-dimensional model by adjusting the landscape change data to obtain a garden landscape three-dimensional digital enhanced model; Step S45: outputting digital information of the garden landscape based on the three-dimensional digital enhanced model of the garden landscape to obtain digital information of the garden landscape; processing the digital information of the garden landscape into information charts to obtain visualization charts of the garden landscape; compiling visualization reports on the visualization charts of the garden landscape to generate visualization reports of the garden landscape.

[0015] The present invention captures the reflection of light and shadow environment on the garden landscape space mapping data, and can accurately capture the light and shadow changes in the garden; performs garden landscape light and shadow path tracing on the light and shadow environment reflection data, which is helpful to simulate and analyze the propagation path of light in the garden; performs illumination angle measurement on the garden landscape light and shadow path data, and provides angle information for illumination analysis of the garden landscape; performs illumination simulation on the garden landscape space mapping data based on the garden landscape illumination angle data, thereby simulating landscape illumination information; performs plant seasonal change analysis on the garden multi-dimensional feature data according to the landscape illumination simulation data, and can analyze the growth status and changes of plants in different seasons; performs landscape change dynamic adjustment on the garden landscape three-dimensional model based on the plant seasonal change data, so that the garden The three-dimensional model of forest landscape can reflect the actual status of plants changing with the seasons, and at the same time enhances the timeliness and adaptability of the model; the three-dimensional model of garden landscape is enhanced by intelligent digital visualization through landscape change adjustment data, which improves the visual effect and information richness of the garden landscape model, making the display of garden landscape more intuitive and vivid; digital information of garden landscape is output based on the three-dimensional digital enhancement model of garden landscape, so as to obtain digital information of garden landscape; digital information of garden landscape is processed into information charts, so that garden data is presented in the form of charts; visualization reports are compiled for garden landscape visualization charts, and finally a garden landscape visualization report is generated, which provides an intuitive and detailed visual report for the planning, management and display of garden landscape.

[0016] Preferably, step S42 includes the following steps: Step S421: marking the landscape lighting simulation data with seasonal lighting features to obtain seasonal lighting marking features; performing seasonal lighting pattern recognition on the garden multidimensional feature data using the seasonal lighting marking features to generate a seasonal lighting pattern; Step S422: performing light path detection on the seasonal light pattern to obtain seasonal light path data; performing light intensity measurement on the seasonal light path data to generate seasonal light intensity data; Step S423: recording plant photosynthesis response of the garden multidimensional feature data based on the seasonal light path data and the seasonal light intensity data to obtain plant photosynthesis response data; performing seasonal photoperiod effect detection on the plant photosynthesis response data to generate seasonal photoperiod effect data; Step S424: monitoring seasonal morphological characteristics of plants on the garden multi-dimensional feature data according to the seasonal photoperiod effect data to obtain seasonal morphological characteristics of plants; performing landscape visual morphological recognition on the seasonal morphological characteristics of plants to generate landscape visual morphological data; Step S425: Determine seasonal growth changes of plants based on seasonal morphological characteristics of plants based on landscape visual morphological data to obtain seasonal change data of plants.

[0017] The present invention detects the light path of seasonal light patterns and clarifies the propagation path of light in the garden; measures the light intensity of seasonal light path data and can quantify the light intensity in different seasons, providing important parameters for plant growth and landscape effect evaluation; Recording plant photosynthesis responses to garden multidimensional feature data based on seasonal light path data and seasonal light intensity data is helpful to monitor the physiological responses of plants to light changes; detecting seasonal photoperiod effects on plant photosynthesis response data can evaluate the impact of photoperiod on plant growth; monitoring plant seasonal morphological characteristics based on seasonal photoperiod effect data on garden multidimensional feature data can monitor the appearance characteristics of plants that change with the seasons; identifying landscape visual morphology based on plant seasonal morphological characteristics can convert plant seasonal changes into visual information and enhance the seasonal visual effects of garden landscapes; measuring plant seasonal growth changes based on plant seasonal morphological characteristics based on landscape visual morphology data can accurately record the growth of plants that change with the seasons and provide data support for the renewal of garden landscapes.

[0018] Preferably, step S43 includes the following steps: Step S431: performing seasonal change feature recognition on the plant seasonal change data to obtain the plant seasonal change feature; performing plant species change extraction on the plant seasonal change feature to obtain plant species change data; dynamically adjusting the plant species of the garden landscape three-dimensional model based on the plant species change data to generate plant species update data; Step S432: performing plant morphological feature recognition on the seasonal change characteristics of the plants to obtain plant morphological change data; performing morphological size measurement on the plant morphological change data to generate plant morphological size data; performing plant morphological update on the garden landscape three-dimensional model based on the plant morphological size data to obtain plant morphological update data; Step S433: recording plant leaf color changes for the plant morphology change data to generate plant leaf color change data; updating the leaf color of the garden landscape three-dimensional model based on the plant leaf color change data to obtain plant leaf color update data; Step S434: measuring the plant distribution area change according to the seasonal change characteristics of the plants to obtain plant distribution area change data; adjusting the vegetation coverage of the garden landscape three-dimensional model based on the plant distribution area change data to generate vegetation coverage adjustment data; Step S435: The plant species update data, the plant morphology update data, the plant leaf color update data and the vegetation coverage adjustment data are combined for landscape change adjustment to obtain landscape change adjustment data.

[0019] The present invention identifies seasonal change characteristics of plant seasonal change data, and can capture the regular characteristics of plant changes with seasons; extracts plant species changes from plant seasonal change characteristics, and can identify changes in plant species in different seasons; dynamically adjusts plant species in a three-dimensional garden landscape model based on plant species change data, and ensures that the garden landscape model can reflect the actual distribution of plant species, thereby improving the authenticity and timeliness of the model; identifies plant morphological characteristics from plant seasonal change characteristics, obtains plant morphological change data, and clarifies changes in plant morphology with seasons; and measures morphological size from plant morphological change data, and can quantify specific changes in plant morphology. Based on the plant morphology and size data, the plant morphology of the garden landscape three-dimensional model is updated, making the plant morphology in the garden landscape model more accurate and enhancing the detail and accuracy of the model; the plant leaf color change data is recorded to capture the dynamic characteristics of plant leaf color changing with the seasons; the leaf color of the garden landscape three-dimensional model is updated based on the plant leaf color change data to enhance the visual expression of the garden landscape; the seasonal change characteristics of the plants are measured to determine the changes in the plant distribution area to clarify the spatial distribution changes of plants in the garden; the vegetation coverage of the garden landscape three-dimensional model is adjusted based on the plant distribution area change data to ensure that the vegetation coverage of the garden landscape can reflect the actual ecological status; the plant species update data, plant morphology update data, plant leaf color update data and vegetation coverage adjustment data are merged for landscape change adjustment to ensure that the garden landscape can fully reflect the seasonal changes of plants, thereby improving the overall coordination of the garden landscape.

[0020] Preferably, step S44 includes the following steps: Step S441: performing garden geometry correction on the garden landscape three-dimensional model based on the landscape change adjustment data to obtain geometry correction data; performing plant landscape texture mapping on the garden landscape three-dimensional model according to the geometry correction data to obtain a plant landscape texture mapping three-dimensional model; Step S442: performing garden landscape light and shadow optimization on the plant landscape texture mapping three-dimensional model to generate garden landscape light and shadow optimization data; performing real-time landscape rendering enhancement on the plant landscape texture mapping three-dimensional model based on the garden landscape light and shadow optimization data to obtain a landscape rendering enhanced three-dimensional model; Step S443: interactively visually enhance the landscape rendering enhanced three-dimensional model to generate garden landscape visual enhancement data; perform landscape scene perception enhancement on the landscape rendering enhanced three-dimensional model according to the garden landscape visual enhancement data to obtain a landscape scene perception enhanced three-dimensional model; Step S444: Perform intelligent digital visualization enhancement on the landscape scene perception enhanced three-dimensional model to obtain a three-dimensional digital enhanced model of the garden landscape.

[0021] The present invention performs garden geometry correction on the garden landscape three-dimensional model based on the landscape change adjustment data, thereby ensuring that the geometry of the model is accurate and correct; performs plant landscape texture mapping on the garden landscape three-dimensional model according to the geometry correction data, thereby enhancing the visual effect of the model and making it closer to the texture and details of the actual landscape; performs garden landscape light and shadow optimization on the plant landscape texture mapping three-dimensional model, thereby improving the lighting effect in the model and making it more realistic; performs real-time landscape rendering enhancement on the plant landscape texture mapping three-dimensional model based on the garden landscape light and shadow optimization data, thereby further improving the rendering quality of the model; performs interactive visual enhancement on the landscape rendering enhanced three-dimensional model, thereby improving the interactivity and visual appeal of the model; performs landscape scene perception enhancement on the landscape rendering enhanced three-dimensional model according to the garden landscape visual enhancement data, thereby enabling the model to better simulate the human eye's perception of the landscape and enhancing the immersiveness of the model display; performs intelligent digital visualization enhancement on the landscape scene perception enhanced three-dimensional model, and combines the previous enhancement effects to provide a fully optimized garden landscape model; and provides a visualized digital tool for the design, planning and display of the garden landscape.

[0022] In this specification, a garden landscape intelligent digital design system based on a three-dimensional model is provided, which is used to execute the above-mentioned garden landscape intelligent digital design method based on a three-dimensional model. The garden landscape intelligent digital design system based on a three-dimensional model includes: The garden landscape data acquisition module is used to obtain garden landscape data; pre-process the garden landscape data to obtain standard garden landscape data; perform multi-dimensional feature recognition on the standard garden landscape data to obtain garden multi-dimensional feature data; The garden landscape spatial correlation analysis module is used to extract the garden terrain features from the garden multi-dimensional feature data to obtain the garden terrain features; to measure the spatial layout of the garden terrain features to obtain the garden spatial layout data; to extract the plant landscape features from the garden multi-dimensional feature data to obtain the plant landscape features; to perform correlation analysis on the garden spatial layout data and the plant landscape features to generate the garden landscape spatial correlation data; The garden landscape three-dimensional model construction module is used to perform three-dimensional data mapping on the garden landscape space correlation data to obtain the garden landscape three-dimensional data; the garden landscape three-dimensional model is constructed on the garden multi-dimensional feature data using the garden landscape three-dimensional data to obtain the garden landscape three-dimensional model; The intelligent digital visualization enhancement module of garden landscape is used to simulate the lighting of garden landscape space mapping data to obtain landscape lighting simulation data; analyze the seasonal changes of plants in the garden multi-dimensional feature data according to the landscape lighting simulation data to generate plant seasonal change data; dynamically adjust the landscape change of the garden landscape three-dimensional model based on the plant seasonal change data to generate landscape change adjustment data; perform intelligent digital visualization enhancement on the garden landscape three-dimensional model through the landscape change adjustment data to obtain a three-dimensional digital enhancement model of the garden landscape; display the garden landscape visualization report based on the three-dimensional digital enhancement model of the garden landscape to generate a garden landscape visualization report.

[0023] The present invention obtains garden landscape data through a garden landscape data acquisition module to ensure the basis for comprehensive understanding and analysis of garden landscape; then, the garden landscape data is preprocessed and converted into standard garden landscape data, which can eliminate noise and inconsistency in the data, thereby improving the quality and availability of the data; then, multi-dimensional feature recognition is performed on the standard garden landscape data to reveal the deep-level characteristics of the garden landscape. Through the garden landscape spatial association analysis module, the garden terrain feature is extracted from the garden multi-dimensional feature data to clarify the terrain structure feature information of the garden; the spatial layout of the garden terrain feature is measured to quantify the spatial layout of the garden, providing a data basis for garden planning and design; the plant landscape feature is extracted from the garden multi-dimensional feature data to identify the distribution and types of plants in the garden; the garden space layout data and the plant landscape feature are associated and analyzed to reveal the relationship between the spatial layout and the plant distribution. Through the garden landscape three-dimensional model construction module, the garden landscape space-related data is mapped into three-dimensional data, so that the spatial information of the garden landscape can be intuitively displayed in three-dimensional space, enhancing the visualization effect of the garden landscape; the garden landscape three-dimensional data is used to construct the garden landscape three-dimensional model for the garden multi-dimensional feature data to obtain the garden landscape three-dimensional model, so that the multi-dimensional features of the garden landscape are converted into a three-dimensional model, providing a powerful tool for the display and analysis of the garden landscape. Through the intelligent digital visualization enhancement module of the garden landscape, the illumination simulation is performed on the garden landscape space mapping data, which can simulate the illumination effects under different time and weather conditions, and provide a scientific basis for the illumination design of the garden landscape; according to the landscape illumination simulation data, the seasonal change analysis of the garden multidimensional feature data is performed, and the growth status and landscape effect of the plants in different seasons can be evaluated; based on the seasonal change data of the plants, the landscape change dynamic adjustment is performed on the three-dimensional model of the garden landscape, so that the garden landscape model can reflect the plant growth status that changes with time, and improve the timeliness and adaptability of the model; through the landscape change adjustment data, the three-dimensional model of the garden landscape is intelligently digitally visualized and enhanced, which can improve the visual effect and information richness of the garden landscape model; based on the three-dimensional digital enhancement model of the garden landscape, the garden landscape visualization report is displayed, and the garden landscape visualization report is generated, which provides an intuitive and detailed visual report for the display and communication of the garden landscape. Therefore, the present invention realizes the correlation analysis of the garden space layout characteristics and the plant landscape characteristics through data processing technology, geographic information system technology, pattern recognition technology and three-dimensional mapping technology; and realizes the intelligent digital adjustment of the three-dimensional model of the garden landscape, thereby improving the accuracy of the garden landscape presentation. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] Figure 1 A schematic diagram of the steps of a garden landscape intelligent digital design method based on a three-dimensional model; Figure 2for Figure 1 Detailed implementation steps of step S3 in FIG. Figure 3 for Figure 2 Detailed implementation steps of step S34 are shown in the flowchart; The realization of the purpose, functional features and advantages of the present invention will be further explained in conjunction with embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0025] The technical method of the present invention is described clearly and completely below in conjunction with the accompanying drawings. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by technicians in this field without creative work are within the scope of protection of the present invention.

[0026] In addition, the accompanying drawings are only schematic illustrations of the present invention and are not necessarily drawn to scale. The same reference numerals in the figures represent the same or similar parts, and their repeated description will be omitted. Some of the block diagrams shown in the accompanying drawings are functional entities and do not necessarily correspond to physically or logically independent entities. The functional entities can be implemented in software form, or implemented in one or more hardware modules or integrated circuits, or implemented in different networks and / or processor methods and / or microcontroller methods.

[0027] It should be understood that, although the terms "first", "second", etc. may be used herein to describe various units, these units should not be limited by these terms. These terms are used only to distinguish one unit from another unit. For example, without departing from the scope of the exemplary embodiments, the first unit may be referred to as the second unit, and similarly the second unit may be referred to as the first unit. The term "and / or" used herein includes any and all combinations of one or more of the listed associated items.

[0028] To achieve this, please refer to Figures 1 to 3 , a garden landscape intelligent digital design method based on a three-dimensional model, the method comprising the following steps: Step S1: obtaining garden landscape data; preprocessing the garden landscape data to obtain standard garden landscape data; performing multi-dimensional feature recognition on the standard garden landscape data to obtain garden multi-dimensional feature data; Step S2: extracting garden terrain features from the garden multidimensional feature data to obtain garden terrain features; measuring the spatial layout of the garden terrain features to obtain garden spatial layout data; extracting plant landscape features from the garden multidimensional feature data to obtain plant landscape features; and performing correlation analysis between the garden spatial layout data and the plant landscape features to generate garden landscape spatial correlation data; Step S3: performing three-dimensional data mapping on the garden landscape space association data to obtain the garden landscape three-dimensional data; using the garden landscape three-dimensional data to construct a garden landscape three-dimensional model on the garden multi-dimensional feature data to obtain the garden landscape three-dimensional model; Step S4: Perform lighting simulation on the garden landscape space mapping data to obtain landscape lighting simulation data; perform plant seasonal change analysis on the garden multidimensional feature data based on the landscape lighting simulation data to generate plant seasonal change data; perform landscape change dynamic adjustment on the garden landscape three-dimensional model based on the plant seasonal change data to generate landscape change adjustment data; perform intelligent digital visualization enhancement on the garden landscape three-dimensional model through the landscape change adjustment data to obtain a garden landscape three-dimensional digital enhancement model; perform garden landscape visualization report display based on the garden landscape three-dimensional digital enhancement model to generate a garden landscape visualization report.

[0029] The present invention obtains garden landscape data to ensure the basis for comprehensive understanding and analysis of garden landscape; then, the garden landscape data is preprocessed and converted into standard garden landscape data, which can eliminate noise and inconsistency in the data, thereby improving the quality and availability of the data; then, the standard garden landscape data is multi-dimensionally identified, which can reveal the deep-level characteristics of the garden landscape. Garden terrain features are extracted from the garden multi-dimensional feature data to clarify the terrain structure feature information of the garden; spatial layout measurement is performed on the garden terrain features, so that the spatial layout of the garden can be quantified, providing data basis for garden planning and design; plant landscape features are extracted from the garden multi-dimensional feature data to identify the distribution and types of plants in the garden; garden spatial layout data and plant landscape features are associated and analyzed to reveal the relationship between spatial layout and plant distribution. Three-dimensional data mapping is performed on the garden landscape spatial association data, so that the spatial information of the garden landscape can be intuitively displayed in three-dimensional space, enhancing the visualization effect of the garden landscape; the garden landscape three-dimensional model is constructed on the garden multi-dimensional feature data using the garden landscape three-dimensional data to obtain the garden landscape three-dimensional model, so that the multi-dimensional features of the garden landscape are converted into a three-dimensional model, providing a powerful tool for the display and analysis of the garden landscape. The illumination simulation of the garden landscape space mapping data can simulate the illumination effects under different time and weather conditions, and provide a scientific basis for the illumination design of the garden landscape; the seasonal change analysis of the garden multidimensional feature data according to the landscape illumination simulation data can evaluate the growth status and landscape effect of plants in different seasons; the dynamic adjustment of the landscape change of the garden landscape three-dimensional model based on the seasonal change data of the plant enables the garden landscape model to reflect the plant growth status that changes with time, and improves the timeliness and adaptability of the model; the intelligent digital visualization enhancement of the garden landscape three-dimensional model through the landscape change adjustment data can enhance the visual effect and information richness of the garden landscape model; the garden landscape visualization report is displayed based on the garden landscape three-dimensional digital enhancement model, and the garden landscape visualization report is generated, which provides an intuitive and detailed visual report for the display and communication of the garden landscape. Therefore, the present invention realizes the correlation analysis of the garden space layout characteristics and the plant landscape characteristics through data processing technology, geographic information system technology, pattern recognition technology and three-dimensional mapping technology; and realizes the intelligent digital adjustment of the garden landscape three-dimensional model, thereby improving the accuracy of the garden landscape presentation.

[0030] In the embodiment of the present invention, reference Figure 1 The above is a schematic diagram of the steps of a garden landscape intelligent digital design method based on a three-dimensional model of the present invention. In this example, the garden landscape intelligent digital design method based on a three-dimensional model includes the following steps: Step S1: obtaining garden landscape data; preprocessing the garden landscape data to obtain standard garden landscape data; performing multi-dimensional feature recognition on the standard garden landscape data to obtain garden multi-dimensional feature data; In an embodiment of the present invention, aerial photography is performed by a motion camera carried by an unmanned aerial vehicle to obtain image data of a garden landscape; specifically, the unmanned aerial vehicle is used to perform grid photography over the garden, and the photography coverage includes plants, vegetation coverage, hard ground, water surface and buildings; the photographed garden landscape images are digitized to obtain garden landscape data; image processing software, such as Adobe Photoshop software, is used to pre-process the garden landscape data; specifically, operations such as image correction, denoising and enhancement are included; for example, radiation correction and atmospheric correction are performed on plant feature data of the garden landscape data; thereby, standard garden landscape data is obtained; computer vision technology, such as OpenCV technology, is used, specifically including edge detection, color segmentation and texture analysis methods, to extract features from the standard garden landscape data; specifically, different elements in the garden, such as trees, grass, water bodies, etc. are identified; green areas in the image are identified as grass through color segmentation technology; tree outlines are identified through edge detection technology; and different types of vegetation are distinguished through texture analysis technology; thereby, multi-dimensional feature data of the garden is obtained.

[0031] Step S2: extracting garden terrain features from the garden multidimensional feature data to obtain garden terrain features; measuring the spatial layout of the garden terrain features to obtain garden spatial layout data; extracting plant landscape features from the garden multidimensional feature data to obtain plant landscape features; and performing correlation analysis between the garden spatial layout data and the plant landscape features to generate garden landscape spatial correlation data; In the embodiment of the present invention, the feature recognition technology is used to identify the garden terrain features of the garden multidimensional feature data to obtain the garden terrain features; the terrain analysis tool, such as the contour tool, is used to extract the contour map of the garden multidimensional feature data; the slope tool is used to calculate the terrain slope distribution of the garden multidimensional feature data; the terrain undulation change of the garden multidimensional feature data is analyzed by the terrain undulation tool; the garden space layout is determined by using the terrain analysis result, and the high points and low points in the garden and the undulation change of the terrain are determined by analyzing the contour distribution and the slope change, so as to obtain the spatial layout characteristics of the garden; the garden multidimensional feature data is processed by using a method based on deep learning, such as a convolutional neural network (CNN), the plant landscape features are specifically segmented, and the feature information of the plants in different color spaces is extracted; further, the feature selection technology, such as the relief algorithm, is used to screen out the color features that change most obviously with the seasons; the garden space layout data and the plant landscape features are correlated and analyzed by using the data association analysis method, so as to obtain the garden landscape space association data.

[0032] Step S3: performing three-dimensional data mapping on the garden landscape space association data to obtain the garden landscape three-dimensional data; using the garden landscape three-dimensional data to construct a garden landscape three-dimensional model on the garden multi-dimensional feature data to obtain the garden landscape three-dimensional model; In the embodiment of the present invention, data mapping technology is used to perform three-dimensional data mapping on garden landscape space association data. Specifically, the garden two-dimensional data is combined with the landscape three-dimensional space data to realize three-dimensional visualization of the data and obtain the garden landscape three-dimensional data; the three-dimensional modeling software is used to construct a garden landscape three-dimensional model for the garden multi-dimensional feature data according to the garden landscape three-dimensional data; specifically, 3DS MAX three-dimensional modeling software is used to construct a three-dimensional model of the garden according to the actual size and shape of the garden and on the premise of ensuring the basic characteristics of the garden, and the texture veneer is pasted and edited; in this step, modeling is performed according to three levels of fine model, standard model and simple model to meet the requirements of different precisions; further, through computer simulation, the topography, surface runoff, hydrology and hydrodynamics, aerodynamics, sunshine and shadow of the garden are dynamically analyzed and designed to finally form a three-dimensional model of the garden landscape.

[0033] Step S4: Perform lighting simulation on the garden landscape space mapping data to obtain landscape lighting simulation data; perform plant seasonal change analysis on the garden multidimensional feature data based on the landscape lighting simulation data to generate plant seasonal change data; perform landscape change dynamic adjustment on the garden landscape three-dimensional model based on the plant seasonal change data to generate landscape change adjustment data; perform intelligent digital visualization enhancement on the garden landscape three-dimensional model through the landscape change adjustment data to obtain a garden landscape three-dimensional digital enhancement model; perform garden landscape visualization report display based on the garden landscape three-dimensional digital enhancement model to generate a garden landscape visualization report.

[0034] In the embodiment of the present invention, computer-aided design software (CAD) is used in combination with illumination simulation software to perform illumination simulation on the garden landscape space mapping data; the specific operation includes establishing a three-dimensional model of the garden in the CAD software, and then importing it into the illumination simulation software to set parameters such as the position, intensity and sky model of the sun; and performing illumination simulation analysis at different time periods throughout the year to obtain landscape illumination simulation data, which includes data such as illumination intensity and shadow length at each time period of the garden; according to the landscape illumination simulation data, and using a plant growth model such as the SPOTLIGHT model, seasonal changes of plants are analyzed; specifically, the growth status and appearance changes of plants are analyzed according to environmental factors such as light and temperature, and the color and morphology and other characteristic changes to generate plant seasonal change data; use 3D modeling software, such as 3DSMAX, to dynamically adjust the 3D model of the garden landscape based on the seasonal change data of the plants; adjust the 3D model texture and morphology of the plants according to the seasonal change data to reflect the impact of seasonal changes on the garden landscape, and the adjustment parameters of the model are recorded in the landscape change adjustment data; use augmented reality (AR) technology and virtual reality (VR) technology to combine the landscape change adjustment data to perform intelligent digital visualization enhancement on the 3D model of the garden landscape; use AR / VR technology to display the dynamic changes of the garden landscape in real time in a virtual environment, and finally obtain a 3D digital enhanced model of the garden landscape; use professional visualization reporting software, such as Power BI, to display a garden landscape visualization report based on the 3D digital enhanced model of the garden landscape, where the report contains static images and charts, as well as dynamic 3D models and interactive timelines, and finally obtains a garden landscape visualization report.

[0035] Preferably, step S2 comprises the following steps: Step S21: performing terrain feature recognition on the garden multidimensional feature data to generate terrain feature data; performing garden terrain feature marking on the garden multidimensional feature data according to the terrain feature data to obtain garden terrain marking data; performing garden terrain feature extraction on the garden multidimensional feature data through the garden terrain marking data to obtain garden terrain feature data; Step S22: performing geometric feature recognition on the garden terrain feature data to generate garden terrain geometric features; performing garden geometric relationship measurement on the garden terrain geometric features to obtain garden geometric relationship data; performing spatial structure recognition on the garden terrain features based on the garden geometric relationship data to generate garden spatial structure data; performing spatial layout measurement on the garden spatial structure data to obtain garden spatial layout data; Step S23: performing plant feature recognition on the garden multidimensional feature data to generate garden plant feature data; performing landscape feature marking on the garden multidimensional feature data based on the garden plant feature data to obtain garden landscape feature marking data; performing plant landscape feature extraction on the garden multidimensional feature data through the garden landscape feature marking data to obtain plant landscape features; Step S24: performing correlation analysis on the garden space layout data and the plant landscape characteristics to generate garden landscape space correlation data.

[0036] In the embodiment of the present invention, a terrain analysis tool in a geographic information system (GIS) software is used to specifically identify terrain parameters such as the elevation, slope and aspect of a garden, thereby obtaining garden terrain feature data; the map annotation function of the GIS software is used to perform garden terrain and visually mark the multidimensional feature data of the garden according to the terrain feature data, thereby obtaining garden terrain marked data; the query and statistical analysis tools of the GIS software are used again to extract specific terrain features, such as peaks and valleys, according to the marked data, thereby forming garden terrain feature data; computer-aided design (CAD) software, such as AutoCAD, is used to perform geometric modeling on the garden terrain feature data, thereby specifically identifying the geometric features of the terrain, such as area and perimeter; in the CAD software, the measurement tool is used to perform spatial relationship analysis on the geometric features, specifically analyzing the adjacent relationships, thereby generating garden geometric relationship data; the spatial analysis tool of the GIS software is used to combine the garden The geometric relationship data identifies the spatial structural characteristics of the terrain, such as the hierarchical structure of the terrain, and forms the garden spatial structure data; the spatial layout of the garden spatial structure data set is calculated, specifically the spatial layout of the garden landscape is calculated, and the visual angle of the landscape view is analyzed; image recognition technology, such as support vector machine (SVM) or convolutional neural network (CNN), is used to identify the plant images in the garden multidimensional feature data, and specifically extract the species and morphology of the plants; the garden multidimensional feature data is marked with landscape features based on the garden plant feature data to obtain the garden landscape feature marking data; statistical analysis tools are used to extract plant landscape features according to plant distribution density and seasonal changes to obtain plant landscape features; in GIS software, spatial association analysis tools are used to associate and analyze the garden space layout data and plant landscape feature data, specifically identifying the spatial distribution pattern and mutual influence, thereby generating garden landscape spatial association data.

[0037] Preferably, step S24 includes the following steps: Step S241: performing spatial structural element recognition on the garden space layout data to obtain spatial structural element data; performing garden boundary detection on the spatial structural element data to generate garden space boundary information; performing garden space range measurement on the garden space layout data based on the garden space boundary information to generate garden space range information; Step S242: performing garden area feature recognition on the garden space range information to obtain garden area feature data; performing area type classification on the garden area feature data to generate garden area type data; Step S243: dividing the garden space range information into garden space composition according to the garden area type data to obtain garden space composition data; identifying plant placement areas on the garden space composition data to generate plant placement area data; Step S244: calculating the plant landscape space coverage rate of the plant placement area data based on the plant landscape characteristics to obtain the plant landscape space coverage rate; performing garden space sight line detection on the garden space layout data according to the plant landscape space coverage rate to obtain garden space sight line information; Step S245: Associating the garden space layout data and the plant landscape features with the garden landscape space visual positions based on the garden space sight line information to generate garden landscape space associated data.

[0038] In the embodiment of the present invention, the spatial structural elements such as terrain, water bodies, plants and buildings in the garden are specifically identified by the spatial analysis tool of GIS software, and each spatial structural element is extracted to generate spatial structural element data; the boundary detection tool of ArcGIS is used to perform boundary contour detection on the spatial structural element data to specifically identify the boundary line of the garden; the area calculation tool of ArcGIS is used to calculate the spatial range and area of ​​the garden by using the garden space boundary information data; the image recognition technology is used to analyze the image in the garden space range information data to identify the different regional features of the garden; the regional feature data of the garden is classified into regional types to obtain the garden regional type data; the GIS software is used to calculate the spatial range and area of ​​the garden by using the area calculation tool of ArcGIS. The zoning tool, combined with the garden area type data, divides the garden space range information data into spatial structures; based on feature recognition technology, the plant areas in the garden space structure data set are identified to specifically determine the plant placement areas; the spatial coverage rate of the plant placement area data is calculated by calculating the percentage of the vertical projection area of ​​the vegetation on the ground to the total area; the viewshed analysis tool of ArcGIS is used to combine the plant landscape spatial coverage rate data to conduct line of sight analysis on the garden space layout data to specifically identify the line of sight corridors and blocked areas in the garden; the visual position association analysis of the garden space layout and the plant landscape characteristics is conducted based on the garden space line of sight information data and the plant landscape feature data, thereby generating garden landscape space association data.

[0039] As an example of the present invention, refer to Figure 2 As shown, in this example, step S3 includes: Step S31: performing three-dimensional space coordinate conversion on the garden landscape space association data to obtain garden landscape three-dimensional coordinate data; performing geometric voxelization on the garden landscape three-dimensional coordinate data to obtain geometric voxelized three-dimensional data; performing spatial topological feature recognition on the geometric voxelized three-dimensional data to generate garden landscape spatial topological features; Step S32: determining the spatial topological relationship of the geometric voxelized three-dimensional data according to the spatial topological characteristics of the garden landscape to obtain spatial topological relationship data; performing garden landscape spatial dimension recognition on the spatial topological relationship data to obtain garden landscape spatial dimension data; performing spatial hierarchical division on the garden landscape spatial dimension data to generate garden landscape spatial hierarchical data; Step S33: performing entity object recognition on the garden landscape space associated data based on the garden landscape space hierarchical data to obtain landscape entity object data; performing entity object relationship extraction on the landscape entity object data to generate entity object relationship data; performing three-dimensional mapping on the garden landscape space associated data according to the entity object relationship data to obtain garden landscape three-dimensional data; Step S34: constructing a three-dimensional model of the garden landscape by using the three-dimensional garden landscape data and the multi-dimensional characteristic data of the garden to obtain the three-dimensional model of the garden landscape.

[0040] In the embodiment of the present invention, a geometric coordinate transformation technology is used to perform a three-dimensional spatial coordinate transformation on the spatial association data of the garden landscape. Specifically, one or more common blocks are selected as the central block, and the rotation, translation and scale transformation relationship between the blocks is determined according to the coordinate information of the registration points between the blocks, and then the coordinates of each block are transformed into a global coordinate system according to these relationships, so as to obtain the three-dimensional coordinate data of the garden landscape; a three-dimensional reconstruction algorithm (such as stereo matching, multi-view geometry) is used to geometrically voxelize the three-dimensional coordinate data of the garden landscape to obtain geometric voxelized three-dimensional data; a feature recognition technology is used to perform spatial topological feature recognition on the geometric voxelized three-dimensional data; by analyzing the spatial topological features, the topological relationships such as adjacency and inclusion between the spatial elements are determined; a spatial analysis tool is used to identify the two-dimensional plane and three-dimensional space dimensions of the garden landscape, so as to form The spatial dimension data of garden landscape is generated; the spatial hierarchy is divided according to the spatial dimension data, and the spatial structures at different levels are identified; information extraction (IE) technology, especially named entity recognition (NER) and relationship extraction (RE), is used to extract entity objects from structured, semi-structured or unstructured data to obtain landscape entity object data; the information extraction technology is then used to extract the relationship between entities; the entity object relationship data and the garden landscape space association data are three-dimensionally mapped using three-dimensional GIS software to obtain the garden landscape three-dimensional data; the spatial attribute three-dimensional data of the vertical high-rise structure and each landscape element of the garden landscape is holographically collected and visualized by combining three-dimensional laser scanning and multi-view image reconstruction technology; and through multi-source data fusion, the unified construction of multi-level three-dimensional models of different granularities is completed, and finally the garden landscape three-dimensional model is obtained.

[0041] As an example of the present invention, refer to Figure 3 As shown, in this example, step S34 includes: Step S341: Based on the three-dimensional data of the garden landscape, the landscape granularity framework feature recognition is performed on the garden multidimensional feature data to generate landscape granularity framework feature data; based on the landscape granularity framework feature data, the terrain level granularity framework is constructed on the garden multidimensional feature data to obtain the terrain level granularity framework; Step S342: constructing a real-scene-level granularity framework for the garden multi-dimensional feature data through the landscape granularity framework feature data to obtain a real-scene-level granularity framework; merging the terrain-level granularity framework and the real-scene-level granularity framework into a multi-granularity framework to generate a multi-granularity garden landscape framework; Step S343: performing three-dimensional vector recognition on the three-dimensional garden landscape data based on the multi-granularity garden landscape framework to obtain the three-dimensional garden landscape vector data; performing vector feature processing on the three-dimensional vector features of the garden landscape to generate vectorized three-dimensional data; Step S344: constructing a preliminary three-dimensional garden landscape model for the vectorized three-dimensional data according to the multi-granularity garden landscape framework to obtain a preliminary three-dimensional garden landscape model; rendering the preliminary three-dimensional garden landscape model with landscape colors to generate a three-dimensional garden landscape model.

[0042] In the embodiment of the present invention, the spatial analysis tool of ArcGIS software is used to quantitatively analyze the three-dimensional data of the garden landscape, and specifically identify framework features of different granularities, such as terrain, vegetation and water bodies; the terrain analysis tools in ArcGIS, such as slope and elevation analysis, are used to construct a terrain-level granularity framework, which specifically includes key terrain features such as terrain undulation and slope; three-dimensional modeling software, such as SketchUp, is used to combine the real-scene data of the garden landscape to construct the real-scene granularity framework of the garden landscape; in ArcGIS, the granularity frameworks of the terrain level and the real-scene level are merged through layer overlay and spatial analysis functions to form a multi-granularity garden landscape framework; the three-dimensional data of the garden landscape is vectorized by AutoCAD software, so as to obtain the three-dimensional vector data of the garden landscape; in the CAD software, the three-dimensional vector data is characterized, specifically including extracting edge lines and filling areas; in 3D modeling software, such as 3ds Max, constructs a preliminary 3D model of the garden landscape based on vectorized 3D data, specifically constructing the relationships between elements such as terrain, vegetation and architecture; uses rendering software, such as Lumion, to render the preliminary 3D model in color, including the green of vegetation, the blue of water bodies, the colors of buildings, etc., to generate a 3D model of the garden landscape.

[0043] Preferably, step S4 comprises the following steps: Step S41: capturing the light and shadow environment reflection of the garden landscape space mapping data to obtain the light and shadow environment reflection data; tracing the garden landscape light and shadow path of the light and shadow environment reflection data to generate the garden landscape light and shadow path data; calculating the illumination angle of the garden landscape light and shadow path data to obtain the garden landscape illumination angle data; simulating the illumination of the garden landscape space mapping data based on the garden landscape illumination angle data to obtain the landscape illumination simulation data; Step S42: performing seasonal plant change analysis on the garden multi-dimensional feature data according to the landscape illumination simulation data to generate seasonal plant change data; Step S43: dynamically adjusting the landscape change of the garden landscape three-dimensional model based on the seasonal change data of the plants to generate landscape change adjustment data; Step S44: performing intelligent digital visualization enhancement on the garden landscape three-dimensional model by adjusting the landscape change data to obtain a garden landscape three-dimensional digital enhanced model; Step S45: outputting digital information of the garden landscape based on the three-dimensional digital enhanced model of the garden landscape to obtain digital information of the garden landscape; processing the digital information of the garden landscape into information charts to obtain visualization charts of the garden landscape; compiling visualization reports on the visualization charts of the garden landscape to generate visualization reports of the garden landscape.

[0044] In the embodiment of the present invention, light and shadow simulation software (such as Radiance) is used to capture the light and shadow environment reflection of the garden landscape; specifically, by setting different light sources and reflection parameters, the light and shadow reflection characteristics of each element in the garden are obtained, thereby obtaining light and shadow environment reflection data; using ray tracing technology, the propagation path of light in the garden space is analyzed; by analyzing the light and shadow path data, the illumination angle of the garden landscape at different times and locations is calculated to form the garden landscape illumination angle data; using illumination simulation software to perform illumination simulation, and combining the garden landscape illumination angle data, the garden landscape effects under different illumination conditions are displayed to obtain landscape illumination simulation data; using remote sensing technology and hyperspectral imaging technology, the spectral characteristics of plants in different seasons are analyzed, and the growth of plants is recorded. status and color changes; in 3D modeling software (such as 3dsMax), dynamically adjust the 3D model of the garden landscape according to the seasonal change data of the plants, so that the model reflects the seasonal changes of the plants and forms a landscape change adjustment data set; use augmented reality (AR) and virtual reality (VR) technology to visualize and enhance the 3D model of the garden landscape, so as to obtain a 3D digital enhanced model of the garden landscape; extract and organize the data in the 3D digital enhanced model of the garden landscape, including the spatial characteristics of the garden and plant information; use data visualization tools (such as Tableau) to graphically process the digital information of the garden landscape to obtain a visualization chart of the garden landscape; compile a visualization report for the visualization chart of the garden landscape to generate a visualization report of the garden landscape.

[0045] Preferably, step S42 includes the following steps: Step S421: marking the landscape lighting simulation data with seasonal lighting features to obtain seasonal lighting marking features; performing seasonal lighting pattern recognition on the garden multidimensional feature data using the seasonal lighting marking features to generate a seasonal lighting pattern; Step S422: performing light path detection on the seasonal light pattern to obtain seasonal light path data; performing light intensity measurement on the seasonal light path data to generate seasonal light intensity data; Step S423: recording plant photosynthesis response of the garden multidimensional feature data based on the seasonal light path data and the seasonal light intensity data to obtain plant photosynthesis response data; performing seasonal photoperiod effect detection on the plant photosynthesis response data to generate seasonal photoperiod effect data; Step S424: monitoring seasonal morphological characteristics of plants on the garden multi-dimensional feature data according to the seasonal photoperiod effect data to obtain seasonal morphological characteristics of plants; performing landscape visual morphological recognition on the seasonal morphological characteristics of plants to generate landscape visual morphological data; Step S425: Determine seasonal growth changes of plants based on seasonal morphological characteristics of plants based on landscape visual morphological data to obtain seasonal change data of plants.

[0046] In the embodiment of the present invention, digital simulation software (such as Ecotect Analysis) is used to analyze the illumination simulation data of the garden landscape, and the data is marked according to the seasonal variation characteristics of the sunshine hours to form seasonal illumination marking characteristics; pattern recognition technology is used in combination with seasonal illumination marking characteristics to identify the illumination pattern in the multi-dimensional characteristic data of the garden to form a seasonal illumination pattern; ray tracing technology (such as Radiance) is used to perform path detection on the seasonal illumination pattern to specifically analyze the propagation path of light in the garden space; in combination with the illumination path data, photometric measurement technology is used to calculate the illumination intensity in different seasons to obtain seasonal illumination intensity data; based on the seasonal illumination path data and Seasonal light intensity data is used to detect plant photosynthesis in the garden's multidimensional feature data and record the response of plant photosynthesis. Plant phenology remote sensing monitoring technology is used to analyze the seasonal photoperiod effect of plant photosynthesis response data to obtain seasonal photoperiod effect data. Hyperspectral imaging technology is used in combination with seasonal photoperiod effect data to monitor the seasonal morphological changes of plants. Image recognition technology is used to perform visual morphological recognition of plant seasonal morphological characteristics to form landscape visual morphological data. Time series analysis technology is used in combination with landscape visual morphological data to measure the seasonal growth changes of plants, thereby obtaining plant seasonal change data.

[0047] Preferably, step S43 includes the following steps: Step S431: performing seasonal change feature recognition on the plant seasonal change data to obtain the plant seasonal change feature; performing plant species change extraction on the plant seasonal change feature to obtain plant species change data; dynamically adjusting the plant species of the garden landscape three-dimensional model based on the plant species change data to generate plant species update data; Step S432: performing plant morphological feature recognition on the seasonal change characteristics of the plants to obtain plant morphological change data; performing morphological size measurement on the plant morphological change data to generate plant morphological size data; performing plant morphological update on the garden landscape three-dimensional model based on the plant morphological size data to obtain plant morphological update data; Step S433: recording plant leaf color changes for the plant morphology change data to generate plant leaf color change data; updating the leaf color of the garden landscape three-dimensional model based on the plant leaf color change data to obtain plant leaf color update data; Step S434: measuring the plant distribution area change according to the seasonal change characteristics of the plants to obtain plant distribution area change data; adjusting the vegetation coverage of the garden landscape three-dimensional model based on the plant distribution area change data to generate vegetation coverage adjustment data; Step S435: The plant species update data, the plant morphology update data, the plant leaf color update data and the vegetation coverage adjustment data are combined for landscape change adjustment to obtain landscape change adjustment data.

[0048] In the embodiment of the present invention, hyperspectral remote sensing technology is used to analyze the changes in the spectral reflectance of plants in different seasons to specifically identify the seasonal change characteristics of plants and obtain the seasonal change characteristics of plants; feature extraction technology is used to specifically extract the change information of plant species in different seasons to form plant species change data; in the three-dimensional modeling software, the plant species in the three-dimensional model of the garden landscape are dynamically adjusted according to the plant species change data, and the plant species information is specifically updated; the plant morphological feature recognition technology is used to identify the seasonal morphological changes of plants, and the changes in plant morphology are recorded to form plant morphological change data; the geometric measurement technology is used to measure the plant morphological dimensions in the plant morphological change data; and in the three-dimensional model of the garden landscape, the plant species are dynamically adjusted according to the plant species change data to specifically update the plant species information. In the 3D model of the garden landscape, the plant morphology is updated according to the plant morphology size data, so as to obtain the plant morphology update data; the plant leaf intelligent color simulation technology is used to record the color changes of plant leaves in different seasons to form plant leaf color change data; in the 3D model of the garden landscape, the color of the plant leaves is updated according to the plant leaf color change data; the mixed pixel decomposition method in the remote sensing image is used to measure the changes in the plant distribution area, and the vegetation coverage is adjusted in the 3D model of the garden landscape according to the plant distribution area change data; the plant species update data, the plant morphology update data, the plant leaf color update data and the vegetation coverage adjustment data are combined for landscape change adjustment using the data integration technology to obtain the landscape change adjustment data.

[0049] Preferably, step S44 includes the following steps: Step S441: performing garden geometry correction on the garden landscape three-dimensional model based on the landscape change adjustment data to obtain geometry correction data; performing plant landscape texture mapping on the garden landscape three-dimensional model according to the geometry correction data to obtain a plant landscape texture mapping three-dimensional model; Step S442: performing garden landscape light and shadow optimization on the plant landscape texture mapping three-dimensional model to generate garden landscape light and shadow optimization data; performing real-time landscape rendering enhancement on the plant landscape texture mapping three-dimensional model based on the garden landscape light and shadow optimization data to obtain a landscape rendering enhanced three-dimensional model; Step S443: interactively visually enhance the landscape rendering enhanced three-dimensional model to generate garden landscape visual enhancement data; perform landscape scene perception enhancement on the landscape rendering enhanced three-dimensional model according to the garden landscape visual enhancement data to obtain a landscape scene perception enhanced three-dimensional model; Step S444: Perform intelligent digital visualization enhancement on the landscape scene perception enhanced three-dimensional model to obtain a three-dimensional digital enhanced model of the garden landscape.

[0050] In the embodiment of the present invention, a three-dimensional model geometry correction technology, such as mesh patching, surface fitting and point cloud filtering, is used to correct the geometric structure in the garden landscape three-dimensional model, specifically to eliminate the geometric structure defects and errors in the model; the three-dimensional plant modeling software PlantFactory is used to perform plant landscape texture mapping on the corrected garden landscape three-dimensional model, specifically to add the texture and material of the plant, so as to obtain a plant landscape texture mapping three-dimensional model; through virtual reality technology, object-oriented three-dimensional modeling and parameterization methods are used to optimize the light and shadow of the plant landscape texture mapping three-dimensional model; OpenGL extension function is used to perform real-time rendering enhancement, and the three-dimensional model after light and shadow optimization is rendered to obtain a landscape rendering enhanced three-dimensional model; combined with virtual reality technology, the landscape rendering enhanced three-dimensional model is interactively visually enhanced to generate garden landscape visual enhancement data; big data monitoring technology is used to analyze the garden landscape visual enhancement data, enhance the perception effect of the landscape scene, and generate a landscape scene perception enhanced three-dimensional model; digital landscape technology is used to perform intelligent digital visualization enhancement on the landscape scene perception enhanced three-dimensional model, and finally obtain a garden landscape three-dimensional digital enhancement model.

[0051] In this specification, a garden landscape intelligent digital design system based on a three-dimensional model is provided, which is used to execute the above-mentioned garden landscape intelligent digital design method based on a three-dimensional model. The garden landscape intelligent digital design system based on a three-dimensional model includes: The garden landscape data acquisition module is used to obtain garden landscape data; pre-process the garden landscape data to obtain standard garden landscape data; perform multi-dimensional feature recognition on the standard garden landscape data to obtain garden multi-dimensional feature data; The garden landscape spatial correlation analysis module is used to extract the garden terrain features from the garden multi-dimensional feature data to obtain the garden terrain features; to measure the spatial layout of the garden terrain features to obtain the garden spatial layout data; to extract the plant landscape features from the garden multi-dimensional feature data to obtain the plant landscape features; to perform correlation analysis on the garden spatial layout data and the plant landscape features to generate the garden landscape spatial correlation data; The garden landscape three-dimensional model construction module is used to perform three-dimensional data mapping on the garden landscape space correlation data to obtain the garden landscape three-dimensional data; the garden landscape three-dimensional model is constructed on the garden multi-dimensional feature data using the garden landscape three-dimensional data to obtain the garden landscape three-dimensional model; The intelligent digital visualization enhancement module of garden landscape is used to simulate the lighting of garden landscape space mapping data to obtain landscape lighting simulation data; analyze the seasonal changes of plants in the garden multi-dimensional feature data according to the landscape lighting simulation data to generate plant seasonal change data; dynamically adjust the landscape change of the garden landscape three-dimensional model based on the plant seasonal change data to generate landscape change adjustment data; perform intelligent digital visualization enhancement on the garden landscape three-dimensional model through the landscape change adjustment data to obtain a three-dimensional digital enhancement model of the garden landscape; display the garden landscape visualization report based on the three-dimensional digital enhancement model of the garden landscape to generate a garden landscape visualization report.

[0052] The present invention obtains garden landscape data through a garden landscape data acquisition module to ensure the basis for comprehensive understanding and analysis of garden landscape; then, the garden landscape data is preprocessed and converted into standard garden landscape data, which can eliminate noise and inconsistency in the data, thereby improving the quality and availability of the data; then, multi-dimensional feature recognition is performed on the standard garden landscape data to reveal the deep-level characteristics of the garden landscape. Through the garden landscape spatial association analysis module, the garden terrain feature is extracted from the garden multi-dimensional feature data to clarify the terrain structure feature information of the garden; the spatial layout of the garden terrain feature is measured to quantify the spatial layout of the garden, providing a data basis for garden planning and design; the plant landscape feature is extracted from the garden multi-dimensional feature data to identify the distribution and types of plants in the garden; the garden space layout data and the plant landscape feature are associated and analyzed to reveal the relationship between the spatial layout and the plant distribution. Through the garden landscape three-dimensional model construction module, the garden landscape space-related data is mapped into three-dimensional data, so that the spatial information of the garden landscape can be intuitively displayed in three-dimensional space, enhancing the visualization effect of the garden landscape; the garden landscape three-dimensional data is used to construct the garden landscape three-dimensional model for the garden multi-dimensional feature data to obtain the garden landscape three-dimensional model, so that the multi-dimensional features of the garden landscape are converted into a three-dimensional model, providing a powerful tool for the display and analysis of the garden landscape. Through the intelligent digital visualization enhancement module of the garden landscape, the illumination simulation is performed on the garden landscape space mapping data, which can simulate the illumination effects under different time and weather conditions, and provide a scientific basis for the illumination design of the garden landscape; according to the landscape illumination simulation data, the seasonal change analysis of the garden multidimensional feature data is performed, and the growth status and landscape effect of the plants in different seasons can be evaluated; based on the seasonal change data of the plants, the landscape change dynamic adjustment is performed on the three-dimensional model of the garden landscape, so that the garden landscape model can reflect the plant growth status that changes with time, and improve the timeliness and adaptability of the model; through the landscape change adjustment data, the three-dimensional model of the garden landscape is intelligently digitally visualized and enhanced, which can improve the visual effect and information richness of the garden landscape model; based on the three-dimensional digital enhancement model of the garden landscape, the garden landscape visualization report is displayed, and the garden landscape visualization report is generated, which provides an intuitive and detailed visual report for the display and communication of the garden landscape. Therefore, the present invention realizes the correlation analysis of the garden space layout characteristics and the plant landscape characteristics through data processing technology, geographic information system technology, pattern recognition technology and three-dimensional mapping technology; and realizes the intelligent digital adjustment of the three-dimensional model of the garden landscape, thereby improving the accuracy of the garden landscape presentation.

[0053] Therefore, the embodiments should be regarded as illustrative and non-restrictive from all points, and the scope of the present invention is limited by the appended claims rather than the above description, and it is therefore intended that all changes falling within the meaning and range of equivalent elements of the application documents are included in the present invention.

[0054] The above description is only a specific embodiment of the present invention, so that those skilled in the art can understand or implement the present invention. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to the embodiments shown herein, but should conform to the widest scope consistent with the principles and novel features invented herein.

Claims

1. A method for intelligent digital design of garden landscape based on three-dimensional model, characterized in that: The following steps are involved: Step S1: Acquire garden landscape data; Preprocessing the garden landscape data to obtain standard garden landscape data; Perform multi-dimensional feature recognition on standard garden landscape data to obtain garden multi-dimensional feature data; Step S2: extracting garden terrain features from the garden multidimensional feature data to obtain garden terrain features; measuring the spatial layout of the garden terrain features to obtain garden spatial layout data; extracting plant landscape features from the garden multidimensional feature data to obtain plant landscape features; and performing correlation analysis between the garden spatial layout data and the plant landscape features to generate garden landscape spatial correlation data; Step S3: performing three-dimensional data mapping on the garden landscape space association data to obtain the garden landscape three-dimensional data; using the garden landscape three-dimensional data to construct a garden landscape three-dimensional model on the garden multi-dimensional feature data to obtain the garden landscape three-dimensional model; Step S4: performing illumination simulation on the garden landscape space mapping data to obtain landscape illumination simulation data; performing seasonal plant change analysis on the garden multidimensional feature data according to the landscape illumination simulation data to generate seasonal plant change data; dynamically adjusting the landscape change of the garden landscape three-dimensional model based on the seasonal plant change data to generate landscape change adjustment data; performing intelligent digital visualization enhancement on the garden landscape three-dimensional model through the landscape change adjustment data to obtain a three-dimensional digital enhanced model of the garden landscape; Based on the three-dimensional digital enhanced model of the garden landscape, the garden landscape visualization report is displayed and a garden landscape visualization report is generated.

2. The intelligent digital design method for garden landscape based on three-dimensional model according to claim 1 is characterized in that: Step S2 includes the following steps: Step S21: performing terrain feature recognition on the garden multidimensional feature data to generate terrain feature data; performing garden terrain feature marking on the garden multidimensional feature data according to the terrain feature data to obtain garden terrain marking data; performing garden terrain feature extraction on the garden multidimensional feature data through the garden terrain marking data to obtain garden terrain feature data; Step S22: performing geometric feature recognition on the garden terrain feature data to generate garden terrain geometric features; performing garden geometric relationship measurement on the garden terrain geometric features to obtain garden geometric relationship data; performing spatial structure recognition on the garden terrain features based on the garden geometric relationship data to generate garden spatial structure data; performing spatial layout measurement on the garden spatial structure data to obtain garden spatial layout data; Step S23: performing plant feature recognition on the garden multidimensional feature data to generate garden plant feature data; performing landscape feature marking on the garden multidimensional feature data based on the garden plant feature data to obtain garden landscape feature marking data; performing plant landscape feature extraction on the garden multidimensional feature data through the garden landscape feature marking data to obtain plant landscape features; Step S24: performing correlation analysis on the garden space layout data and the plant landscape characteristics to generate garden landscape space correlation data.

3. The intelligent digital design method for garden landscape based on three-dimensional model according to claim 2 is characterized in that: Step S24 includes the following steps: Step S241: performing spatial structural element recognition on the garden space layout data to obtain spatial structural element data; performing garden boundary detection on the spatial structural element data to generate garden space boundary information; performing garden space range measurement on the garden space layout data based on the garden space boundary information to generate garden space range information; Step S242: performing garden area feature recognition on the garden space range information to obtain garden area feature data; performing area type classification on the garden area feature data to generate garden area type data; Step S243: dividing the garden space range information into garden space composition according to the garden area type data to obtain garden space composition data; identifying plant placement areas on the garden space composition data to generate plant placement area data; Step S244: calculating the plant landscape space coverage rate of the plant placement area data based on the plant landscape characteristics to obtain the plant landscape space coverage rate; performing garden space sight line detection on the garden space layout data according to the plant landscape space coverage rate to obtain garden space sight line information; Step S245: Associating the garden space layout data and the plant landscape features with the garden landscape space visual positions based on the garden space sight line information to generate garden landscape space associated data.

4. The intelligent digital design method for garden landscape based on three-dimensional model according to claim 1 is characterized in that: Step S3 includes the following steps: Step S31: performing three-dimensional space coordinate conversion on the garden landscape space association data to obtain garden landscape three-dimensional coordinate data; performing geometric voxelization on the garden landscape three-dimensional coordinate data to obtain geometric voxelized three-dimensional data; performing spatial topological feature recognition on the geometric voxelized three-dimensional data to generate garden landscape spatial topological features; Step S32: determining the spatial topological relationship of the geometric voxelized three-dimensional data according to the spatial topological characteristics of the garden landscape to obtain spatial topological relationship data; performing garden landscape spatial dimension recognition on the spatial topological relationship data to obtain garden landscape spatial dimension data; performing spatial hierarchical division on the garden landscape spatial dimension data to generate garden landscape spatial hierarchical data; Step S33: performing entity object recognition on the garden landscape space associated data based on the garden landscape space hierarchical data to obtain landscape entity object data; performing entity object relationship extraction on the landscape entity object data to generate entity object relationship data; performing three-dimensional mapping on the garden landscape space associated data according to the entity object relationship data to obtain garden landscape three-dimensional data; Step S34: constructing a three-dimensional model of the garden landscape by using the three-dimensional garden landscape data and the multi-dimensional characteristic data of the garden to obtain the three-dimensional model of the garden landscape.

5. The intelligent digital design method for garden landscape based on three-dimensional model according to claim 4 is characterized in that: Step S34 includes the following steps: Step S341: Based on the three-dimensional data of the garden landscape, the landscape granularity framework feature recognition is performed on the garden multidimensional feature data to generate landscape granularity framework feature data; based on the landscape granularity framework feature data, the terrain level granularity framework is constructed on the garden multidimensional feature data to obtain the terrain level granularity framework; Step S342: constructing a real-scene-level granularity framework for the garden multi-dimensional feature data through the landscape granularity framework feature data to obtain a real-scene-level granularity framework; merging the terrain-level granularity framework and the real-scene-level granularity framework into a multi-granularity framework to generate a multi-granularity garden landscape framework; Step S343: performing three-dimensional vector recognition on the three-dimensional garden landscape data based on the multi-granularity garden landscape framework to obtain the three-dimensional garden landscape vector data; performing vector feature processing on the three-dimensional vector features of the garden landscape to generate vectorized three-dimensional data; Step S344: constructing a preliminary three-dimensional garden landscape model for the vectorized three-dimensional data according to the multi-granularity garden landscape framework to obtain a preliminary three-dimensional garden landscape model; rendering the preliminary three-dimensional garden landscape model with landscape colors to generate a three-dimensional garden landscape model.

6. The intelligent digital design method for garden landscape based on three-dimensional model according to claim 1 is characterized in that: Step S4 includes the following steps: Step S41: capturing the light and shadow environment reflection of the garden landscape space mapping data to obtain the light and shadow environment reflection data; tracing the garden landscape light and shadow path of the light and shadow environment reflection data to generate the garden landscape light and shadow path data; calculating the illumination angle of the garden landscape light and shadow path data to obtain the garden landscape illumination angle data; simulating the illumination of the garden landscape space mapping data based on the garden landscape illumination angle data to obtain the landscape illumination simulation data; Step S42: performing seasonal plant change analysis on the garden multi-dimensional feature data according to the landscape illumination simulation data to generate seasonal plant change data; Step S43: dynamically adjusting the landscape change of the garden landscape three-dimensional model based on the seasonal change data of the plants to generate landscape change adjustment data; Step S44: performing intelligent digital visualization enhancement on the garden landscape three-dimensional model by adjusting the landscape change data to obtain a garden landscape three-dimensional digital enhanced model; Step S45: outputting digital information of the garden landscape based on the three-dimensional digital enhanced model of the garden landscape to obtain digital information of the garden landscape; processing the digital information of the garden landscape into information charts to obtain visualization charts of the garden landscape; compiling visualization reports on the visualization charts of the garden landscape to generate visualization reports of the garden landscape.

7. The intelligent digital design method for garden landscape based on three-dimensional model according to claim 6 is characterized in that: Step S42 includes the following steps: Step S421: marking the landscape lighting simulation data with seasonal lighting features to obtain seasonal lighting marking features; performing seasonal lighting pattern recognition on the garden multidimensional feature data using the seasonal lighting marking features to generate a seasonal lighting pattern; Step S422: performing light path detection on the seasonal light pattern to obtain seasonal light path data; performing light intensity measurement on the seasonal light path data to generate seasonal light intensity data; Step S423: recording plant photosynthesis response of the garden multidimensional feature data based on the seasonal light path data and the seasonal light intensity data to obtain plant photosynthesis response data; performing seasonal photoperiod effect detection on the plant photosynthesis response data to generate seasonal photoperiod effect data; Step S424: monitoring seasonal morphological characteristics of plants on the garden multi-dimensional feature data according to the seasonal photoperiod effect data to obtain seasonal morphological characteristics of plants; performing landscape visual morphological recognition on the seasonal morphological characteristics of plants to generate landscape visual morphological data; Step S425: Determine seasonal growth changes of plants based on seasonal morphological characteristics of plants based on landscape visual morphological data to obtain seasonal change data of plants.

8. The intelligent digital design method for garden landscape based on three-dimensional model according to claim 6 is characterized in that: Step S43 includes the following steps: Step S431: performing seasonal change feature recognition on the plant seasonal change data to obtain the plant seasonal change feature; performing plant species change extraction on the plant seasonal change feature to obtain plant species change data; dynamically adjusting the plant species of the garden landscape three-dimensional model based on the plant species change data to generate plant species update data; Step S432: performing plant morphological feature recognition on the seasonal change characteristics of the plants to obtain plant morphological change data; performing morphological size measurement on the plant morphological change data to generate plant morphological size data; performing plant morphological update on the garden landscape three-dimensional model based on the plant morphological size data to obtain plant morphological update data; Step S433: recording plant leaf color changes for the plant morphology change data to generate plant leaf color change data; updating the leaf color of the garden landscape three-dimensional model based on the plant leaf color change data to obtain plant leaf color update data; Step S434: measuring the plant distribution area change according to the seasonal change characteristics of the plants to obtain plant distribution area change data; adjusting the vegetation coverage of the garden landscape three-dimensional model based on the plant distribution area change data to generate vegetation coverage adjustment data; Step S435: The plant species update data, the plant morphology update data, the plant leaf color update data and the vegetation coverage adjustment data are combined for landscape change adjustment to obtain landscape change adjustment data.

9. The intelligent digital design method for garden landscape based on three-dimensional model according to claim 6 is characterized in that: Step S44 includes the following steps: Step S441: performing garden geometry correction on the garden landscape three-dimensional model based on the landscape change adjustment data to obtain geometry correction data; performing plant landscape texture mapping on the garden landscape three-dimensional model according to the geometry correction data to obtain a plant landscape texture mapping three-dimensional model; Step S442: performing garden landscape light and shadow optimization on the plant landscape texture mapping three-dimensional model to generate garden landscape light and shadow optimization data; performing real-time landscape rendering enhancement on the plant landscape texture mapping three-dimensional model based on the garden landscape light and shadow optimization data to obtain a landscape rendering enhanced three-dimensional model; Step S443: interactively visually enhance the landscape rendering enhanced three-dimensional model to generate garden landscape visual enhancement data; perform landscape scene perception enhancement on the landscape rendering enhanced three-dimensional model according to the garden landscape visual enhancement data to obtain a landscape scene perception enhanced three-dimensional model; Step S444: Perform intelligent digital visualization enhancement on the landscape scene perception enhanced three-dimensional model to obtain a three-dimensional digital enhanced model of the garden landscape.

10. A garden landscape intelligent digital design system based on a three-dimensional model, characterized in that: Used to execute the garden landscape intelligent digital design method based on the three-dimensional model as claimed in claim 1, the garden landscape intelligent digital design system based on the three-dimensional model comprises: The garden landscape data acquisition module is used to obtain garden landscape data; pre-process the garden landscape data to obtain standard garden landscape data; perform multi-dimensional feature recognition on the standard garden landscape data to obtain garden multi-dimensional feature data; The garden landscape spatial correlation analysis module is used to extract the garden terrain features from the garden multi-dimensional feature data to obtain the garden terrain features; to measure the spatial layout of the garden terrain features to obtain the garden spatial layout data; to extract the plant landscape features from the garden multi-dimensional feature data to obtain the plant landscape features; to perform correlation analysis on the garden spatial layout data and the plant landscape features to generate the garden landscape spatial correlation data; The garden landscape three-dimensional model construction module is used to perform three-dimensional data mapping on the garden landscape space correlation data to obtain the garden landscape three-dimensional data; the garden landscape three-dimensional model is constructed on the garden multi-dimensional feature data using the garden landscape three-dimensional data to obtain the garden landscape three-dimensional model; The intelligent digital visualization enhancement module of garden landscape is used to simulate the lighting of garden landscape space mapping data to obtain landscape lighting simulation data; analyze the seasonal changes of plants in the garden multi-dimensional feature data according to the landscape lighting simulation data to generate plant seasonal change data; dynamically adjust the landscape change of the garden landscape three-dimensional model based on the plant seasonal change data to generate landscape change adjustment data; perform intelligent digital visualization enhancement on the garden landscape three-dimensional model through the landscape change adjustment data to obtain a three-dimensional digital enhancement model of the garden landscape; display the garden landscape visualization report based on the three-dimensional digital enhancement model of the garden landscape to generate a garden landscape visualization report.

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