Garden landscape three-dimensional scene seasonal phase change simulation method and system

Abstract

The invention discloses a landscape three-dimensional scene seasonal phase change simulation method and system, and relates to the technical field of virtual reality. According to the method, a phenological parameter database containing a leaf density change curve, a crown breadth scaling curve and a leaf color gradient curve is constructed, and a correction relation between leaf density parameters and crown breadth scaling parameters and a correlation modulation mechanism between the leaf density parameters and lightness components and between the crown breadth scaling parameters and saturation components are established; the generation of the leaf color does not independently depend on a preset value of season or time any more, but can dynamically respond to the change of geometric morphology; when the value of the leaf density parameter is relatively high, the canopy presents a dark effect conforming to the visual principle; and when the value of the crown breadth zooming parameter is large, the canopy presents a bright effect conforming to the visual principle, so that the problem of unreasonable vision caused by disjunction of color and geometric morphology in the prior art is solved, and the sense of reality and immersion of seasonal phase change simulation are remarkably improved.

Description

Technical Field

[0001] This invention relates to the field of virtual reality technology, specifically to a method and system for simulating seasonal changes in three-dimensional garden landscape scenes. Background Technology

[0002] Simulation of seasonal changes in 3D landscape scenes is an important technological direction in the fields of digital landscape, virtual reality, and smart gardens. Its core lies in using computer graphics to realistically reproduce the changes in the appearance and morphology of plants in different seasons and phenological stages, including changes in crown size, leaf density, and the periodic evolution of leaf color. With the widespread application of 3D visualization technology in landscape planning, urban landscape design, and virtual tourism, higher requirements have been placed on the realism, continuity, and parameter adjustability of seasonal change simulation.

[0003] In existing technologies, the simulation of seasonal changes in 3D garden landscape scenes is usually achieved by switching texture maps or directly rendering with preset color values. Specifically, for the 3D model of the same plant, multiple sets of leaf texture maps corresponding to different seasons are pre-made or multiple sets of leaf color RGB values ​​are set. During the simulation, the corresponding texture map is switched or the corresponding color value is called for rendering according to the target season to present the seasonal effects of different periods. Some existing solutions further introduce time axis interpolation methods to perform linear interpolation between the color values ​​of two preset seasons to achieve a smooth color transition.

[0004] The limitation of existing technologies lies in the fact that the generation of leaf color relies solely on preset values ​​for season or time, failing to consider the coupled influence of changes in plant canopy geometry on the visual representation of leaf color. In realistic 3D scene rendering, the visual perception of leaf color is closely related to the geometry of the canopy: when leaf density is high, the mutual occlusion of leaves leads to a decrease in the overall brightness of the canopy; when the canopy width is large, the increased light-receiving area enhances the visual saturation of color. However, because existing technologies have failed to establish a correlation modulation mechanism between leaf density, canopy width scaling, and leaf color, the color representation of the canopy lacks visual rationality under different geometric conditions, reducing the realism and immersiveness of the simulation of seasonal changes. Summary of the Invention

[0005] To address the shortcomings of existing technologies, this invention provides a method and system for simulating seasonal changes in three-dimensional garden landscape scenes.

[0006] To achieve the above objectives, the technical solution of the present invention is as follows: a method for simulating seasonal changes in a three-dimensional landscape scene, comprising:

[0007] Construct a phenological parameter database for the target landscape, storing a set of phenological parameter curves for each plant species.

[0008] Receive the target date, obtain the meteorological data of the target landscape on the target date, and determine the phenological stage of the current plants based on the meteorological data;

[0009] Based on the phenological period identifier, leaf density parameters, initial crown width scaling parameters, and initial leaf color parameters are obtained from the phenological parameter curve set.

[0010] The leaf density parameter is input into a preset correction function to calculate the crown width correction coefficient. The initial crown width scaling parameter is then corrected based on the crown width correction coefficient to obtain the current crown width scaling parameter.

[0011] The initial leaf color parameters are converted from RGB values ​​to HSV color space to obtain hue components, lightness components, and saturation components.

[0012] A lightness attenuation coefficient is determined based on the leaf density parameter, and the lightness component is attenuated using the lightness attenuation coefficient to obtain a corrected lightness component; a saturation adjustment coefficient is determined based on the current crown scaling parameter, and the saturation component is adjusted using the saturation adjustment coefficient to obtain a corrected saturation component; the hue component, the corrected lightness component, and the corrected saturation component are synthesized and converted back to RGB values ​​to generate the current leaf color parameters;

[0013] The leaf density parameter, the current crown scaling parameter, and the current leaf color parameter are output to the 3D rendering engine.

[0014] A 3D simulation system for seasonal changes in garden landscape scenes, including:

[0015] Phenological parameter database construction module: used to construct a phenological parameter database for the target landscape, storing the phenological parameter curve set for each plant species;

[0016] Phenological Period Identification Module: Used to receive the target date, obtain the meteorological data of the target landscape on the target date, and determine the phenological period identifier of the current plant based on the meteorological data;

[0017] Initial parameter acquisition module: used to acquire leaf density parameters, initial crown width scaling parameters, and initial leaf color parameters from the phenological parameter curve set based on the phenological period identifier;

[0018] Crown width scaling parameter determination module: used to input the leaf density parameter into a preset correction function, calculate the crown width correction coefficient, and correct the initial crown width scaling parameter according to the crown width correction coefficient to obtain the current crown width scaling parameter;

[0019] HSV component acquisition module: used to convert the initial leaf color parameters from RGB values ​​to HSV color space to obtain hue component, lightness component and saturation component;

[0020] Leaf color parameter determination module: used to determine the lightness attenuation coefficient based on the leaf density parameter, and use the lightness attenuation coefficient to attenuate the lightness component to obtain the corrected lightness component; to determine the saturation adjustment coefficient based on the current crown scaling parameter, and use the saturation adjustment coefficient to adjust the saturation component to obtain the corrected saturation component; to synthesize the hue component, the corrected lightness component, and the corrected saturation component, and convert them back to RGB values ​​to generate the current leaf color parameters;

[0021] Current parameter output module: Outputs the leaf density parameter, the current crown scaling parameter, and the current leaf color parameter to the 3D rendering engine.

[0022] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0023] This invention constructs a phenological parameter database containing leaf density variation curves, canopy scaling curves, and leaf color gradation curves. It also establishes a chain-like correction relationship between leaf density and canopy scaling parameters, as well as a correlation modulation mechanism between leaf density and lightness components, and between canopy scaling and saturation components. This allows leaf color generation to no longer rely solely on preset values ​​for season or time, but to dynamically respond to changes in canopy geometry: when leaf density is high, the lightness component decreases accordingly, resulting in a darker canopy that conforms to visual norms; when canopy scaling is high, the saturation component increases accordingly, resulting in a brighter canopy that conforms to visual norms. This solves the visual inconsistency caused by the disconnect between color and geometry in existing technologies, significantly improving the realism and immersiveness of seasonal change simulation. Attached Figure Description

[0024] The disclosure of this invention is illustrated with reference to the accompanying drawings. It should be understood that the drawings are for illustrative purposes only and are not intended to limit the scope of protection of this invention. In the drawings, the same reference numerals are used to refer to the same parts. Wherein:

[0025] Figure 1 This is a flowchart of the method of the present invention;

[0026] Figure 2 This is a system structure diagram of the present invention. Detailed Implementation

[0027] It is readily understood that, based on the technical solutions of this invention, those skilled in the art can propose various interchangeable structural methods and implementations without altering the essential spirit of the invention. Therefore, the following detailed embodiments and accompanying drawings are merely illustrative examples of the technical solutions of this invention and should not be considered as the entirety of the invention or as limitations or restrictions on the technical solutions of this invention.

[0028] like Figure 1 As shown, this invention provides a method for simulating seasonal changes in a three-dimensional landscape scene. The specific implementation steps include the following:

[0029] Construct a phenological parameter database for the target landscape, storing a set of phenological parameter curves for each plant species.

[0030] It should be specifically noted that the construction steps of the phenological parameter database specifically include:

[0031] A1. For each plant species in the target landscape, collect phenological observation data throughout its entire growth cycle, including the time nodes of key phenological stages such as leaf unfolding, full leaf stage, coloring leaf stage, and leaf fall stage.

[0032] A2. During each phenological stage, multi-angle image data of the plant canopy are acquired using image acquisition equipment, and the canopy size, leaf density, and leaf color parameters are extracted using image processing algorithms.

[0033] A3. Interpolate and fit the collected discrete parameter values ​​along the time axis to generate a continuous parameter change curve;

[0034] A4. Store the generated parameter change curves in a structured data format in the database, and associate each curve with the corresponding plant species identifier and phenological period identifier.

[0035] For plant species for which data cannot be obtained through field collection, the construction of phenological parameter databases can also be achieved by: obtaining the phenological parameter data of the plant from a publicly available plant phenological database; or by using parameter migration methods for similar plants of the same family and genus, and adjusting the parameters based on the stored parameter curves of similar plants.

[0036] The phenological parameter database adopts a relational database or key-value pair storage structure. Each plant record contains the following fields: plant species identifier, plant type identifier, and phenological parameter curve set.

[0037] The plant species identifier is used to uniquely identify a plant, while the plant type identifier is used to identify the type group to which the plant belongs, including fast-growing tree type, slow-growing shrub type, coniferous tree type, and flowering shrub type.

[0038] It should be specifically noted that the set of phenological parameter curves includes leaf density variation curves, crown width scaling curves, and leaf color gradient curves;

[0039] Each curve uses phenological stage indicators as the horizontal axis and parameter values ​​as the vertical axis, covering the complete growth cycle of plants from leaf unfolding to leaf fall.

[0040] The leaf density change curve records the leaf density parameter values ​​of plants at different phenological stages. The leaf density parameter reflects the number of leaves or the degree of coverage per unit canopy volume, and the value ranges from 0 to 1, where 0 indicates no leaves and 1 indicates complete leaf coverage. During the leaf expansion stage, the leaf density parameter gradually increases from the initial value; during the full leaf stage, the leaf density parameter reaches its peak and remains stable; during the color-changing leaf stage, the leaf density parameter begins to decrease; and during the leaf fall stage, the leaf density parameter drops to its lowest value.

[0041] The canopy scaling curve records the canopy scaling parameters of plants at different phenological stages. The canopy scaling coefficient reflects the ratio of the canopy geometry to the reference size, and the value range is preset to 0.5 to 1.5, where 1 represents the reference size, less than 1 indicates canopy contraction, and greater than 1 indicates canopy expansion. During the leaf expansion stage, the canopy scaling coefficient gradually increases from the initial value; during the full leaf stage, the canopy scaling coefficient reaches its peak; and during the leaf fall stage, the canopy scaling coefficient gradually decreases.

[0042] The leaf color gradient curve records the RGB values ​​of the plant's leaves at different phenological stages. The RGB values ​​of the leaves are composed of three components: red channel value, green channel value, and blue channel value. The value range of each component is preset to 0 to 1. During the leaf unfolding stage, the leaf color RGB values ​​are light green. During the full leaf stage, the leaf color RGB values ​​are bright green. During the color leaf stage, the leaf color RGB values ​​gradually transition to yellow, orange, or red. During the leaf fall stage, the leaf color RGB values ​​are withered yellow or brown.

[0043] The specific method for generating the phenological parameter curve set is as follows:

[0044] For plant species whose discrete parameter values ​​are obtained through field collection, the phenological parameter curve set is generated by interpolation fitting. The collected discrete data points are sorted according to the time axis, and a spline interpolation algorithm is used to generate smooth transition curves between adjacent data points, so that the curves cover the entire phenological cycle and are continuous and differentiable.

[0045] For plant species generated through parameter migration, source plants of the same family and genus with similar phenological characteristics are selected to obtain a set of phenological parameter curves for the source plants. Based on the differences in phenological characteristics of the target plants, the source plant curves are subjected to time axis stretching or compression transformation, as well as parameter value scaling transformation, to generate a set of phenological parameter curves for the target plants.

[0046] This step established a basic data support system for plant seasonal changes. The leaf color gradient curve records the continuous process of leaf color change over time from leaf expansion to leaf fall. The canopy scaling curve reflects the relative change of canopy geometry at different phenological stages. The leaf density change curve describes the temporal evolution of leaf density with phenological stages.

[0047] By pre-constructing a phenological parameter database and classifying, storing, and managing phenological parameter curve sets according to plant species, a unified data source can be provided for subsequent parameter extraction and dynamic modulation. During simulation, the corresponding parameter values ​​can be quickly indexed based on plant species and phenological period identifiers, avoiding real-time calculations or repeated data collection, thus improving processing efficiency and parameter consistency. At the same time, the continuous processing of parameter curve sets ensures the smoothness of parameter changes between phenological periods, avoiding jumps in the seasonal change process.

[0048] In practice, phenological parameter curve sets can be stored in a time-series database, supporting quick querying and aggregation of parameter values ​​by time range; parameterized models can be used to replace parameter curve sets, and parameter values ​​for any phenological period can be calculated in real time using preset mathematical model parameters; phenological parameters can also be obtained in real time through external data interfaces, and real-time phenological observation data can be obtained by connecting with meteorological data service platforms.

[0049] Receive the target date, obtain the meteorological data of the target landscape on the target date, and determine the phenological stage of the current plants based on the meteorological data.

[0050] It should be noted that the target date input by the user is received. The target date is represented in a standard date format and includes three components: year, month, and day. The target date is used to determine the time point to be simulated and serves as the time reference for determining the phenological period.

[0051] Based on the target date and the geographic coordinates of the target landscape, the corresponding meteorological data is obtained from the meteorological data service interface. The meteorological data includes the following key factors: the accumulated temperature from January 1 of the current year to the target date, the average sunshine duration within a preset number of days before the target date, and the cumulative precipitation within a preset number of days before the target date. The accumulated temperature is calculated by summing the daily average temperatures that are higher than a preset temperature threshold from January 1 of the current year. The geographic coordinates are obtained through the preset location information of the target landscape or through the geographic coordinate parameters input by the user.

[0052] In cases where real-time meteorological data cannot be obtained, an alternative acquisition method is adopted: obtain the multi-year average meteorological data of the geographical coordinates on the corresponding date from the historical meteorological database, or directly query the phenological period identifier corresponding to the date from the preset phenological calendar table.

[0053] It should be specifically noted that the method for determining the phenological period markers is as follows:

[0054] The phenological response model parameters of the plant are obtained from the phenological parameter database based on the plant species identifier;

[0055] The acquired meteorological data is compared with the thresholds in the phenological response model;

[0056] The current phenological stage is determined based on the comparison results;

[0057] The phenological response model is based on each plant species pre-stored in a phenological parameter database and associated with plant species identifiers. It describes the mapping relationship between meteorological data and phenological stages, specifically including: the accumulated temperature threshold for the leaf unfolding stage, the accumulated temperature threshold for the full leaf stage, the light duration threshold for the color-leaf stage, and the accumulated temperature decrease threshold for the leaf fall stage.

[0058] Taking the determination of the leaf unfolding period as an example: when the accumulated temperature reaches the preset threshold for the leaf unfolding period, the plant is determined to have entered the leaf unfolding period; when the accumulated temperature exceeds the threshold for the peak leafing period, the plant is determined to have entered the peak leafing period. Taking the determination of the coloring period as an example: when the average light duration is lower than the preset light duration threshold and the accumulated temperature reaches the threshold for the coloring period, the plant is determined to have entered the coloring period. Taking the determination of the leaf falling period as an example: when the rate of decrease of the accumulated temperature exceeds the preset rate of decrease threshold, the plant is determined to have entered the leaf falling period.

[0059] Phenological stage markers can be represented in discrete or continuous form; the discrete form includes preset phenological stage markers such as leaf unfolding stage, full leaf stage, coloring leaf stage, and leaf fall stage, with each marker corresponding to one phenological stage;

[0060] The continuous form uses values ​​between 0 and 1 to represent phenological processes, where 0 represents the start point of leaf unfolding, 1 represents the end point of leaf fall, and intermediate values ​​represent the proportional position of the phenological process; specifically, the proportional value of the phenological process is calculated based on the relative position of meteorological data and the threshold values ​​of each stage in the phenological response model.

[0061] For example, when the accumulated temperature is between the leaf-spreading stage initiation threshold and the full-blown stage initiation threshold, the phenological process ratio is calculated by linear interpolation: the phenological process ratio is equal to (current accumulated temperature minus leaf-spreading stage initiation threshold) divided by (full-blown stage initiation threshold minus leaf-spreading stage initiation threshold) multiplied by the first preset ratio coefficient.

[0062] After determining the phenological period identifier, a verification step is performed: it is determined whether the determination result is consistent with the phenological period identifier of the same date in the preset phenological calendar. If they are inconsistent, the determination result is corrected according to the preset confidence weight. The correction method is to take a weighted average of the determination result and the query result of the phenological calendar.

[0063] This step transforms the user-specified time conditions and spatial location into phenological stages of plant growth, enabling the simulation results to respond to climate differences in different years and regions, rather than simply changing according to fixed calendar dates.

[0064] By introducing a meteorological data-driven phenological period determination mechanism, the simulation of seasonal changes can respond to climate fluctuations in different years, such as early leaf unfolding due to warm winters and early leaf coloring due to drought, thus enhancing the dynamic adaptability and realism of the simulation. At the same time, through the phenological response model associated with plant species, the differences in the responses of different plants to the same meteorological conditions can be reflected, such as the difference in sensitivity of early-flowering plants and late-flowering plants to accumulated temperature, thereby enhancing the dynamic adaptability and realism of the simulation.

[0065] In practice, one can also directly query the phenological calendar and obtain the phenological period identifier from the preset phenological calendar based on the target date. This method is suitable for rapid simulation of standard years. Alternatively, one can use remote sensing image inversion to obtain the vegetation index of the target date through satellite remote sensing data and determine the phenological period identifier based on the mapping relationship between the vegetation index and the phenological period. Another method is to use machine learning models to construct a phenological period prediction model by training the correlation between historical phenological observation data and meteorological data.

[0066] Leaf density parameters, initial crown width scaling parameters, and initial leaf color parameters are obtained from the phenological parameter curve set based on the phenological period identifier.

[0067] It should be specifically explained that, using the phenological stage identifier as an index, samples are taken from the leaf density change curve, the crown scaling curve, and the leaf color gradient curve to obtain three parameter values ​​corresponding to that phenological stage. The obtained leaf density parameter, initial crown scaling parameter, and initial leaf color parameter are stored in the system memory in the form of parameter groups for subsequent steps to call. Among them, the initial leaf color parameter is stored in the form of three-channel values ​​in the RGB color space, including red channel value, green channel value, and blue channel value.

[0068] Phenological periods can be identified in discrete or continuous form, and the sampling method for the parametric curves is determined based on the representation of the phenological periods, specifically as follows:

[0069] When the phenological period markers are in a discrete form, the corresponding phenological stage interval is located in the parameter curve based on the phenological period markers, and the preset representative parameter values ​​within that interval are obtained.

[0070] Specifically, this includes: for the leaf expansion stage, obtaining the leaf density parameter value corresponding to the midpoint of the leaf density change curve at the leaf expansion stage, obtaining the crown scaling parameter value corresponding to the midpoint of the crown scaling curve at the leaf expansion stage, and obtaining the leaf color parameter value corresponding to the midpoint of the leaf color gradient curve at the leaf expansion stage; for the peak leaf stage, the colored leaf stage, and the leaf fall stage, the same sampling method is used to obtain the parameter values ​​corresponding to the midpoint of each stage.

[0071] The representative parameter values ​​for discrete phenological stages are obtained by sampling the parameter values ​​within each phenological stage interval during the parameter curve generation process, selecting the parameter value at the midpoint of the interval as the representative parameter value for that stage, and storing it in the parameter mapping table of the phenological parameter database.

[0072] When the phenological period indicator is in a continuous form, the continuous phenological period indicator value is used as the horizontal axis input parameter curve function, and the corresponding parameter value is output through function calculation; the position of the continuous phenological period indicator value within the horizontal axis interval of the parameter curve is determined, and the vertical axis value corresponding to that position is calculated according to the curve function type.

[0073] For the parameter curve generated by linear interpolation, locate the discrete point interval where the continuous phenological period indicator value is located, obtain the x-coordinate value and corresponding parameter value of the discrete points at both ends of the interval, and calculate the parameter value corresponding to the current phenological period indicator using the linear interpolation formula.

[0074] For parametric curves generated using spline interpolation, obtain the spline function expression of the parametric curve, substitute the continuous phenological period identifiers into the spline function expression, and calculate and output the corresponding parameter values.

[0075] When the phenological period indicator exceeds the coverage area of ​​the parameter curve, boundary processing is performed; if the phenological period indicator is less than the minimum horizontal coordinate value of the parameter curve, the parameter value corresponding to the minimum horizontal coordinate is obtained as the current parameter value; if the phenological period indicator is greater than the maximum horizontal coordinate value of the parameter curve, the parameter value corresponding to the maximum horizontal coordinate is obtained as the current parameter value.

[0076] This step extracts the standard parameter values ​​corresponding to the current phenological stage from the phenological parameter database, providing an initial baseline for subsequent dynamic adjustments. These parameter values ​​reflect the ideal performance of plants under standard growth conditions, and subsequent steps will make dynamic adjustments based on actual growth conditions.

[0077] By standardizing the mapping from phenological stage identifiers to parameter values, a unified correspondence between phenological stages and plant appearance parameters is achieved. This enables the simulation system to quickly and accurately obtain standard plant appearance parameters based on phenological stages, providing a stable and reliable initial benchmark for subsequent dynamic corrections. At the same time, it supports both discrete and continuous phenological stage identifiers and is compatible with phenological data input methods of different granularities.

[0078] In practice, a lookup table method can be used to replace curve sampling. A mapping table between phenological period identifiers and parameter values ​​can be pre-constructed, and parameter values ​​can be directly obtained by looking up the table. Alternatively, a parameterized model can be used to replace the parameter curve set. The parameter values ​​for any phenological period can be calculated in real time using preset mathematical model parameters. Parameter sampling can also be performed using an interpolation function library, which supports various interpolation methods such as linear interpolation, cubic spline interpolation, and Bézier curve interpolation.

[0079] The leaf density parameter is input into a preset correction function to calculate the crown width correction coefficient. The initial crown width scaling parameter is then corrected based on the crown width correction coefficient to obtain the current crown width scaling parameter.

[0080] It should be specifically noted that the correction function describes the mapping relationship between leaf density and crown width correction coefficient. The specific form of this function is selected and configured according to the plant type, because the response mechanism of crown width to leaf density varies significantly among different plants. The specific selection of the correction function type is as follows:

[0081] The preset plant type identifiers include fast-growing tree type, slow-growing shrub type, coniferous tree type, and flowering shrub type. Each type corresponds to a preset correction function form and parameter set:

[0082] When the plant type is identified as a fast-growing tree type, a linear correction function is selected. In this function, the crown width correction coefficient is linearly related to the leaf density parameter, and the preset linear coefficient range is 0.7 to 1.0.

[0083] When the plant type is identified as a slow-growing shrub, a linear correction function is selected. In this function, the crown width correction coefficient is linearly related to the leaf density parameter, and the preset linear coefficient range is 0.2 to 0.4.

[0084] When the plant type is identified as a coniferous species, the exponential correction function is selected. In this function, the crown width correction coefficient is exponentially related to the leaf density parameter, and the preset base parameter range is 2.0 to 3.0.

[0085] When the plant type is identified as a flowering shrub, a segmented correction function is selected. This function divides the plant into a first segment and a second segment based on the phenological period identifier, and each segment is assigned an independent coefficient.

[0086] The coefficients involved in the correction function are stored in the parameter configuration table corresponding to the plant type. Each plant type identifier is associated with a set of preset coefficients. The values ​​of the coefficients are obtained by pre-calibrating the parameters of the plant samples. The crown width measurement data of the plants under different leaf density conditions are collected, and the weight coefficient value that minimizes the fitting error is determined by the fitting algorithm and stored in the parameter configuration table.

[0087] Once the crown width correction coefficient is determined, it is multiplied by the initial crown width scaling parameter to generate the current crown width scaling parameter;

[0088] When the leaf density is higher than the reference value, the deviation is positive, the crown width correction coefficient is greater than one, and the current crown width scaling parameter is increased relative to the initial value.

[0089] When the leaf density is lower than the reference value, the deviation is negative, the crown width correction coefficient is less than one, and the current crown width scaling parameter is reduced relative to the initial value.

[0090] This step enables the leaf density parameter to drive the crown scaling factor in a unidirectional manner, establishing a chain-like influence relationship between parameters.

[0091] By selecting the corresponding correction function form and preset parameters by identifying the plant type, the canopy size can dynamically respond to changes in leaf density in a quantifiable way, and the response method conforms to the growth characteristics data of different plants, which enhances the visual consistency between canopy geometry and leaf density, and improves the biological rationality of the simulation.

[0092] In practice, a neural network model can be used to replace the preset correction function, and the mapping relationship between leaf density and crown width correction coefficient can be learned through training sample data; a lookup table method can be used to store the crown width correction coefficient corresponding to discrete leaf density intervals, and continuous values ​​can be calculated by interpolation; the correction function can also be associated with phenological period identifiers, and independent coefficients can be stored for different phenological periods in the parameter configuration table.

[0093] The initial leaf color parameters are converted from RGB values ​​to HSV color space to obtain hue components, lightness components, and saturation components.

[0094] It's important to clarify that the RGB color space represents color as a linear combination of three channels: red, green, and blue. These three channels are coupled, meaning that directly adjusting any one of the RGB channels will simultaneously affect the color's brightness, saturation, and hue, making it difficult to achieve independent control over a single attribute. In contrast, the HSV color space decomposes color into three independent components: hue, saturation, and brightness. Hue determines the basic tone type of the color, saturation determines the vividness or purity of the color, and brightness determines the lightness or darkness of the color. These three components are independent of each other, and adjusting one component will not affect the values ​​of the others. Therefore, the HSV color space is more suitable for multi-dimensional color modulation.

[0095] The initial leaf color parameters are stored in the RGB color space, containing three components: red channel value, green channel value, and blue channel value. The value range of each component is preset to be 0 to 1. The conversion process of the initial leaf color parameters from RGB values ​​to HSV color space includes the following steps:

[0096] B1. Find the maximum and minimum values ​​from the red channel values, green channel values, and blue channel values, and assign the maximum value to the maximum value variable and the minimum value to the minimum value variable. Calculate the difference between the maximum and minimum values ​​and assign it to the difference variable.

[0097] B2. Calculate the lightness and saturation components;

[0098] The value component is calculated as follows: the value component equals the maximum value variable;

[0099] The saturation component is calculated as follows: when the maximum value variable is equal to 0, the saturation component is preset to 0; when the maximum value variable is not equal to 0, the saturation component is equal to the difference variable divided by the maximum value variable.

[0100] B3. Calculate the hue components based on the color channel corresponding to the maximum value variable;

[0101] When the maximum value variable equals the red channel value and the difference variable is not equal to 0, the hue component equals 60 multiplied by (green channel value minus blue channel value) divided by the difference variable;

[0102] When the maximum value variable equals the green channel value, the hue component equals 60 multiplied by (2 plus (blue channel value minus red channel value) divided by the difference variable).

[0103] When the maximum value variable equals the blue channel value, the hue component equals 60 multiplied by (4 plus (red channel value minus green channel value) divided by the difference variable).

[0104] When the difference variable is equal to 0, the hue component is preset to 0;

[0105] If the calculated hue component is less than 0, then add 360 to the hue component so that the hue component value falls between 0 and 360.

[0106] To facilitate subsequent calculations, the three components after conversion are normalized. The hue component's value range is preset to 0 to 360, and this range will remain unchanged in subsequent calculations or be normalized to the 0 to 1 range by dividing by 360. The saturation component's value range is 0 to 1, and the lightness component's value range is 0 to 1. These two components will directly remain in the 0 to 1 range after conversion without additional normalization.

[0107] The converted hue, lightness, and saturation components are stored in the system memory in three forms for use in subsequent steps. The hue, lightness, and saturation components remain independent during storage. Subsequent steps modulate the lightness and saturation components independently, while the hue component remains unchanged.

[0108] This step converts the color representation from the RGB color space to the HSV color space, so that different attributes of the color can be adjusted independently, providing independently controllable components for subsequent brightness attenuation and saturation adjustments.

[0109] By converting RGB colors to the HSV color space, color attributes are decoupled, allowing subsequent steps to independently control brightness and saturation without affecting hue. This avoids mutual interference between channels when directly adjusting in RGB space. This multi-dimensional control method makes color modulation more precise and controllable, and can accurately achieve the visual coupling effect of leaf density affecting brightness and crown width affecting saturation.

[0110] In practice, the HSL color space can be used instead of the HSV color space. The HSL space decomposes color into hue, saturation, and brightness. The definition of the brightness component is slightly different from that of the lightness component in HSV, but it can still achieve dimensional control. The YUV color space can also be used instead of the HSV color space. The YUV space decomposes color into a brightness component and two chromaticity components, which is suitable for brightness-related modulation operations. The Lab color space can also be used instead of the HSV color space. The Lab space decomposes color into a brightness component and two contrasting color components, which is more suitable for color adjustments based on visual perception.

[0111] The lightness attenuation coefficient is determined based on the leaf density parameter, and the lightness component is attenuated using the lightness attenuation coefficient to obtain the corrected lightness component; the saturation adjustment coefficient is determined based on the current crown scaling parameter, and the saturation component is adjusted using the saturation adjustment coefficient to obtain the corrected saturation component; the hue component, the corrected lightness component, and the corrected saturation component are synthesized and converted back to RGB values ​​to generate the current leaf color parameters.

[0112] It should be specifically noted that the steps for obtaining the corrected lightness components are as follows:

[0113] Obtain the preset baseline brightness attenuation coefficient;

[0114] The first deviation is calculated based on the leaf density parameter and the preset reference leaf density parameter, and the brightness attenuation offset is determined based on the first deviation.

[0115] The reference lightness attenuation coefficient is superimposed with the lightness attenuation offset to generate the lightness attenuation coefficient, and the lightness component is attenuated using the lightness attenuation coefficient to obtain the corrected lightness component.

[0116] Determine whether the corrected luminance component exceeds the preset luminance threshold range. If it does, perform amplitude limiting and output the amplitude-limited luminance component as the corrected luminance component.

[0117] The brightness attenuation coefficient is generated by superimposing the reference brightness attenuation coefficient and the brightness attenuation offset;

[0118] The baseline brightness attenuation coefficient is stored in the parameter configuration table corresponding to the plant type, with a default value of 1.0, indicating that brightness does not decrease or increase under standard growth conditions.

[0119] The brightness attenuation offset is calculated as follows: the brightness attenuation offset is equal to the product of the first weighting coefficient and the first deviation.

[0120] The first weighting coefficient is obtained from the parameter configuration table based on the plant type identifier, and its value range is preset to 0.3 to 0.8. It is used to control the influence intensity of leaf density deviation on lightness attenuation. The value of the first weighting coefficient is obtained by pre-calibrating the parameters of the plant samples, collecting canopy lightness measurement data of the plants under different leaf density conditions, and determining the weighting coefficient value that minimizes the fitting error through a fitting algorithm.

[0121] The first deviation is calculated by dividing the difference between the leaf density parameter and the reference leaf density parameter by the reference leaf density parameter. The reference leaf density parameter is obtained from the leaf density change curve and corresponds to a preset standard phenological period. The standard phenological period is configured according to the plant type identifier.

[0122] When the leaf density parameter is greater than the reference leaf density parameter, the deviation is positive, indicating that the leaf density is higher than the standard state. The brightness attenuation offset is positive, the brightness attenuation coefficient is greater than the reference value of 1.0, the brightness component is enhanced, and the overall visual brightness of the canopy increases.

[0123] When the leaf density parameter is less than the reference leaf density parameter, the deviation is negative, indicating that the leaf density is lower than the standard state. The brightness attenuation offset is negative, the brightness attenuation coefficient is less than the reference value of 1.0, the brightness component is attenuated, and the overall visual brightness of the canopy decreases.

[0124] After obtaining the corrected brightness components, a limiting process is performed to prevent the corrected brightness components from exceeding the effective range of the color space and to avoid rendering distortion; specifically:

[0125] Obtain the preset brightness threshold range corresponding to the plant type identifier, calculate the deviation of the corrected brightness component from this threshold range, and perform amplitude limiting processing based on the comparison results of the deviation with the preset first deviation threshold and second deviation threshold:

[0126] When the deviation is less than the first deviation threshold, the amplitude limiting process is not performed.

[0127] When the deviation is between the first deviation threshold and the second deviation threshold, soft limiting is performed through a smoothing limiting function to smoothly return the excess part to the threshold range;

[0128] When the deviation exceeds the second deviation threshold, hard limiting is performed, directly restricting the corrected luminance component to the threshold interval boundary value.

[0129] After limiting, the limited luminance component is output as the corrected luminance component for subsequent HSV color synthesis.

[0130] The specific steps for obtaining the corrected saturation component are as follows:

[0131] Obtain the preset baseline saturation adjustment coefficient;

[0132] The second deviation is calculated based on the current crown scaling parameter and the preset reference crown scaling parameter, and the saturation adjustment offset is determined based on the second deviation.

[0133] The saturation adjustment coefficient is generated by superimposing the reference saturation adjustment coefficient and the saturation adjustment offset.

[0134] The saturation component is adjusted using the saturation adjustment coefficient to obtain the corrected saturation component;

[0135] Determine whether the corrected saturation component exceeds the preset saturation threshold range. If it does, perform amplitude limiting and output the amplitude-limited saturation component as the corrected saturation component.

[0136] The saturation adjustment coefficient is generated by superimposing the reference saturation adjustment coefficient and the saturation adjustment offset.

[0137] The preset baseline saturation adjustment coefficient is stored in the parameter configuration table corresponding to the plant type. The default value is 1.0, which means that the saturation will not be adjusted under standard growth conditions. The preset standard phenological period is obtained from the crown scaling curve with reference to the crown scaling parameter. The standard phenological period is configured according to the plant type identifier.

[0138] The saturation adjustment offset is calculated as the product of the second weighting coefficient and the second deviation.

[0139] The second weighting coefficient is obtained from the parameter configuration table based on the plant type identifier, and its value range is preset to 0.2 to 0.6. It is used to control the influence intensity of the crown deviation on the saturation adjustment. The value of the second weighting coefficient is obtained by pre-calibrating the parameters of the plant samples, collecting crown saturation measurement data of the plants under different crown conditions, and determining the weighting coefficient value that minimizes the fitting error through the fitting algorithm.

[0140] The second deviation is calculated as follows: the second deviation is equal to the difference between the current crown scaling parameter and the reference crown scaling parameter divided by the reference crown scaling parameter. The reference crown scaling parameter is obtained from the crown scaling curve and corresponds to a preset standard phenological period. The standard phenological period is configured according to the plant type identifier.

[0141] When the crown scaling parameter is greater than the reference crown scaling parameter, the deviation is positive, indicating that the crown is greater than the standard state. The saturation adjustment offset is positive, the saturation adjustment coefficient is greater than the reference value of 1.0, the saturation component is enhanced, and the crown color is visually more vivid.

[0142] When the crown scaling parameter is less than the reference crown scaling parameter, the deviation is negative, indicating that the crown is less than the standard state. The saturation adjustment offset is negative, the saturation adjustment coefficient is less than the reference value of 1.0, the saturation component is attenuated, and the crown color becomes visually darker.

[0143] After obtaining the corrected saturation components, a limiting process is performed to prevent the corrected saturation components from exceeding the effective range of the color space, thus avoiding saturation overflow that could lead to color anomalies; specifically:

[0144] Obtain the preset saturation threshold range corresponding to the plant type identifier, calculate the deviation of the corrected saturation component from this threshold range, and perform limiting processing based on the comparison results of the deviation with the preset third and fourth deviation thresholds: when the deviation is less than the third deviation threshold, no limiting processing is performed; when the deviation is between the third and fourth deviation thresholds, soft limiting processing is performed through a smoothing limiting function to smoothly return the excess part to the threshold range; when the deviation is greater than the fourth deviation threshold, hard limiting processing is performed to directly limit the corrected saturation component to the threshold range boundary value.

[0145] After the limiting process, the system outputs the limited saturation component as the corrected saturation component for subsequent HSV color synthesis.

[0146] It's important to clarify that brightness modulation and saturation modulation employ the same computational logic because they are mathematically symmetrical. Both use the deviation of the current parameter from the reference parameter as input, control the influence intensity through weighting coefficients, generate modulation coefficients, and then multiply them by the corresponding color components to achieve linear modulation of the color components. This unified computational framework reduces the system's implementation complexity, reduces code redundancy by reusing the same computational modules, and improves system maintainability. It also ensures the consistency of parameter configuration; the modulation coefficients for both brightness and saturation are generated based on the linear superposition of deviations, facilitating the uniform configuration of weighting coefficients for different plant types. Furthermore, if it's necessary to add other geometric parameters to influence the color components in the future, this framework can be directly reused, requiring only the configuration of new input parameters and weighting coefficients.

[0147] After completing the brightness attenuation and saturation adjustment, the system re-synthesizes the original hue component, the corrected brightness component, and the corrected saturation component into an HSV color. The synthesis method is as follows: the hue component retains the original value, the brightness component is replaced with the corrected brightness component, and the saturation component is replaced with the corrected saturation component. The three components together constitute a new HSV color value. The synthesized HSV color value is then converted back to the RGB color space to generate the final current leaf color parameters.

[0148] This step enables independent modulation of brightness by leaf density parameter and independent modulation of saturation by canopy scaling parameter. The two are processed in parallel without interference, allowing the visual representation of leaf color to dynamically respond to changes in canopy geometry.

[0149] By establishing a correlation modulation mechanism between leaf density and brightness, and between canopy width and saturation, and selecting corresponding preset parameters based on plant type identifiers, the visual representation of leaf color can dynamically respond to changes in canopy geometry in a quantifiable manner: when leaf density is high, the overall brightness of the canopy decreases, and when leaf density is low, the overall brightness of the canopy increases; when the canopy width is large, color saturation is enhanced, and when the canopy width is small, color saturation is diminished. This multi-dimensional, independently controlled modulation method solves the problem of the disconnect between color and geometric shape in existing technologies, achieves color representation that conforms to visual norms, and significantly enhances the realism and immersiveness of seasonal change simulation.

[0150] In practice, nonlinear mapping functions can be used instead of linear superposition methods, and the nonlinear relationship between the brightness attenuation coefficient and the deviation can be realized through exponential or logarithmic functions. Alternatively, a lookup table can be used to store the modulation coefficients corresponding to discrete parameter intervals, and continuous values ​​can be calculated through interpolation. Furthermore, the modulation coefficients can be associated with phenological period identifiers, and independent first and second weight coefficients can be stored for different phenological periods in the parameter configuration table.

[0151] The leaf density parameter, the current crown scaling parameter, and the current leaf color parameter are output to the 3D rendering engine.

[0152] The three parameters are encapsulated and output according to the preset data interface format of the 3D rendering engine; the leaf density parameter is output in floating-point form, with a preset value range of 0 to 1, representing the density of leaves within the canopy; the current canopy scaling parameter is output in floating-point form, with a preset value range of 0.5 to 1.5, representing the scaling ratio of the overall size of the plant model relative to the standard size; the current leaf color RGB parameter is output in three-channel floating-point form, including red channel value, green channel value, and blue channel value, with each channel value preset to a range of 0 to 1, and the combination of the three channels represents the final color of the leaf material.

[0153] The 3D rendering engine is pre-configured with a parameter receiving interface associated with the plant model. Through this interface, three parameters are passed to the rendering engine, and the rendering engine updates the rendering status of the plant model based on the received parameter values.

[0154] The leaf density parameter is passed to the leaf density control module of the rendering engine, which adjusts the rendering method of the leaves in the canopy according to the parameter value. When the leaf density parameter value is high, the rendering engine increases the number of leaf instances drawn or decreases the transparency between leaves to make the canopy appear lush; when the leaf density parameter value is low, the rendering engine reduces the number of leaf instances drawn or increases the transparency between leaves to make the canopy appear sparse.

[0155] The current crown scaling parameter is passed to the model transformation module of the rendering engine. This module scales the transformation matrix of the plant model according to the parameter value. The rendering engine obtains the original transformation matrix of the plant model, uses the current crown scaling parameter as a scaling factor, and applies it to the scaling components of the model in the X, Y, and Z axes respectively to generate a new transformation matrix, which is then applied to the model rendering.

[0156] The current leaf color RGB parameters are passed to the material management module of the rendering engine, which updates the color attributes of the leaf material based on the parameter values. The rendering engine obtains the color attribute parameters of the leaf material and assigns the current leaf color RGB parameters to the diffuse color channel, specular color channel, or emissive color channel of the material, so that the leaf presents the corresponding color visual effect under the scene lighting conditions.

[0157] The system transmits parameters to the 3D rendering engine through a preset output frequency. The output frequency is synchronized with the rendering frame rate of the 3D rendering engine to ensure that the latest parameter values ​​are used in each frame of rendering. When the target date or geographical coordinates change, the system calculates the updated parameter values ​​and outputs them to the rendering engine in the next rendering frame to achieve a continuous transition of seasonal changes.

[0158] Before outputting the parameters, the system performs a verification step to determine whether all three parameters are within the preset valid value range. If any parameter exceeds the preset valid value range, the system limits the parameter to the boundary value of the valid value range and outputs the limited parameter to the rendering engine. The preset values ​​of the valid value range are as follows: the valid value range of the leaf density parameter is 0 to 1, the valid value range of the current crown scaling parameter is 0.5 to 1.5, and the valid value range of each channel of the current leaf color RGB parameter is 0 to 1.

[0159] This step passes the three calculated core parameters to the 3D rendering engine, driving the real-time rendering of the 3D scene and achieving a complete mapping from phenological parameters to 3D visual effects.

[0160] By using a standardized parameter output interface, the phenological parameter calculation module and the 3D rendering engine are decoupled, allowing parameter calculation and scene rendering to be developed and optimized independently. At the same time, the real-time and continuous nature of the parameter output ensures a smooth transition in the simulation of seasonal changes, avoiding jumps or flickering during scene transitions.

[0161] In practice, a data-driven event notification mechanism can be used to trigger rendering updates only when parameter changes exceed a preset threshold, reducing the rendering computation load. Parameter interpolation can also be used to linearly interpolate parameters between adjacent frames, making seasonal transitions smoother. Batch output can be used to package the parameters of multiple plants and send them to the rendering engine at once, reducing the number of interface calls and improving rendering efficiency. Alternatively, the rendering engine's built-in material parameter animation system can be used to preset parameter change curves into the material animation track, allowing the rendering engine to drive parameter changes automatically.

[0162] like Figure 2 As shown, this invention provides a three-dimensional scene seasonal change simulation system for garden landscapes, specifically including:

[0163] Phenological parameter database construction module: used to construct a phenological parameter database for the target landscape, storing the phenological parameter curve set for each plant species;

[0164] Phenological Period Identification Module: Used to receive the target date, obtain the meteorological data of the target landscape on the target date, and determine the phenological period identifier of the current plant based on the meteorological data;

[0165] Initial parameter acquisition module: used to acquire leaf density parameters, initial crown width scaling parameters, and initial leaf color parameters from the phenological parameter curve set based on the phenological period identifier;

[0166] Crown width scaling parameter determination module: used to input the leaf density parameter into a preset correction function, calculate the crown width correction coefficient, and correct the initial crown width scaling parameter according to the crown width correction coefficient to obtain the current crown width scaling parameter;

[0167] HSV component acquisition module: used to convert the initial leaf color parameters from RGB values ​​to HSV color space to obtain hue component, lightness component and saturation component;

[0168] Leaf color parameter determination module: used to determine the lightness attenuation coefficient based on the leaf density parameter, and use the lightness attenuation coefficient to attenuate the lightness component to obtain the corrected lightness component; to determine the saturation adjustment coefficient based on the current crown scaling parameter, and use the saturation adjustment coefficient to adjust the saturation component to obtain the corrected saturation component; to synthesize the hue component, the corrected lightness component, and the corrected saturation component, and convert them back to RGB values ​​to generate the current leaf color parameters;

[0169] Current parameter output module: Outputs the leaf density parameter, the current crown scaling parameter, and the current leaf color parameter to the 3D rendering engine.

[0170] The technical scope of this invention is not limited to the content described above. Those skilled in the art can make various modifications and variations to the above embodiments without departing from the technical concept of this invention, and all such modifications and variations should fall within the protection scope of this invention.

Claims

1. A method for simulating seasonal changes in three-dimensional garden landscape scenes, characterized in that, Specifically: Construct a phenological parameter database for the target landscape, storing a set of phenological parameter curves for each plant species. Receive the target date, obtain the meteorological data of the target landscape on the target date, and determine the phenological stage of the current plants based on the meteorological data; Based on the phenological period identifier, leaf density parameters, initial crown width scaling parameters, and initial leaf color parameters are obtained from the phenological parameter curve set. The leaf density parameter is input into a preset correction function to calculate the crown width correction coefficient. The initial crown width scaling parameter is then corrected based on the crown width correction coefficient to obtain the current crown width scaling parameter. The initial leaf color parameters are converted from RGB values ​​to HSV color space to obtain hue components, lightness components, and saturation components. A lightness attenuation coefficient is determined based on the leaf density parameter, and the lightness component is attenuated using the lightness attenuation coefficient to obtain a corrected lightness component; a saturation adjustment coefficient is determined based on the current crown scaling parameter, and the saturation component is adjusted using the saturation adjustment coefficient to obtain a corrected saturation component. The hue component, the corrected lightness component, and the corrected saturation component are combined and converted back to RGB values ​​to generate the current leaf color parameters. The leaf density parameter, the current crown scaling parameter, and the current leaf color parameter are output to the 3D rendering engine.

2. The method for simulating seasonal changes in a three-dimensional landscape scene according to claim 1, characterized in that: The set of phenological parameter curves includes: Leaf density change curve, crown width scaling curve, and leaf color gradient curve; Each curve uses phenological stage indicators as the horizontal axis and parameter values ​​as the vertical axis, covering the complete growth cycle of plants from leaf unfolding to leaf fall. The leaf density change curve records the leaf density parameter values ​​of the plant at different phenological stages, the crown scaling curve records the crown scaling parameters of the plant at different phenological stages, and the leaf color gradient curve records the RGB values ​​of the leaf color of the plant at different phenological stages. The RGB values ​​of the leaf color consist of three components: red channel value, green channel value, and blue channel value.

3. The method for simulating seasonal changes in a three-dimensional landscape scene according to claim 1, characterized in that: The specific method for determining the phenological period markers is as follows: The phenological response model parameters of the plant are obtained from the phenological parameter database based on the plant species identifier; The acquired meteorological data is compared with the thresholds in the phenological response model; The current phenological stage is determined based on the comparison results; The phenological response model is based on each plant species pre-stored in a phenological parameter database and associated with plant species identifiers; Phenological stages are represented in either discrete or continuous form. The discrete form uses pre-defined phenological stage identifiers, with each identifier corresponding to one phenological stage. The continuous form uses numerical values ​​between 0 and 1 to represent the phenological process.

4. The method for simulating seasonal changes in a three-dimensional landscape scene according to claim 1, characterized in that: The specific form of the correction function is selected and configured according to the plant type, specifically as follows: When the plant type is identified as a fast-growing tree type, a linear correction function is selected. In this function, the crown width correction coefficient is linearly related to the leaf density parameter, and the preset linear coefficient range is 0.7 to 1.

0. When the plant type is identified as a slow-growing shrub, a linear correction function is selected. In this function, the crown width correction coefficient is linearly related to the leaf density parameter, and the preset linear coefficient range is 0.2 to 0.

4. When the plant type is identified as a coniferous species, the exponential correction function is selected. In this function, the crown width correction coefficient is exponentially related to the leaf density parameter, and the preset base parameter range is 2.0 to 3.

0. When the plant type is identified as a flowering shrub, a segmented correction function is selected. This function divides the plant into a first segment and a second segment based on the phenological period identifier, and each segment is assigned an independent coefficient.

5. The method for simulating seasonal changes in a three-dimensional landscape scene according to claim 1, characterized in that: The conversion process of the initial leaf color parameters from RGB values ​​to HSV color space includes the following steps: B1. Find the maximum and minimum values ​​from the red channel values, green channel values, and blue channel values, and assign the maximum value to the maximum value variable and the minimum value to the minimum value variable. Calculate the difference between the maximum and minimum values ​​and assign it to the difference variable. B2. Calculate the lightness and saturation components; The value component is calculated as follows: the value component equals the maximum value variable; The saturation component is calculated as follows: when the maximum value variable is equal to 0, the saturation component is preset to 0; when the maximum value variable is not equal to 0, the saturation component is equal to the difference variable divided by the maximum value variable. B3. Calculate the hue components based on the color channel corresponding to the maximum value variable; If the calculated hue component is less than 0, then add 360 to the hue component so that the hue component value falls between 0 and 360.

6. The method for simulating seasonal changes in a three-dimensional landscape scene according to claim 1, characterized in that: The specific steps for obtaining the corrected lightness component are as follows: Obtain the preset baseline brightness attenuation coefficient; The first deviation is calculated based on the leaf density parameter and the preset reference leaf density parameter, and the brightness attenuation offset is determined based on the first deviation. The reference lightness attenuation coefficient is superimposed with the lightness attenuation offset to generate the lightness attenuation coefficient, and the lightness component is attenuated using the lightness attenuation coefficient to obtain the corrected lightness component. Determine whether the corrected luminance component exceeds the preset luminance threshold range. If it does, perform a limiting process and output the limited luminance component as the corrected luminance component. The baseline brightness attenuation coefficient is stored in the parameter configuration table corresponding to the plant type; The first deviation is calculated by dividing the difference between the leaf density parameter and the reference leaf density parameter by the reference leaf density parameter, which is obtained from the leaf density change curve and corresponds to a preset standard phenological period.

7. The method for simulating seasonal changes in a three-dimensional landscape scene according to claim 6, characterized in that: Determine whether the corrected luminance component exceeds a preset luminance threshold range. If it does, perform amplitude limiting processing, specifically: Obtain the preset brightness threshold range corresponding to the plant type identifier, calculate the deviation of the corrected brightness component from this threshold range, and perform amplitude limiting processing based on the comparison results of the deviation with the preset first deviation threshold and second deviation threshold: When the deviation is less than the first deviation threshold, the amplitude limiting process is not performed. When the deviation is between the first deviation threshold and the second deviation threshold, soft limiting is performed through a smoothing limiting function to smoothly return the excess part to the threshold range; When the deviation exceeds the second deviation threshold, hard limiting is performed, directly restricting the corrected luminance component to the threshold interval boundary value.

8. The method for simulating seasonal changes in a three-dimensional landscape scene according to claim 1, characterized in that: The specific steps for obtaining the corrected saturation component are as follows: Obtain the preset baseline saturation adjustment coefficient; The second deviation is calculated based on the current crown scaling parameter and the preset reference crown scaling parameter, and the saturation adjustment offset is determined based on the second deviation. The saturation adjustment coefficient is generated by superimposing the reference saturation adjustment coefficient and the saturation adjustment offset. The saturation component is adjusted using the saturation adjustment coefficient to obtain the corrected saturation component; Determine whether the corrected saturation component exceeds the preset saturation threshold range. If it does, perform a limiting process and output the limited saturation component as the corrected saturation component. The preset baseline saturation adjustment coefficient is stored in the parameter configuration table corresponding to the plant type; The second deviation is calculated as follows: the difference between the current crown scaling parameter and the reference crown scaling parameter is divided by the reference crown scaling parameter, which is obtained from the crown scaling curve and corresponds to a preset standard phenological period.

9. The method for simulating seasonal changes in a three-dimensional landscape scene according to claim 8, characterized in that: Determine whether the corrected saturation component exceeds a preset saturation threshold range. If it does, perform amplitude limiting processing, specifically: Obtain the preset saturation threshold range corresponding to the plant type identifier, calculate the deviation of the corrected saturation component from this threshold range, and perform amplitude limiting processing based on the comparison results of the deviation with the preset third and fourth deviation thresholds: When the deviation is less than the third deviation threshold, the amplitude limiting process is not performed. When the deviation is between the third and fourth deviation thresholds, soft limiting is performed through a smoothing limiting function to smoothly return the excess portion to the threshold range. When the deviation exceeds the fourth deviation threshold, hard limiting is performed, directly restricting the corrected saturation component to the threshold interval boundary value.

10. A three-dimensional scene seasonal change simulation system for garden landscapes, characterized in that, Specifically, it includes: Phenological parameter database construction module: used to construct a phenological parameter database for the target landscape, storing the phenological parameter curve set for each plant species; Phenological Period Identification Module: Used to receive the target date, obtain the meteorological data of the target landscape on the target date, and determine the phenological period identifier of the current plant based on the meteorological data; Initial parameter acquisition module: used to acquire leaf density parameters, initial crown width scaling parameters, and initial leaf color parameters from the phenological parameter curve set based on the phenological period identifier; Crown width scaling parameter determination module: used to input the leaf density parameter into a preset correction function, calculate the crown width correction coefficient, and correct the initial crown width scaling parameter according to the crown width correction coefficient to obtain the current crown width scaling parameter; HSV component acquisition module: used to convert the initial leaf color parameters from RGB values ​​to HSV color space to obtain hue component, lightness component and saturation component; Leaf color parameter determination module: used to determine the lightness attenuation coefficient based on the leaf density parameter, and use the lightness attenuation coefficient to attenuate the lightness component to obtain the corrected lightness component; and to determine the saturation adjustment coefficient based on the current crown scaling parameter, and use the saturation adjustment coefficient to adjust the saturation component to obtain the corrected saturation component. The hue component, the corrected lightness component, and the corrected saturation component are combined and converted back to RGB values ​​to generate the current leaf color parameters. Current parameter output module: Outputs the leaf density parameter, the current crown scaling parameter, and the current leaf color parameter to the 3D rendering engine.

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