Vegetation greening layout optimization method

Abstract

The invention provides a vegetation greening layout optimization method, and relates to the technical field of environmental engineering and garden design, and the method comprises the steps: carrying out the homologous retrieval through taking a target region plant growth influence factor as a constraint, and obtaining a plant type and a planting density; calculating a high-frequency planting density threshold value of the plant type; inputting the plant types into a type-attribute comparison table for matching to obtain time zone attributes, determining a plant combination and performing adaptive screening; carrying out vegetation layout design based on the adaptive plant combination and the high-frequency planting density threshold to generate an initial layout scheme; constructing a simulated growth environment to perform growth prediction and simulated viewing on the layout scheme; and performing evaluation and feasibility evaluation on the simulated landscape according to the landscape evaluation function, and determining an optimal layout scheme, thereby solving the technical problems that the vegetation configuration cannot be accurately performed and the vegetation growth effect and the landscape aesthetic property cannot be effectively predicted and evaluated due to lack of systematic analysis on the plant growth. The technical effect of improving the accuracy and predictability of the plant layout is achieved.

Description

Technical Field

[0001] The invention relates to the technical field of environmental engineering and garden design, and in particular to a method for optimizing the layout of vegetation greening. Background Art

[0002] With the acceleration of urbanization and the emphasis on ecological environmental protection, urban greening layout plays a vital role in improving the beauty of cities and improving the ecological environment. However, with the expansion of urban scale and the complexity of urban structure, the design and management of greening layout are also facing increasing challenges. Traditional greening layout methods often rely on the experience and intuitive judgment of gardeners, which may lead to uneven greening effects and increased maintenance costs in the later stage of plant growth to a certain extent. Traditional greening methods often ignore the specific needs and environmental adaptability of plant growth in plant selection and layout design, and fail to fully consider soil conditions, climate characteristics and terrain effects, which have a significant impact on plant growth and development and landscape effects. In addition, these methods are usually unable to effectively predict the dynamic changes of plant growth, resulting in the inability to adjust and optimize vegetation configuration in real time, affecting the quality and durability of greening.

[0003] In summary, the existing vegetation greening layout methods lack systematic analysis and real-time monitoring of plant growth, resulting in the inability to accurately configure vegetation, and the inability to effectively predict and evaluate the growth effect and landscape aesthetics after vegetation layout, which in turn causes technical problems such as loss of ecological benefits and economic efficiency, and poor greening quality and durability. Summary of the invention

[0004] The present application provides a method for optimizing the layout of vegetation and greening, which is used to solve the technical problems that the existing vegetation and greening layout methods lack systematic analysis and real-time monitoring of plant growth, resulting in the inability to accurately configure vegetation, and the inability to effectively predict and evaluate the growth effect and landscape aesthetics after vegetation layout, thereby causing losses in ecological benefits and economic efficiency, and poor greening quality and durability.

[0005] The present application provides a method for optimizing the layout of vegetation greening, the method comprising:

[0006] Collect plant growth influencing factors in the target area, use the plant growth influencing factors as constraints, perform homology retrieval based on garden big data, and obtain a sample plant information set, wherein the sample plant information includes plant type and planting density; determine multiple plant types based on the sample plant information set, and calculate multiple high-frequency planting density thresholds for the multiple plant types; input the multiple plant types into a type-attribute comparison table, match and obtain multiple time zone attributes, and use the expected plant diversity as a constraint to enumerate based on the multiple time zone attributes to determine multiple plant combinations, perform adaptation screening on the multiple plant combinations, and determine multiple adapted plant combinations; based on the multiple adapted plant combinations and the multiple The high-frequency planting density threshold is used to design vegetation layout and generate multiple initial layout schemes; the ecological simulation software is used to construct a simulated growth environment according to the plant growth influencing factors, and the growth of the multiple initial layout schemes after a predetermined period is predicted through the simulated growth environment to generate multiple growth prediction scenarios; the multiple growth prediction scenarios are simulated for viewing based on a predetermined viewing strategy to obtain multiple simulated landscape image sets, and the multiple simulated landscape image sets are evaluated according to the landscape evaluation function to determine multiple optimized layout schemes; the construction feasibility of the multiple optimized layout schemes is evaluated respectively, and the optimal layout scheme is determined based on the evaluation results, and the vegetation layout of the target area is executed.

[0007] One or more technical solutions provided in this application have at least the following technical effects or advantages:

[0008] The vegetation greening layout optimization method provided by the present application collects plant growth influencing factors in the target area, takes the plant growth influencing factors as constraints, performs homology retrieval based on garden big data, and obtains a sample plant information set, wherein the sample plant information includes plant type and planting density; determines multiple plant types based on the sample plant information set, and calculates multiple high-frequency planting density thresholds for the multiple plant types; inputs the multiple plant types into a type-attribute comparison table, matches multiple time zone attributes, and enumerates based on the multiple time zone attributes to meet the expected plant diversity as a constraint, determines multiple plant combinations, performs adaptation screening on the multiple plant combinations, and determines multiple adapted plant combinations; performs vegetation layout design based on the multiple adapted plant combinations and the multiple high-frequency planting density thresholds, and generates multiple initial layout plans; uses ecological simulation software to construct a simulated growth environment according to the plant growth influencing factors, and respectively screens the multiple plant combinations through the simulated growth environment; The method comprises the following steps: performing growth prediction after a predetermined period for each initial layout scheme, generating a plurality of growth prediction scenes; performing simulated viewing of the plurality of growth prediction scenes based on a predetermined viewing strategy, obtaining a plurality of simulated landscape image sets, and evaluating the plurality of simulated landscape image sets according to a landscape evaluation function, and determining a plurality of optimized layout schemes; performing construction feasibility evaluation on the plurality of optimized layout schemes, determining the optimal layout scheme based on the evaluation results, and executing vegetation layout in the target area, thereby solving the technical problems that the existing vegetation greening layout methods lack systematic analysis and real-time monitoring of plant growth, resulting in the inability to accurately configure vegetation, and the inability to effectively predict and evaluate the growth effect and landscape aesthetics after vegetation layout, thereby causing the loss of ecological benefits and economic efficiency, and poor greening quality and durability, and achieving the technical effect of improving the accuracy and predictability of plant layout, thereby ensuring the maximization of the aesthetics, ecological benefits and economic efficiency of vegetation layout, and ensuring the quality and durability of greening. BRIEF DESCRIPTION OF THE DRAWINGS

[0009] Figure 1 A flow chart of a vegetation greening layout optimization method is provided for this application.

[0010] Figure 2 A flow chart of garden big data mining in the vegetation greening layout optimization method is provided for this application. DETAILED DESCRIPTION

[0011] In order to make the purpose, technical solution and advantages of the present application more clearly understood, the present application is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0012] Examples, such as Figure 1 As shown, the present application provides a method for optimizing vegetation greening layout, the method comprising:

[0013] Step S100: collecting plant growth influencing factors in the target area, taking the plant growth influencing factors as constraints, performing homologous search based on garden big data, and obtaining a sample plant information set, wherein the sample plant information includes plant type and planting density.

[0014] Specifically, the factors affecting plant growth in the target area are first collected. These factors mainly include but are not limited to soil type, soil fertility, climate conditions (such as temperature and humidity), light conditions, and terrain. These parameters are crucial to determining which plants can grow best in the area. For example, if an area has sufficient sunlight but poor soil drainage, it is suitable for planting light-loving and moisture-resistant plants. The target area refers to an area where vegetation greening is about to be laid out, such as a real estate park, park greening, and traffic green belt.

[0015] After collecting the growth influencing factors, these factors are used as constraints to cooperate with the garden big data platform for homologous retrieval. Garden big data refers to a database that collects a large amount of information about plant growth data, plant species information, historical vegetation layout cases, etc. Homologous retrieval refers to searching and identifying other data sets with the same or similar environmental characteristics based on known data of a specific environment or condition when performing data query. Homologous retrieval can use the garden big data platform to search for plant species and planting density information under similar conditions by comparing key growth influencing factors such as soil type, climate conditions, and light conditions. This information reflects the adaptability and growth performance of plants in similar environments and can provide valuable reference and guidance for new vegetation layout. In this way, sample plant information sets with similar growth conditions to the target area can be retrieved. This information set records in detail the types of various plants and their planting densities under similar conditions. The planting density refers to the number of plants planted in a certain area, which is usually used to describe the distance between plants and the density of plant distribution. The calculation expression is the number of plants per unit area. Planting density is an important agricultural and horticultural parameter because it directly affects plant growth, health, yield and the final landscape effect. For example, if the climate of the target area is temperate and the soil is loam, then the types of plants that grow successfully in temperate loam areas, such as oaks, pines, etc., and the planting densities at which they grow successfully will be retrieved from the big data. Of course, these data are not only based on historical vegetation layout cases, but may also include information from experimental data or field tests to ensure the accuracy and practicality of the retrieval results.

[0016] By conducting homologous searches based on factors affecting plant growth and preliminarily determining plant species and planting density, an effective reference is provided for subsequent layout decisions, which not only improves product selection efficiency and decision-making speed, but also ensures the predictability and accuracy of the layout.

[0017] Step S200: determining a plurality of plant types based on the sample plant information set, and calculating a plurality of high-frequency planting density thresholds of the plurality of plant types.

[0018] Next, the performance and adaptability of various types of plants in similar growth environments are analyzed based on the sample plant information set. The selection of plant types is based not only on their viability, but also on their aesthetic value and ecological functions, such as providing shade, air purification, or enhancing biodiversity. For example, if the target area is located in an urban park, tree species and shrubs with high ornamental value and low maintenance requirements, such as cherry trees and crape myrtles, may be preferred. Subsequently, for each plant type, its high-frequency planting density threshold is calculated. This calculation is based on the distribution of planting density of the corresponding plant type in the collected data, and the planting density interval with the highest frequency is counted. The planting density threshold refers to the optimal plant spacing that can achieve the expected landscape effect while ensuring the healthy growth of plants. For example, for lawn plants, the planting density may be calculated as how many grass seeds need to be planted per square meter to achieve uniform coverage without being too dense. For urban greening, taking cherry trees as an example, assuming that in a similar urban park environment, historical data shows that the planting density of cherry trees is usually 3-5 trees per 100 square meters. This data reflects the best practices of planting density while ensuring the healthy growth of trees and good visual effects of blooming flowers. In this way, a standard range of planting density can be developed for each plant type, thus guiding the execution of the actual vegetation layout.

[0019] Through the above steps, the appropriate plant types and their planting density can be scientifically determined, which greatly improves the practicality and feasibility of vegetation greening layout and ensures that the final greening effect is both beautiful and eco-friendly.

[0020] Step S300: Input the multiple plant types into a type-attribute comparison table, match multiple time zone attributes, and use expected plant diversity as a constraint to enumerate and determine multiple plant combinations based on the multiple time zone attributes. Perform adaptation screening on the multiple plant combinations to determine multiple adapted plant combinations.

[0021] Optionally, multiple plant types are classified according to their seasonal characteristics to ensure that the expected greening effect can be displayed throughout the year in the designed vegetation layout and meet the requirements of ecological diversity. Specifically, each plant is first classified into its best display season, that is, time zone attributes, including spring, summer, autumn, winter and all seasons according to its growth habits and seasonal changes. In this step, the season of the plant (that is, the time zone attribute) is entered into a type-attribute comparison table. For example, cherry blossoms mainly bloom in spring, so they are classified in the spring plant category; while some evergreen tree species such as pine trees are classified as all-season plants because of their evergreen characteristics throughout the year.

[0022] Subsequently, based on the principle of plant diversity as a constraint, a diverse plant display is ensured in each season, which means that in any given season, the park or green area can present a rich and colorful vegetation state, thereby enhancing the attractiveness and ecological value of the landscape. This constraint requires that at least a predetermined number of plant species be included in each season to ensure seasonal visual and ecological diversity. Next, the plant species are enumerated based on the classification and diversity constraints, and multiple plant combination cases are generated by randomly combining plant types in different seasons. For example, cherry blossoms, tulips and winter jasmine may be combined in spring; sunflowers, crape myrtles and lavender may be selected in summer. Further, each generated plant combination then needs to go through an adaptation screening process, which takes into account the compatibility between plants (such as root competition, light requirements, etc.), maintenance requirements and the harmony of the overall landscape design. Adaptation screening can determine which plant combinations can coexist well in the actual environment and achieve the greening effect originally designed.

[0023] Through the above steps, the seasonal diversity and aesthetics of vegetation layout are effectively achieved, while also meeting the needs of ecological diversity and sustainable development. This plant combination screening method based on seasonal attributes and diversity constraints provides a scientific and practical solution for modern urban greening.

[0024] Step S400: Designing vegetation layout based on the multiple adaptive plant combinations and the multiple high-frequency planting density thresholds to generate multiple initial layout schemes.

[0025] Furthermore, detailed information on topography, soil type, light conditions, etc. of the area to be designed is obtained based on the geographic information system and on-site survey data. This information is essential to ensure the suitability of plant species and planting density. For each plant combination, the corresponding high-frequency planting density threshold is applied for layout planning. At the same time, the growth habits, mature size and seasonal changes of plants, as well as their needs for light and water, are considered in the layout design. Layout personnel need to ensure that there is enough space between plants to support their healthy growth, while also considering aesthetics and visual coherence. Through software tools or manual calculations, plants are configured in the layout in the most aesthetic and functional way. Finally, all data and parameters are combined to generate multiple initial plans for vegetation layout, each of which should show in detail the planting location, species, and expected growth and development status of the plants. After the initial plan is completed, it can also be reviewed internally to consider the feasibility, cost-effectiveness and expected ecological benefits of its implementation. If necessary, adjustments can be made to better adapt to geographical and ecological conditions, or to better meet the needs of customers and the public.

[0026] Through the above steps, a systematic approach is provided to design and implement vegetation layout, ensuring the healthy growth of plants and the overall beauty of the landscape design, while also considering the maximization of ecological and economic benefits.

[0027] Step S500: using ecological simulation software to construct a simulated growth environment according to the plant growth influencing factors, and using the simulated growth environment to perform growth predictions on the multiple initial layout schemes after a predetermined period to generate multiple growth prediction scenarios.

[0028] Exemplary, ecological simulation software is a computer program or system specially designed to simulate and analyze the interaction and dynamic changes of various factors in an ecosystem. This type of software uses multidisciplinary knowledge such as ecology, botany, meteorology, and soil science, combined with mathematical models and algorithms, to predict the behavior and development trend of ecosystems under specific environmental conditions. Therefore, the plant growth simulation of the layout scheme is performed using ecological simulation software. First, all growth influencing factor data related to the target area are input, including but not limited to soil pH, nutrients, average annual rainfall, temperature range, light intensity, etc. These parameters are crucial for accurately simulating the plant growth environment. Appropriate simulation parameters are set in the software, such as the length of the growth cycle (e.g., 3 years, 5 years, or 10 years), simulated seasonal changes, etc. These parameters will help the software to more accurately predict the growth and maturation process of the plant.

[0029] Next, the ecological simulation software simulates the development status of each plant in the set growth cycle based on the input environmental data and plant characteristics, including growth indicators such as plant height, crown width, leaf color change, etc. Finally, the software will generate detailed growth prediction scenarios for all plant species, which visualize the growth of plants at various time points in the future, such as the coverage of trees and the density of shrubs.

[0030] Through the above steps, advanced ecological simulation technology can be effectively used to predict and optimize greening projects to ensure the scientific nature and long-term sustainability of vegetation layout.

[0031] Step S600: simulating viewing of the plurality of growth prediction scenes based on a predetermined viewing strategy, obtaining a plurality of simulated landscape image sets, and respectively evaluating the plurality of simulated landscape image sets according to a landscape evaluation function to determine a plurality of optimized layout schemes.

[0032] Optionally, determine a predetermined viewing strategy. Set a series of viewing points based on the geographic characteristics of the target area and the public access path, each of which is equipped with multiple viewing angles to cover the viewing angles of vegetation from different directions and heights. For example, viewing points may be set at the entrance of a park, a rest area, or around a water body, and the viewing angles include horizontal, downward, and upward views. Then, use ecological simulation software and three-dimensional visualization tools to generate vegetation layout views from each viewing point and angle based on the above viewing strategy. These views reflect the growth status of plants in different seasons and different time periods, as well as their visual effects in the landscape. In this process, the views of each viewing point and angle are captured and saved as images to form a set of simulated landscape images, which will be used for subsequent evaluation and analysis to determine which vegetation layout schemes are most visually attractive and meet the design goals.

[0033] Next, each simulated landscape image set was evaluated using a landscape evaluation function, which includes multiple evaluation indicators, such as the harmony of plant colors, structural diversity, visual impact, and integration with the surrounding environment. Each indicator has a corresponding weight, which is adjusted according to its importance in the overall aesthetics and functionality. Finally, the advantages and disadvantages of each layout scheme are analyzed according to the results of the landscape evaluation function. The high-scoring layout scheme indicates that its visual effects and functions have achieved the expected results, while the low-scoring ones may need to adjust the type, location, or density of plants. For example, in a park design project in the city center, five main viewing points were set, and three viewing angles were set for each point. Landscape image sets in spring and autumn were generated using simulation software and evaluated by applying the landscape evaluation function. The results showed that the cherry blossom layout in the entrance area scored higher in spring, but in autumn, due to the yellowing of cherry blossom leaves, the contrast with evergreen plants was not enough, and the visual effect was poor. Therefore, the planting position and number of evergreen plants were adjusted to enhance the visual effect in autumn. The above steps not only optimized the visual aesthetics of the vegetation layout scheme, but also improved its ability to adapt to changes in different seasons, ensuring continuous landscape attractiveness throughout the year.

[0034] Step S700: Conduct construction feasibility assessments on the multiple optimized layout schemes respectively, determine the optimal layout scheme based on the assessment results, and execute vegetation layout in the target area.

[0035] Specifically, a construction feasibility assessment is conducted for each optimized layout scheme to ensure that the selected scheme is not only visually and ecologically optimal, but also technically and economically feasible. This process involves multiple evaluation levels, including cost estimation, construction technical requirements, long-term maintenance, and environmental impact assessment. Based on these evaluation results, the optimal vegetation layout scheme is determined and vegetation layout is implemented in the target area.

[0036] In this embodiment, the construction feasibility assessment includes a technical feasibility assessment, an economic feasibility assessment, and an environmental feasibility assessment. Among them, the technical feasibility assessment is used to assess the terrain adaptability and construction difficulty, specifically to assess whether the terrain is suitable for the growth of the selected plants. For example, some plants may require a good drainage system, which may not be provided by some terrains; at the same time, the construction technology and equipment required for the planting and landscape construction of plants are considered. For example, large trees may require special lifting equipment for implantation. The economic feasibility assessment is used to make cost estimates, and the cost estimates of the required materials, manpower and equipment are made, including all related costs such as plant purchase, soil improvement, and irrigation system installation. The environmental feasibility assessment is used to consider ecological impact and sustainability, assess the impact of the selected plants on the local ecosystem, ensure that the scheme will not have a negative impact on the local ecology, and consider the long-term maintenance needs of the plants and their sustainability, for example, select drought-resistant plants to reduce water consumption. Finally, the technical, economic and environmental assessment results of all schemes are summarized and compared. The expert team and project managers discuss the advantages and disadvantages of each scheme based on the evaluation results, and select the scheme that performs best in all evaluation criteria. This is usually a scheme that balances various factors, such as cost-effectiveness, construction difficulty, long-term maintenance needs and environmental impact. The vegetation layout is completed by arranging vegetation on site according to the determined optimal plan. Through the above steps, not only the feasibility and economy of the vegetation layout plan are ensured, but also the environmental sustainability and ecological impact are taken into consideration, thus achieving a fully optimized greening solution.

[0037] Furthermore, to obtain the sample plant information set, step S100 of the present application further includes:

[0038] Step S110: collecting plant growth influencing factors in the target area, wherein the plant growth influencing factors include soil information, climate information and landform information.

[0039] Step S120: extracting features from the soil information, climate information and landform information, and constructing a soil feature matrix, a climate feature matrix and a landform feature matrix.

[0040] Step S130: taking the soil characteristic matrix, the climate characteristic matrix and the landform characteristic matrix as constraints, data mining is performed based on garden big data to obtain the sample plant information set.

[0041] Optionally, define key factors affecting plant growth in the target area, including soil information, climate information and geomorphic information, which can be collected through field measurements, historical climate data analysis and geological surveys. For example, soil samples can be analyzed to determine their pH value, organic matter content, texture, etc.; climate information includes annual average temperature, precipitation, sunshine hours, etc.; geomorphic information focuses on the slope, altitude and orientation of the terrain.

[0042] Furthermore, the collected soil, climate and landform data are feature extracted, and statistical and geographic information system software is used to identify and quantify key variables that affect plant growth. For example, the features extracted from the soil sample data may include the concentration of nutrient elements, drainage capacity and soil type. Then, the extracted features are used to construct soil feature matrices, climate feature matrices and landform feature matrices, which will be used as inputs for subsequent data mining. Each matrix contains data of multiple dimensions and describes the characteristics of each environmental factor in detail. The constructed feature matrix is ​​input into the garden big data analysis system, and data mining technology is used to identify plant species suitable for specific environmental conditions. This process can use machine learning algorithms to predict the growth performance and survival rate of different plants under specific environmental conditions. Finally, a sample plant information set containing recommended plant species and their planting density is formed based on the data mining results. For example, if data mining shows that a certain plant performs well under specific soil and climate conditions, this plant and its suitable planting density will be included in the information set. For example, in an urban park project, data mining results showed that cherry trees and crape myrtles grew best in soils with high organic matter content and moderate drainage capacity. This information will be used to guide the layout design of these plants in the park to ensure that they can thrive in the most suitable environment.

[0043] Through the above steps, scientific, data-driven plant selection and layout recommendations can be effectively provided for the vegetation layout of a specific area, thereby maximizing the ecological benefits and aesthetics of the vegetation.

[0044] Furthermore, if Figure 2 As shown, data mining is performed based on garden big data, and step S130 of this application also includes:

[0045] Step S131: performing data mining based on garden big data to obtain first search data, wherein the first search data includes a first soil feature, a first climate feature, and a first landform feature.

[0046] Step S132: performing a similarity analysis on the soil feature matrix and the first soil feature according to a predetermined similarity comparison algorithm to determine a first similarity.

[0047] Step S133: If the first similarity is greater than a predetermined threshold, a similarity analysis is performed on the climate characteristic matrix and the first climate characteristic to determine a second similarity.

[0048] Step S134: If the second similarity is greater than a predetermined threshold, a similarity analysis is performed on the geomorphic feature matrix and the first geomorphic feature to determine a third similarity.

[0049] Step S135: If the third similarity is greater than a predetermined threshold, the first search data is added to the sample plant information set.

[0050] Furthermore, a predetermined similarity analysis algorithm is used to perform multi-stage data comparison and screening to ensure that the plant species finally selected are most suitable for the designated area. Specifically, the garden big data resources are first used for preliminary data mining to obtain the first retrieval data, including retrieving the first soil characteristics, the first climate characteristics and the first geomorphic characteristics from a large amount of stored environmental and plant growth data. These characteristics are extracted from environmental conditions known to have a positive impact on plant growth, reflecting a set of ecological environments that may be similar to the target area.

[0051] The soil features in the retrieved first search data are analyzed, and these features are compared with the soil feature matrix of the target area using a predetermined similarity comparison algorithm to determine a first similarity. The comparison algorithm is set based on a series of quantitative indicators, such as soil pH, organic matter content, texture, etc., to calculate the similarity score between the two. If the first similarity is higher than the set threshold, it means that the first soil feature has a high similarity with the soil of the target area. Next, the climate feature will be subjected to the same similarity analysis to calculate the second similarity between the first climate feature and the climate feature matrix of the target area. The climate features may include annual average temperature, precipitation, sunshine hours, etc. The calculation of the second similarity is also based on a predetermined algorithm to evaluate the degree of match between the two. If the second similarity also exceeds the predetermined threshold, the geomorphic features are analyzed. This step includes comparing the first geomorphic feature with the geomorphic feature matrix of the target area to determine a third similarity. The analysis of geomorphic features may focus on factors such as terrain slope and altitude. The level of the third similarity will determine whether this set of data is added to the sample plant information set. If the third similarity also exceeds the threshold, it indicates that the first retrieval data is highly similar to the target area in terms of soil, climate and landform. Therefore, it is added to the sample plant information set. This information set will be used for vegetation layout design in the future to ensure that the selected plant species and planting strategies are highly consistent with environmental conditions, thereby improving the vegetation success rate and ecological benefits.

[0052] Through the above steps, not only the scientificity and accuracy of plant selection are improved, but also the adaptability and sustainability of vegetation layout are optimized, providing solid data support for the realization of efficient and eco-friendly greening projects.

[0053] Furthermore, to obtain a plurality of high-frequency planting density thresholds, step S200 of the present application further includes:

[0054] Step S210: randomly selecting a first plant type from the multiple plant types, and performing planting density extraction on the sample plant information set based on the first plant type to obtain a first density set.

[0055] Step S220: determining a first initial density threshold based on the maximum density value and the minimum density value of the first density set, dividing the first initial density threshold according to a predetermined step length, and determining a plurality of first initial density intervals.

[0056] Step S230: Count the number of data in multiple first initial density intervals to obtain multiple first frequencies, select the first initial density interval corresponding to the first frequency greater than a predetermined frequency threshold as the first high-frequency density interval, construct a first high-frequency planting density threshold based on the multiple first high-frequency density intervals, and add it to the multiple high-frequency planting density thresholds.

[0057] Exemplarily, a plant type is randomly selected from the sample plant information set as the first plant type. This selection relies on the diversity and completeness of the sample plant information set to ensure that a wide range of plant types are selected to fairly evaluate the planting density of different plants. Based on the selected first plant type, all relevant planting density data are extracted from the information set to form a first density set. This data set includes the planting density values ​​actually applied to the plant type in historical records, and these density values ​​are collected from multiple different locations and conditions.

[0058] Further, the first density set is analyzed to determine its maximum density value and minimum density value, which reflect the planting density limit of the plant type under different conditions. Then, the first initial density threshold is set based on these two extreme values, and this density range is divided into multiple first initial density intervals according to a predetermined step size (for example, one plant is added per square meter). Data statistics are performed on each first initial density interval, and the number of data in each interval, that is, the first frequency, is calculated to obtain multiple first frequencies. This step is intended to identify the most common planting density intervals in practical applications. The interval whose first frequency is greater than the predetermined frequency threshold is selected and defined as the first high-frequency density interval, which represents the most commonly used and most effective density range in actual planting. The first high-frequency planting density threshold is constructed based on multiple first high-frequency density intervals. This threshold provides a scientific basis for designers and gardeners to guide them to use the most effective planting density in actual vegetation layout. Among them, the predetermined step size and the predetermined frequency threshold can be set according to actual needs, and are not limited here.

[0059] Through the above steps, not only a systematic and scientific method is provided to determine the ideal planting density of plants, but also the aesthetics and growth effect of plant layout are ensured, and the practicality and sustainability of vegetation design are enhanced.

[0060] Furthermore, the plurality of plant combinations are subjected to adaptation screening, and step S300 of the present application further includes:

[0061] Step S310: randomly selecting a first plant combination from the multiple plant combinations, performing big data retrieval with the first plant combination and the plant growth influencing factors as constraints, and obtaining multiple plant survival rate sets.

[0062] Step S320: abnormal state triggering judgment is performed on the plurality of plant survival rate sets respectively according to the predetermined abnormal rule to determine the first abnormal proportion, wherein if the survival rate of any plant in the plant survival rate set is less than the predetermined survival rate threshold, an abnormal state is triggered.

[0063] Step S330: If the first abnormality ratio is less than a predetermined abnormality threshold, the first plant combination is set as an adapted plant combination; otherwise, the first plant combination is discarded.

[0064] Specifically, one of the pre-designed plant combinations is randomly selected as the first plant combination. This step ensures that each plant combination has an equal opportunity to be evaluated, so that the screening process is fair and unbiased. Then, using the garden big data platform, data retrieval is performed based on the first plant combination and its related factors affecting plant growth (such as soil type, climatic conditions, etc.) to obtain the plant survival rate set of the combination. This set includes the survival of the plants in the combination under similar environmental conditions in historical data.

[0065] Further, a predetermined abnormal rule is set to evaluate whether the plant survival rate meets the minimum requirement. The specific operation is to analyze the plant survival rate set to determine whether any plant has a survival rate lower than a predetermined survival rate threshold (e.g., 60%). This threshold can be determined based on sufficient scientific research and practical experience to ensure the overall health and growth potential of the plant combination. If the survival rate of any plant in the plant survival rate set is less than the predetermined survival rate threshold, the abnormal state is triggered. The proportion of plants that trigger the abnormal state, i.e., the first abnormal proportion, is calculated. This value represents the proportion of plants that do not meet the minimum survival rate requirement in the plant combination. If the first abnormal proportion is less than the predetermined abnormal threshold (e.g., if the abnormal proportion is less than 20%), it is considered that the first plant combination has a higher overall adaptability and survival potential, so it is set as an adaptive plant combination. On the contrary, if the abnormal proportion is higher than this threshold, it means that the survival rate of the plant combination in the target environment may not meet expectations, so it is discarded from the consideration range. The predetermined abnormal threshold can be determined based on actual conditions.

[0066] Through the above steps, it is possible to effectively ensure that the selected plant combination has the highest survival rate and optimal growth conditions in a specific environment, improve the layout accuracy, and thus enhance the sustainability and ecological benefits of the vegetation layout.

[0067] Furthermore, to obtain a plurality of simulated landscape image sets, step S600 of the present application further includes:

[0068] Step S610: configuring a predetermined viewing strategy, wherein the predetermined viewing strategy includes a plurality of predetermined viewing points, and each predetermined viewing point includes a plurality of viewing angles.

[0069] Step S620: Based on the multiple predetermined viewing points and multiple viewing angles, the multiple growth prediction scenes are collected to obtain multiple simulated landscape image sets.

[0070] Optionally, multiple predetermined viewing points are set, which are selected as potential important viewing locations, such as the entrance of the park, the central lake, the rest area, etc. Each viewing point is further subdivided into multiple viewing angles, covering the perspectives of observing the landscape from different directions and heights, such as front view, side view and overhead view. The setting of the predetermined viewing strategy is based on the focus and viewing value of the overall landscape design, ensuring that important vegetation elements and landscape features can be displayed from the best perspective, thereby enhancing the viewing experience of tourists and the visual appeal of the site. Further, using advanced graphics and rendering software, each growth prediction scenario is visually simulated according to each viewing point and viewing angle, including simulating the lighting and shadow effects in different seasons and different time periods, as well as the actual appearance of the plants after maturity. High-quality images are captured from each viewing angle, and these images are synthesized into a set of simulated landscape images, each of which represents the predicted vegetation layout effect seen from a specific viewing point and angle, providing rich visual data for further landscape evaluation.

[0071] Through the above steps, we can not only systematically capture and evaluate the visual effects of vegetation layout, but also optimize vegetation layout based on actual viewing strategies, ensuring that the final design is both beautiful and practical, and meets the public's viewing and functional needs.

[0072] Furthermore, to determine a plurality of optimized layout solutions, step S600 of the present application further includes:

[0073] Step S630: Using the garden expert system, respectively evaluate the multiple simulated landscape image sets in terms of landscape color, plant size and plant morphology, and determine multiple color evaluation coefficient sets, multiple size evaluation coefficient sets and multiple morphology evaluation coefficient sets.

[0074] Step S640: According to the landscape evaluation function, the plurality of color evaluation coefficient sets, the plurality of size evaluation coefficient sets and the plurality of form evaluation coefficient sets are comprehensively evaluated respectively to determine a plurality of landscape evaluation coefficients, and the coefficients are arranged from large to small to generate a landscape evaluation coefficient sequence.

[0075] Step S650: Selecting the initial layout schemes corresponding to the first Q landscape evaluation coefficients of the landscape evaluation coefficient sequence and setting them as the optimized layout schemes to obtain the multiple optimized layout schemes, wherein Q is an integer greater than 3 and less than 10.

[0076] Furthermore, to construct a landscape evaluation function, step S640 of the present application further includes:

[0077] The expression of the landscape evaluation function is:

[0078]

[0079] Among them, F is the landscape evaluation coefficient, M is the number of scheduled viewing spots, m represents any one of the M scheduled viewing spots, and w m is the weight of the mth scheduled viewing point, N is the number of viewing angles of the mth scheduled viewing point, n represents any one of the N viewing angles, and w n is the weight of the nth viewing angle, v1 is the color weight, v2 is the size weight, v3 is the morphology weight, AR is the color evaluation coefficient, BR is the size evaluation coefficient, and CR is the morphology evaluation coefficient.

[0080] Specifically, the garden expert system is configured to receive and process the simulated landscape image set. The system should have advanced image analysis capabilities that can identify and evaluate features such as color, size, and morphology in the image. Evaluation parameters are set in the system, including weights (v1, v2, v3) for color, size, and morphology, which are pre-set according to the design goals of the vegetation layout and the expected visual effects. Then, the simulated landscape image of each predetermined viewing point and angle is uploaded to the expert system, and the system automatically identifies key visual elements in the image, such as the color brightness, size ratio, and morphological structure of the plant, and scores the color, size, and morphology of each image, generating a color rating coefficient (AR), a size rating coefficient (BR), and a morphological rating coefficient (CR). The rating is obtained based on advanced image analysis techniques such as color histogram matching, object size analysis, and morphological recognition algorithms.

[0081] Further, according to the formula The comprehensive landscape evaluation coefficient F of each image set is calculated, which takes into account the importance of each viewing point (w m ) and the effect of each angle (w n ). Next, all calculated F values ​​are sorted from high to low to identify the best performing vegetation layout. Finally, based on the sorting results, the top Q high-scoring layouts are selected as the final optimized layouts. This selection is based on a comprehensive consideration of the aesthetic performance, ecological benefits, and maintenance sustainability of the vegetation.

[0082] Through the above evaluation and selection process, the vegetation layout plan is optimized in aesthetics, ecology and function, meeting the public's viewing and functional needs while maintaining the sustainability and environmental friendliness of the design.

[0083] Through the technical solutions of the above-mentioned embodiments, the vegetation greening layout optimization method provided by the present application solves the technical problems that the existing vegetation greening layout methods lack systematic analysis and real-time monitoring of plant growth, resulting in the inability to accurately configure vegetation, and the inability to effectively predict and evaluate the growth effect and landscape aesthetics after vegetation layout, thereby causing losses in ecological benefits and economic efficiency, and poor greening quality and durability. It achieves the goal of improving the accuracy and predictability of plant layout, thereby ensuring the maximization of the aesthetics, ecological benefits and economic efficiency of vegetation layout, and ensuring the technical effect of greening quality and durability.

[0084] The technical features of the above embodiments may be arbitrarily combined. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0085] The above-mentioned embodiments only express several implementation methods of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the invention patent. It should be pointed out that, for ordinary technicians in this field, several variations and improvements can be made without departing from the concept of the present application, and these all belong to the protection scope of the present application.

Claims

1. A method for optimizing vegetation greening layout, characterized in that: include: Collect plant growth influencing factors in the target area, and use the plant growth influencing factors as constraints to perform homology search based on garden big data to obtain a sample plant information set, wherein the sample plant information includes plant type and planting density; Determine multiple plant types based on the sample plant information set, and calculate multiple high-frequency planting density thresholds of the multiple plant types; Input the multiple plant types into a type-attribute comparison table, match to obtain multiple time zone attributes, enumerate based on the multiple time zone attributes to meet the expected plant diversity constraint, determine multiple plant combinations, perform adaptation screening on the multiple plant combinations, and determine multiple adapted plant combinations; Performing vegetation layout design based on the multiple adaptive plant combinations and the multiple high-frequency planting density thresholds to generate multiple initial layout schemes; Using ecological simulation software, constructing a simulated growth environment according to the plant growth influencing factors, and using the simulated growth environment to perform growth predictions on the multiple initial layout schemes after a predetermined period to generate multiple growth prediction scenarios; Performing simulated viewing of the plurality of growth prediction scenes based on a predetermined viewing strategy, obtaining a plurality of simulated landscape image sets, and evaluating the plurality of simulated landscape image sets respectively according to a landscape evaluation function, and determining a plurality of optimized layout schemes; Conduct construction feasibility assessments on the multiple optimized layout schemes respectively, determine the optimal layout scheme based on the assessment results, and execute vegetation layout in the target area.

2. The method for optimizing vegetation greening layout according to claim 1, characterized in that: Get a sample plant information set including: Collect plant growth influencing factors in the target area, where the plant growth influencing factors include soil information, climate information and landform information; Extracting features of the soil information, climate information and landform information to construct a soil feature matrix, a climate feature matrix and a landform feature matrix; Taking the soil characteristic matrix, climate characteristic matrix and landform characteristic matrix as constraints, data mining is performed based on garden big data to obtain the sample plant information set.

3. The method for optimizing vegetation greening layout according to claim 2, characterized in that: Data mining based on garden big data, including: Performing data mining based on the garden big data to obtain first search data, wherein the first search data includes a first soil feature, a first climate feature, and a first landform feature; Performing a similarity analysis on the soil feature matrix and the first soil feature according to a predetermined similarity comparison algorithm to determine a first similarity; If the first similarity is greater than a predetermined threshold, performing a similarity analysis on the climate feature matrix and the first climate feature to determine a second similarity; If the second similarity is greater than a predetermined threshold, performing a similarity analysis on the geomorphic feature matrix and the first geomorphic feature to determine a third similarity; If the third similarity is greater than a predetermined threshold, the first search data is added to the sample plant information set.

4. The method for optimizing vegetation greening layout according to claim 1, characterized in that: Multiple high-frequency planting density thresholds are obtained, including: Randomly selecting a first plant type from the multiple plant types, and performing planting density extraction on the sample plant information set based on the first plant type to obtain a first density set; Determine a first initial density threshold based on a maximum density value and a minimum density value of the first density set, divide the first initial density threshold according to a predetermined step length, and determine a plurality of first initial density intervals; Count the number of data in multiple first initial density intervals to obtain multiple first frequencies, select the first initial density interval corresponding to the first frequency greater than a predetermined frequency threshold and set it as the first high-frequency density interval, construct a first high-frequency planting density threshold based on the multiple first high-frequency density intervals, and add it to the multiple high-frequency planting density thresholds.

5. The method for optimizing vegetation greening layout according to claim 1, characterized in that: The plurality of plant combinations are screened for suitability, comprising: Randomly selecting a first plant combination from the multiple plant combinations, performing big data retrieval with the first plant combination and the plant growth influencing factors as constraints, and obtaining multiple plant survival rate sets; According to the predetermined abnormal rule, the plurality of plant survival rate sets are respectively subjected to abnormal state triggering judgment to determine a first abnormal proportion, wherein if the survival rate of any plant in the plant survival rate set is less than a predetermined survival rate threshold, an abnormal state is triggered; If the first abnormality ratio is less than a predetermined abnormality threshold, the first plant combination is set as an adapted plant combination; otherwise, the first plant combination is discarded.

6. The method for optimizing vegetation greening layout according to claim 1, characterized in that: Get multiple simulated landscape image sets, including: Configuring a predetermined viewing strategy, wherein the predetermined viewing strategy includes a plurality of predetermined viewing points, and each predetermined viewing point includes a plurality of viewing angles; Based on the multiple predetermined viewing points and multiple viewing angles, viewing images of the multiple growth prediction scenes are collected to obtain multiple simulated landscape image sets.

7. The method for optimizing vegetation greening layout according to claim 1, characterized in that: Identify multiple optimized layout solutions, including: Through the garden expert system, the landscape color, plant size and plant morphology of the plurality of simulated landscape image sets are evaluated respectively, and a plurality of color evaluation coefficient sets, a plurality of size evaluation coefficient sets and a plurality of morphology evaluation coefficient sets are determined; According to the landscape evaluation function, the plurality of color evaluation coefficient sets, the plurality of size evaluation coefficient sets and the plurality of morphology evaluation coefficient sets are comprehensively evaluated respectively to determine a plurality of landscape evaluation coefficients, and the coefficients are arranged from large to small to generate a landscape evaluation coefficient sequence; The initial layout schemes corresponding to the first Q landscape evaluation coefficients of the landscape evaluation coefficient sequence are selected and set as the optimized layout schemes to obtain the multiple optimized layout schemes, wherein Q is an integer greater than 3 and less than 10.

8. The method for optimizing vegetation greening layout according to claim 7, characterized in that: Construct a landscape evaluation function, including: The expression of the landscape evaluation function is: Among them, F is the landscape evaluation coefficient, M is the number of scheduled viewing spots, m represents any one of the M scheduled viewing spots, and w m is the weight of the mth scheduled viewing point, N is the number of viewing angles of the mth scheduled viewing point, n represents any one of the N viewing angles, and w n is the weight of the nth viewing angle, v1 is the color weight, v2 is the size weight, v3 is the morphology weight, AR is the color evaluation coefficient, BR is the size evaluation coefficient, and CR is the morphology evaluation coefficient.

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