A carbon neutrality analysis and calculation method, system and storage medium for low-carbon gardens

Abstract

The present application discloses a carbon neutrality analysis and calculation method, system and storage medium for a low-carbon garden, and relates to the field of electrical digital data processing technology. The carbon neutrality analysis and calculation method for the low-carbon garden includes: obtaining parameter data of the low-carbon garden to form a database; based on the database, obtaining a target local sample plot from the low-carbon garden; based on the target local sample plot, obtaining a first carbon fixation estimation model and a first carbon emission estimation model for the target local sample plot; based on the first carbon fixation estimation model and the first carbon emission estimation model, obtaining a carbon neutrality prediction model for the low-carbon garden; based on the carbon neutrality prediction model, obtaining carbon neutrality prediction data for the low-carbon garden. Compared with the direct measurement method of the prior art, the labor cost required for the present application is relatively small, that is, the use cost is reduced; compared with the indirect estimation method of the prior art, the labor invested in the present application is also relatively small, and the estimation accuracy is high.

Description

Technical Field

[0001] The present application relates to the technical field of electrical digital data processing, and specifically to a carbon neutrality analysis and calculation method, system and storage medium for low-carbon gardens. Background Art

[0002] In recent years, energy conservation and emission reduction have become a new trend in environmental protection. In this era, traditional gardens can no longer fully meet human needs. "Ecology, green and low carbon" have become one of the themes of garden construction in the new era, so the concept of "low-carbon garden" came into being. Low-carbon gardens refer to gardens that adopt a series of low-carbon technologies and management measures during planning, construction, operation and maintenance to reduce carbon emissions, enhance carbon sink capacity, achieve energy self-sufficiency or close to self-sufficiency, and try to achieve zero or negative total carbon emissions.

[0003] Accurately analyzing the carbon neutrality capacity of low-carbon gardens is a basic requirement for building low-carbon gardens. The steps to calculate the carbon neutrality capacity of carbon gardens are: first calculate the carbon emissions and carbon absorption of the garden, and then compare the two to analyze the carbon neutrality capacity of the garden. In the above process, commonly used methods for calculating carbon emissions and carbon absorption include direct measurement (that is, direct calculation through on-site measurement or sample survey) and indirect estimation (that is, indirect estimation using geographic information systems, remote sensing technology and other methods). However, due to the complexity of measuring carbon emissions and carbon fixation of related items such as vegetation, soil and buildings, the direct measurement method requires a lot of labor and increases costs; indirect estimation is highly dependent on analysis models (for example: plant carbon fixation calculation system and The calculation model, etc.) and input data require extremely high accuracy of the model. If the analysis model is inaccurate or the input data is inaccurate, the obtained carbon neutrality prediction data will be inaccurate. Summary of the invention

[0004] The purpose of this application is to provide a carbon neutrality analysis and calculation method, system and storage medium for low-carbon gardens, so as to solve the technical problems in the prior art that the carbon neutrality prediction data of the garden cannot be accurately obtained or the cost of obtaining the carbon neutrality prediction data is high.

[0005] To achieve the above objectives, this application provides the following technical solutions:

[0006] In the first aspect, the present application proposes a technical solution for a carbon neutrality analysis and calculation method for a low-carbon garden, and the carbon neutrality analysis and calculation method for a low-carbon garden includes:

[0007] Acquiring parameter data of the low-carbon garden to form a database;

[0008] Based on the database, the low-carbon garden obtains a target local sample plot; the target local sample plot belongs to the low-carbon garden, and the similarity of plant data between the target local sample plot and the low-carbon garden is greater than or equal to a first preset value;

[0009] Based on the target local sample plot, obtaining a first carbon fixation estimation model and a first carbon emission estimation model for the target local sample plot;

[0010] Based on the first carbon fixation estimation model and the first carbon emission estimation model, obtaining a carbon neutrality prediction model for the low-carbon garden;

[0011] Based on the carbon neutrality prediction model, the carbon neutrality prediction data of the low-carbon garden is obtained.

[0012] As a specific scheme in the technical scheme of the present application, the parameter data of the low-carbon garden include the total area of ​​the low-carbon garden, vegetation types, vegetation coverage, plant diameter and root-to-stem ratio, biomass conversion factor, the number and type of buildings and public facilities in the low-carbon garden, the operating energy consumption of the low-carbon garden, the number of tourists in the low-carbon garden, and the activity intensity of tourists.

[0013] As a specific solution in the technical solution of the present application, the method of obtaining a target local sample plot from the low-carbon garden based on the database includes:

[0014] Obtaining a bird's-eye view of the low-carbon garden;

[0015] Based on the bird's-eye view, a sliding window is set to obtain a plurality of first areas;

[0016] Obtaining pixel similarity between each first area and the bird's-eye view image;

[0017] Based on the similarities of each pixel, a plurality of second regions are obtained; the second region is any first region whose pixel similarity is greater than a second preset value;

[0018] Acquire a first local sample plot corresponding to each second area in the low-carbon garden;

[0019] Based on the database, obtaining first plant data corresponding to the low-carbon garden;

[0020] Based on the database, obtaining second plant data corresponding to each first local sample plot;

[0021] Based on each second plant data and the first plant data, obtaining a plurality of plant data similarities;

[0022] Based on the similarity of multiple plant data, a target local sample plot is obtained from each first local sample plot.

[0023] As a specific solution in the technical solution of the present application, the obtaining of the pixel similarity between each first area and the bird's-eye view image includes:

[0024] Based on the multiple first areas, a third area is acquired; the third area is any area among the multiple first areas;

[0025] Obtaining a pixel mean value of each pixel point in the third area;

[0026] Acquire a first pixel value of each pixel point based on the bird's-eye view image;

[0027] Based on each first pixel value and the pixel mean, a pixel similarity between the third area and the bird's-eye view image is obtained.

[0028] As a specific solution in the technical solution of the present application, the calculation formula for obtaining the pixel similarity between the third area and the bird's-eye view image based on each first pixel value and the pixel mean is as follows:

[0029]

[0030] in, Indicates the bird's-eye view The pixel value of a pixel; represents the pixel mean of each pixel point in the b-th first area; Represents the minimum value function; Indicates the absolute value. It represents the mean value of the function value corresponding to different pixel points in the bird's-eye view; is an exponential function with a natural constant as its base.

[0031] As a specific solution in the technical solution of the present application, the obtaining of multiple plant data similarities based on each second plant data and the first plant data includes:

[0032] Based on the first plant data, obtaining a first ecological weight corresponding to each plant in the low-carbon garden; the ecological weight is at least used to represent the ratio of the number of each plant to the total number of all plants;

[0033] Based on the second plant data, obtaining a second ecological weight corresponding to each plant in the first local sample plot;

[0034] Based on the first ecological weight and the second ecological weight, the similarity of plant data between the first local sample plot and the low-carbon garden is obtained.

[0035] As a specific solution in the technical solution of the present application, based on the first ecological weight and the second ecological weight, the calculation formula for obtaining the similarity of the plant data of the first local sample plot and the low-carbon garden is as follows:

[0036]

[0037] in, Indicates The first local sample plot The ecological weight of plant species; It means that the first The ecological weight of plant species; Indicates The total number of plant species in the first local sample plot; Indicates the total number of plant species in the entire low-carbon garden; Indicates The first local sample plot The total number of plants grown; It means that the first The total number of plants grown; Indicates different calculations The standard deviation of the corresponding function value.

[0038] As a specific solution in the technical solution of the present application, the carbon neutrality prediction model of the low-carbon garden is obtained based on the first carbon fixation estimation model and the first carbon emission estimation model, including:

[0039] Based on the database, obtaining a first vegetation coverage rate of the target local sample plot;

[0040] Based on the database, obtaining a second vegetation coverage rate of the low-carbon garden;

[0041] Acquire a confidence weight based on the first vegetation coverage rate and the second vegetation coverage rate;

[0042] Based on the confidence weight and the first carbon sequestration estimation model, obtaining a second carbon sequestration estimation model for the low-carbon garden;

[0043] Based on the confidence weight and the first carbon emission estimation model, obtaining a second carbon emission estimation model of the low-carbon garden;

[0044] Based on the second carbon fixation estimation model and the second carbon emission estimation model, a carbon neutrality prediction model of the low-carbon garden is obtained.

[0045] In the second aspect, the present application proposes a technical solution for a carbon neutrality analysis and calculation system for a low-carbon garden, and the carbon neutrality analysis and calculation system for a low-carbon garden includes:

[0046] A reader, used for acquiring parameter data of the low-carbon garden to form a database;

[0047] A processor, configured to obtain a target local sample plot from the low-carbon garden based on the database; the target local sample plot belongs to the low-carbon garden, and the similarity of plant data between the target local sample plot and the low-carbon garden is greater than or equal to a first preset value;

[0048] And, based on the target local sample plot, obtaining a first carbon fixation estimation model and a first carbon emission estimation model of the target local sample plot;

[0049] And, based on the first carbon fixation estimation model and the first carbon emission estimation model, obtaining a carbon neutrality prediction model for the low-carbon garden;

[0050] And, based on the carbon neutrality prediction model, the carbon neutrality prediction data of the low-carbon garden is obtained.

[0051] As a specific scheme in the technical scheme of the present application, the parameter data of the low-carbon garden include the total area of ​​the low-carbon garden, vegetation types, vegetation coverage, plant diameter and root-to-stem ratio, biomass conversion factor, the number and type of buildings and public facilities in the low-carbon garden, the operating energy consumption of the low-carbon garden, the number of tourists in the low-carbon garden, and the activity intensity of tourists.

[0052] As a specific solution in the technical solution of the present application, the reader is also used to obtain a bird's-eye view of the low-carbon garden;

[0053] The processor is further configured to, based on the bird's-eye view, set a sliding window to obtain a plurality of first areas;

[0054] and obtaining pixel similarity between each first area and the bird's-eye view image;

[0055] And, based on the similarities of each pixel, a plurality of second regions are obtained; the second regions are any first regions whose pixel similarities are greater than a second preset value;

[0056] And, obtaining first local sample plots corresponding to each second area in the low-carbon garden;

[0057] The reader is also used to obtain first plant data corresponding to the low-carbon garden based on the database;

[0058] and, based on the database, obtaining second plant data corresponding to each first local sample plot;

[0059] The processor is further configured to obtain a plurality of plant data similarities based on each second plant data and the first plant data;

[0060] And, based on the similarities of the plurality of plant data, a target local sample plot is obtained from each of the first local sample plots.

[0061] As a specific solution in the technical solution of the present application, the processor is further used to obtain a third area based on the multiple first areas; the third area is any area among the multiple first areas;

[0062] And, obtaining the pixel mean value of each pixel point in the third area;

[0063] And, acquiring a first pixel value of each pixel point based on the bird's-eye view;

[0064] And, based on each first pixel value and the pixel mean, a pixel similarity between the third area and the bird's-eye view image is obtained.

[0065] As a specific solution in the technical solution of the present application, the processor is further used to obtain the pixel similarity between the third area and the bird's-eye view image based on each first pixel value and the pixel mean value by the following calculation formula:

[0066]

[0067] in, Indicates the bird's-eye view The pixel value of a pixel; represents the pixel mean of each pixel point in the b-th first area; Represents the minimum value function; Indicates the absolute value. It represents the mean value of the function value corresponding to different pixel points in the bird's-eye view; is an exponential function with a natural constant as its base.

[0068] As a specific solution in the technical solution of the present application, the processor is also used to obtain a first ecological weight corresponding to each plant in the low-carbon garden based on the first plant data; the ecological weight is at least used to characterize the ratio of the number of each plant to the total number of all plants;

[0069] and, based on the second plant data, obtaining a second ecological weight corresponding to each plant in the first local sample plot;

[0070] And, based on the first ecological weight and the second ecological weight, the similarity of plant data between the first local sample plot and the low-carbon garden is obtained.

[0071] As a specific solution in the technical solution of the present application, the processor is also used to obtain the similarity of plant data between the first local sample plot and the low-carbon garden based on the first ecological weight and the second ecological weight through the following calculation formula:

[0072]

[0073] in, Indicates The first local sample plot The ecological weight of plant species; It means that the first The ecological weight of plant species; Indicates The total number of plant species in the first local sample plot; Indicates the total number of plant species in the entire low-carbon garden; Indicates The first local sample plot The total number of plants grown; It means that the first The total number of plants grown; Indicates different calculations The standard deviation of the corresponding function value.

[0074] As a specific solution in the technical solution of the present application, the reader is also used to obtain a first vegetation coverage rate of the target local sample plot based on the database;

[0075] And, based on the database, obtaining a second vegetation coverage rate of the low-carbon garden;

[0076] The processor is further configured to obtain a confidence weight based on the first vegetation coverage rate and the second vegetation coverage rate;

[0077] And, based on the confidence weight and the first carbon sequestration estimation model, obtaining a second carbon sequestration estimation model for the low-carbon garden;

[0078] And, based on the confidence weight and the first carbon emission estimation model, obtaining a second carbon emission estimation model of the low-carbon garden;

[0079] And, based on the second carbon fixation estimation model and the second carbon emission estimation model, a carbon neutrality prediction model of the low-carbon garden is obtained.

[0080] In the third aspect, the present application proposes a technical solution of a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, it implements the carbon neutrality analysis and calculation method for low-carbon gardens as described in any one of the first aspects.

[0081] Compared with the prior art, the beneficial effects of this application are:

[0082] This application selects target local plots in low-carbon gardens that show high similarity with the ecosystem of low-carbon gardens. And based on the target local plots, the first carbon fixation estimation model and the first carbon emission estimation model are accurately constructed. Finally, the carbon neutrality prediction model of the low-carbon garden is obtained based on the first carbon fixation estimation model and the first carbon emission estimation model. Compared with the direct measurement method of the prior art, the labor cost required for this application is relatively small, that is, the cost of use is reduced; compared with the indirect estimation method of the prior art, the labor cost invested in this application is also relatively small, and the estimation accuracy is high. BRIEF DESCRIPTION OF THE DRAWINGS

[0083] Figure 1 A schematic diagram of a process for analyzing and calculating the carbon neutrality of a low-carbon garden proposed in an embodiment of the present application;

[0084] Figure 2 This is a structural schematic diagram of a carbon neutrality analysis and calculation system for a low-carbon garden proposed in an embodiment of the present application. DETAILED DESCRIPTION

[0085] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0086] The terms "first", "second", etc. in the description of the embodiments of the present application and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence, such as the first area and the second area proposed below, which belong to different areas. It should be understood that the areas used in this way can be interchanged where appropriate, so that the embodiments described here can be implemented in an order other than the content illustrated or described here. In addition, the terms "including" and "having" and any of their variations are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device including a series of steps or modules is not necessarily limited to those steps or modules clearly listed, but may include other steps or modules that are not clearly listed or inherent to these processes, methods, products or devices. The division of the modules that appear in the embodiments of the present application is only a logical division. There may be other division methods when implemented in practical applications, such as multiple modules can be combined or integrated into another system, or some features can be ignored, or not executed. In addition, the coupling or direct coupling or communication connection between each other shown or discussed can be through some interfaces, and the indirect coupling or communication connection between modules can be electrical or other similar forms, which are not limited in the embodiments of the present application. Furthermore, the modules or sub-modules described as separate components may or may not be physically separated, may or may not be physical modules, or may be distributed in multiple circuit modules, and some or all of the modules may be selected according to actual needs to achieve the purpose of the embodiment of the present application.

[0087] In order to solve the technical problem that the existing technology mentioned in the background technology cannot accurately obtain the carbon neutrality prediction data of the garden or the cost of obtaining the carbon neutrality prediction data is high, this application proposes a carbon neutrality analysis and calculation method for low-carbon gardens, such as Figure 1 As shown, the carbon neutrality analysis and calculation method of the low-carbon garden includes steps S100 to S500.

[0088] Step S100: Acquire parameter data of the low-carbon garden to form a database.

[0089] It should be clear that in the embodiments of the present application, the parameter data of the low-carbon garden can be any parameter related to the carbon neutrality calculation. For example, the parameter data of the low-carbon garden can include the total area of ​​the low-carbon garden, vegetation type, vegetation coverage, plant diameter and root-to-stem ratio, and biomass conversion factor. These data can be determined according to the latest research and standards, for example, according to GB / T43648-2024, to determine these parameters. The parameter data of the low-carbon garden can also include the number and type of buildings and public facilities in the low-carbon garden, the operating energy consumption of the low-carbon garden (including irrigation, lighting, other maintenance, etc.), the number of tourists in the low-carbon garden, and the activity intensity of tourists.

[0090] Step S200: Based on the database, the low-carbon garden obtains a target local sample plot.

[0091] It should be clear that in a garden, the plant species are fixed, and considering the factors of aesthetics, ecological cycle and matching level, various plants and related buildings will be scattered more evenly throughout the garden. Therefore, according to the types, quantity ratios and other locations of various plants and buildings in the low-carbon garden, local plots that are highly similar to the entire garden ecosystem can be screened out, and the selected plots have carbon sink characteristics similar to those of the garden as a whole. Due to the similarity in carbon sink mechanism between the local plots and the garden as a whole, the carbon neutrality of the garden as a whole can be estimated based on the carbon neutrality of the local plots and the carbon emission data of tourists. That is to say, in an embodiment of the present application, the target local plot belongs to the low-carbon garden, and the similarity of the plant data between the target local plot and the low-carbon garden is greater than or equal to the first preset value.

[0092] In the embodiments of the present application, the target local sample plot can be selected in the low-carbon garden based on artificial selection. It should be noted that in a low-carbon garden, it is necessary to select suitable plant species and consider how to lay out the corresponding vegetation during planning and construction; that is, according to the climate (temperature, precipitation and other related conditions) of the location of the garden, select suitable plant species, and then according to the growth habits (growth rate, root characteristics, etc.) of these selected plants and their space utilization in the garden, combined with the complementarity and competition between plants, reasonably arrange the vegetation layout in the garden. In addition, in order to better support the operation of the ecosystem in the garden and enhance the attractiveness and usability of the low-carbon garden, various related buildings are relatively evenly placed in the garden according to the vegetation layout of the garden. Due to the limited resources (time, manpower, financial resources, etc.) when analyzing the carbon neutrality of the garden, it is impossible to conduct a detailed investigation of the entire garden when analyzing the carbon neutrality of the garden. Therefore, it is necessary to select local sample plots for analysis; and considering the complexity of the garden, in order to improve the reliability and scientificity of the carbon neutrality analysis of the low-carbon garden, the most representative sample plots are selected as the carbon sink estimation model of the garden based on the similarity of the ecosystem and ecological environment between the garden as a whole and the sample plot, that is, according to the structure, function and dynamic changes of the ecosystem, and the relationship between vegetation and other things. If only the target local sample plot with a high similarity to the plant data of the low-carbon garden is manually selected, the operation difficulty is also relatively large. In order to reduce the difficulty of selecting the target local sample plot, in one embodiment of the present application, step S200, based on the database, obtains the target local sample plot from the low-carbon garden, including steps S210 to S290.

[0093] Step S210: Obtaining a bird's-eye view of the low-carbon garden.

[0094] In the embodiment of the present application, a bird's-eye view of the low-carbon garden can be obtained based on a drone. Since obtaining a bird's-eye view based on a drone is a mature technology, it will not be described in detail here.

[0095] Step S220: Based on the bird's-eye view, a sliding window is set to obtain a plurality of first areas.

[0096] It should be clear that in the embodiments of the present application, the sliding window can be set as required, and there is no restriction on the size of the sliding window and the sliding step. For example, in a specific embodiment of the present application, the width of the sliding window is K and the length is C. Among them, C can be equal to K, or C and K can be determined based on the aspect ratio of the bird's-eye view (for example: if the aspect ratio of the bird's-eye view is 2:1, then C:K can also be equal to 2:1), and the sliding step is preset to 3.

[0097] Step S230: Obtain pixel similarity between each first area and the bird's-eye view image.

[0098] It should be clear that the colors of each area in the bird's-eye view can represent the position and proportion of plants and buildings in the low-carbon garden. For example, plants are generally green in the bird's-eye view, buildings are generally gray, and lakes are generally gray-black. That is to say, in the embodiment of the present application, the first area that may be used as the target local sample site in the future can be preliminarily screened out by calculating the pixel similarity between each first area and the bird's-eye view.

[0099] It should be noted that in the embodiment of the present application, any similarity calculation algorithm can be used to calculate the pixel similarity of each first area bird's-eye view image. For example, a histogram algorithm, a hash algorithm, a mean square error algorithm, etc. In order to accurately and quickly obtain the pixel similarity between each first area and the bird's-eye view image. In one embodiment of the present application, step S230, obtaining the pixel similarity between each first area and the bird's-eye view image, includes steps S231 to S234.

[0100] Step S231: Based on the multiple first regions, obtain a third region.

[0101] As can be seen from the foregoing, it is necessary to calculate the pixel similarity between each first region and the bird's-eye view image. That is, in this embodiment, the third region is any region among the plurality of first regions.

[0102] Step S232: Obtain the pixel mean value of each pixel point in the third area.

[0103] It should be clear that obtaining the pixel mean of each pixel point in a certain area is a mature technology and will not be described in detail here.

[0104] Step S233: obtaining a first pixel value of each pixel point based on the bird's-eye view image.

[0105] It should be clear that obtaining the pixel value of each pixel in an image is also a mature technology and will not be described in detail here.

[0106] Step S234: based on each first pixel value and the pixel mean, obtaining pixel similarity between the third area and the bird's-eye view image.

[0107] It should be clear that if the pixel mean in the third area is relatively similar to the pixel values ​​of each pixel point in the bird's-eye view, it means that the local garden plot corresponding to the third area has the possibility of becoming the target local plot. In the embodiment of the present application, the pixel similarity between the third area and the bird's-eye view can be obtained in any manner based on each first pixel value and the pixel mean. For example, in step S234, the calculation formula for obtaining the pixel similarity between the third area and the bird's-eye view based on each first pixel value and the pixel mean is as follows:

[0108]

[0109] in, Indicates pixel similarity; Indicates the bird's-eye view The pixel value of a pixel; represents the pixel mean of each pixel in the bth first region (i.e., the third region), where b is from 1 to n, and n is the number of first regions; It means taking the minimum function, that is, first traverse the first area and the first area in the bird's-eye view. The most similar area is selected based on the similarity of the pixels. Indicates the absolute value. It represents the mean of the function values ​​corresponding to different pixels in the bird's-eye view image. That is, the bird's-eye view image area that is most similar to each pixel is firstly selected, and then the average value of the similar situations is calculated. is an exponential function with a natural constant as its base.

[0110] Step S240: acquiring a plurality of second regions based on the similarities of the pixels.

[0111] As can be seen from the foregoing, the second area is the first area with a high similarity to the entire bird's-eye view. That is, in the embodiment of the present application, the second area is any first area whose pixel similarity is greater than the second preset value. In the embodiment of the present application, the second preset value can be set according to demand, for example: the second preset value can be 0.60 or 0.75, etc.

[0112] Step S250: Acquire the first local sample plots corresponding to each second area in the low-carbon garden.

[0113] As can be seen from the foregoing, each second area corresponds to a local sample plot. It is not possible to directly determine whether the plant data of the local sample plot is similar to the plant data of the entire low-carbon garden just from the pixels. It is easy to understand that if the plant data of the subsequent target local sample plot is less similar to the plant data of the low-carbon garden, it is difficult to accurately simulate the carbon neutrality data of the low-carbon garden based on the analysis model. In order to accurately obtain the carbon neutrality data of the low-carbon garden based on the analysis model, in the embodiments of the present application, it is also necessary to determine whether the plant data in each first local sample plot is similar to the plant data of the low-carbon garden.

[0114] Step S260: Based on the database, first plant data corresponding to the low-carbon garden is obtained.

[0115] In the embodiment of the present application, the plant data may be the vegetation type, vegetation coverage, plant diameter and root-to-stem ratio, biomass conversion factor, etc. Acquiring data from a database is a mature technology and will not be described in detail here.

[0116] Step S270: Based on the database, obtain the second plant data corresponding to each first local sample plot.

[0117] Acquiring data from a database is a mature technology and will not be described in detail here.

[0118] Step S280: obtaining a plurality of plant data similarities based on each second plant data and the first plant data.

[0119] It should be clear that in the embodiments of the present application, multiple plant data similarities can be obtained based on each second plant data and the first plant data in any manner. For example, multiple plant data similarities are obtained using the Jaccard similarity algorithm or the Gower distance algorithm. In a specific embodiment of the present application, step S280, based on each second plant data and the first plant data, multiple plant data similarities are obtained, including steps S281 to S283.

[0120] Step S281: Based on the first plant data, obtain the first ecological weight corresponding to each plant in the low-carbon garden.

[0121] It should be clear that in the embodiments of the present application, the ecological weight is at least used to characterize the ratio of the number of each plant to the total number of all plants. For example, if the total number of trees in the low-carbon garden is 10,000, of which the number of willows is 100, the ecological weight of the willow is 0.01.

[0122] In one embodiment of the present application, step S281: based on the first plant data, a calculation formula for obtaining a first ecological weight corresponding to each plant in the low-carbon garden is as follows:

[0123]

[0124] in, represents the first ecological weight of the i-th plant; Indicates The total number of plants belonging to the same plant category (plant categories include: trees, shrubs, vines, grasses, ferns, green algae, lichens, etc.), used to describe the proportion of this type of plant in the garden ecosystem. The higher the proportion, the greater the weight of this type of plant; Indicates The number of plant species that belong to the same category as a plant is used to describe the uniqueness of this plant in this large category, that is, the number of other plant species that can replace this plant in the ecological chain. The greater the substitutability, the smaller the weight of the plant; Represents the normalization function.

[0125] Step S282: Based on the second plant data, obtain a second ecological weight corresponding to each plant in the first local sample plot.

[0126] It should be clear that the calculation method of the second ecological weight here is similar to the ecological weight mentioned above.

[0127] Step S283: Based on the first ecological weight and the second ecological weight, the similarity of plant data between the first local sample plot and the low-carbon garden is obtained.

[0128] It should be clear that if the first ecological weight and the second ecological weight are highly similar, it means that the plant data similarity between the first local sample plot and the low-carbon garden is also highly similar. In a specific embodiment of the present application, step S283, based on the first ecological weight and the second ecological weight, obtains the calculation formula for the similarity of the plant data between the first local sample plot and the low-carbon garden as follows:

[0129]

[0130] in, Indicates The first local sample plot The ecological weight of plant species; It means that the first The ecological weight of plant species; Indicates The total number of plant species in the first local sample plot; Indicates the total number of plant species in the entire low-carbon garden; Indicates The first local sample plot The total number of plants grown; It means that the first The total number of plants grown; Indicates different calculations The standard deviation of the corresponding function value. That is to say, in this embodiment, the first The first local sample plot The proportion of different plants in the garden, and then calculate the standard deviation of the proportion of different plants to describe the The differences between the first local sample plot and the entire garden in the ecological chain.

[0131] Step S290: Based on the similarities of multiple plant data, a target local sample plot is obtained from each first local sample plot.

[0132] In an embodiment of the present application, all first local plots can be traversed to calculate the corresponding plant data similarities, and the first local plot with the largest plant data similarity can be selected as the target local plot. Of course, in an embodiment of the present application, in order to reduce the amount of calculation, any first local plot with a plant data similarity greater than or equal to a first preset value can also be selected as the target local plot. In an embodiment of the present application, the first preset value can be set according to demand, for example: the first preset value can be 0.60 or 0.75, etc. It is easy to understand that if the plant data similarity of a first local plot is greater than or equal to the first preset value, it is considered that the local plot has a high similarity with the overall garden in terms of biological community and ecosystem, that is, the carbon sequestration mechanism of the local plot and the overall garden is highly consistent.

[0133] In an embodiment of the present application, the target local sample plot may be any one of a plurality of first local sample plots meeting the requirements or a combination of a plurality of local sample plots.

[0134] Step S300: Based on the target local sample plot, a first carbon fixation estimation model and a first carbon emission estimation model of the target local sample plot are obtained.

[0135] Specifically, in this embodiment, according to the planting batches and time of various plants in the target local sample plot in the low-carbon garden, several other carbon layer division methods (such as Carbon layer division method Then select many representative plants, measure their tree diameter, root-to-shoot ratio, biomass conversion factor and other related data, and build a biomass model based on the relevant data of these trees, and estimate the corresponding total forest biomass; these parameters and related models that need to be collected can be determined based on the latest research and standards, such as GB / T43648-2024 (mature technology, no more elaboration).

[0136] In the embodiment of the present application, the calculation formula of the first carbon fixation estimation model is as follows:

[0137]

[0138] in, represents the total biomass carbon storage of the target local sample plot in year t; Represents the carbon layer Plants in In the Area occupied per year; Represents the carbon layer Plants In the Total forest biomass per unit area per year; Represents the carbon layer Plants In the The annual biomass carbon content is obtained by the combustion method in chemical analysis; Indicates The total number of plant species in the annual target local sample plots; Represents the total number of carbon layers within the target local sample plot.

[0139] Furthermore, a soil greenhouse gas analyzer is set up in the target local sample plot to collect the corresponding carbon emissions.

[0140] In the embodiment of the present application, the calculation formula of the first carbon emission estimation model is as follows:

[0141]

[0142] in, represents the total carbon emissions of the target local sample plot in year t; Indicates Operational projects are run times per second The amount of operational carbon emitted; Indicates Projects in operation during the year The total amount of operational carbon emitted, that is, the total amount of carbon emitted by each operation project The sum of carbon emissions, the sum of operation items such as irrigation and lighting that calculate the duration needs to be calculated with respect to time, and the sum of operation items such as maintenance and fertilization that calculate the number of times needs to be calculated with respect to times; Indicates Construction projects during the year the amount of construction carbon emitted; Indicates Projects in operation during the year Total number of items; Indicates New construction projects in the year The amount of carbon emissions is generated during the construction process and project operation process. Carbon emissions from energy consumption Specifically, In the formula Display items energy consumption, Representing the project process The carbon emission factor corresponding to the energy consumed; it should be noted that the carbon emission factor can be determined with the help of the "China Product Life Cycle Greenhouse Gas Emission Coefficient Database", which will not be further elaborated here.

[0143] It should be noted that, as can be seen from the previous text, the target local sample plots are screened based on their similarity with the garden ecosystem, etc. Since the screened target local sample plots are highly similar to the low-carbon garden in terms of ecosystem and ecological structure, the target local sample plots are highly similar to the low-carbon garden in terms of carbon neutrality mechanism. It is only necessary to amplify the various data in the carbon neutrality mechanism according to the ratio of the two to predict the overall carbon neutrality prediction model of the low-carbon garden. In other words, based on the various carbon neutrality data in the target local sample plots, combined with the various relationships between the low-carbon garden and the target local sample plots in terms of buildings and other related data, the carbon neutrality data of the low-carbon garden can be inferred.

[0144] Step S400: Based on the first carbon fixation estimation model and the first carbon emission estimation model, a carbon neutrality prediction model of the low-carbon garden is obtained.

[0145] In an embodiment of the present application, the first carbon fixation estimation model and the first carbon emission estimation model can be directly amplified based on the area ratio of the low-carbon garden to the target local sample plot, thereby obtaining a carbon neutrality prediction model for the low-carbon garden. In order to obtain a more accurate carbon neutrality prediction model for the low-carbon garden, in an embodiment of the present application, step S400, based on the first carbon fixation estimation model and the first carbon emission estimation model, obtains the carbon neutrality prediction model for the low-carbon garden, including steps S410 to S460.

[0146] Step S410: Based on the database, obtain a first vegetation coverage rate of the target local sample plot.

[0147] It should be noted that the vegetation coverage rate refers to the ratio of the vertical projection area of ​​plants in a certain region to the area of ​​the region, expressed as a percentage. In this embodiment, the vegetation coverage rate can be calculated manually or by computer based on the bird's-eye view.

[0148] Step S420: Based on the database, a second vegetation coverage rate of the low-carbon garden is obtained.

[0149] In this embodiment, the calculation method of the second vegetation coverage rate is the same as above and will not be described in detail here.

[0150] Step S430: Acquire a confidence weight based on the first vegetation coverage rate and the second vegetation coverage rate.

[0151] It should be clear that the higher the similarity between the target local sample plot and the low-carbon garden, the more accurate the low-carbon garden carbon neutrality prediction model obtained using the first carbon fixation estimation model and the first carbon emission estimation model. In an embodiment of the present application, the confidence weight refers to the degree of credibility of the similarity between the target local sample plot and the low-carbon garden. In an embodiment of the present application, the confidence weight can be the ratio of the first vegetation coverage rate to the second vegetation coverage rate. In another embodiment of the present application, the calculation formula of the confidence weight is as follows:

[0152]

[0153] in, represents the confidence weight; Indicates the similarity of plant data of the target local sample plot; Indicates the pixel similarity of the target local sample plot; Indicates the plants in the target local sample plot vegetation coverage; Indicates plants in low-carbon gardens vegetation coverage; It indicates the total number of plant species in the target local sample plot. It is easy to understand that the larger the confidence weight, the higher the credibility of the similarity between the target local sample plot and the low-carbon garden.

[0154] Step S440: Based on the confidence weight and the first carbon sequestration estimation model, a second carbon sequestration estimation model of the low-carbon garden is obtained.

[0155] It should be clear that in the embodiment of the present application, the calculation formula for obtaining the second carbon fixation estimation model of the low-carbon garden is as follows:

[0156]

[0157] in, represents the total biomass carbon storage of the low-carbon garden in year t; The total amount of biomass carbon storage within the target local sample plot; Represents the confidence weight of the target local sample plot, which is used to describe the similarity between the target local sample plot and the low-carbon garden as a whole in various aspects; Represents the weighted average function, that is, calculating the weighted average of the total biomass carbon storage of many target local sample plots, and the weight is the sample plot confidence weight ; Indicates the magnification, used to describe the area ratio of the target local sample plot and the low-carbon garden. , represents the area of ​​the target local sample plot, Indicates the area of ​​low-carbon gardens.

[0158] Step S450: Based on the confidence weight and the first carbon emission estimation model, a second carbon emission estimation model of the low-carbon garden is obtained.

[0159] It should be clear that in the embodiment of the present application, the calculation formula for obtaining the second carbon emission estimation model of the low-carbon garden is as follows:

[0160]

[0161] in, represents the total carbon emissions of low-carbon gardens in year t; Indicates the total carbon emissions of the target local sample plot; represents the plot confidence weight of the target local plot; represents the weighted average function, with weights ; Indicates the magnification, calculated in the same way as above.

[0162] Step S460: Based on the second carbon fixation estimation model and the second carbon emission estimation model, a carbon neutrality prediction model of the low-carbon garden is obtained.

[0163] It should be clear that after obtaining the second carbon fixation estimation model and the second carbon emission estimation model, the carbon neutrality prediction model of the low-carbon garden can be directly obtained. Specifically, the calculation formula of the carbon neutrality prediction model of the low-carbon garden is as follows:

[0164]

[0165] in, represents the carbon neutrality of the low-carbon garden in year t; represents the total biomass carbon storage of the low-carbon garden in year t; Represents the total carbon emissions of the low-carbon garden in year t.

[0166] Step S500: Based on the carbon neutrality prediction model, obtain the carbon neutrality prediction data of the low-carbon garden.

[0167] It should be clear that in the embodiments of the present application, it is only necessary to input the year into the carbon neutrality prediction model to obtain the carbon neutrality prediction data of the low-carbon garden. It should be clear that the carbon neutrality analysis and calculation method for low-carbon gardens proposed in the present application selects target local plots in the low-carbon garden that show a high similarity with the ecosystem of the low-carbon garden. And based on the target local plots, the first carbon fixation estimation model and the first carbon emission estimation model are accurately constructed. Finally, based on the first carbon fixation estimation model and the first carbon emission estimation model, the carbon neutrality prediction model of the low-carbon garden is obtained. Compared with the direct measurement method of the prior art, the method of the present application requires less labor cost, that is, it reduces the cost of use; compared with the indirect estimation method of the prior art, the labor invested in the model of the present application is also relatively small, and the estimation accuracy is high.

[0168] After introducing a carbon neutrality analysis and calculation method for a low-carbon garden proposed in an embodiment of the present application, the following introduces a carbon neutrality analysis and calculation system for a low-carbon garden proposed in an embodiment of the present application. Figure 2 As shown, the carbon neutrality analysis and calculation system 10 of the low-carbon garden includes:

[0169] The reader 11 is used to obtain the parameter data of the low-carbon garden and form a database;

[0170] The processor 12 is used to obtain a target local sample plot from the low-carbon garden based on the database; the target local sample plot belongs to the low-carbon garden, and the similarity of plant data between the target local sample plot and the low-carbon garden is greater than or equal to a first preset value;

[0171] And, based on the target local sample plot, obtaining a first carbon fixation estimation model and a first carbon emission estimation model of the target local sample plot;

[0172] And, based on the first carbon fixation estimation model and the first carbon emission estimation model, obtaining a carbon neutrality prediction model for the low-carbon garden;

[0173] And, based on the carbon neutrality prediction model, the carbon neutrality prediction data of the low-carbon garden is obtained.

[0174] As a specific embodiment of the present application, the parameter data of the low-carbon garden include the total area of ​​the low-carbon garden, vegetation types, vegetation coverage, plant diameter and root-to-stem ratio, biomass conversion factor, the number and type of buildings and public facilities in the low-carbon garden, the operating energy consumption of the low-carbon garden, the number of tourists in the low-carbon garden, and the activity intensity of tourists.

[0175] As a specific embodiment of the present application, the reader 11 is also used to obtain a bird's-eye view of the low-carbon garden;

[0176] The processor 12 is further configured to set a sliding window based on the bird's-eye view to obtain a plurality of first areas;

[0177] and obtaining pixel similarity between each first area and the bird's-eye view image;

[0178] And, based on the similarities of each pixel, a plurality of second regions are obtained; the second regions are any first regions whose pixel similarities are greater than a second preset value;

[0179] And, obtaining first local sample plots corresponding to each second area in the low-carbon garden;

[0180] The reader 11 is also used to obtain first plant data corresponding to the low-carbon garden based on the database;

[0181] and, based on the database, obtaining second plant data corresponding to each first local sample plot;

[0182] The processor 12 is further configured to obtain a plurality of plant data similarities based on each second plant data and the first plant data;

[0183] And, based on the similarities of multiple plant data, a target local sample plot is obtained from each first local sample plot.

[0184] As a specific embodiment of the present application, the processor 12 is further configured to obtain a third area based on the multiple first areas; the third area is any area among the multiple first areas;

[0185] And, obtaining a pixel mean value of each pixel point in the third area;

[0186] And, acquiring a first pixel value of each pixel point based on the bird's-eye view image;

[0187] And, based on each first pixel value and the pixel mean, a pixel similarity between the third area and the bird's-eye view image is obtained.

[0188] As a specific embodiment of the present application, the processor 12 is further configured to obtain the pixel similarity between the third area and the bird's-eye view image based on each first pixel value and the pixel mean value by using the following calculation formula:

[0189]

[0190] in, Indicates the bird's-eye view The pixel value of a pixel; represents the pixel mean of each pixel point in the b-th first area; Represents the minimum value function; Indicates the absolute value. It represents the mean value of the function value corresponding to different pixel points in the bird's-eye view; is an exponential function with a natural constant as its base.

[0191] As a specific embodiment of the present application, the processor 12 is further used to obtain, based on the first plant data, a first ecological weight corresponding to each plant in the low-carbon garden; the ecological weight is at least used to characterize the ratio of the number of each plant to the total number of all plants;

[0192] and, based on the second plant data, obtaining a second ecological weight corresponding to each plant in the first local sample plot;

[0193] And, based on the first ecological weight and the second ecological weight, the similarity of plant data between the first local sample plot and the low-carbon garden is obtained.

[0194] As a specific embodiment of the present application, the processor 12 is further used to obtain the similarity of plant data between the first local sample plot and the low-carbon garden based on the first ecological weight and the second ecological weight through the following calculation formula:

[0195]

[0196] in, Indicates The first local sample plot The ecological weight of plant species; It means that the first The ecological weight of plant species; Indicates The total number of plant species in the first local sample plot; Indicates the total number of plant species in the entire low-carbon garden; Indicates The first local sample plot The total number of plants grown; It means that the first The total number of plants grown; Indicates different calculations The standard deviation of the corresponding function value.

[0197] As a specific embodiment of the present application, the reader 11 is further used to obtain a first vegetation coverage rate of the target local sample plot based on the database;

[0198] And, based on the database, obtaining a second vegetation coverage rate of the low-carbon garden;

[0199] The processor 12 is further configured to obtain a confidence weight based on the first vegetation coverage rate and the second vegetation coverage rate;

[0200] And, based on the confidence weight and the first carbon sequestration estimation model, obtaining a second carbon sequestration estimation model for the low-carbon garden;

[0201] And, based on the confidence weight and the first carbon emission estimation model, obtaining a second carbon emission estimation model of the low-carbon garden;

[0202] And, based on the second carbon fixation estimation model and the second carbon emission estimation model, a carbon neutrality prediction model of the low-carbon garden is obtained.

[0203] It should be clear that the carbon neutrality analysis and calculation system for low-carbon gardens proposed in this application selects target local plots in low-carbon gardens that show high similarity with the ecosystem of low-carbon gardens. And based on the target local plots, the first carbon fixation estimation model and the first carbon emission estimation model are accurately constructed. Finally, based on the first carbon fixation estimation model and the first carbon emission estimation model, the carbon neutrality prediction model of the low-carbon garden is obtained. Compared with the direct measurement method of the prior art, the method of the present application requires less labor cost, that is, it reduces the cost of use; compared with the indirect estimation method of the prior art, the model of the present application also requires relatively less labor and has high estimation accuracy.

[0204] After introducing a carbon neutrality analysis and calculation system for a low-carbon garden proposed in an embodiment of the present application, the following introduces a computer-readable storage medium proposed in an embodiment of the present application, on which a computer program is stored. When the computer program is executed by the processor 12, it implements the carbon neutrality analysis and calculation method for the low-carbon garden as described in any of the above embodiments.

[0205] It should be clear that the computer-readable storage media in this application include permanent and non-permanent, removable and non-removable media that can be used to store information by any method or technology. Information can be computer-readable instructions, data structures, modules of programs or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, read-only compact disk read-only memory (CD-ROM), digital versatile disk (DVD) or other optical storage, magnetic cassettes, disk storage or other magnetic storage devices or any other non-transmission media that can be used to store information that can be accessed by a computing device. As defined herein, computer-readable media does not include temporary computer-readable media (transitory media), such as modulated data signals and carrier waves.

[0206] In the above embodiments, the description of each embodiment has its own emphasis. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0207] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the methods, devices and equipment described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0208] In the several embodiments provided in the embodiments of the present application, it should be understood that the disclosed devices, apparatuses and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of the modules is only a logical function division. There may be other division methods in actual implementation, such as multiple modules or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be an indirect coupling or communication connection through some interfaces, devices or modules, which can be electrical, mechanical or other forms.

[0209] The modules described as separate components may or may not be physically separated, and the components shown as modules may or may not be physical modules, that is, they may be located in one place or distributed on multiple network modules. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0210] In addition, each functional module in each embodiment of the present application can be integrated into a processing module, or each module can exist physically separately, or two or more modules can be integrated into one module. The above-mentioned integrated module can be implemented in the form of hardware or in the form of a software functional module. If the integrated module is implemented in the form of a software functional module and sold or used as an independent product, it can be stored in a computer-readable storage medium.

[0211] In the above embodiments, all or part of the embodiments may be implemented by software, hardware, firmware or any combination thereof. When implemented by software, all or part of the embodiments may be implemented in the form of a computer program product.

[0212] The computer program product includes one or more computer instructions. When the computer program is loaded and executed on a computer, the process or function described in the embodiment of the present application is generated in whole or in part. The computer may be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions may be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions may be transmitted from a website site, a computer, a server, or a data center to another website site, a computer, a server, or a data center by wired (e.g., coaxial cable, optical fiber, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium may be any available medium that a computer can store or a data storage device such as a server or a data center that includes one or more available media integrated. The available medium may be a magnetic medium (e.g., a floppy disk, a hard disk, a tape), an optical medium (e.g., a DVD), or a semiconductor medium (e.g., a solid-state hard disk Solid State Disk (SSD)), etc.

[0213] Although the embodiments of the present application have been shown and described, it will be appreciated by those skilled in the art that many changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles of the present application.

Claims

1. A carbon neutrality analysis and calculation method for low-carbon gardens, characterized in that: include: Acquiring parameter data of the low-carbon garden to form a database; Based on the database, the low-carbon garden obtains a target local sample plot; the target local sample plot belongs to the low-carbon garden, and the similarity of plant data between the target local sample plot and the low-carbon garden is greater than or equal to a first preset value; The step of obtaining a target local sample plot from the low-carbon garden based on the database includes: Obtaining a bird's-eye view of the low-carbon garden; Based on the bird's-eye view, a sliding window is set to obtain a plurality of first areas; Obtaining pixel similarity between each first area and the bird's-eye view image; Based on the similarities of each pixel, a plurality of second regions are obtained; the second region is any first region whose pixel similarity is greater than a second preset value; Acquire a first local sample plot corresponding to each second area in the low-carbon garden; Based on the database, obtaining first plant data corresponding to the low-carbon garden; Based on the database, obtaining second plant data corresponding to each first local sample plot; Based on each second plant data and the first plant data, obtaining a plurality of plant data similarities; Based on the similarity of multiple plant data, a target local sample plot is obtained from each first local sample plot; The obtaining of pixel similarities between each first area and the bird's-eye view image includes: Based on the multiple first areas, a third area is acquired; the third area is any area among the multiple first areas; Obtaining a pixel mean value of each pixel point in the third area; Acquire a first pixel value of each pixel point based on the bird's-eye view image; Based on each first pixel value and the pixel mean, obtaining pixel similarity between the third area and the bird's-eye view image; The calculation formula for obtaining the pixel similarity between the third area and the bird's-eye view image based on each first pixel value and the pixel mean is as follows: in, Indicates the bird's-eye view The pixel value of a pixel; represents the pixel mean of each pixel point in the b-th first area; Represents the minimum value function; Indicates the absolute value. It represents the mean value of the function value corresponding to different pixel points in the bird's-eye view; is an exponential function with a natural constant as base; The obtaining a plurality of plant data similarities based on each second plant data and the first plant data comprises: Based on the first plant data, obtaining a first ecological weight corresponding to each plant in the low-carbon garden; the ecological weight is at least used to represent the ratio of the number of each plant to the total number of all plants; Based on the second plant data, obtaining a second ecological weight corresponding to each plant in the first local sample plot; Based on the first ecological weight and the second ecological weight, obtaining the similarity of plant data between the first local sample plot and the low-carbon garden; The calculation formula for obtaining the similarity of the plant data between the first local sample plot and the low-carbon garden based on the first ecological weight and the second ecological weight is as follows: in, Indicates The first local sample plot The ecological weight of plant species; It means that the first The ecological weight of plant species; Indicates The total number of plant species in the first local sample plot; Indicates the total number of plant species in the entire low-carbon garden; Indicates The first local sample plot The total number of plants grown; It means that the first The total number of plants grown; Indicates different calculations The standard deviation of the corresponding function value; Based on the target local sample plot, obtaining a first carbon fixation estimation model and a first carbon emission estimation model for the target local sample plot; Based on the first carbon fixation estimation model and the first carbon emission estimation model, obtaining a carbon neutrality prediction model for the low-carbon garden; Based on the carbon neutrality prediction model, the carbon neutrality prediction data of the low-carbon garden is obtained.

2. The carbon neutrality analysis and calculation method for low-carbon gardens according to claim 1 is characterized in that: The parameter data of the low-carbon garden include the total area of ​​the low-carbon garden, vegetation types, vegetation coverage, plant diameter and root-to-stem ratio, biomass conversion factor, the number and type of buildings and public facilities in the low-carbon garden, the operating energy consumption of the low-carbon garden, the number of visitors to the low-carbon garden and the activity intensity of visitors.

3. The carbon neutrality analysis and calculation method for low-carbon gardens according to any one of claims 1 or 2, characterized in that: The step of obtaining the carbon neutrality prediction model of the low-carbon garden based on the first carbon fixation estimation model and the first carbon emission estimation model includes: Based on the database, obtaining a first vegetation coverage rate of the target local sample plot; Based on the database, obtaining a second vegetation coverage rate of the low-carbon garden; Acquire a confidence weight based on the first vegetation coverage rate and the second vegetation coverage rate; Based on the confidence weight and the first carbon sequestration estimation model, obtaining a second carbon sequestration estimation model for the low-carbon garden; Based on the confidence weight and the first carbon emission estimation model, obtaining a second carbon emission estimation model of the low-carbon garden; Based on the second carbon fixation estimation model and the second carbon emission estimation model, a carbon neutrality prediction model of the low-carbon garden is obtained.

4. A carbon neutrality analysis and calculation system for low-carbon gardens according to the carbon neutrality analysis and calculation method for low-carbon gardens according to any one of claims 1 to 3, characterized in that: include: A reader, used for acquiring parameter data of the low-carbon garden to form a database; A processor, configured to obtain a target local sample plot from the low-carbon garden based on the database; the target local sample plot belongs to the low-carbon garden, and the similarity of plant data between the target local sample plot and the low-carbon garden is greater than or equal to a first preset value; And, based on the target local sample plot, obtaining a first carbon fixation estimation model and a first carbon emission estimation model of the target local sample plot; And, based on the first carbon fixation estimation model and the first carbon emission estimation model, obtaining a carbon neutrality prediction model for the low-carbon garden; And, based on the carbon neutrality prediction model, the carbon neutrality prediction data of the low-carbon garden is obtained.

5. A computer-readable storage medium, characterized in that: A computer program is stored thereon, and when the computer program is executed by a processor, the carbon neutrality analysis and calculation method for a low-carbon garden as described in any one of claims 1 to 3 is implemented.

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