A method, device and electronic equipment for predicting carbon fixation of garden trees

Abstract

The present application provides a method, device and electronic equipment for predicting the carbon sequestration of garden trees, which relates to the field of carbon sink measurement, including obtaining basic information of target tree species and constructing a number of morphological simulation data, wherein the basic information includes fixed morphological information; performing volume estimation based on the morphological simulation data to obtain branch volume information corresponding to the fixed morphological information; combining the unit carbon content corresponding to the fixed morphological information and the branch volume information to obtain the branch carbon content; constructing a correlation between the fixed morphological information and the branch carbon content; obtaining an assessment task for the target tree species in the current garden; and combining the assessment task, the simulated growth model and the correlation to predict the future carbon sequestration of the target tree species in the current garden, so as to assist relevant personnel in evaluating the carbon sequestration potential of the target tree species in the current garden.

Description

Technical Field

[0001] The present invention relates to the field of carbon sink measurement, and in particular to a method, device and electronic equipment for predicting the carbon sequestration amount of garden trees. Background Art

[0002] In the landscaping industry, tree planting plays an important role in expanding the area of ​​landscaping and improving the greening effect. The estimation of the future carbon sequestration of trees is also an important part of the urban carbon sink capacity background survey and reserve assessment.

[0003] At present, the calculation of carbon sequestration of garden trees mainly refers to the measurement method of forestry carbon sink. There are many measurement methods for forestry carbon sink. However, due to the differences between garden trees and forestry trees in growth environment, management methods and functional positioning, the existing forestry carbon sink measurement method is not fully applicable to the prediction of carbon sequestration of garden trees.

[0004] Therefore, it is necessary to propose a method, device and electronic equipment for predicting the carbon fixation of garden trees. Summary of the invention

[0005] This manual provides a method, device and electronic equipment for predicting the carbon sequestration of garden trees. It predicts future carbon sequestration through morphological simulation, volume estimation, carbon content determination, and combines assessment tasks, branch carbon content and simulated growth models to assist relevant personnel in accurately assessing the carbon sequestration potential of target tree species in current gardens.

[0006] The present application provides a method for predicting the carbon sequestration of garden trees using the following technical solutions, including:

[0007] Obtaining basic information of target tree species and constructing a number of morphological simulation data, wherein the basic information includes fixed morphological information;

[0008] Performing volume estimation according to the morphological simulation data to obtain branch volume information corresponding to the fixed morphological information;

[0009] Combining the unit carbon content corresponding to the fixed morphological information and the branch volume information to obtain the branch carbon content; constructing a correlation relationship between the fixed morphological information and the branch carbon content;

[0010] Obtain the evaluation task of the target tree species in the current garden;

[0011] The future carbon sequestration of the target tree species in the current garden is predicted by combining the assessment task, the simulated growth model and the association relationship.

[0012] Optionally, the basic information of the target tree species is obtained to construct a plurality of morphological simulation data, including:

[0013] Collect basic information of the target tree species; the basic information includes: fixed morphological information;

[0014] Acquire data of several branches and trunks of the target tree species;

[0015] Based on the fixed morphological information and a plurality of the branch data, the morphological simulation data corresponding to the branch data is constructed.

[0016] Optionally, the step of performing volume estimation in combination with the morphological simulation data to generate branch volume information of the target tree species includes:

[0017] Performing volume quantification on the morphological simulation data to obtain original volume information of the target tree species;

[0018] Based on all the original volume information corresponding to the fixed morphological information, the branch volume information corresponding to the fixed morphological information in the target type of tree is determined.

[0019] Optionally, determining the branch volume information of the target type of tree based on the fixed morphological information and all the morphological correspondences includes:

[0020] All the original volume information corresponding to the fixed shape information is acquired, and an average value of all the original volume information is taken as the branch volume information corresponding to the fixed shape information.

[0021] Optionally, the step of combining the unit carbon content corresponding to the fixed morphological information and the branch volume information to obtain the branch carbon content; and constructing a correlation between the fixed morphological information and the branch carbon content includes:

[0022] Constructing a plant sample set corresponding to the target tree species;

[0023] Obtaining the original carbon content of the tree sample;

[0024] Based on all the original carbon contents corresponding to the same fixed form information, determining the unit carbon content corresponding to the fixed form information in the target tree species;

[0025] The branch volume information and the unit carbon content corresponding to the same fixed morphological information are combined to predict the branch carbon content corresponding to each fixed morphological information in the target tree species to obtain the association relationship.

[0026] Optionally, the assessment task includes: planting information and predicted range;

[0027] The combining of the assessment task, the simulated growth model and the association relationship to predict the future carbon sequestration of the target tree species in the current garden includes:

[0028] Generate a branch prediction result according to the planting information, the prediction range and the simulated growth model;

[0029] Based on the branch prediction results, the corresponding branch carbon content is searched to predict the future carbon fixation of the target tree species.

[0030] Optionally, it also includes: constructing a simulated growth model of the target tree species;

[0031] Optionally, constructing a simulated growth model of the target tree species includes:

[0032] Constructing the initial state of the target tree species and determining one or more growth nodes;

[0033] Setting a growth rule; the growth rule includes a plurality of growth sub-models; wherein the growth sub-models are determined respectively in combination with the characteristics of different growth parts of the target tree species;

[0034] The growth node is adjusted based on the growth sub-model.

[0035] The present application provides a device for predicting the carbon sequestration of garden trees, which adopts the following technical solutions, including:

[0036] A simulation module is used to obtain basic information of target tree species and construct a number of morphological simulation data, wherein the basic information includes fixed morphological information;

[0037] A volume estimation module, used to perform volume estimation based on the morphological simulation data to obtain branch volume information corresponding to the fixed morphological information;

[0038] A carbon content estimation module is used to combine the unit carbon content corresponding to the fixed shape information and the branch volume information to obtain the branch carbon content; and to establish a correlation between the fixed shape information and the branch carbon content;

[0039] The task acquisition module is used to obtain the evaluation tasks of the target tree species in the current garden;

[0040] The carbon fixation prediction module is used to predict the future carbon fixation of the target type of trees in the current garden by combining the evaluation task, the simulated growth model and the association relationship.

[0041] Optionally, the simulation module includes:

[0042] The basic information collection submodule is used to collect the basic information of the target tree species; the basic information includes: fixed morphological information;

[0043] A branch data acquisition submodule is used to acquire a number of branch data of the target tree species;

[0044] The morphology simulation data construction submodule is used to construct the morphology simulation data corresponding to the branch data based on the fixed morphology information and a plurality of the branch data.

[0045] Optionally, the volume estimation module includes:

[0046] A volume quantification submodule, used to perform volume quantification on the morphological simulation data to obtain original volume information of the target tree species;

[0047] The branch volume determination submodule is used to determine the branch volume information corresponding to the fixed morphological information in the target type of tree according to all the original volume information corresponding to the fixed morphological information.

[0048] Optionally, the branch volume determination submodule includes:

[0049] The volume optimization unit is used to obtain all the original volume information corresponding to the fixed shape information, and take the average value of all the original volume information as the branch volume information corresponding to the fixed shape information.

[0050] Optionally, the carbon content estimation module includes:

[0051] A plant sample set construction submodule is used to construct a plant sample set corresponding to the target tree species;

[0052] A carbon content corresponding relationship construction submodule is used to obtain the original carbon content of the tree sample;

[0053] A unit carbon content calculation submodule, used to determine the unit carbon content corresponding to the fixed form information in the target type of tree based on all the original carbon contents corresponding to the same fixed form information;

[0054] The branch carbon content prediction submodule is used to combine the branch volume information and the unit carbon content corresponding to the same fixed morphological information, predict the branch carbon content corresponding to each fixed morphological information in the target type of tree, and obtain the association relationship.

[0055] Optionally, the assessment task includes: planting information and predicted range;

[0056] Optionally, the carbon sequestration prediction module includes:

[0057] A branch prediction submodule, used for generating a branch prediction result according to the planting information, the prediction range and the simulated growth model;

[0058] The carbon fixation prediction submodule is used to find the corresponding branch carbon content based on the branch prediction result and predict the future carbon fixation of the target tree species.

[0059] Optionally, it also includes: a growth model construction module, used to construct a simulated growth model of the target tree species;

[0060] The growth model building module comprises:

[0061] An initial state construction submodule is used to construct the initial state of the target tree species and determine one or more growth nodes;

[0062] A configuration submodule is used to set a growth rule; the growth rule includes a plurality of growth submodels; wherein the growth submodels are determined respectively in combination with the characteristics of different growth parts of the target tree species;

[0063] An adjustment submodule is used to adjust the growth node based on the growth submodel.

[0064] This specification also provides an electronic device, wherein the electronic device includes:

[0065] processor; and,

[0066] A memory storing computer executable instructions, which when executed cause the processor to perform any of the above methods.

[0067] The present specification also provides a computer-readable storage medium, wherein the computer-readable storage medium stores one or more programs, and when the one or more programs are executed by a processor, any of the above methods is implemented.

[0068] In the present application, by acquiring basic information of target species of trees, a number of morphological simulation data are constructed, wherein the basic information includes fixed morphological information; volume estimation is performed according to the morphological simulation data to obtain branch volume information corresponding to the fixed morphological information; the branch carbon content is obtained by combining the unit carbon content corresponding to the fixed morphological information and the branch volume information; a correlation between the fixed morphological information and the branch carbon content is constructed; an assessment task for target species of trees in the current garden is obtained; and the future carbon fixation of target species of trees in the current garden is predicted by combining the assessment task, the simulated growth model and the correlation, thereby improving the prediction accuracy of the carbon fixation of trees in the garden, so as to improve the scientificity and accuracy of carbon fixation benefit assessment. BRIEF DESCRIPTION OF THE DRAWINGS

[0069] Figure 1 A schematic diagram of the principle of a method for predicting carbon sequestration of garden trees provided in an embodiment of this specification;

[0070] Figure 2 Another schematic diagram of a process for predicting carbon sequestration of garden trees provided in an embodiment of this specification;

[0071] Figure 3 A schematic diagram of the structure of a device for predicting the carbon fixation of garden trees provided in an embodiment of this specification;

[0072] Figure 4 A schematic diagram of the structure of an electronic device provided in an embodiment of this specification;

[0073] Figure 5 A schematic diagram of a computer-readable medium provided for an embodiment of this specification. DETAILED DESCRIPTION

[0074] The following description is used to disclose the present invention so that those skilled in the art can implement the present invention. The preferred embodiments described below are only examples, and those skilled in the art can think of other obvious variations. The basic principles of the present invention defined in the following description can be applied to other embodiments, variations, improvements, equivalents, and other technical solutions that do not deviate from the spirit and scope of the present invention.

[0075] Exemplary embodiments of the present invention will now be described more fully with reference to the accompanying drawings. However, exemplary embodiments can be implemented in a variety of forms, and should not be construed as limiting the present invention to the embodiments set forth herein. On the contrary, providing these exemplary embodiments enables the present invention to be more comprehensive and complete, and is more convenient for fully conveying the inventive concept to those skilled in the art. The same reference numerals in the figures represent the same or similar elements, components or parts, and thus their repeated description will be omitted.

[0076] Under the premise of being consistent with the technical concept of the present invention, the features, structures, characteristics or other details described in a specific embodiment do not exclude that they can be combined in one or more other embodiments in a suitable manner.

[0077] In the description of specific embodiments, the features, structures, characteristics or other details described in the present invention are intended to enable those skilled in the art to fully understand the embodiments. However, it does not exclude that those skilled in the art can practice the technical solutions of the present invention without one or more of the specific features, structures, characteristics or other details.

[0078] The flowcharts shown in the accompanying drawings are only exemplary and do not necessarily include all the contents and operations / steps, nor must they be executed in the order described. For example, some operations / steps can be decomposed, and some operations / steps can be combined or partially combined, so the actual execution order may change according to actual conditions.

[0079] The block diagrams shown in the accompanying drawings are merely functional entities and do not necessarily correspond to physically independent entities. That is, these functional entities may be implemented in software form, or in one or more hardware modules or integrated circuits, or in different networks and / or processor devices and / or microcontroller devices.

[0080] The term "and / or" or "and / or" includes all combinations of any one or more of the associated listed items.

[0081] Figure 1 A schematic diagram of the principle of a method for predicting carbon sequestration of garden trees provided in an embodiment of this specification, the method comprising:

[0082] S2 obtains basic information of target tree species and constructs a number of morphological simulation data, wherein the basic information includes fixed morphological information;

[0083] S3 performs volume estimation according to the morphological simulation data to obtain branch volume information corresponding to the fixed morphological information;

[0084] S4: combining the unit carbon content corresponding to the fixed shape information and the branch volume information to obtain the branch carbon content; and constructing a correlation relationship between the fixed shape information and the branch carbon content;

[0085] S5 obtains the evaluation task of the target tree species in the current garden;

[0086] S6 combines the assessment task, the simulated growth model and the association relationship to predict the future carbon fixation of the target type of trees in the current garden.

[0087] In the landscaping industry, newly planted trees are an important component of urban carbon sinks, and the estimation of their carbon sink volume is crucial for the assessment of urban carbon sink capacity.

[0088] In the current stage of landscaping, the carbon sequestration of landscaping trees is mainly estimated by referring to the measurement methods of forestry carbon sinks. Although there are many methods for measuring forestry carbon sinks, including: accumulation method, eddy method, remote sensing method, expansion factor method, allometric method, planting type-area method, photosynthetic rate method, etc., the above-mentioned forestry carbon sink measurement methods cannot be effectively used to predict the carbon sequestration of landscaping trees. Specifically:

[0089] (1) The volume method estimates carbon storage mainly based on the volume of trees. However, in landscaping, due to the diversity of tree species and complex growth environments, volume measurements are often not accurate enough, resulting in large errors in carbon sink estimation.

[0090] (2) The eddy method estimates carbon sinks by measuring eddy-related fluxes, but this method is limited by the measurement period and cannot provide long-term carbon sink assessments.

[0091] (3) Remote sensing methods use satellite or drone images to analyze vegetation coverage and growth conditions to estimate carbon storage. However, in landscaping, the application of remote sensing methods is limited because trees are scattered and the area is relatively small.

[0092] (4) The expansion factor method and the allometric method require extensive and in-depth survey data. Both methods require detailed tree growth and biomass data, but these data are often difficult to obtain in landscaping.

[0093] (5) The planting type-area method estimates carbon sequestration based on planting type and area. However, the tree species, growth rate, and carbon storage vary greatly in different regions. Therefore, detailed investigations are needed to obtain specific parameters for local conditions.

[0094] (6) The photosynthetic rate method estimates carbon fixation by measuring the photosynthesis rate of plants. However, this method requires long-term monitoring and complex parameter derivation, which is costly and complex to operate.

[0095] The above method is mainly aimed at estimating carbon sequestration at the forest scale. Based on this, in order to improve the prediction efficiency and accuracy of carbon sequestration of garden trees, the present invention provides a prediction method for carbon sequestration of garden trees based on digital simulation technology, which is used to calculate small-scale and scattered greening carbon sequestration. Specifically, Figure 2 As shown, the method includes:

[0096] S1 builds a simulated growth model of the target tree species;

[0097] A simulated growth model of the target tree species is constructed based on the L-system (Lindenmayer system) to simulate the growth process of the target tree species.

[0098] Arbors are tall trees with independent trunks growing from the roots, and the trunk and crown are clearly distinguished. There are many types of arbor, and each type of arbor has a different growth trend. Therefore, different types of arbor need to construct simulation growth models separately in the future to predict carbon sequestration.

[0099] In order to efficiently store and manage the data of virtual plant morphology and facilitate dynamic simulation, a morphological information management database is also constructed; the morphological information management database includes: growth equation parameters, L-system strings, growth node parameters, etc. Growth node parameters include but are not limited to: growth node location, height, branch angle and other attributes. At the same time, a unique identifier is assigned to each node for reference in subsequent calculations.

[0100] For ease of management, the morphological information management database uses data structures such as linked lists, trees or graphs to store and manage the growth paths and branching of plants, so as to dynamically add and delete nodes to adapt to changes in plant morphology.

[0101] Specifically, in the L-system, the initial state of the target tree is constructed, and one or more growth nodes are determined. The growth nodes may be parts such as roots, stems or leaves of the target tree.

[0102] Set growth rules; growth rules include but are not limited to: growth direction, speed, length and branch angle, etc. Preferably, the growth rules include several growth sub-models; combined with the characteristics of different growth parts of the target species of trees, the growth sub-models are determined respectively to construct growth rules; wherein the growth parts include but are not limited to: breast diameter, tree height, crown width. Growth models include but are not limited to: Logistic equation, Gompertz equation, Richards equation, etc. Different equations can describe the changes in various parts of the plant at different growth stages. Adjust the growth node based on the growth sub-model.

[0103] In one embodiment of the present specification, the change of DBH is predicted based on the Weibul equation, and the growth rules of DBH of target tree species are constructed. Taking camphor tree as an example, the growth rules of DBH include:

[0104]

[0105] Based on the Richards equation, the tree height change prediction is carried out to construct the growth rules of the target tree species. Taking camphor as an example, the tree height growth rules include:

[0106]

[0107] Based on the Logistic equation, the crown width change prediction is carried out, and the crown width growth rules of the target tree species are constructed. Taking camphor as an example, the crown width growth rules include:

[0108]

[0109] Among them, X1 is the current tree age.

[0110] The growth process of the target tree species is described by the growth rule. Specifically, the node information of each growth node is parsed according to the L-system string generated by the growth rule. The node information includes: node position, node type (such as stem, branch, leaf), node length, node angle, etc.

[0111] According to the continuous iteration of growth rules, the complex morphology of the target tree species is generated based on the L-system. In each iteration, the growth node will generate new nodes and branches according to the rules, thus simulating the growth process of the target tree species.

[0112] By simulating the growth model, the present invention can simulate the growth process of the target species of trees based on the species and age of the trees, and convert the calculated plant morphological data into a visual three-dimensional digital model by selecting appropriate three-dimensional tree modeling tools (such as Speed-Tree, OpenGL, DirectX, Three.js).

[0113] S2 obtains basic information of target tree species and constructs a number of morphological simulation data, wherein the basic information includes fixed morphological information;

[0114] S21 collects basic information of the target tree species;

[0115] The basic information includes: target species and basic morphological information. The basic morphological information includes several fixed morphological information. That is, the basic information includes fixed morphological information. Preferably, the fixed morphological information may be: tree height, diameter at breast height, crown width, etc.

[0116] In one embodiment of the present specification, the target type of the tree and the basic morphological information of the tree can be preset by the user. Of course, the present invention can obtain several basic information to finally obtain the corresponding relationship between the fixed morphological information and the carbon content of the branches and trunks.

[0117] S22 obtains data of several branches and trunks of the target tree species;

[0118] Based on the fixed morphological information, several branch data of the target tree species are obtained. Preferably, the fixed morphological information includes but is not limited to: diameter at breast height. In one embodiment of the present specification, m branch data of the same diameter at breast height are obtained, where m∈N + .

[0119] The branch data include: the main trunk of the tree and the diameter, length, number, etc. of branches at all levels.

[0120] The branch data can be obtained based on field measurements, literature review, real-life photo processing, and existing data from simulation software.

[0121] Specifically, in one embodiment of the present specification, random selection is performed based on a data set of the morphology and structure of the trees contained in a simulated growth model of the trees of the target species.

[0122] In another embodiment of the present specification, firstly determine a branch data of a target type of tree, and then randomly generate other branch data by simulating and controlling the growth conditions such as the direction and curvature of the secondary branches.

[0123] S23 constructs the morphology simulation data corresponding to the branch data based on the fixed morphology information and the plurality of branch data;

[0124] Each branch data corresponds to a morphological simulation data.

[0125] In one embodiment of the present specification, basic information of a target type of tree is obtained, and a number of morphological simulation data are constructed based on a simulated growth model; specifically, based on the fractal theory of plants, with the aid of a simulated growth model of the target type of tree, a fractal algorithm is used to determine the basic information of the target type of tree; after obtaining the branch data, a digital model of the tree is obtained by combining the fixed morphological information and the branch data as the morphological simulation data of the target type of tree.

[0126] Of course, in order to improve the simulation effect of the morphological simulation data, the morphological simulation data can also be improved and corrected based on manual processing.

[0127] S3 performs volume estimation according to the morphological simulation data to obtain branch volume information corresponding to the fixed morphological information;

[0128] S31 performs volume quantification on the morphological simulation data to obtain original volume information of the target tree species;

[0129] In one embodiment of the present specification, the morphological simulation data is processed and quantified by Rhino software to obtain original volume information corresponding to the morphological simulation data.

[0130] In the above steps, for the same type of trees, multiple morphological simulation data are generated. Each morphological simulation data is processed respectively to obtain the original volume information corresponding to the morphological simulation data. That is, each morphological simulation data corresponds to an original volume information x.

[0131] S32 determines the branch volume information corresponding to the fixed morphological information in the target type of tree based on all the original volume information corresponding to the fixed morphological information.

[0132] S321 builds a volume optimization model;

[0133] Volume Optimization Model:

[0134] Y b represents the branch volume information of the target tree species when the diameter at breast height is b, x i Represents the i-th original volume information.

[0135] S322 substitutes all the original volume information corresponding to the fixed shape information into the volume optimization model to obtain the branch volume information corresponding to the fixed shape information.

[0136] The fixed morphological information in the basic information is associated with m branch data, each branch data corresponds to a morphological simulation data, and each morphological simulation data corresponds to an original volume information. Therefore, the same fixed morphological information is associated with m original volume information. That is, the same diameter at breast height is associated with m original volume information.

[0137] All original volume information of the same DBH is obtained, and an original volume information group is constructed, where the DBH corresponds to the original volume information group one by one.

[0138] In order to improve subsequent volume optimization, in one embodiment of the present specification, data cleaning is performed on the original volume information group;

[0139] The original volume information in the original volume information group is identified, and when the difference between the original volume information and other original volume information is greater than a first preset error, the original volume information is determined to be abnormal original volume information; and the abnormal original volume information is removed from the original volume information group.

[0140] After data cleaning, the original volume information group includes n original volume information x i , where n≤m, n∈N + , 1≤i≤n.

[0141] Based on the original volume information group, all the original volume information corresponding to the fixed form information is acquired, and an average value of all the original volume information is used as the branch volume information corresponding to the fixed form information.

[0142] Specifically, all original volume information corresponding to the diameter at breast height b is obtained, substituted into the volume optimization model, and the average value of all original volume information is calculated and used as the branch volume information corresponding to the diameter at breast height b in the target tree species.

[0143] According to all the original volume information corresponding to the fixed morphological information, the branch volume information corresponding to each fixed morphological information in the target tree type is determined. That is, based on this step, the branch volume information corresponding to different breast diameters in each target tree type can be calculated.

[0144] In one embodiment of the present specification, taking a camphor tree with a diameter of 15 cm as an example, the target tree species is camphor tree with a breast diameter of 15 cm. By simulating and controlling the growth conditions such as the direction and curvature of secondary branches, 10 groups of digital models of camphor trees with a breast diameter of 15 cm are generated as morphological simulation data; combined with the morphological simulation data, 10 corresponding groups of original volume information are calculated; after data cleaning of the original volume information, the average value of the original volume information is calculated through the volume optimization model, and finally the branch volume information of the camphor tree with a breast diameter of 15 cm is obtained.

[0145] S4: combining the unit carbon content corresponding to the fixed shape information and the branch volume information to obtain the branch carbon content; and constructing a correlation relationship between the fixed shape information and the branch carbon content;

[0146] S41 constructs a plant sample set corresponding to the target tree species;

[0147] The plant sample set includes a number of tree samples; the tree samples are target species, wherein the tree samples are preferably plant samples of the woody parts of the trunk of the tree.

[0148] Specifically, N trees of the target type with the same DBH were randomly selected as original trees; for each original tree, sampling was performed once in each season, that is, each original tree was sampled 4 times in total; for trees of the target type with the same DBH, a total of 4N tree samples were obtained.

[0149] S42 obtains the original carbon content of the tree sample;

[0150] Specifically, the carbon content of tree samples in the plant sample set is measured by an element analyzer to obtain the original carbon content.

[0151] The original tree includes four tree samples. Get the original carbon content x of the four tree samples of each original tree ′ .

[0152] S43, based on all the original carbon contents corresponding to the same fixed form information, determining the unit carbon content corresponding to the fixed form information in the target type of tree;

[0153] S431 builds a carbon content optimization model;

[0154] Carbon content optimization model

[0155] Y′ b Indicates the unit carbon content when the diameter at breast height is b, x′ i′ Represents the carbon content of the i′th unit.

[0156] S432 substitutes the original carbon content corresponding to each breast diameter into the carbon content optimization model to obtain the unit carbon content corresponding to the breast diameter.

[0157] The fixed morphological information in the basic information is associated with 4N tree samples, and each tree sample corresponds to an original carbon content. Therefore, the same fixed morphological information is associated with 4N original carbon contents. That is, the same DBH is associated with 4N original carbon contents x ′ .

[0158] All original carbon contents corresponding to the same DBH are obtained, and an original carbon content group is constructed, with the DBH corresponding to the original carbon content group one by one.

[0159] In order to improve subsequent carbon content prediction, in one embodiment of the present specification, data cleaning is performed on the original carbon content group;

[0160] The original carbon content in the original carbon content group is identified, and when the difference between the original carbon content and other original carbon contents is greater than a second preset error, the original carbon content is determined to be an abnormal original carbon content; and the abnormal original carbon content is removed from the original carbon content group.

[0161] After data cleaning, the original carbon content group includes n ′ Original carbon content x i ′ ′ , where n ′ ≤4N, n ′ ∈N + , 1≤i ′ ≤n ′ .

[0162] Based on the original carbon content group, all the original carbon contents corresponding to the fixed form information are obtained, and the average value of all the original carbon contents is used as the unit carbon content corresponding to the fixed form information.

[0163] Specifically, all original carbon contents corresponding to the breast diameter b are obtained, substituted into the carbon content optimization model, and the average value of all original carbon contents is calculated and used as the unit carbon content of the target tree species corresponding to the breast diameter b.

[0164] Based on all the original carbon contents corresponding to the same fixed morphological information, the unit carbon content corresponding to each fixed morphological information in the target tree species is determined. That is, based on this step, the unit carbon content corresponding to different breast diameters of the target tree species can be calculated.

[0165] S44 combines the branch volume information and the unit carbon content corresponding to the same fixed morphological information to predict the branch carbon content corresponding to each fixed morphological information in the target tree species to obtain the association relationship.

[0166] Multiply the branch volume information corresponding to the diameter at breast height b by the unit carbon content corresponding to the diameter at breast height b to obtain the branch carbon content corresponding to the diameter at breast height b in the target tree species.

[0167] Based on the above steps, the branch volume information and unit carbon content corresponding to each DBH of each target type of tree can be obtained. Based on the product of the branch volume information and unit carbon content of the same DBH in the same target type of tree, the branch carbon content corresponding to the DBH in the target type of tree can be obtained. Furthermore, based on this, the correlation between different DBH and branch carbon content of trees of different target types can be summarized to facilitate the prediction of carbon fixation in the later stage and improve the prediction efficiency. The correlation includes: target type of tree - DBH - branch carbon content.

[0168] The present invention calculates the carbon content of branches and trunks based on the detection results of the carbon content in the tree samples, thereby avoiding the huge differences in measurement results at different time periods caused by plant respiration.

[0169] S5 obtains the evaluation task of the target tree species in the current garden;

[0170] Assessment tasks include: planting information and forecast coverage;

[0171] Planting information includes: growing conditions and age of trees of each target species.

[0172] The prediction range includes: a prediction time period for clearly predicting the future carbon fixation amount. In one embodiment of the present specification, the prediction range includes: prediction years arranged in order. For example, the current year is 20XX, and the carbon fixation amount in the next Y years is predicted, then the prediction time range includes: 20XX, 20XX+1, 20XX+2, ..., 20XX+Y, and each year is a prediction year.

[0173] S6 combines the assessment task, the simulated growth model and the association relationship to predict the future carbon fixation of the target type of trees in the current garden.

[0174] S61, generating a branch prediction result according to the planting information, the prediction range and the simulated growth model;

[0175] The planting information is input into the simulated growth model, and the corresponding branch prediction results are obtained according to the tree age and prediction range of the target tree species in the current garden; the branch prediction results include: the target morphological information of each target tree species in each year within the prediction range. The target morphological information includes: diameter at breast height, height, and crown width.

[0176] Preferably, based on the sequentially arranged forecast years, the morphological changes of each target species of trees within the forecast range in each forecast year are predicted in turn to obtain branch and trunk prediction results.

[0177] S62 searches for the corresponding branch carbon content based on the branch prediction result, and predicts the future carbon fixation amount of the target tree species.

[0178] Based on the forecast time range, determine the current forecast period;

[0179] According to the target morphological information in the current forecast period, the carbon content of branches and trunks of the corresponding target tree species is searched. Specifically, the diameter at breast height in the current forecast period is determined, and based on the corresponding relationship between the diameter at breast height and the carbon content of branches and trunks, the carbon content of branches and trunks corresponding to the diameter at breast height in the current forecast period is searched.

[0180] In another embodiment of the present specification, if the corresponding branch carbon content cannot be found, the branch carbon content corresponding to the target morphological information can be generated based on the target morphological information according to the above steps S2-S4, and the specific steps are not repeated here.

[0181] Calculate the predicted carbon sequestration amount of the target tree species in the current predicted year based on the target morphological information and branch carbon content of each target tree species in the current predicted year;

[0182] The total carbon content of the current forecast year is obtained; based on the difference between the total carbon content of the forecast year and the two consecutive years before the forecast year, the carbon increase is determined; and the carbon increase is used as the predicted carbon fixation amount of the target species of trees in the current forecast year.

[0183] The predicted carbon sequestration of all target tree species in the current garden is accumulated to obtain the total carbon sequestration of target tree species within the current predicted period.

[0184] In one embodiment of the present specification, target trees are classified according to their age, and the number of target trees of the same age is determined, that is, the number of target trees of the age α is N. α .

[0185] Predict the target morphological information of the target tree species with age α in the current prediction period, find the corresponding branch carbon content, obtain the total carbon content of the target tree species with age α in the current prediction period, and then obtain the predicted carbon fixation amount C of the target tree species with age α in the current prediction period. α ; Get the first predicted carbon fixation N of all target species of trees with age α α ×C α ; Sum all the first predicted carbon fixation amounts involved in the current garden to obtain the total carbon fixation amount of the target tree species within the current predicted period∑ α (Nα ×C α ).

[0186] The year after the current forecast period is taken as the new current forecast period, and the total carbon sequestration of trees of the target species within the new current forecast period is obtained through the above steps.

[0187] Summarize the total carbon sequestration in each forecast year to determine the forecast results of future carbon sequestration.

[0188] The present invention achieves accurate estimation of the volume of tree branches by acquiring basic information of target tree species and constructing morphological simulation data; by combining unit carbon content information, the carbon content of tree branches can be accurately determined, providing a reliable basis for carbon sequestration assessment. In this process, there is no need to cut down trees, and the test time is shortened.

[0189] In addition, by obtaining the current assessment tasks in the garden and combining the carbon content of branches and trunks with the simulated growth model, the future carbon sequestration of the target tree species is predicted, so that relevant personnel can evaluate the carbon sequestration potential and environmental impact of the target tree species in the current garden within the next Y years, which provides a scientific basis for garden planning and management, helps to improve the carbon sequestration capacity of the garden, and promotes ecological environmental protection and sustainable development.

[0190] Figure 3 A schematic diagram of a device for predicting carbon fixation of garden trees provided in an embodiment of this specification, the device comprising:

[0191] The simulation module 320 is used to obtain basic information of the target tree species and construct a plurality of morphological simulation data, wherein the basic information includes fixed morphological information;

[0192] A volume estimation module 330, configured to perform volume estimation based on the morphological simulation data to obtain branch volume information corresponding to the fixed morphological information;

[0193] The carbon content estimation module 340 is used to combine the unit carbon content corresponding to the fixed shape information and the branch volume information to obtain the branch carbon content; and to establish a correlation relationship between the fixed shape information and the branch carbon content;

[0194] The task acquisition module 350 is used to acquire the evaluation task of the target tree species in the current garden;

[0195] The carbon fixation prediction module 360 ​​is used to predict the future carbon fixation of the target type of trees in the current garden by combining the evaluation task, the simulated growth model and the association relationship.

[0196] Optionally, the simulation module 320 includes:

[0197] The basic information collection submodule is used to collect the basic information of the target tree species; the basic information includes: fixed morphological information;

[0198] A branch data acquisition submodule is used to acquire a number of branch data of the target tree species;

[0199] The morphology simulation data construction submodule is used to construct the morphology simulation data corresponding to the branch data based on the fixed morphology information and a plurality of the branch data.

[0200] Optionally, the volume estimation module 330 includes:

[0201] A volume quantification submodule, used to perform volume quantification on the morphological simulation data to obtain original volume information of the target tree species;

[0202] The branch volume determination submodule is used to determine the branch volume information corresponding to the fixed morphological information in the target type of tree according to all the original volume information corresponding to the fixed morphological information.

[0203] Optionally, the branch volume determination submodule includes:

[0204] The volume optimization unit is used to obtain all the original volume information corresponding to the fixed shape information, and take the average value of all the original volume information as the branch volume information corresponding to the fixed shape information.

[0205] Optionally, the carbon content estimation module 340 includes:

[0206] A plant sample set construction submodule is used to construct a plant sample set corresponding to the target tree species;

[0207] A carbon content corresponding relationship construction submodule is used to obtain the original carbon content of the tree sample;

[0208] A unit carbon content calculation submodule, used to determine the unit carbon content corresponding to the fixed form information in the target type of tree based on all the original carbon contents corresponding to the same fixed form information;

[0209] The branch carbon content prediction submodule is used to combine the branch volume information and the unit carbon content corresponding to the same fixed morphological information, predict the branch carbon content corresponding to each fixed morphological information in the target type of tree, and obtain the association relationship.

[0210] Optionally, the assessment task includes: planting information and predicted range;

[0211] Optionally, the carbon sequestration prediction module 360 ​​includes:

[0212] A branch prediction submodule, used for generating a branch prediction result according to the planting information, the prediction range and the simulated growth model;

[0213] The carbon fixation prediction submodule is used to find the corresponding branch carbon content based on the branch prediction result and predict the future carbon fixation of the target tree species.

[0214] Optionally, it also includes: a growth model construction module, used to construct a simulated growth model of the target tree species;

[0215] The growth model building module comprises:

[0216] An initial state construction submodule is used to construct the initial state of the target tree species and determine one or more growth nodes;

[0217] A configuration submodule is used to set a growth rule; the growth rule includes a plurality of growth submodels; wherein the growth submodels are determined respectively in combination with the characteristics of different growth parts of the target tree species;

[0218] An adjustment submodule is used to adjust the growth node based on the growth submodel.

[0219] The functions of the device in the embodiment of the present invention have been described in the above method embodiment, so for details not provided in the description of this embodiment, please refer to the relevant description in the above embodiment, and no further description will be given here.

[0220] It will be appreciated by those skilled in the art that embodiments of the present invention may be provided as methods, devices, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0221] The electronic device embodiment of the present invention is described below, and the electronic device can be regarded as a physical implementation of the method and device embodiments of the present invention described above. The details described in the electronic device embodiment of the present invention should be regarded as a supplement to the above method or device embodiments; details not disclosed in the electronic device embodiment of the present invention can be implemented with reference to the above method or device embodiments.

[0222] Figure 4 A schematic diagram of the structure of an electronic device provided in an embodiment of this specification. Figure 4 The computer device shown is only an example and should not bring any limitation to the functions and scope of use of the embodiments of the present invention.

[0223] like Figure 4 As shown, the computer device 400 of this exemplary embodiment is in the form of a general data processing device. The components of the computer device 400 may include but are not limited to: at least one processor 410, at least one memory 420, a network interface 430, a display unit 440, an input component 450, etc.

[0224] The memory 420 stores a computer-readable program, which may be a source program or a code of a read-only program. The program may be executed by the processor 410, so that the processor 410 performs the steps of various embodiments of the present invention. For example, the processor 410 may perform the following steps: Figure 1 Steps shown.

[0225] The memory 420 may include a readable medium in the form of a volatile storage unit, such as a random access memory unit (RAM) and / or a cache memory unit, and may further include a read-only memory unit (ROM). The memory 420 may also include a program / utility having a set (at least one) of program modules, such program modules including but not limited to: an operating system, one or more application programs, other program modules, and program data, each of which or some combination may include the implementation of a network environment.

[0226] Also included is a bus (not shown) which may represent one or more of several types of bus structures, including a memory unit bus or memory unit controller, a peripheral bus, an accelerated graphics port, a processing unit, or a local bus using any of a variety of bus architectures.

[0227] The computer device 400 may also communicate with one or more external devices (e.g., keyboard, display, network device, Bluetooth device, etc.) so that a user can interact with the computer device 400 via these external devices, and / or so that the computer device 400 can communicate with one or more other data processing devices (e.g., routers, modems, etc.). Such communication may be performed through the network interface 430, and may also be performed through a network adapter with one or more networks (e.g., local area network (LAN), wide area network (WAN) and / or public network, such as the Internet). The network adapter may communicate with other modules of the computer device 400 via a bus. It should be understood that, although not shown in the figure, other hardware and / or software modules may be used in the computer device 400, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage systems.

[0228] Figure 5Schematic diagram of a computer readable medium embodiment of the present invention. Figure 5 As shown, the computer program can be stored on one or more computer-readable media. The computer-readable medium can be a readable signal medium or a readable storage medium. The readable storage medium can be, for example, but not limited to, a system, device or device of electricity, magnetism, light, electromagnetic, infrared, or semiconductor, or any combination of the above. More specific examples (non-exhaustive list) of readable storage media include: an electrical connection with one or more wires, a portable disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. When the computer program is executed by one or more data processing devices, the computer-readable medium is enabled to implement the above method of the present invention.

[0229] Through the description of the above implementation modes, it is easy for those skilled in the art to understand that the exemplary embodiments described in the present invention can be implemented by software, or by software combined with necessary hardware. Therefore, the technical solution according to the implementation mode of the present invention can be embodied in the form of a software product, which can be stored in a computer-readable storage medium (which can be a CD-ROM, a USB flash drive, a mobile hard disk, etc.) or on a network, and includes several instructions to enable a data processing device (which can be a personal computer, a server, or a network device, etc.) to perform the above method according to the present invention.

[0230] The computer readable storage medium may include a data signal propagated in a baseband or as part of a carrier wave, wherein a readable program code is carried. This propagated data signal may take a variety of forms, including but not limited to an electromagnetic signal, an optical signal, or any suitable combination of the above. The readable storage medium may also be any readable medium other than a readable storage medium, which may send, propagate, or transmit a program for use by an instruction execution system, an apparatus, or a device or used in combination with it. The program code contained on the readable storage medium may be transmitted with any appropriate medium, including but not limited to wireless, wired, optical cable, RF, etc., or any suitable combination of the above.

[0231] Program code for performing the operations of the present invention may be written in any combination of one or more programming languages, including object-oriented programming languages ​​such as Java, C++, etc., and conventional procedural programming languages ​​such as "C" or similar programming languages. The program code may be executed entirely on the user computing device, partially on the user device, as a separate software package, partially on the user computing device and partially on a remote computing device, or entirely on a remote computing device or server. In cases involving a remote computing device, the remote computing device may be connected to the user computing device through any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computing device (e.g., through the Internet using an Internet service provider).

[0232] In summary, the present invention can be implemented by a method, apparatus, electronic device or computer-readable medium that executes a computer program. In practice, a general data processing device such as a microprocessor or a digital signal processor (DSP) can be used to implement some or all functions of the present invention.

[0233] The specific embodiments described above further describe the purpose, technical solutions and beneficial effects of the present invention in detail. It should be understood that the present invention is not inherently related to any specific computer, virtual device or electronic device, and various general devices can also implement the present invention. The above description is only a specific embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A method for predicting carbon sequestration of garden trees, characterized in that: include: Obtaining basic information of target tree species and constructing a number of morphological simulation data, wherein the basic information includes fixed morphological information; Performing volume estimation according to the morphological simulation data to obtain branch volume information corresponding to the fixed morphological information; Combining the unit carbon content corresponding to the fixed morphological information and the branch volume information to obtain the branch carbon content; constructing a correlation relationship between the fixed morphological information and the branch carbon content; Obtain the evaluation task of the target tree species in the current garden; The future carbon sequestration of the target tree species in the current garden is predicted by combining the assessment task, the simulated growth model and the association relationship.

2. The method for predicting carbon sequestration of garden trees according to claim 1, characterized in that: The basic information of the target tree species is obtained to construct a number of morphological simulation data, including: Collect basic information of the target tree species; Acquire data of several branches and trunks of the target tree species; Based on the fixed morphological information and a plurality of the branch data, the morphological simulation data corresponding to the branch data is constructed.

3. The method for predicting carbon sequestration of garden trees according to claim 1, characterized in that: The step of performing volume estimation according to the morphological simulation data to obtain branch volume information corresponding to the fixed morphological information includes: Performing volume quantification on the morphological simulation data to obtain original volume information of the target tree species; Based on all the original volume information corresponding to the fixed morphological information, the branch volume information corresponding to the fixed morphological information in the target type of tree is determined.

4. A method for predicting carbon sequestration of garden trees as claimed in claim 3, characterized in that: The determining, based on all the original volume information corresponding to the fixed form information, the branch volume information corresponding to the fixed form information in the target type of tree comprises: All the original volume information corresponding to the fixed shape information is acquired, and an average value of all the original volume information is taken as the branch volume information corresponding to the fixed shape information.

5. The method for predicting carbon sequestration of garden trees according to claim 1, characterized in that: The step of combining the unit carbon content corresponding to the fixed morphological information and the branch volume information to obtain the branch carbon content; and constructing a correlation relationship between the fixed morphological information and the branch carbon content includes: Constructing a plant sample set corresponding to the target tree species; Obtaining the original carbon content of the tree sample; Based on all the original carbon contents corresponding to the same fixed form information, determining the unit carbon content corresponding to the fixed form information in the target tree species; The branch volume information and the unit carbon content corresponding to the same fixed morphological information are combined to predict the branch carbon content corresponding to each fixed morphological information in the target tree species to obtain the association relationship.

6. The method for predicting carbon sequestration of garden trees according to claim 1, characterized in that: The assessment tasks include: planting information and forecast range; The combining of the assessment task, the simulated growth model and the association relationship to predict the future carbon sequestration of the target tree species in the current garden includes: Generate a branch prediction result according to the planting information, the prediction range and the simulated growth model; Based on the branch prediction results, the corresponding branch carbon content is searched to predict the future carbon fixation of the target tree species.

7. The method for predicting carbon sequestration of garden trees according to claim 1, characterized in that: Also includes: Constructing a simulated growth model of the target tree species; The step of constructing a simulated growth model of the target tree species includes: Constructing the initial state of the target tree species and determining one or more growth nodes; Setting a growth rule; the growth rule includes a plurality of growth sub-models; wherein the growth sub-models are determined respectively in combination with the characteristics of different growth parts of the target tree species; The growth node is adjusted based on the growth sub-model.

8. A device for predicting carbon sequestration of garden trees, characterized in that: include: A simulation module is used to obtain basic information of target tree species and construct a number of morphological simulation data, wherein the basic information includes fixed morphological information; A volume estimation module, used to perform volume estimation based on the morphological simulation data to obtain branch volume information corresponding to the fixed morphological information; A carbon content estimation module is used to combine the unit carbon content corresponding to the fixed shape information and the branch volume information to obtain the branch carbon content; and to establish a correlation between the fixed shape information and the branch carbon content; The task acquisition module is used to obtain the evaluation tasks of the target tree species in the current garden; The carbon fixation prediction module is used to predict the future carbon fixation of the target type of trees in the current garden by combining the evaluation task, the simulated growth model and the association relationship.

9. An electronic device, wherein: The electronic device includes: processor; and, A memory storing computer executable instructions which, when executed, cause the processor to perform a method according to any one of claims 1-7.

10. A computer-readable storage medium, wherein: The computer-readable storage medium stores one or more programs, and when the one or more programs are executed by a processor, the method of any one of claims 1 to 7 is implemented.

Images