Method, equipment and storage medium for determining life characteristic parameters of ancient trees

Abstract

The embodiment of the present application provides a method, device and storage medium for determining the life characteristic parameters of an ancient tree, which belongs to the technical field of ancient tree maintenance; the method includes obtaining the physical parameters, geometric parameters and environmental stress data of the ancient tree to be maintained; performing stress analysis on the components of the ancient tree to be maintained according to the physical parameters and environmental stress data to obtain a wind resistance index; performing biomass analysis on the ancient tree to be maintained according to the geometric parameters to obtain a biomass index; obtaining the new branch index of the ancient tree to be maintained; obtaining life characteristic parameters according to the new branch index, wind resistance index and biomass index; wherein the life characteristic parameters are used to characterize the vitality and growth potential of the ancient tree to be maintained. The embodiment of the present application can realize the automatic calculation of life characteristic parameters after collecting the physical parameters, geometric parameters and environmental stress data of the ancient tree to be maintained, thereby reducing the requirements for maintenance personnel and improving the convenience of ancient tree maintenance.

Description

Technical Field

[0001] The present application relates to the technical field of ancient tree maintenance, and in particular to a method, device and storage medium for determining life characteristic parameters of ancient trees. Background Art

[0002] In the related art, although it is proposed that the growth status of trees can be evaluated based on growth potential and vitality. However, there are many factors affecting growth potential and vitality, and the factors often affect each other, which makes it difficult to quantitatively evaluate the growth status of trees. Especially for ancient trees, the factors affecting their growth potential and vitality are more different from those of conventional trees. Therefore, there is no quantitative standard for the growth status of ancient trees in the related art. It usually depends on experienced people to conduct real-time artificial inspections to determine the life characteristic parameters that characterize the state of growth potential and vitality, and perform artificial maintenance based on the determined life characteristic parameters. This method is highly dependent on the technical ability of the maintenance personnel, and the maintenance effect of ancient trees is often uncertain, which makes the maintenance of ancient trees more difficult. Therefore, there is an urgent need for a method for determining the life characteristic parameters of ancient trees that can realize the quantitative evaluation of the growth status of ancient trees and improve the convenience of ancient tree maintenance. Summary of the invention

[0003] The main purpose of the embodiments of the present application is to propose a method, device and storage medium for determining the life characteristic parameters of ancient trees, which can improve the convenience of ancient tree maintenance.

[0004] To achieve the above-mentioned purpose, a first aspect of an embodiment of the present application proposes a method for determining life characteristic parameters of an ancient tree, the method comprising:

[0005] Obtain the physical parameters, geometric parameters and environmental stress data of the ancient trees to be maintained;

[0006] Performing stress analysis on each component of the ancient tree to be maintained according to the physical parameters and the environmental stress data to obtain a wind resistance index;

[0007] Performing biomass analysis on the ancient trees to be maintained according to the geometric parameters to obtain biomass indicators;

[0008] Obtaining new branch indicators of the ancient tree to be maintained;

[0009] According to the new branch index, the wind resistance index and the biomass index, life characteristic parameters are obtained; wherein the life characteristic parameters are used to characterize the vitality and growth potential of the ancient tree to be maintained.

[0010] To achieve the above-mentioned purpose, the second aspect of an embodiment of the present application proposes an electronic device, which includes a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, it implements the method for determining the life characteristic parameters of the ancient tree as described in any one of the first aspects.

[0011] To achieve the above-mentioned purpose, the third aspect of the embodiments of the present application proposes a computer-readable storage medium, which stores a computer program. When the computer program is executed by a processor, it implements the method for determining the life characteristic parameters of the ancient tree as described in any one of the first aspects.

[0012] The method, device and storage medium for determining the life characteristic parameters of the ancient tree proposed in the present application collect the physical parameters, geometric parameters and environmental stress data of the ancient tree to be maintained, and perform stress analysis on the ancient tree to be maintained based on the physical parameters and environmental stress data to obtain the wind resistance index, analyze the biomass based on the geometric parameters to obtain the biomass index, and collect the new branch index of the ancient tree to be maintained, so that the life characteristic parameters of the ancient tree to be maintained can be estimated based on the wind resistance index, biomass index and new branch index; thus, the growth state can be quantified. Compared with the related art, the embodiment of the present application can realize the automatic calculation of the life characteristic parameters based on the physical parameters, geometric parameters and environmental stress data of the ancient tree to be maintained, reduce the requirements for maintenance personnel, and improve the convenience of ancient tree maintenance. BRIEF DESCRIPTION OF THE DRAWINGS

[0013] Figure 1 A schematic flow chart of a method for determining life characteristic parameters of ancient trees provided in this application;

[0014] Figure 2 A schematic diagram of a scenario for applying the method for determining life characteristic parameters of ancient trees provided in this application;

[0015] Figure 3 A schematic diagram of another scenario for applying the method for determining life characteristic parameters of ancient trees provided in this application;

[0016] Figure 4 A schematic diagram of the hardware structure of the equipment corresponding to the method for determining the life characteristic parameters of ancient trees provided in this application. DETAILED DESCRIPTION

[0017] The embodiments of the present application are described in detail below, and examples of the embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain the present application, and cannot be understood as limiting the present application.

[0018] In the description of the present application, it should be understood that descriptions involving orientation, such as up, down, front, back, left, right, etc., indicating orientations or positional relationships, are based on the orientations or positional relationships shown in the accompanying drawings, and are only for the convenience of describing the present application and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore should not be understood as a limitation on the present application.

[0019] In the description of this application, "several" means one or more, "more" means more than two, "greater than", "less than", "exceed", etc. are understood to exclude the number itself, and "above", "below", "within", etc. are understood to include the number itself. If there is a description of "first" or "second", it is only used for the purpose of distinguishing technical features, and cannot be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features or implicitly indicating the order of the indicated technical features.

[0020] The following is an explanation of the terms used in this application:

[0021] Vitality: refers to the ability and intensity of life activities of organisms under certain external environmental conditions;

[0022] Growth potential: refers to the vigor of plant growth and development, such as the length, thickness and leaf size of new shoots. The larger, stronger and faster the growth, the stronger the growth potential; the smaller and weaker the growth, the weaker the growth potential.

[0023] Central trunk: The central trunk is the extension of the trunk, located in the center of the crown. It is the main branch that grows upright and leads the growth of various branches in the entire crown.

[0024] Trunk: The trunk is part of the tree trunk, usually refers to the thickest part of the tree, located in the center of the trunk, playing a supporting and conductive role, the part from the root neck to the first branch.

[0025] Trunk: The main part of a tree, including the main trunk and central trunk.

[0026] Main branch: A permanent large branch directly attached to the main trunk is called a main branch.

[0027] Canopy: refers to the dense top layer of tree branches and leaves; the part 20-100 meters above the outer edge of the crown. The crown is mainly composed of backbone branches and auxiliary branches. The backbone branches include the central trunk, main branches and secondary branches, while the auxiliary branches are to assist the growth of the main trunk and backbone branches (such as crown branches and twigs).

[0028] In the prior art, although it is proposed that the growth state of trees can be evaluated based on the two aspects of growth potential and vitality. However, there are many factors affecting growth potential and vitality, and the factors often affect each other. In addition, due to the differences in their morphology and living environment, the factors affecting growth potential and vitality of different plants are different, which makes it difficult to quantitatively evaluate the growth state of trees. In particular, the factors affecting the growth potential and vitality of ancient trees are more different from those of conventional trees. Therefore, there is no quantitative standard for the growth state of ancient trees in the prior art. Usually, it depends on experienced people to conduct artificial real-time inspections to determine the life characteristic parameters that characterize the growth potential and vitality, and artificial maintenance is performed based on the determined life characteristic parameters. It is strongly dependent on the technical ability of the maintenance personnel, and the maintenance effect of ancient trees is often uncertain, which makes the maintenance of ancient trees more difficult. Therefore, there is an urgent need for a method for determining the life characteristic parameters of ancient trees that can realize the quantitative evaluation of the growth state of ancient trees and improve the convenience of ancient tree maintenance. Based on this, the embodiment of the present application provides a method, device and storage medium for determining the life characteristic parameters of ancient trees, which can improve the convenience of ancient tree maintenance.

[0029] It is understandable that according to the embodiment of the present application, a method for determining life characteristic parameters of an ancient tree includes:

[0030] Step S100, obtaining physical parameters, geometric parameters and environmental stress data of the ancient tree to be maintained;

[0031] Step S200, performing stress analysis on each component of the ancient tree to be maintained according to the physical parameters and environmental stress data to obtain a wind resistance index;

[0032] Step S300, analyzing the biomass of the old trees to be maintained according to the geometric parameters to obtain a biomass index;

[0033] Step S400, obtaining new branch indicators of the ancient tree to be maintained;

[0034] Step S500, obtaining life characteristic parameters according to the new branch index, wind resistance index and biomass index; wherein the life characteristic parameters are used to characterize the vitality and growth potential of the ancient tree to be maintained.

[0035] Therefore, by collecting the physical parameters, geometric parameters and environmental stress data of the ancient trees to be maintained, and performing stress analysis on the ancient trees to be maintained based on the physical parameters and environmental stress data, wind resistance index is obtained, biomass is analyzed based on geometric parameters to obtain biomass index, and new branch index of the ancient trees to be maintained is collected, so that the life characteristic parameters of the ancient trees to be maintained can be estimated based on the wind resistance index, biomass index and new branch index; thus, the growth status can be quantified. Compared with the related art, the embodiment of the present application can realize the automatic calculation of life characteristic parameters based on the physical parameters, geometric parameters and environmental stress data of the ancient trees to be maintained, reduce the requirements for maintenance personnel, and improve the convenience of ancient tree maintenance.

[0036] It is understandable that steps S100 to S500 can be used to evaluate the vitality and growth potential of the ancient tree in its current state, or can be used to estimate the vitality and growth potential of the ancient tree based on the maintenance strategy. This embodiment of the application does not limit this.

[0037] It is understandable that, for the ancient trees to be maintained, their components may be the trunk, main branches, secondary branches, crown branches and twigs. The physical parameters include the cross-sectional radius, length, etc. of each component involved in the calculation of life characteristic parameters in the ancient trees to be maintained; such as the physical parameters include the cross-sectional radius of the trunk, the cross-sectional radius of the main branches, the cross-sectional radius of the secondary branches, etc. The environmental force data is the natural force borne by each component involved in the calculation of life characteristic parameters, including gravity and wind load. Among them, the physical parameters can be obtained by taking a video / image of the ancient trees to be maintained, and based on the taken data, the components of each ancient tree to be maintained that participate in the calculation of life characteristic parameters are fitted with cylinders, and then the physical parameters of each component are determined based on the fitted data. Among them, gravity can be estimated according to the density and volume of each component, and the volume can be calculated based on the physical parameters of the fitted cylinder. For this, the embodiments of the present application will not be elaborated in detail. Physical parameters and environmental stress data can be calculated in real time based on collected image data of ancient trees, meteorological data, etc., or the measurement results can be directly input for evaluation. In this regard, the embodiments of the present application do not limit how the platform for determining the method for determining the life characteristic parameters of ancient trees using physical parameters and environmental stress data is determined.

[0038] It is understandable that the force analysis can be performed on the old trees to be maintained based on the existing structural mechanics model. In this regard, the embodiments of the present application do not limit the structural mechanics model used, and those skilled in the art can selectively set it according to actual needs. The wind resistance index characterizes the wind resistance of the old trees to be maintained, and can include the wind resistance index of each component of the old trees to be maintained.

[0039] The geometric parameters characterize the parameters related to the shape of the old tree to be maintained, such as the geometric parameters may include the trunk volume, the canopy area (i.e., the total area of ​​the leaves). Among them, the total area of ​​the leaves can be input after field measurement or obtained by image processing technology based on the photographed pictures. Other parameters in the geometric parameters can also be determined with reference to the total area of ​​the leaves. To this end, the embodiments of the present application do not limit how the geometric parameters are determined. The geometric parameters can be data input after calculation by other platform software, or can be obtained by real-time calculation based on image data. To this end, the embodiments of the present application do not limit how the geometric parameters are obtained, and those skilled in the art can selectively set them according to actual needs. Exemplary, such as in the process of maintaining old trees, it is necessary to construct a three-dimensional view for simulation. At this time, two platforms can be constructed, one for dynamic display and simulation of the three-dimensional view, and the other for the estimation of life characteristic parameters. At this time, the platform based on the dynamic display of the three-dimensional view can extract the physical parameters, geometric parameters and environmental stress data of the old tree to be maintained, and input the extracted physical parameters, geometric parameters and environmental stress data into the platform for estimating life characteristic parameters.

[0040] The new branch index characterizes the number of new branches grown within a preset time period; the preset time period can be configured according to actual needs. The new branch index can be obtained based on image processing or determined by field measurement. Exemplarily, the new branch index can be expressed as N=N1+N2+N3+N4; wherein N1 represents the number of new branches on the trunk, N2 represents the number of new branches on the main branch, N3 represents the number of new branches on the secondary branch, and N4 represents the number of new branches on the twig. Since the number of new branches can reflect the growth rate and vitality of trees, the vitality characteristic parameters can be made more accurate by statistically analyzing the number of new branches at each branch level of the ancient trees to be maintained.

[0041] The life characteristic parameters can be obtained through a neural network model, or can be determined through a fitting model corresponding to the life characteristic parameters, new branch index, wind resistance index, and biomass index, wherein both the fitting model and the neural network model can be trained based on historical maintenance data. This embodiment of the present application does not limit this, and those skilled in the art can selectively set it according to actual needs.

[0042] The life characteristic parameters can determine the scores of various influencing factors based on the new branch index, wind resistance index and biomass index, and then determine the index scores representing vitality and growth potential based on the scores of various influencing factors. In some embodiments, the life characteristic parameters may include the index scores of vitality and growth potential, and the scores of various influencing factors; they may also include maintenance instructions to instruct the maintainer to start maintenance; they may also include the index scores of vitality and growth potential. Exemplarily, take the life characteristic parameters including the index scores of vitality and growth potential, and the scores of various influencing factors as an example; when it is determined that the index score of vitality is low, the lowest score of the various influencing factors in the vitality index score can be selected for adjustment. Similarly, the same method can be used for the index score of growth potential. For example, taking the life characteristic parameters including maintenance instructions as an example, when it is determined that the maintenance instructions indicate that the index score of vitality is low, a maintenance strategy can be formulated offline based on the parameters related to vitality, and then the theoretical physical parameters, environmental force data and geometric parameters of the ancient tree are re-determined based on the maintenance strategy, and the maintenance strategy of the ancient tree is evaluated through the above steps S100 to S500.

[0043] It is understandable that the components of the ancient trees to be maintained include the trunk and branches. Based on the physical parameters and environmental stress data, the stress analysis of the components of the ancient trees to be maintained is carried out to obtain wind resistance indicators, including:

[0044] Input the trunk physical parameters and the trunk force data of the environmental force data into a preset trunk force analysis model to obtain the trunk average stress index corresponding to the trunk one by one;

[0045] When the old tree to be maintained has main branches, the main branch data corresponding to each main branch in the physical parameters and environmental stress data are respectively input into the preset main branch stress analysis model to obtain the main branch stress index corresponding to the main branch one by one;

[0046] When there are secondary branches in the ancient tree to be maintained, the secondary branch data corresponding to each secondary branch in the physical parameters and environmental stress data are respectively input into the preset secondary branch stress analysis model to obtain the branch stress index corresponding to the secondary branch;

[0047] According to the average stress index of the trunk, the stress index of each main branch and the stress index of each branch, the wind resistance index is obtained;

[0048] Among them, the main trunk force analysis model is different from the branch force analysis model and the main branch force analysis model.

[0049] The trunk physical parameters are the physical parameters corresponding to the trunk, and the trunk force data are the environmental force data corresponding to the trunk.

[0050] The subordinate branches are a general term for the remaining components other than the main trunk and main branches, and the subordinate branches include at least one of the secondary branches, crown branches and twigs. Since the secondary branches, crown branches and twigs are far away from the main trunk, the force they are subjected to is relatively small. In some embodiments, when there are secondary branches, crown branches and twigs in the ancient tree to be maintained, at least one of the secondary branches, crown branches or twigs in the ancient tree to be maintained can be subjected to force analysis, or all of them can be subjected to force analysis. To this end, technical personnel in this field can selectively set it according to actual needs. If the total number of secondary branches, crown branches and twigs on each main branch of the ancient tree to be maintained accounts for a relatively small total area and total volume (such as less than a preset threshold), it is judged that the subordinate branches on the main branch do not meet the preset first force analysis condition, and the secondary branches, crown branches and twigs on the main branch are not subjected to force separation. When the subordinate branches on the main branch meet the preset first force analysis conditions, determine whether the crown branches and twigs of each secondary branch on the main branch that meet the first force condition meet the preset second force analysis conditions. If the total number of crown branches and twigs on the secondary branches accounts for a small amount of the total area and total volume of the corresponding secondary branches (such as less than a preset threshold), the crown branches and twigs on the secondary branches do not meet the second force analysis.

[0051] By making the main branch stress analysis model different from the branch stress analysis model, the main branch stress analysis model and the main trunk stress analysis model are set to be different, so that the main branch, the branch and the main trunk can be subjected to targeted stress separation, and the wind resistance index is more reliable. The main branch stress analysis model and the branch stress analysis model can be the same or different, and this is not limited in the embodiments of the present application.

[0052] In some embodiments, the main trunk force analysis model is set to Euler-Ber-Bernoulli, and the main branch force analysis model adopts a cantilever beam model; for example, taking the main trunk force analysis model as Euler-Ber-Bernoulli, and the main branch force analysis model and the secondary branch force analysis model as cantilever beam models, the force data for the main trunk, main branch and secondary branch are as follows:

[0053]

[0054]

[0055] M branch =F branch1 ×L branch1 ×sin(θ1);

[0056] M minor =F branch2 ×L branch2 ×sin(θ2);

[0057] Among them, σ trunk Indicates the stress borne by the trunk; σ maxIt represents the maximum stress on the trunk, M is the bending moment, M = F × L; y is the distance from the trunk axis to the surface; F is the force determined based on wind load and gravity (including wind load, or the sum of wind load and the weight of each component except the trunk), which is one of the trunk force data, L is the length of the trunk, which is one of the trunk physical parameters. I is the trunk section inertia moment, R is the cross-sectional radius of the trunk, which is one of the physical parameters of the trunk. branch represents the stress of the main branch, F branch1 is the external force acting on the main branch (including wind load, or the sum of wind load and weight of secondary branches), which is one of the main branch data in the environmental force data; L branch1 is the length of the main branch, which is one of the main branch data in the physical parameters; θ1 is the angle between the main branch and the trunk, which is one of the main branch data in the physical parameters; M minor Indicates the maximum stress borne by the branch, L branch1 is the length of the secondary branch; θ2 is the angle between the primary branch and the secondary branch, which is one of the primary branch data in the physical parameters. branch2 is the external force acting on the branch (including wind load). The stress state of the entire trunk can be obtained, that is, the average stress index of the trunk. The maximum stress of the trunk (also known as the trunk maximum stress index) can be determined, and then it can be judged whether the trunk is at risk of fracture and whether support reinforcement is required.

[0058] It is understandable that, in some embodiments, the wind load can be obtained by combining the following wind load model with the weather station data, wherein the wind load model is as follows:

[0059]

[0060] Among them, F wind represents wind load, v is the wind speed in the main wind direction; C d is the resistance coefficient, ρ air Air density; θ azimuth is the azimuth, A facing (θ azimuth ) is θ azimuth The windward area at the azimuth. The windward area can be determined based on the area of ​​the canopy at the azimuth. The specific area can be calculated by obtaining the graphic outline of the canopy at the azimuth and then calculating based on the graphic outline. air It can be obtained based on the data of the weather station, C dIt can be obtained by predicting the wind resistance of the old tree to be maintained. The same wind load model can be constructed for the entire old tree, or different wind load models can be constructed based on the trunk and crown. This embodiment of the application does not restrict this, and those skilled in the art can selectively set it according to actual needs.

[0061] Therefore, by analyzing the stress of each component separately, the complex mechanical structure of the ancient tree can be fully captured, and the introduction of angles in the main branches and secondary branches can reflect the influence of angles on stress, so that the growth structure of the tree can be optimized through angles in practical applications to ensure that stress meets the actual growth needs. If the vitality characteristic parameter represents insufficient vitality, and the vitality can be improved by adjusting the tree shape, the angle can be adjusted according to the above model, such as determining the stability M of the main branch. branch If the wood tensile strength is greater than the preset value, it means that the main branch is at risk of breaking. The vitality can be improved by adjusting the angle of the branches on the main branch, thereby ensuring that the vitality in the vitality characteristic parameters meets the requirements.

[0062] The wind resistance index can be obtained by weighted calculation of the average stress index of the trunk, the stress index of each main branch, and the stress index of each branch (such as weighted calculation based on the influence ratio of the trunk, main branch, and secondary branch, and the influence ratio can be set manually) to obtain a specific index value, or by summing the average stress index of the trunk, the stress index of each main branch, and the stress index of each branch to obtain a specific index value. In this regard, the embodiment of the present application does not limit how the wind resistance index is determined based on the average stress index of the trunk, the stress index of each main branch, and the stress index of each branch.

[0063] It is understandable that the geometric parameters include trunk geometric parameters and canopy geometric parameters; according to the geometric parameters, the biomass of the old trees to be maintained is analyzed to obtain biomass indicators, including:

[0064] The biomass of the trunks of the ancient trees to be maintained is evaluated according to the trunk geometric parameters to obtain the trunk biomass;

[0065] The canopy biomass of the old trees to be maintained is evaluated based on the canopy geometric parameters to obtain the canopy biomass.

[0066] Determine the root biomass of the ancient trees to be maintained based on the trunk biomass and canopy biomass;

[0067] Root biomass, trunk biomass and canopy biomass were all used as biomass indicators.

[0068] Biomass is a key indicator for evaluating tree monitoring and productivity, which is affected by trunk, branch, leaf density and root biomass.

[0069] In some embodiments, the trunk geometric parameters can be input into the trunk geometric biomass assessment model to evaluate the biomass of the trunk, which can more accurately determine the trunk biomass compared to the empirical formula. The trunk geometric biomass assessment model characterizes the relationship between the trunk volume, trunk density and trunk biomass. In some embodiments, the canopy geometric parameters can be input into the canopy geometric biomass assessment model. The canopy geometric biomass assessment model characterizes the relationship between the canopy leaf area, leaf density and canopy biomass.

[0070] The root-crown biomass is usually proportional to the above-ground biomass. Therefore, in some embodiments, the root biomass can be determined based on the root-crown ratio method. root = k·B above Among them, B root represents root biomass; B above It represents the above-ground biomass, which is the sum of trunk biomass and canopy biomass. k is the coefficient when the root-crown biomass and above-ground biomass are usually proportional. It is an empirical constant and can be determined based on historical data. Different tree species have different environmental conditions.

[0071] For example, taking the trunk biomass using the trunk geometric biomass assessment model and the canopy biomass using the canopy geometric biomass assessment model as an example, the trunk geometric biomass assessment model and the canopy geometric biomass assessment model are as follows:

[0072] B m =V trunk ·ρ trunk ;

[0073] B c =A leaf ·ρ leaf ;

[0074] Among them, B m is the trunk biomass; V trunk is the volume of the trunk, which is one of the geometric parameters of the trunk; ρ trunk Characterizes the density of the tree trunk, which can be obtained by sampling and is one of the geometric parameters of the tree trunk. c is the canopy biomass; A leaf is the canopy area, which is one of the canopy geometric parameters; ρ leaf It is the leaf density per unit area of ​​the canopy. It can be estimated by the total area and the number of leaves, or by density sampling in multiple areas and calculating the mean of the adopted density. It is one of the canopy geometric parameters.

[0075] It is understandable that, in some embodiments, the canopy area can be estimated by the canopy projection area. Taking the canopy as an ellipsoid as an example, the canopy area is expressed as follows:

[0076] A leaf =πab;

[0077] Wherein, a is the major axis of the ellipse formed by the crown projection, and b is the minor axis of the ellipse formed by the crown projection. It can be determined by modeling the ancient tree, or by image processing, to which the embodiments of the present application are not limited. At this time, by combining the shape of the canopy and the leaf density, the canopy biomass assessed by the canopy geometric biomass can be made more accurate, and can better capture the complex biomass changes of the canopy.

[0078] It is understood that the method further comprises:

[0079] Obtain radar detection data of the root system;

[0080] Based on the radar detection data and root biomass, a root distribution map of the root system was constructed.

[0081] The root distribution map can reflect the growth of the roots of ancient trees and can be used as an evaluation indicator of life characteristic parameters.

[0082] It is understandable that according to the new branch index, wind resistance index and biomass index, life characteristic parameters are obtained, including:

[0083] Obtain the weight coefficients corresponding to the new branch index, wind resistance index and biomass index;

[0084] The new branch index, wind resistance index and biomass are weightedly calculated according to each weight coefficient to obtain the vitality parameter.

[0085] Vitality parameter is one of the life characteristic parameters.

[0086] In some embodiments, there are two biomass indicators, and corresponding weight coefficients can be set for both biomass indicators.

[0087] Assume that the weight coefficient of the new branch index is β; the weight coefficient of the wind resistance index is δ; illustratively, there is one biomass index, and the weight coefficient of the biomass index is α, then P = αB + βN + δF; wherein P is the vitality parameter, B represents the biomass index, N represents the new branch index, and F represents the wind resistance index.

[0088] For example, it is assumed that there are two biomass indicators, namely, trunk biomass B m and canopy biomass B c , and the weight coefficient of trunk biomass is α1, and the weight coefficient of canopy biomass is α2; then P = α1B m +α2B c +βN+δF.

[0089] It can be understood that the weight coefficients corresponding to the new branch index, wind resistance index and biomass index are determined by the following steps:

[0090] The first weight combination is obtained by performing multivariate linear regression fitting on the historical new branch index, historical wind resistance index, historical biomass index and historical vitality parameters of the sample ancient trees; wherein the first weight combination is formed by combining the weight coefficients corresponding to the historical new branch index, the historical wind resistance index and the historical biomass index;

[0091] The new branch index, wind resistance index and biomass index of the sample ancient trees are weighted by a preset genetic algorithm to obtain a second weight combination when the prediction error of the vitality of the sample ancient trees meets the preset error condition;

[0092] The new branch index, wind resistance index and biomass index of the sample ancient trees are weighted by the preset gradient descent algorithm to obtain the third weight combination when the prediction error of the vitality of the sample ancient trees meets the preset error condition;

[0093] The new branch index, wind resistance index and biomass index of the sample ancient trees are weighted by the preset Bayesian optimization algorithm to obtain the fourth weight combination when the prediction error of the vitality of the sample ancient trees meets the preset error condition;

[0094] The same parameter type in the first weight combination, the second weight combination, the third weight combination and the fourth weight combination is weighted respectively to obtain the weight coefficients corresponding to the new branch index, the wind resistance index and the biomass index.

[0095] Due to the complexity of ancient trees, a single weight coefficient may have errors. Determining the weight coefficient through multiple methods can minimize the overall error, thereby improving the accuracy of the weight coefficient and the accuracy of vitality parameter estimation.

[0096] It is understandable that the maintenance of ancient trees is an ongoing process. During the maintenance process, various parameters of the ancient trees are recorded. At this time, the historical maintenance records of the ancient trees to be maintained can be used as sample data for training, and then different weight coefficients can be obtained.

[0097] It is understandable that the geometric parameters, physical parameters and environmental stress data of ancient trees are the basic data in the maintenance process of ancient trees. Therefore, when there is no historical data on wind resistance indicators and biomass indicators, the historical wind resistance indicators and historical biomass indicators can also be calculated based on historical basic data.

[0098] For example, the weight coefficient of the new branch index is β, the weight coefficient of the wind resistance index is δ, the weight coefficient of the trunk biomass is α1, and the weight coefficient of the canopy biomass is α2; assuming that the first weight combination obtained by multivariate linear regression fitting is {β1, δ1, α 11 , α 21}; The second weight combination obtained by genetic algorithm is {β2, δ2, α 12 , α 22}; The third weight combination obtained by the gradient descent algorithm is {β3, δ3, α 13 , α 23}; The fourth weight combination obtained by the Bayesian optimization algorithm is {β4, δ4, α 14 , α 24}; At this time, we can get β=λ 11 β1+λ 12 β2+λ 13 β3+λ 14 β4;δ=λ 21 δ1+λ 22 δ2+λ 23 δ3+λ 24 δ4; α1 = λ 31 α 11 +λ 32 α 12 +λ 33 δ 13 +λ 34 α 14 ; α2=λ 42 α 21 +λ 42 α 22 +λ 43 δ 23 +λ 44 α 24 ; Among them, the weight coefficients of indicators obtained by the same algorithm of different types can be the same or different, such as λ 11 , 21 , 31 , 42 They can be the same or different. The weight coefficients of indicators obtained by different algorithms of the same type are different, such as λ 11 , 12 , 13 , 14 The sum of the weight coefficients of the indicators obtained by different algorithms of the same type is equal to 1 and each weight coefficient is greater than or equal to 0, that is, λ 11 +λ 21 +λ 31 +λ 42 =1;λ 21 +λ 22 +λ23 +λ 24 =1;λ 31 +λ 32 +λ 33 +λ 34 =1;λ 41 +λ 42 +λ 43 +λ 44 =1.

[0099] The embodiment of the present application does not restrict how to determine the coefficients for weighting the same parameter type in the first weight combination, the second weight combination, the third weight combination, and the fourth weight combination. The weight coefficients correspond to the weight combination types one by one, that is, each weight combination type corresponds to a weight coefficient. In some embodiments, the weight coefficients corresponding to each weight combination can be ultimately determined by constructing a total error function. Among them, the total error function characterizes the sum of square errors between the real vitality parameters of multiple sample data and the predicted vitality parameters after fusion. Taking the weight coefficients of each weight coefficient in the same weight combination type as the same, the weight coefficient of the first weight combination is λ1; the weight coefficient of the second weight combination is λ2; the weight coefficient of the third weight combination is λ3; the weight coefficient of the fourth weight combination is λ4 as an example, then the total error function is as follows:

[0100]

[0101] Where n represents the number of samples, P 1,i represents the predicted vitality parameter of the i-th sample calculated by the first weight combination; P 2,i represents the predicted vitality parameter of the i-th sample calculated by the second weight combination; P 3,i represents the predicted vitality parameter of the i-th sample calculated by the third weight combination; P 4,i P represents the predicted vitality parameter of the i-th sample calculated by the fourth weight combination. i represents the true vitality parameter of the i-th sample, and E(λ1,λ2,λ3,λ4) represents the error value.

[0102] Among them, by setting the objective function The optimal λ1, λ2, λ3, λ4 can be determined by the constraint condition λ1+λ2+λ3+λ4=1 and λ1, λ2, λ3, λ4≥0. This application does not restrict how to determine the optimal λ1, λ2, λ3, λ4 based on the objective function and the constraint condition, and those skilled in the art can selectively set it according to actual needs.

[0103] For example, in some embodiments, the Lagrangian function can be determined by the Lagrangian multiplier method or the gradient descent method. Taking the Lagrangian multiplier method as an example, the Lagrangian function is defined as: Where μ is the Lagrange multiplier. By taking partial derivatives of each variable λ1,λ2,λ3,λ4 and μ and solving the corresponding optimal value, the optimal λ1,λ2,λ3,λ4 can be obtained. For another example, the weighted coefficients in λ1,λ2,λ3,λ4 are iteratively updated by the gradient descent method to gradually approach the optimal solution. The update formula is: Based on the above update formula, each weighted coefficient can converge to the optimal value after multiple rounds of iterations.

[0104] It is understandable that for multiple linear regression, the following fitting formula can be constructed: i =α1B mi +βN i +α2B ci +δF i +ε i ; Among them, ε i represents the error term, P i Represents the predicted vitality parameter of the i-th sample. For n sample data, the regression model of multiple linear regression can be expressed as P = Xw + ε; where P represents the n*1 vitality parameter vector, X is an n*4-dimensional matrix composed of trunk biomass, number of new branches, canopy biomass, and wind resistance index, and each row corresponds to the trunk biomass, number of new branches, canopy biomass, and wind resistance index of a sample data. w is the expected weight vector, expressed as w = [α1, β, α2, δ] T ; ε represents the error vector. The objective function is constructed as Based on the objective function, the initial estimate of the weight is determined as w = (X T X) -1 X T P.

[0105] It is understandable that the second weight combination can be determined by the genetic algorithm through the following steps:

[0106] Step 1. Coding:

[0107] The weight coefficient of the new branch index is β, the weight coefficient of the wind resistance index is δ, the weight coefficient of the trunk biomass is α1, and the weight coefficient of the canopy biomass is α2. The gene sequence of each individual is obtained as

[0108] [α1,β,α2,δ].

[0109] Step 2: Initial population:

[0110] Randomly generate multiple individuals as the initial population, and each individual corresponds to a set of weight parameters.

[0111] Step 3, fitness function:

[0112] The fitness function is defined as the inverse of the square error based on the vitality parameter as follows:

[0113]

[0114] Among them, the larger f(α1, β, α2, δ) is, the better (α1, β, α2, δ) is.

[0115] Step 4: Selection: Use the roulette wheel selection method to select individuals with higher fitness for reproduction, so that individuals with higher fitness have a greater chance of being passed on to the next generation.

[0116] Step 5: Crossover: Perform a crossover operation on the selected individuals to generate new offspring. The crossover can be a single-point crossover or a multi-point crossover.

[0117] Step 6, mutation: select a preset proportion of individuals for traversal operation, so that the genes of each individual have a certain probability of randomly changing the weight parameters to maintain the diversity of the population.

[0118] Step 7, iteration: Repeat steps 4 to 6 above to continuously generate new populations, and select the individual with the highest fitness as the second weighted combination when the preset iteration stop condition is met.

[0119] Therefore, the global optimal weight combination can be obtained through the genetic algorithm of the above steps 1 to 7, and the optimization of complex vitality models can be achieved.

[0120] It is understandable that for the gradient descent algorithm to determine the third weight combination, the loss function can be constructed as follows:

[0121]

[0122] The weight update formula is set as follows:

[0123]

[0124] Where η is the learning rate, represents the gradient of the loss function with respect to α2, represents the gradient of the loss function with respect to δ, represents the gradient of the loss function with respect to β, Represents the gradient of the loss function with respect to α1. For example, the gradient It is expressed as follows: At this point, through continuous iteration, when the loss function meets the convergence condition, the second weight combination determined by the gradient descent algorithm can be obtained.

[0125] It is understandable that the fourth weight combination determined based on Bayesian optimization can be determined by the following steps:

[0126] Step 1: Construct the objective function. The objective function is as follows:

[0127] Step 2: Set up a proxy model of the objective function, where the proxy model can be determined using a Gaussian process (GP) to predict possible future points with minimum error based on historical data points.

[0128] Step 3: Define the sampling strategy: select the next sampling point by expected improvement (EI) or probability of improvement (PI).

[0129] Step 4: Iterative optimization: Sampling is performed according to the recommended points of Bayesian optimization. During the optimization process, the weights (α1, β, α2, δ) are continuously adjusted to update the iterative model until the objective function converges or the preset number of optimizations is reached, thereby obtaining the optimal (α1, β, α2, δ) output.

[0130] Bayesian optimization is used to efficiently approximate the optimal solution with less sampling, which is suitable for optimization problems with high computational cost. The weight combination obtained by Bayesian optimization can be combined with the weight combination obtained by other algorithms, resulting in smaller errors and more accurate vitality parameters.

[0131] It is understandable that according to the new branch index, wind resistance index and biomass index, the life characteristic parameters obtained include:

[0132] Determine the trunk coordination parameters based on the total cross-sectional diameter of the branches and the cross-sectional diameter of the trunk of the ancient tree to be maintained;

[0133] Obtain the root distribution map of the ancient trees to be maintained and determine the root distribution data;

[0134] The biomass index, root distribution data, trunk coordination parameters and pre-configured deceleration rate coefficient are input into a preset tree type evaluation model to determine the tree type evaluation data.

[0135] It is understandable that the tree shape evaluation data can be used to evaluate growth potential, and a tree with a good shape has better growth potential.

[0136] The trunk coordination parameter is used to measure the coordination between the trunk and branches.

[0137] In some embodiments, the backbone coordination parameters in, is the total branch cross-sectional diameter of all branches, d iIndicates the cross-sectional diameter of the i-th branch. B = D main The cross-sectional diameter of the trunk is also called the cross-sectional diameter of the trunk. A branch can be a main branch or a combination of a main branch and a secondary branch.

[0138] The tree type evaluation model is an independent model that can be used to evaluate the morphology and water distribution of trees. In some embodiments, the tree type evaluation model includes two independent models: a tree morphology scoring model and a tree comprehensive scoring model. The tree comprehensive scoring model adds an evaluation index for water distribution relative to the tree morphology scoring model, so that the advantages of ancient trees can be evaluated from multiple different dimensions. Among them, the trunk coordination parameter and the deceleration rate coefficient can be used for the index scoring of water distribution, and the biomass index data and the root distribution data are used for the scoring of tree morphology.

[0139] In some embodiments, the tree morphology scoring model is set up as follows:

[0140] S adv =ω1f(H)+ω2f(D canopy )+ω3f(θ)+ω4f(B m )+ω5f(R d );

[0141] Where f(x) is a normalization function that normalizes the parameter value x to be normalized to between [0,1] so that it has the same comparison scale. adv is the comprehensive advantage value of tree morphology, indicating the overall growth potential of the tree. H is the height of the old tree to be maintained, where the higher H is, the larger the photosynthesis area is. canopy represents the canopy diameter, where D canopy The larger the value, the greater the ability to capture sunlight, which is more conducive to photosynthesis. θ represents the angle trend of the main branches of the ancient tree. The angles of each main branch and the trunk of the ancient tree can be averaged to determine θ. In some implementations, the most concentrated angle θ can also be determined through a clustering algorithm. m is the trunk biomass, R d Characterizes the root distribution area and can be set to the radius or area of ​​the root system. ω1, ω2, ω3, ω4, and ω5 are evaluation coefficients, indicating the relative importance of each parameter to the tree's dominance.

[0142] In some embodiments, f(x) can be set to x is the parameter value to be normalized, x max Indicates the maximum value that x can take, that is, the maximum setting parameter value; x min In other embodiments, it can also be selectively set according to actual conditions. For example, f(H), f(D canopy)、f(θ)、f(B m )、f(R d ) are as follows:

[0143]

[0144] f(θ)=cos(θ-θ opt );

[0145]

[0146] Among them, R d,max Indicates the maximum area that the root system can reach, which can be manually input dynamically. m,max Indicates the maximum biomass of the tree trunk, which can be set based on empirical data; B m,min Describes the minimum biomass of a tree trunk, which can be set artificially based on empirical data. opt It represents the optimal main branch angle under ideal conditions (i.e., the main branch angle when both water conduction efficiency and mechanical stability meet the expected conditions, and the expected conditions can be set artificially), which can be determined by simulation or calculated by geometric model. opt It is the ideal tree height, which can be set artificially, such as based on the density of surrounding plants, light range, etc. K1 represents the optimal ratio coefficient between canopy diameter and tree height, which can be set based on experience, and the value is different for different types of trees.

[0147] It is understandable that the present application embodiment does not limit how to determine ω1, ω2, ω3, ω4, and ω5 in the tree morphology scoring model. In some embodiments, they can be obtained by mechanical learning through historical data. In other embodiments, they can be determined based on the analytic hierarchy process (AHP), that is, by comparing the importance of parameters two by two, constructing a judgment matrix, and thus obtaining the weight of each parameter. The judgment matrix is ​​shown in Table 1 below:

[0148] parameter Tree height H <![CDATA[Canopy diameter D canopy > Main branch angle θ <![CDATA[Trunk biomass B m > <![CDATA[Root distribution R d > Tree height H 1 2 3 4 2 <![CDATA[Canopy diameter D canopy > 0.5 1 2 3 2 Main branch angle θ 0.33 0.5 1 2 3 <![CDATA[Trunk biomass B m > 0.25 0.33 0.5 1 2 <![CDATA[Root distribution R d > 0.5 0.5 0.33 0.5 1

[0149] Table 1

[0150] It is understandable that after obtaining the first tree dominance score (the first tree dominance score is one of the tree evaluation data) based on the tree morphology scoring model, the first tree dominance score can be compared with a preset first tree dominance critical value, wherein the first tree dominance critical value can be obtained based on historical data. Specifically as follows:

[0151]

[0152] Among them, S threshold Represents the first tree dominance critical value.

[0153] For example, it is assumed that the parameters of the ancient tree are: tree height H = 12 meters, canopy diameter D canopy = 8 meters, main branch angle θ = 35°; trunk biomass B m = 600 kg, root distribution R d = 15 square meters; assuming that the above tree type evaluation data is substituted into S adv ≥S threshold , then the ancient tree meets the standards of a dominant tree type.

[0154] In some embodiments, the tree comprehensive scoring model is configured as follows:

[0155] S' adv =ω'1f(H)+ω'2f(D canopy )+ω'3f(θ)+ω'4f(B m )+ω'5f(R d )+ω6S A / B +ω7S Q ;

[0156] Among them, S' adv is the comprehensive score of the output of the tree comprehensive scoring model. ω'1, ω'2, ω'3, ω'4, ω'5, ω6, ω7 are the evaluation coefficients of the tree comprehensive scoring model, which can be determined based on historical data training. S A / B represents the score of the backbone coordination parameter; S Q Indicates the moisture distribution score.

[0157] In some embodiments, in, Characterize the backbone coordination parameters under ideal conditions. It can be manually input or determined by simulation. The value may be different for different ancient trees. In this regard, the embodiment of the present application does not limit how to determine it.

[0158] In some embodiments, Among them, Var(Q1,Q2,...,Q n ) represents the variance of water flow in branches of the same type. The smaller the variance, the more uniform the water distribution. Q1,Q2,...,Q n Represents branches of the same type, such as the water flow of main branch 1 to main branch n.

[0159] The water distribution decline model characterizes the water change trend of each component of the ancient tree. The water flow of each branch can be determined based on the water distribution decline model and the water distribution decline model. In some embodiments, the water distribution decline model is set as follows:

[0160]

[0161] Among them, Q i represents the water flow of the ith branch (such as main branch, secondary branch, etc.); h j represents the height of the j-th branch (i.e., the height from the ground); γ is the decreasing rate coefficient; Q max is the maximum water supply of the trunk, and n represents the total number of branches. When the decreasing rate coefficient is determined, the water flow of each branch that needs to participate in the calculation can be determined based on the water distribution decreasing model.

[0162] It is understandable that after obtaining the second tree dominance score (the second tree dominance score is one of the tree evaluation data) based on the tree morphology scoring model, the obtained second tree dominance score can be compared with a preset second tree dominance critical value, wherein the second tree dominance critical value can be obtained based on historical data. Specifically as follows:

[0163]

[0164] Among them, S t ' hreshold Represents the second tree dominance critical value. S' adv ≥S t ' hreshold When it is the dominant tree type, S' adv t ' hreshold It is a non-dominant tree type.

[0165] For example, it is assumed that the parameters of the ancient tree are: tree height H = 12 meters, canopy diameter D canopy = 8 meters, main branch angle θ = 35°; trunk biomass B m = 600 kg, root distribution R d = 15 square meters; A / B ratio is 0.6, water reduction coefficient is 0.5; Suppose the above tree type evaluation data is substituted to obtain S' adv ≥S t ' hreshold , then the ancient tree meets the standards of a dominant tree type.

[0166] It is understandable that the tree morphology scoring model and the tree comprehensive scoring model can be used to jointly evaluate whether the current state of the ancient tree is a dominant tree type, and then determine whether human participation is required for pruning or reinforcement operations.

[0167] It is understood that the method further comprises:

[0168] ​The wind resistance curve of the ancient trees to be maintained is constructed according to the azimuth of the ancient trees to be maintained, the tree height-crown relationship curve is constructed according to the type of the ancient trees to be maintained, and the biomass curve is determined according to the azimuth of the ancient trees to be maintained and the type of the ancient trees to be maintained; the tree height-crown relationship curve represents the relationship between the trunk height and the canopy diameter, the wind resistance curve represents the relationship between the canopy windward area corresponding to the canopy diameter and the wind resistance; the biomass curve represents the relationship between the biomass and the canopy diameter;

[0169] Determine the first intersection point of the wind resistance curve and the tree height and crown relationship curve;

[0170] Determine the expected canopy diameter, expected trunk height and expected biomass according to the first intersection point and the second intersection point of the wind resistance curve and the biomass curve; wherein the expected canopy diameter, expected trunk height and expected biomass all meet the corresponding set maintenance conditions;

[0171] Determine the main branch force analysis model and water conduction model according to the type of ancient trees to be maintained;

[0172] According to the main branch stress analysis model and water conduction model, the branch angle simulation processing is performed on each main branch of the ancient tree to be maintained to determine the expected branch angle of each main branch. The expected branch angle indicates that when the corresponding main branch is at the expected branch angle with the trunk of the ancient tree to be maintained, the stress and water conduction efficiency expected to be generated meet the stability conditions.

[0173] The expected canopy diameter, expected trunk height, expected biomass, each expected branch angle, root distribution data, deceleration rate coefficient, and expected trunk coordination parameters are input into the tree type evaluation model to determine the expected tree type evaluation data.

[0174] Therefore, by redetermining the expected canopy diameter, expected trunk height, expected biomass, and expected branch angle, the tree shape advantages after reinforcement, pruning, and other operations on ancient trees can be predicted, so that the strategy for ancient tree maintenance can be determined more quickly and the maintenance can be more comprehensive.

[0175] It can be understood that the wind resistance curve is a functional expression of the wind load model. The tree height and crown relationship curve is set to D canopy = k1H; where k1 is a proportional coefficient related to tree species and environmental conditions, which can be determined based on actual ancient tree maintenance data or set based on empirical values. The biomass curve represents the relationship between the expected biomass and the canopy diameter. In some embodiments, the biomass curve is set to Among them, B' m represents the expected biomass, D canopy represents the canopy diameter. a, b, and c can be determined by fitting the relationship between biomass and canopy diameter through maintenance records of different tree species and under different growth conditions.

[0176] It is understandable that the first intersection point characterizes that the photosynthesis efficiency and wind resistance are in a state of balance. The second intersection point indicates that the structural stability and nutrient storage capacity of the ancient tree are in a state of balance. In some embodiments, since the wind resistance curve will change with the azimuth, it is possible to construct wind resistance curves of multiple azimuths, so that the expected canopy diameter, expected trunk height, and expected biomass can be determined jointly by the first intersection point and multiple second intersection points. In some embodiments, since the wind resistance curve, the tree height and crown relationship curve, and the biomass curve are all related to the canopy diameter, the wind resistance curve, the tree height and crown relationship curve, and the biomass curve can be expressed as curves with the canopy diameter as the independent variable. At this time, the expected canopy diameter is first determined based on the first intersection point and the second intersection point, and then the expected trunk height and expected biomass are determined based on the expected canopy diameter. In some embodiments, the expected canopy diameter can be determined based on the maintenance conditions. The embodiment of the present application does not limit how the maintenance conditions are set, and those skilled in the art can selectively set them according to the actual maintenance conditions.

[0177] It is understandable that the branch angle will affect the effectiveness of water conduction. Therefore, by combining the main branch force analysis model with the water conduction model, the estimated expected branch angle can be made more reliable. At this time, the growth potential of the ancient tree can be improved by adjusting each branch (such as the main branch) to the expected branch angle.

[0178] It is understandable that the main branch force analysis model and the water conduction model can be converted into a curve with the branch angle as the independent variable, and the desired branch angle can be determined by analyzing the third intersection point of the two curves.

[0179] At this point, based on the expected tree type evaluation data, it can be determined whether the current optimization is feasible, and then the maintenance strategy can be quickly determined.

[0180] For example, refer to Figure 2 As shown, a method for determining life characteristic parameters of an ancient tree according to an embodiment of the present application is described, and the specific steps are as follows:

[0181] Step S1, setting the image data, radar detection data, meteorological data, and water reduction coefficient of the ancient tree to be maintained in a preset view;

[0182] Step S2: determining physical parameters and geometric parameters based on the image data, and determining environmental force data based on the image data and meteorological data; wherein, the process of determining physical parameters based on the image data can refer to the image extraction method in the prior art, and the embodiment of the present application will not be described in detail;

[0183] Step S3: Determine the new branch index, wind resistance index and biomass index, which can be referred to Figure 1The steps S200 to S400 and their sub-steps are determined;

[0184] Step S4: determining root distribution data based on radar detection data and biomass index, which may be specifically referred to in the above examples and will not be described in detail here;

[0185] Step S5, determining vitality parameters based on the new branch index, wind resistance index and biomass index;

[0186] Step S6, determining a first tree type dominance score based on trunk height, canopy diameter, branch angle, biomass index, and root distribution data;

[0187] Step S7: Determine the second tree type dominance score based on the trunk height, canopy diameter, branch angle, biomass index, root distribution data, water decline coefficient, and trunk coordination parameters.

[0188] At this time, the first tree dominance score, the second tree dominance score, the physical parameters, the meteorological data, etc. may be displayed according to a preset view layout.

[0189] For example, refer to Figure 3 As shown, a method for determining life characteristic parameters of an ancient tree according to another embodiment of the present application is described, and the specific steps are as follows:

[0190] Step S1, obtaining physical parameters, geometric parameters and environmental force data. The physical parameters, geometric parameters and environmental force parameters can be obtained from other platforms, or can be input from the user interface, or can be based on Figure 2 S1 is obtained in this embodiment of the present application, and no further details are given in this embodiment;

[0191] Step S2, determining the new branch index, wind resistance index and biomass index;

[0192] Step S3, determining root distribution data based on radar detection data and biomass indicators;

[0193] Step S4, determining vitality parameters and tree type evaluation data;

[0194] Step S5, obtaining the expected canopy diameter, expected trunk height, expected biomass, and each expected branch angle;

[0195] Step S6.1, determining a first expected tree type dominance score based on expected trunk height, expected canopy diameter, expected branch angle, expected biomass, and root distribution data;

[0196] Step S6.2, determining a second tree type dominance score based on the expected trunk height, expected canopy diameter, expected branch angle, expected biomass index, root distribution data, water decline coefficient, and trunk coordination expected parameters;

[0197] Step S7: output the first expected tree type dominance score, the second expected tree type dominance score, the vitality parameter and the tree type evaluation parameter.

[0198] It is understandable that, in addition to the output in step S5, the expected trunk height, expected canopy diameter, expected branch angle, expected biomass index, root distribution data, water decline coefficient, trunk coordination expected parameters, and azimuth corresponding to the expected biomass index, physical parameters of ancient trees, etc. can also be displayed together through user request. The embodiment of the present application does not limit how the above data is displayed, and those skilled in the art can selectively set it according to actual needs.

[0199] It is understandable that, in some embodiments, the life characteristic parameter also includes a tree hole probability, and the tree hole probability is determined by the following steps:

[0200] Obtain the geographical location information of the ancient trees to be maintained;

[0201] Determine the bending direction of the trunk of the ancient tree to be maintained based on the geographical location information;

[0202] In the bending direction of the trunk, obtain the maximum stress index of the trunk of the ancient tree to be maintained;

[0203] The probability of tree hole formation is determined based on the target trunk maximum stress index.

[0204] Under natural conditions, tree trunks grow towards the sun, and in different geographical locations, the trunks will bend to a certain extent. The bending direction of the trunk represents the direction in which the trunk tends to bend under natural growth conditions at the geographical location.

[0205] The maximum stress index of the trunk is based on The maximum stress is calculated at the preset height critical value of the trunk. The height critical value is determined based on historical maintenance data as the place where the probability of a tree hole forming is highest when the trunk is bent. Taking the height from the ground being one-fifth of the trunk height as the height critical value as an example, the difference between the trunk height and the height critical value corresponding to the maximum stress index of the trunk and the duration are calculated. The probability of a tree hole forming can be determined by looking up the table based on the difference and duration.

[0206] It is understandable that, in some embodiments, the vital sign parameter also includes branch break risk data, and the branch break risk data is obtained by the following steps:

[0207] Determine a first target main branch stress index in the X direction and a second target main branch stress index in the Z direction corresponding to each main branch of the ancient tree to be maintained;

[0208] The first target main branch stress index and the second target main branch stress index are respectively subjected to main branch breakage analysis and processing to obtain the main branch breakage probability and main branch breakage direction of each main branch;

[0209] According to the main branch breakage probability and main branch breakage direction of each main branch, the branch breakage risk data is obtained.

[0210] In some embodiments, the branch breakage risk data also includes the probability of trunk branch breakage and the trunk breakage direction, which can be specifically determined by the duration of the trunk maximum stress index at the same position.

[0211] The X direction and the Z direction are two directions perpendicular to each other. By analyzing the forces in the X and Z directions respectively, it is easier to determine the direction of the main branch breakage.

[0212] The probability of main branch breaking can be determined by looking up a table. The table can summarize the relationship between the probability of breaking and the force magnitude in different directions based on historical maintenance data.

[0213] It can be understood that the first target main branch stress index and the second target main branch stress index represent the sum of the forces on each stress point in the region where the main branch and the trunk meet in the corresponding direction. The main branch stress index corresponds to the sum of the forces on each stress point in the region where the main branch and the trunk meet in each direction in space.

[0214] The main branch stress index, the branch trunk stress index, the first target main branch stress index and the second target main branch stress index can all be determined based on the corresponding force analysis model combined with a finite element analysis method (such as abqus).

[0215] Understandably, referring to Figure 4 As shown, an embodiment of the present application further provides an electronic device, including:

[0216] at least one processor 401;

[0217] At least one memory 402 is used to store at least one program, and when the at least one program is executed by at least one processor 401, the above method is implemented.

[0218] The memory 402 is a non-transient network system that can be used to store non-transient software programs and non-transient computer executable programs. In addition, the memory 402 may include a high-speed random access memory, and may also include a non-transient memory, such as at least one disk storage device, a flash memory device, or other non-transient solid-state storage device. In some embodiments, the memory 402 may optionally include a memory 402 remotely arranged relative to the processor 401, and these remote memories 402 may be connected to the processor 401 via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0219] The memory 402 can be implemented in the form of a read-only memory (ROM), a static storage device, a dynamic storage device, or a random access memory (RAM). The memory 402 can store an operating system and other application programs. When the technical solutions provided in the embodiments of this specification are implemented by software or firmware, the relevant program codes are stored in the memory 402, and the processor 401 calls and executes the method of the embodiments of this application.

[0220] The processor 401 can be implemented by a general-purpose CPU (Central Processing Unit), a microprocessor, an application-specific integrated circuit (Application Specific Integrated Circuit, ASIC), or one or more integrated circuits, and is used to execute relevant programs to implement the technical solution provided in the embodiment of the present application.

[0221] In some embodiments, the electronic device further comprises:

[0222] Input / output interface, used to realize information input and output;

[0223] Communication interface, used to realize communication interaction between this device and other devices, which can be realized through wired mode (such as USB, network cable, etc.) or wireless mode (such as mobile network, WIFI, Bluetooth, etc.);

[0224] A bus that transmits information between various components of the device (e.g., processor 401, memory 402, input / output interface, and communication interface);

[0225] The processor 401 , the memory 402 , the input / output interface and the communication interface can be connected to each other in communication within the device via a bus.

[0226] It is understandable that an embodiment of the present application further provides a computer-readable storage medium storing computer-executable instructions for executing the above method.

[0227] It is understandable that an embodiment of the present application also provides a computer program product, including a computer program or computer instructions, which are stored in a computer-readable storage medium. The processor of the wall-climbing robot reads the computer program or computer instructions from the computer-readable storage medium, and the processor executes the computer program or computer instructions, so that the computer device executes the above method.

[0228] The system architecture and application scenarios described in the embodiments of the present application are intended to more clearly illustrate the technical solutions of the embodiments of the present application, and do not constitute a limitation on the technical solutions provided in the embodiments of the present application. Those skilled in the art will appreciate that with the evolution of the system architecture and the emergence of new application scenarios, the technical solutions provided in the embodiments of the present application are equally applicable to similar technical problems.

[0229] It will be appreciated by those skilled in the art that all or some of the steps and systems in the disclosed method above may be implemented as software, firmware, hardware and appropriate combinations thereof. Some physical components or all physical components may be implemented as software executed by a processor, such as a central processing unit, a digital signal processor or a microprocessor, or may be implemented as hardware, or may be implemented as an integrated circuit, such as an application specific integrated circuit. Such software may be distributed on a computer-readable medium, which may include a computer storage medium (or a non-transitory medium) and a communication medium (or a temporary medium). As known to those skilled in the art, the term computer storage medium includes volatile and non-volatile, removable and non-removable media implemented in any method or technology for storing information (such as computer-readable instructions, data structures, program modules or other data). Computer storage media include, but are not limited to, RAM, ROM, EEPROM, flash memory or other memory technologies, CD-ROM, digital versatile disks (DVD) or other optical disk storage, magnetic cassettes, magnetic tapes, disk storage or other magnetic storage devices, or any other medium that may be used to store desired information and may be accessed by a computer. Furthermore, it is well known to those skilled in the art that communication media typically embodies computer readable instructions, data structures, program modules, or other data in a modulated data signal such as a carrier wave or other transport mechanism, and may include any information delivery media.

[0230] The above describes some embodiments of the present application with reference to the accompanying drawings, but does not limit the scope of the present invention. Any modification, equivalent substitution and improvement made by those skilled in the art without departing from the scope and essence of the present invention shall be within the scope of the present application.

Claims

1. A method for determining life characteristic parameters of an ancient tree, characterized in that: The method comprises: Obtain the physical parameters, geometric parameters and environmental stress data of the ancient trees to be maintained; Performing stress analysis on each component of the ancient tree to be maintained according to the physical parameters and the environmental stress data to obtain a wind resistance index; Performing biomass analysis on the ancient trees to be maintained according to the geometric parameters to obtain biomass indicators; Obtaining new branch indicators of the ancient tree to be maintained; According to the new branch index, the wind resistance index and the biomass index, life characteristic parameters are obtained; wherein the life characteristic parameters are used to characterize the vitality and growth potential of the ancient tree to be maintained.

2. The method for determining the life characteristic parameters of an ancient tree according to claim 1, characterized in that: The components of the ancient tree to be maintained include a trunk. The force analysis of the components of the ancient tree to be maintained is performed according to the physical parameters and the environmental force data to obtain wind resistance indicators, including: Inputting the trunk physical parameters of the physical parameters and the trunk stress data of the environmental stress data into a preset trunk stress analysis model to obtain a trunk average stress index corresponding to the trunk one by one; When the old tree to be maintained has main branches, the physical parameters and the main branch data corresponding to each main branch in the environmental stress data are respectively input into a preset main branch stress analysis model to obtain main branch stress indicators corresponding to the main branches one by one; When the old tree to be maintained has secondary branches, the physical parameters and the secondary branch data corresponding to each secondary branch in the environmental stress data are respectively input into a preset secondary branch stress analysis model to obtain branch stress indicators corresponding to each secondary branch; Obtaining the wind resistance index according to the trunk average stress index, each of the main branch stress indexes and each of the branch stress indexes; Among them, the main trunk force analysis model is different from the branch force analysis model and the main branch force analysis model.

3. The method for determining the life characteristic parameters of an ancient tree according to claim 1, characterized in that: The geometric parameters include trunk geometric parameters and canopy geometric parameters; the biomass analysis of the ancient trees to be maintained is performed according to the geometric parameters to obtain biomass indicators, including: Evaluate the biomass of the trunk of the ancient tree to be maintained according to the trunk geometric parameters to obtain the trunk biomass; Evaluate the biomass of the canopy of the ancient tree to be maintained according to the canopy geometric parameters to obtain the canopy biomass; Determining the root biomass of the root system of the ancient tree to be maintained according to the trunk biomass and the canopy biomass; The root biomass, the trunk biomass and the canopy biomass are all used as the biomass indicators.

4. The method for determining the life characteristic parameters of an ancient tree according to claim 3, characterized in that: The method further comprises: Acquiring radar detection data of the root system; A root distribution map of the root system is constructed according to the radar detection data and the root biomass.

5. The method for determining the life characteristic parameters of an ancient tree according to claim 1, characterized in that: The life characteristic parameters also include vitality parameters, which are determined by the following steps: Obtaining weight coefficients corresponding to the new branch index, the wind resistance index, and the biomass index; The new branch index, the wind resistance index and the biomass are weightedly calculated according to each weight coefficient to obtain a vitality parameter.

6. The method for determining the life characteristic parameters of an ancient tree according to claim 5, characterized in that: The weight coefficients corresponding to the new branch index, the wind resistance index and the biomass index are determined by the following steps: A first weight combination is obtained by performing multivariate linear regression fitting on the historical new branch index, historical wind resistance index, historical biomass index and historical vitality parameter of the sample ancient trees; wherein the first weight combination is formed by combining the weight coefficients corresponding to the historical new branch index, historical wind resistance index and historical biomass index respectively; The new branch index, the wind resistance index and the biomass index of the sample ancient tree are weighted by a preset genetic algorithm to obtain a second weight combination when the vitality prediction error of the sample ancient tree meets the preset error condition; The new branch index, the wind resistance index and the biomass index of the sample ancient tree are weighted by a preset gradient descent algorithm to obtain a third weight combination when the vitality prediction error of the sample ancient tree meets the preset error condition; The new branch index, the wind resistance index and the biomass index of the sample ancient tree are weighted by a preset Bayesian optimization algorithm to obtain a fourth weight combination when the vitality prediction error of the sample ancient tree meets the preset error condition; The same parameter types in the first weight combination, the second weight combination, the third weight combination and the fourth weight combination are weighted respectively to obtain weight coefficients corresponding to the new branch index, the wind resistance index and the biomass index.

7. The method for determining the life characteristic parameters of an ancient tree according to claim 1 or 5, characterized in that: The life characteristic parameters also include tree-type evaluation data, and the tree-type evaluation data is determined by the following steps: Determine the trunk coordination parameters according to the total cross-sectional diameter of the branches and the cross-sectional diameter of the trunk of the ancient tree to be maintained; Obtaining a root distribution map of the ancient tree to be maintained and determining root distribution data; The biomass index, the root distribution data, the trunk coordination parameter and the pre-configured decreasing rate coefficient are input into a preset tree type evaluation model to determine the tree type evaluation data.

8. The method for determining the life characteristic parameters of an ancient tree according to claim 7, characterized in that: The method further comprises: A wind resistance curve of the ancient tree to be maintained is constructed according to the azimuth of the ancient tree to be maintained, a tree height-crown relationship curve is constructed according to the type of the ancient tree to be maintained, and a biomass curve is determined according to the azimuth of the ancient tree to be maintained and the type of the ancient tree to be maintained; the tree height-crown relationship curve represents the relationship between the trunk height and the canopy diameter, the wind resistance curve represents the relationship between the canopy windward area corresponding to the canopy diameter and the wind resistance; the biomass curve represents the relationship between the biomass and the canopy diameter; Determine a first intersection point of the wind resistance curve and the tree height and crown relationship curve; Determine an expected canopy diameter, an expected trunk height, and an expected biomass according to the first intersection point and a second intersection point of the wind resistance curve and the biomass curve; wherein the expected canopy diameter, the expected trunk height, and the expected biomass all meet corresponding maintenance conditions; Determine the main branch force analysis model and water conduction model according to the type of the ancient tree to be maintained; According to the main branch stress analysis model and the water conduction model, branch angle simulation processing is performed on each main branch of the ancient tree to be maintained to determine the expected branch angle of each main branch, wherein the expected branch angle indicates that when the corresponding main branch has an angle with the trunk of the ancient tree to be maintained of the expected branch angle, the stress and water conduction efficiency expected to be generated both meet the stability condition; The expected canopy diameter, the expected trunk height, the expected biomass, the expected branch angles, the root distribution data, the decreasing rate coefficient, and the expected trunk coordination parameters are input into the tree type evaluation model to determine the expected tree type evaluation data.

9. The method for determining the life characteristic parameters of an ancient tree according to claim 7, characterized in that: The life characteristic parameters also include tree hole probability, and the tree hole probability is determined by the following steps: Obtaining geographical location information of the ancient tree to be maintained; Determine the bending direction of the trunk of the ancient tree to be maintained according to the geographical location information; In the bending direction of the trunk, obtaining the trunk maximum stress index on the trunk of the ancient tree to be maintained; The probability of tree hole formation is determined based on the target trunk maximum stress index.

10. The method for determining the life characteristic parameters of an ancient tree according to claim 9, characterized in that: The life characteristic parameters also include branch breakage risk data, and the branch breakage risk data is obtained by the following steps: Determine a first target main branch stress index in the X direction and a second target main branch stress index in the Z direction corresponding to each main branch of the ancient tree to be maintained; Performing main branch breakage analysis and processing on the first target main branch stress index and the second target main branch stress index respectively to obtain the main branch breakage probability and main branch breakage direction of each main branch; The branch breaking risk data is obtained according to the main branch breaking probability and main branch breaking direction of each main branch.

11. An electronic device, characterized in that: The electronic device includes a memory and a processor, the memory stores a computer program, and the processor implements the method for determining the life characteristic parameters of the ancient tree as described in any one of claims 1 to 10 when executing the computer program.

12. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the method for determining the life characteristic parameters of the ancient tree according to any one of claims 1 to 10 is implemented.

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