Methods, systems, and media of constructing a tree model to simulate environmental effects on tree growth

Abstract

This invention relates to a method, system, and medium for constructing tree models to simulate the environmental impact on tree growth. In this method, a three-dimensional geometric model containing the spatial location and orientation information of each branch is constructed by scanning the aboveground portion of the tree using three-dimensional point cloud scanning. The hierarchical relationships of each branch are analyzed to determine the subordinate topology of branches at each level with the trunk as the root node. Based on this, the total height of the trunk, the set of deflection angles of each level of branches, the size sequence of each level of branches, and the canopy projection area are extracted. Environmental parameters of the target area are also obtained. These structural feature parameters are input into a preset model trained with tree pruning samples. The model automatically identifies crossing branches, inner branches, and vigorous shoots in the three-dimensional geometric model. After conflict detection and safety distance verification, the pruning scheme for each pruning location is determined. This invention solves the problems of incomplete tree branch structure analysis and lack of environmental adaptability in pruning schemes in existing technologies.

Description

Technical Field

[0001] This application belongs to the field of tree model simulation, and in particular relates to methods, systems and media for constructing tree models to simulate the environmental impact on tree growth. Background Technology

[0002] Tree pruning is a crucial aspect of landscaping and orchard management. Proper pruning improves ventilation and light penetration, regulates nutrient distribution, and inhibits the spread of pests and diseases, thereby enhancing tree growth quality and productivity. However, due to the complex structure and varied spatial distribution of tree branches, accurately identifying problematic branches and developing scientifically sound pruning plans remains a core technical challenge in the field of refined tree management.

[0003] Existing research has applied 3D point cloud scanning technology to tree morphology measurement. By acquiring point cloud data of the above-ground parts of the tree, a 3D geometric model of the tree is reconstructed, and basic morphological parameters such as tree height and crown width are extracted to assist in planting management decisions.

[0004] However, most existing methods can only extract static morphological parameters and cannot automatically analyze the hierarchical relationship and branch topology of each branch. Furthermore, they fail to effectively combine environmental parameters such as light, temperature, and soil moisture with the three-dimensional tree model. They lack the ability to simulate and predict the growth trend of pruned trees under the influence of the real environment, resulting in pruning schemes that only remain at the level of static branch removal and cannot provide managers with forward-looking visual decision support. Summary of the Invention

[0005] This application provides a method for constructing tree models to simulate the environmental impact on tree growth. This method is used for automated identification of tree branch structure and planning of pruning schemes, and for simulating and predicting the growth status of pruned trees by combining environmental parameters. The method constructs a geometric model of the tree through 3D point cloud scanning, and automatically identifies problematic branches and generates a visualized pruning plan and post-pruning growth prediction results by combining branch topology and environmental parameters.

[0006] Firstly, this application provides a method for constructing tree models to simulate the environmental impacts on tree growth, including: A 3D point cloud scan of the above-ground part of the tree is performed to obtain a 3D geometric model of the above-ground part of the tree containing information on the spatial position and orientation of each branch. Based on the aforementioned three-dimensional geometric model, the hierarchical membership of each branch is analyzed, and the hierarchical topology of each branch with the trunk as the root node is determined. Based on the branch hierarchy topology, the total height of the trunk, the set of branch deflection angles at each level, the size sequence of branches at each level, and the canopy projection area are extracted, and the environmental parameters of the target area are obtained. Based on the set of branch deflection angles at each level, the size sequence of branches at each level, and the subordinate topology of branches at each level, a preset model trained with tree pruning samples is input to identify the crossing branches, inner branches, and vigorous branches in the three-dimensional geometric model, thereby obtaining a set of branches to be pruned. Based on the set of branches to be pruned, the spatial coordinates of each pruning node are located in the three-dimensional geometric model, and the pruning scheme for each pruning position in the corresponding physical tree is determined. According to the pruning scheme, the removal of the branches to be pruned is simulated in the three-dimensional geometric model. Combined with the environmental parameters, the three-dimensional prediction model after pruning and the updated total height of the main trunk, the set of deflection angles of branches at all levels, the size sequence of branches at all levels and the canopy projection area are obtained. Based on the three-dimensional geometric model and the three-dimensional prediction model after pruning, a comparison model before and after pruning is generated to obtain a visual pruning scheme.

[0007] In the above implementation, a three-dimensional geometric model is constructed by scanning the above-ground parts of the trees with three-dimensional point clouds, and the hierarchical membership and branch topology of each branch are analyzed, thereby realizing the digital expression of the spatial structure of tree branches. By inputting structural feature parameters into a preset model trained on samples, crossing branches, inner branches, and vigorous branches are automatically identified, improving the accuracy and consistency of identifying problematic branches. By combining environmental parameters of the target area to simulate and predict the trees after pruning, the pruning plan can reflect the impact of the real environment on tree growth and has strong environmental adaptability. By generating a visual pruning plan comparing before and after pruning, the pruning effect is presented intuitively, improving the precision of management.

[0008] In one implementation, the step of analyzing the hierarchical membership of each branch based on the three-dimensional geometric model and determining the hierarchical topology of branches at each level with the trunk as the root node specifically includes: The skeleton of the three-dimensional geometric model is extracted to obtain the initial skeleton lines and spatial connection relationships of each branch; Based on the initial skeleton line and spatial connection relationship, the inner branch regions where local point cloud data is missing due to the occlusion of outer branches are detected, and the set of missing branches is obtained. Based on the spatial position and deflection direction of the adjacent known branches of each missing branch in the set of missing branches, the shape of the missing branches is completed by interpolation algorithm to obtain a complete skeleton line; Based on the complete skeleton line, the hierarchical membership relationship of each branch is analyzed step by step with the main trunk as the root node to determine the subordinate topology of each branch.

[0009] In the above implementation, by extracting the skeleton of the three-dimensional geometric model and detecting the missing point cloud data areas caused by the occlusion of outer branches, the missing branch morphology is completed using an interpolation algorithm, which effectively solves the problem of branch hierarchy analysis error caused by incomplete point cloud data in the inner branch area, and improves the integrity and accuracy of the branch topology.

[0010] In one implementation, the step of locating the spatial coordinates of each pruning node in the three-dimensional geometric model based on the set of branches to be pruned, and determining the pruning scheme for each pruning position in the corresponding physical tree, specifically includes: Based on the branch hierarchy topology, traverse the set of branches to be pruned, and detect cases in the set of branches to be pruned where the parent branch and its corresponding child branch on the same hierarchical path are simultaneously marked as to be pruned, thus obtaining a set of conflicting branch groups. Based on the set of conflicting branch groups, retain the pruning mark of the mother branch in each conflicting branch group, remove the pruning mark of the corresponding subordinate branch, and obtain the set of branches to be pruned after conflict removal. Based on the set of branches to be pruned after conflict resolution, the spatial coordinates of each pruning node are located in the three-dimensional geometric model, and the pruning scheme for each pruning position is determined.

[0011] In the above implementation, by traversing the set of branches to be pruned and combining the branch topology to detect the conflict situation where the mother branch and its subordinate branches are simultaneously marked as to be pruned, conflict resolution is carried out by retaining the pruning mark of the mother branch and removing redundant pruning marks of the subordinate branches. This avoids repeated pruning of branches on the same topological path, reduces unnecessary pruning operations, thereby reducing excessive damage to the overall structure of the tree and improving the rationality and operability of the pruning plan.

[0012] In one implementation, the step of locating the spatial coordinates of each pruning node in the three-dimensional geometric model based on the conflict-free set of branches to be pruned, and determining the pruning scheme for each pruning position, specifically includes: Traverse the set of branches to be pruned after conflict resolution, calculate the spatial distance between each pruning node and the surrounding retained branches, and detect cases where the spatial distance between the pruning node and the surrounding retained branches is less than a preset safe distance threshold to obtain a set of risky pruning nodes; Based on the set of risk pruning nodes, the pruning positions corresponding to the risk pruning nodes are highlighted in the three-dimensional geometric model, and the pruning nodes are shifted to the end along the extension direction of the branches to be pruned until the spatial distance between them and the surrounding retained branches meets the preset safe distance threshold, thus obtaining the adjusted spatial coordinates of the pruning nodes. Based on the adjusted spatial coordinates of the trimming nodes and the spatial coordinates of the remaining unadjusted trimming nodes, the trimming scheme for each trimming position is determined.

[0013] In the above implementation, by calculating the spatial distance between each pruning node and the surrounding retained branches, pruning nodes with the risk of accidental damage are detected and automatically offset along the branch extension direction to a position that meets the safe distance threshold. At the same time, the risky pruning nodes are highlighted in the three-dimensional geometric model, which effectively reduces the risk of accidental damage to the surrounding retained branches caused by the pruning operation and improves the safety and accuracy of the pruning scheme.

[0014] In one implementation, the step of inputting a preset model trained on tree pruning samples, based on the set of branch deflection angles at each level, the size sequence of branches at each level, and the subordinate topology of branches at each level, to identify crossing branches, inner branches, and vigorous shoots in the three-dimensional geometric model, and obtaining a set of branches to be pruned, specifically includes: Input the set of branch deflection angles at each level, the sequence of branch size at each level, and the branch subordination topology at each level into the preset model to obtain the pruning category label and corresponding confidence level for each branch; If a branch is detected with a confidence level lower than a preset confidence threshold, a set of low-confidence branches is obtained. Based on the set of low-confidence branches, the set of low-confidence branches is displayed in the three-dimensional geometric model using a distinguishing annotation method to obtain the set of branches to be manually reviewed; Based on the confirmation results of the pruning categories of each branch in the set of branches to be manually reviewed, and combined with the identification results of the remaining branches by the preset model, a complete set of branches to be pruned is obtained.

[0015] In the above implementation, by obtaining the pruning category label and confidence level of each branch by the preset model, branches with confidence levels lower than the preset threshold are extracted separately and displayed in a differentiated labeling manner, guiding the review and confirmation of branches with low confidence levels. This achieves an organic combination of automatic model recognition and manual review, effectively compensating for the problem of high uncertainty in the recognition of the preset model in complex branch structure scenarios while retaining the efficiency of automatic recognition, and improving the recognition accuracy of the set of branches to be pruned.

[0016] In one embodiment, after obtaining the pruned three-dimensional prediction model and the updated total trunk height, the set of branch deflection angles at each level, the branch size sequence at each level, and the canopy projected area, the method further includes: Based on the updated canopy projection area and the set of branch deflection angles at all levels, combined with the wind speed and wind direction data of the target area, the wind pressure distribution of each branch layer of the pruned tree is calculated, and the wind load distribution results after pruning are obtained. Based on the wind load distribution results after pruning, it is detected that there are local branches where the wind pressure exceeds the preset safety threshold. The corresponding branches are marked as supplementary pruning candidates and the visualized pruning scheme is updated to obtain a comprehensive pruning scheme.

[0017] In the above implementation, the wind pressure distribution of each branch layer of the pruned tree is calculated by combining wind speed and wind direction data of the target area, and branches with wind pressure exceeding the preset safety threshold are marked as supplementary pruning candidates. This makes the pruning scheme not only limited to the optimization of tree structure, but also takes into account the wind resistance stability of the pruned trees in the actual wind field environment, reduces the risk of breakage caused by excessive wind load on local branches after pruning, and improves the adaptability of the pruning scheme to the climate environment of the target area.

[0018] In a second aspect, embodiments of this application provide a system for constructing tree models to simulate environmental influences on tree growth, comprising: one or more processors and a memory; the memory is coupled to one or more processors, the memory is used to store computer program code, the computer program code including computer instructions, and one or more processors call the computer instructions to cause the system to perform the method described in the first aspect and any possible implementation thereof.

[0019] Thirdly, embodiments of this application provide a computer-readable storage medium including instructions that, when executed on a system, cause the system to perform the method described in the first aspect and any possible implementation thereof.

[0020] Fourthly, embodiments of this application provide a computer program product that, when run on a system, causes the system to execute the method described in any possible implementation of the first aspect.

[0021] One or more technical solutions provided in the embodiments of this application have at least the following technical effects or advantages: 1. This application provides a method for constructing a tree model to simulate the environmental impact on tree growth. The method constructs a tree geometric model by scanning a three-dimensional point cloud and analyzes the branch topology. Combined with a preset model trained on samples, it automatically identifies crossing branches, inner branches, and vigorous branches. Combined with environmental impact assessment, it realizes the accurate identification of problematic branches of trees and the automated generation of pruning schemes.

[0022] 2. This application provides a method for constructing a tree model to simulate the environmental impact on tree growth. By introducing a conflict resolution mechanism for conflicting branch groups and a safety distance verification mechanism for pruning nodes, it effectively reduces redundant operations and accidental damage risks in the pruning scheme, and improves the rationality of the pruning scheme and the safety of construction.

[0023] 3. This application provides a method for constructing a tree model to simulate the environmental impact on tree growth. By combining the environmental parameters of the target area with the three-dimensional prediction model after pruning, a comprehensive simulation and evaluation of the growth trend and wind resistance stability of pruned trees is achieved. Attached Figure Description

[0024] Figure 1 This is a flowchart illustrating a method for constructing a tree model to simulate the environmental impact on tree growth, as described in an embodiment of this application.

[0025] Figure 2 This is a schematic diagram of a three-dimensional geometric model in a method for constructing a tree model to simulate the environmental impact on tree growth, as described in an embodiment of this application.

[0026] Figure 3 This is a schematic diagram of the physical device structure of a system for constructing a tree model to simulate the environmental impact on tree growth, provided in an embodiment of this application. Detailed Implementation

[0027] The terminology used in the following embodiments of this application is for the purpose of describing particular embodiments only and is not intended to be limiting of this application. As used in the specification and appended claims of this application, the singular expressions “a,” “an,” “the,” “the,” “the,” and “this” are intended to include the plural expressions as well, unless the context clearly indicates otherwise. It should also be understood that the term “and / or” as used in this application refers to any or all possible combinations including one or more of the listed items.

[0028] Hereinafter, the terms "first" and "second" are used for descriptive purposes only and should not be construed as implying or suggesting relative importance or implicitly indicating the number of indicated technical features. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature, and in the description of the embodiments of this application, unless otherwise stated, "multiple" means two or more.

[0029] In the field of tree model simulation, the spatial structure analysis of tree branches and the scientific planning of pruning schemes are core technical requirements in landscape greening management and orchard planting management. Reasonable pruning can effectively improve ventilation and light penetration in trees, regulate nutrient distribution within the tree, and inhibit the spread of pests and diseases, which is of great significance for improving tree growth quality and production efficiency.

[0030] In related technologies, existing research has applied 3D point cloud scanning technology to tree morphology measurement. By reconstructing the 3D geometric model of the tree, basic morphological parameters are extracted to assist planting management decisions. However, most existing methods cannot automatically analyze the hierarchical topology of branches, lack the ability to automatically identify problematic branches such as crossing branches, inner branches, and vigorous branches, and fail to effectively combine environmental parameters with the 3D model. This makes it difficult to simulate and predict the growth trend of trees after pruning, resulting in a lack of forward-looking visual decision support for pruning schemes.

[0031] This application is mainly applied to scenarios such as refined management of landscaping, orchard planting and pruning planning, and urban street tree maintenance, covering various application environments that require periodic pruning management of a large number of trees. In these application scenarios, tree branch structures are complex and varied, manual identification is inefficient and inconsistent, and pruning plans need to fully consider the impact of environmental factors such as light and wind on the subsequent growth of trees. To solve the above technical problems, this application provides a method for constructing tree models to simulate the environmental impact on tree growth. An embodiment is described below, combined with... Figure 1 The present application describes a method for constructing a tree model to simulate the environmental impact on tree growth, as exemplified in the following embodiments: Please see Figure 1 This is a flowchart illustrating a method for constructing a tree model to simulate the environmental impact on tree growth, as described in an embodiment of this application.

[0032] S101. Perform a three-dimensional point cloud scan on the above-ground part of the tree to obtain a three-dimensional geometric model of the above-ground part of the tree containing information on the spatial position and orientation of each branch.

[0033] Among them, the three-dimensional geometric model of the above-ground part of the tree refers to the digital three-dimensional representation that accurately reflects the spatial position, direction and size of the tree trunk and branches at all levels, obtained by filtering, registration and surface reconstruction based on point cloud data.

[0034] Specifically, the aboveground part of the target tree is scanned from multiple angles to obtain raw point cloud data; the raw point cloud data is then denoised and filtered to remove outliers and noise points; the point cloud data obtained from the multi-angle scans is registered and fused to obtain a complete point cloud dataset of the aboveground part of the tree; based on this, the point cloud data is reconstructed to obtain a three-dimensional geometric model of the aboveground part of the tree that includes information on the spatial position and orientation of each branch.

[0035] S102. Based on the three-dimensional geometric model, analyze the hierarchical relationship of each branch and determine the subordinate topology of each branch with the trunk as the root node.

[0036] Hierarchical membership refers to the hierarchical affiliation between branches of a tree based on their growth relationships, that is, the structural description of which level of branch or trunk a particular branch directly belongs to. The branching hierarchy topology refers to a tree-shaped directed graph constructed with the trunk as the root node, extending outwards level by level according to the growth relationships of the branches. Each node represents a branch segment, and the directed edges between nodes represent the hierarchical growth relationships between branches.

[0037] Specifically, the skeleton of the three-dimensional geometric model is first extracted to obtain the initial skeleton lines and spatial connection relationships of each branch. Based on the initial skeleton lines and spatial connection relationships, the main trunk is used as the root node, and the branches are traversed from bottom to top according to the connection points. The hierarchical affiliation of each branch is determined according to the difference in branch diameter and the direction of spatial extension. The subordinate relationship of each branch is recorded level by level to construct the subordinate topology of each level of branches with the main trunk as the root node.

[0038] S103. Based on the hierarchical topology of branches at all levels, extract the total height of the main trunk, the set of deflection angles of branches at all levels, the size sequence of branches at all levels, and the canopy projection area, and obtain the environmental parameters of the target area.

[0039] The total trunk height refers to the vertical distance from the root collar to the branching point at the top of the trunk, reflecting the longitudinal growth of the tree. The set of branch deflection angles refers to the total set of spatial deflection angles formed by branches at each level relative to their parent branches or the extension direction of the trunk, used to characterize the growth posture of the branches. The branch size sequence refers to the numerical sequence of the length and diameter of branches at each level arranged in hierarchical order, used to quantitatively describe the geometric specifications of branches at each level. The canopy projection area refers to the area enclosed by the projected outline of the tree canopy on a horizontal plane, reflecting the overall canopy expansion of the tree. Environmental parameters include external environmental indicators affecting tree growth, such as light intensity, temperature, soil moisture content, and wind speed and direction in the target area.

[0040] Specifically, the process involves traversing the branch nodes along the hierarchical topology of each level of branching, extracting the vertical extension length of the main trunk from the root collar to the top to obtain the total height of the main trunk; calculating the angle between the direction vector of each branch's skeleton line and its corresponding parent branch's skeleton line direction vector, and summarizing these to obtain the set of branch deflection angles at each level; extracting the length of each branch's skeleton line and the corresponding cylinder fitting diameter, and arranging them hierarchically to obtain the branch size sequence at each level; projecting all branch nodes in the 3D geometric model onto the horizontal plane, calculating the convex hull area of ​​the projected contour to obtain the canopy projection area, and referring to... Figure 2 , Figure 2The diagram illustrates the extraction of structural feature parameters from a 3D point cloud model of a target tree. These parameters include the total height of the trunk, the deflection angle of each branch relative to the parent branch, the length of the branch skeleton lines at each level, and the projected area of ​​the canopy. These structural feature parameters collectively constitute the input features for subsequent branch identification using a pre-set model. Simultaneously, environmental parameters such as illumination, temperature, soil moisture, wind speed, and wind direction are acquired from meteorological sensors or meteorological data platforms in the target area.

[0041] S104. Based on the set of branch deflection angles at all levels, the size sequence of branches at all levels, and the subordinate topology of branches at all levels, input a preset model trained by tree pruning samples, identify crossing branches, inner branches, and vigorous branches in the three-dimensional geometric model, and obtain a set of branches to be pruned.

[0042] Crossing branches refer to branches that intersect or rub against other branches in space. Prolonged crossing and friction can damage branches and induce diseases. Inner canopy branches are branches located inside the canopy and growing towards the center of the tree; their presence worsens ventilation and light penetration within the canopy. Vigorous shoots are branches with extremely strong growth, excessively long internodes, and an imbalanced diameter-to-length ratio, consuming large amounts of tree nutrients and negatively impacting yield and tree shape. The pre-set model trained on tree pruning samples refers to a machine learning model trained through supervised learning using manually labeled tree branch pruning category sample data as the training set, capable of classifying the pruning categories of each branch.

[0043] Specifically, the set of branch deflection angles at all levels, the size sequence of branches at all levels, and the subordinate topology of branches at all levels are used as input features and input into a preset model for inference to obtain the pruning category label and corresponding confidence level for each branch; branches labeled as crossing branches, inner branches, or vigorous branches are summarized to obtain the set of branches to be pruned.

[0044] Optionally, the preset model can adopt a branch classification model based on graph neural networks, which directly constructs the graph structure input with the subordinate topology of branches at all levels, and makes full use of the topological relationship information between branches to improve the classification accuracy; alternatively, a traditional machine learning classifier based on gradient boosting trees can be adopted, which uses manual features such as the deflection angle and aspect ratio of each branch as input, and is suitable for application scenarios with a limited number of training samples.

[0045] S105. Based on the set of branches to be pruned, locate the spatial coordinates of each pruning node in the three-dimensional geometric model, and determine the pruning scheme for each pruning position in the corresponding physical tree.

[0046] Among them, a pruning node refers to a specific location on the branch framework line where pruning operations are carried out, and its spatial coordinates determine the actual position of the pruning tools within the corresponding tree. A pruning plan is a complete pruning operation guidance plan that integrates the spatial coordinates of each pruning node, the pruning sequence, and operational precautions.

[0047] Specifically, the set of branches to be pruned is traversed, and the connection point between the branch to be pruned and its parent branch is taken as the initial pruning node according to the hierarchical relationship of each branch. The spatial coordinates of each initial pruning node are recorded.

[0048] Optionally, the location of the pruning node can be optimized by combining the location information of the buds on the branch surface, and the pruning position can be precisely adjusted to be close to the bud to promote the healing of the cut surface and the sprouting of new branches, thereby improving the biological rationality of the pruning operation.

[0049] S106. Based on the pruning plan, simulate the removal of branches to be pruned in the three-dimensional geometric model. Combined with environmental parameters, obtain the three-dimensional prediction model after pruning and the updated total height of the main trunk, the set of deflection angles of branches at all levels, the size sequence of branches at all levels, and the canopy projection area.

[0050] Among them, the post-pruning 3D prediction model refers to a 3D digital model that reflects the future morphology of the tree after pruning, obtained by simulating and extrapolating the subsequent growth trend of the tree in combination with the environmental parameters of the target area after removing the branches to be pruned in the original 3D geometric model according to the pruning plan.

[0051] Specifically, according to the pruning plan, each branch to be pruned is removed sequentially in the three-dimensional geometric model to obtain the initial geometric model after pruning. The environmental parameters of the target area, such as light, temperature, and soil moisture, are combined with the initial geometric model after pruning. Based on the tree growth model, the healing rate of the pruning section, the extension trend of the retained branches, and the sprouting position of new branches are simulated and deduced. The morphology of each branch in the three-dimensional geometric model is updated according to the deduction results to obtain the three-dimensional prediction model after pruning. At the same time, the updated total height of the main trunk, the set of deflection angles of branches at all levels, the size sequence of branches at all levels, and the canopy projection area are extracted and updated.

[0052] S107. Based on the three-dimensional geometric model and the three-dimensional prediction model after pruning, generate a comparison model before and after pruning to obtain a visual pruning scheme.

[0053] The pre- and post-pruning comparison model refers to a visual 3D comparison model that spatially registers the original 3D geometric model with the 3D prediction model after pruning, and uses different colors or transparency to distinguish the tree morphology before and after pruning, intuitively presenting the impact of pruning operations on tree structure. The visualized pruning scheme refers to a comprehensive visual output result that guides actual pruning operations by overlaying spatial coordinate labels of each pruning node, pruning category descriptions, and pruning sequence numbers onto the pre- and post-pruning comparison model.

[0054] Specifically, the original 3D geometric model and the 3D prediction model after pruning are spatially registered, and different colors are used to distinguish between the branches to be retained and the branches to be pruned. The two models are displayed synchronously in the 3D visualization interface, and the spatial coordinates and corresponding pruning categories of each pruning node are marked with highlights. Numbering labels are added to each pruning node according to the pruning order to generate a visual pruning plan for actual operation.

[0055] In the above embodiments, a tree geometric model is constructed by scanning a three-dimensional point cloud and the branch topology is analyzed. Combined with a preset model trained on samples, crossing branches, inner branches and overgrown branches are automatically identified. Combined with environmental impact assessment, the accurate identification of problematic tree branches and the automated generation of pruning plans are realized.

[0056] In one embodiment, when determining the subordinate topology, there may be cases where the occlusion of outer branches leads to the loss of local point cloud data in the inner branch region, resulting in discontinuous skeleton lines and incorrect hierarchical classification of some branches. Therefore, based on the three-dimensional geometric model, the hierarchical membership of each branch is analyzed to determine the subordinate topology of each level of branches with the trunk as the root node. Specifically, this includes: extracting the skeleton of the three-dimensional geometric model to obtain the initial skeleton lines and spatial connection relationships of each branch; based on the initial skeleton lines and spatial connection relationships, detecting the inner branch regions where local point cloud data is missing due to occlusion of outer branches, obtaining a set of missing branches; based on the spatial position and deflection direction of the adjacent known branches of each missing branch in the set of missing branches, using an interpolation algorithm to complete the morphology of the missing branches, obtaining complete skeleton lines; based on the complete skeleton lines, the hierarchical membership of each branch is analyzed level by level with the trunk as the root node to determine the subordinate topology of each level of branches.

[0057] Among them, the initial skeleton line refers to the single-pixel width curve extracted from the point cloud data of the 3D geometric model, which can represent the direction of the central axis of each branch; the spatial connection relationship refers to the topological adjacency relationship between the endpoints of the skeleton lines of each branch, describing which branch skeleton lines are spatially connected to each other; the point cloud density of the branches in the inner branch region is significantly lower than that of the outer branches, which can easily lead to breakpoints or omissions in the skeleton extraction results; the set of missing branches refers to the set of branches in the initial skeleton extraction results whose skeleton lines have breakpoints or are not extracted at all due to the lack of local point cloud data; the adjacent known branches refer to branches that belong to the same level as the missing branches in the subordinate topology of each level of branching, are spatially adjacent, and have complete skeleton lines. Their spatial position and deflection direction can be used as a reference for the morphological interpolation of the missing branches; the complete skeleton line refers to the complete set of skeleton lines that covers the trunk and the complete extension path of branches at all levels after the missing branch completion processing.

[0058] In one specific implementation, the point cloud data of the 3D geometric model is downsampled using voxelization to obtain a uniformly distributed sparse point cloud. A nearest neighbor graph is constructed using the sparse point cloud, and an iterative shrinking operation is performed on the nearest neighbor graph to gradually converge each point towards the central axis along the normal constraint until the convergence result meets the skeleton line accuracy threshold. The initial skeleton lines corresponding to each branch are extracted, and the spatial distance between the endpoints of each skeleton line is detected. Endpoint pairs with a distance less than a preset connection threshold are recorded as spatial connections. Subsequently, each initial skeleton line is traversed to detect the existence of suspended endpoints, i.e., positions where the endpoint does not form a spatial connection relationship with any other skeleton line endpoint. The point cloud density of the local point cloud region around each suspended endpoint is statistically analyzed. If the local point cloud density is lower than a preset density threshold, the suspended endpoint is determined to be the boundary of the missing region. The branch skeleton line segments corresponding to each missing region boundary are summarized to obtain a set of missing branches. The set of missing branches is traversed, and each missing branch is processed... Using the coordinates of the connection point of the parent branch as the starting point for interpolation, the algorithm retrieves adjacent known branches at the same level as the missing branch, extracts their deflection angle, length, and end coordinates, uses the average deflection angle of adjacent known branches as the estimated deflection direction of the missing branch, and the average length of adjacent known branches as the estimated length of the missing branch. A cubic spline interpolation algorithm is used to generate the complete skeleton line segment of the missing branch. This complete skeleton line segment is then merged with the original initial skeleton line to obtain the complete skeleton line. Finally, the topology is initialized with the main skeleton line as the root node. Starting from the root neck of the main trunk, the algorithm traverses upwards along the skeleton line. At each skeleton line bifurcation node, the hierarchy is determined based on the cylinder fitting diameter and the angle between the spatial extension direction of each sub-skeleton line and the current skeleton line. Each branch skeleton line is assigned a corresponding hierarchy number and parent node number. This process is recursively traversed until all end skeleton line nodes are reached, resulting in a complete hierarchical topology of branches at all levels.

[0059] In one embodiment, since there may be cases where a parent branch and its subordinate branches on the same subordinate topological path are marked as to be pruned in the set of branches to be pruned, if the pruning operation is performed independently on the parent branch and its subordinate branches, the pruning of the parent branch will remove all its subordinate branches, resulting in redundant operation of the pruning marking of the subordinate branches, and may cause errors in the spatial coordinate positioning of the pruning node and disorder in the execution order of the pruning scheme. Therefore, based on the set of branches to be pruned, the spatial coordinates of each pruning node are located in the three-dimensional geometric model, and the pruning scheme for each pruning position in the corresponding physical tree is determined. Specifically, this includes: traversing the set of branches to be pruned according to the hierarchical topology of each level of branching, detecting cases where the parent branch and its corresponding subordinate branch on the same hierarchical path are simultaneously marked as to be pruned, thus obtaining a set of conflicting branch groups; based on the set of conflicting branch groups, retaining the pruning marks of the parent branches in each conflicting branch group and removing the pruning marks of the corresponding subordinate branches, thus obtaining a set of branches to be pruned after conflict resolution; and based on the set of branches to be pruned after conflict resolution, locating the spatial coordinates of each pruning node in the three-dimensional geometric model, and determining the pruning scheme for each pruning position.

[0060] Among them, the subordinate topology path refers to the path formed by all nodes and directed edges traversed from the root node to a certain branch node in the subordinate topology structure of each level of branching; the conflict branch set refers to the set of parent branches and subordinate child branches that have a subordinate topology path inclusion relationship in the set of branches to be pruned, where each conflict branch set includes a parent branch and all its subordinate child branches in the set of branches to be pruned; the pruning node refers to the spatial cutting position point corresponding to the pruning operation on a certain branch in the three-dimensional geometric model; the pruning scheme refers to the comprehensive operation scheme determined for each pruning node.

[0061] In one specific implementation, the process involves traversing each branch in the set of branches to be pruned, and sequentially searching all parent branches on the topological path of each branch according to the hierarchical structure of each level of branching. It is then determined whether the parent branch node simultaneously exists in the set of branches to be pruned. If any parent branch node of a branch to be pruned is simultaneously marked as to be pruned, the parent branch and its corresponding subordinate branches are recorded as a conflicting branch group. All detected conflicting branch groups are then aggregated to obtain a conflicting branch group set. The conflicting branch group set is traversed, and the pruning mark of the parent branch is retained for each conflicting branch group. The pruning mark of the corresponding subordinate branches is removed from the set of branches to be pruned, resulting in a conflict-free set of branches to be pruned. For each branch in the conflict-free set, the spatial coordinates of the connection point between the corresponding skeleton line and the parent branch in the 3D geometric model are extracted as the pruning node coordinates. The optimal cutting angle is calculated based on the direction vector of the parent branch skeleton line at the connection point. Combined with the cylindrical fitting diameter of the corresponding branch, the appropriate pruning tool type is matched, and a comprehensive pruning scheme for each pruning position is obtained.

[0062] Furthermore, the spatial coordinates of each pruning node are located in the 3D geometric model to determine the pruning scheme for each pruning position. Specifically, this includes: traversing the set of branches to be pruned after conflict resolution, calculating the spatial distance between each pruning node and the surrounding retained branches, detecting cases where the spatial distance between the pruning node and the surrounding retained branches is less than a preset safe distance threshold, and obtaining a set of risky pruning nodes; based on the set of risky pruning nodes, highlighting the pruning positions corresponding to the risky pruning nodes in the 3D geometric model, and shifting the pruning nodes towards the end along the extension direction of the branches to be pruned until the spatial distance with the surrounding retained branches meets the preset safe distance threshold, thus obtaining the adjusted spatial coordinates of the pruning nodes; and determining the pruning scheme for each pruning position based on the adjusted spatial coordinates of the pruning nodes and the spatial coordinates of the remaining unadjusted pruning nodes.

[0063] Understandably, by calculating the spatial distance between each pruning node and the surrounding retained branches, pruning nodes with the risk of accidental damage are detected and automatically offset along the branch extension direction to a position that meets the safe distance threshold. At the same time, risky pruning nodes are highlighted in the three-dimensional geometric model, which effectively reduces the risk of accidental damage to the surrounding retained branches during pruning operations and improves the safety and accuracy of the pruning scheme.

[0064] In one embodiment, since the preset model may make low confidence in the pruning category judgment of some branches due to reasons such as insignificant branch morphological features, overlapping of multiple categories of features, or uneven point cloud data quality, if the low confidence recognition results are directly included in the set of branches to be pruned, mislabeled branches will be introduced, affecting the accuracy of subsequent pruning schemes. Therefore, based on the set of branch deflection angles at all levels, the sequence of branch sizes at all levels, and the topological structure of branch subordination at all levels, a preset model trained with tree pruning samples is input to identify crossing branches, inner branches, and vigorous branches in the three-dimensional geometric model, resulting in a set of branches to be pruned. Specifically, this includes: inputting the set of branch deflection angles at all levels, the sequence of branch sizes at all levels, and the topological structure of branch subordination at all levels into the preset model to obtain the pruning category label and corresponding confidence level for each branch; detecting cases where the confidence level of a branch is lower than a preset confidence threshold to obtain a set of low-confidence branches; displaying the low-confidence branch set in the three-dimensional geometric model with a distinguishing annotation method based on the low-confidence branch set to obtain a set of branches to be manually reviewed; and obtaining a complete set of branches to be pruned based on the manual confirmation results of the pruning categories of each branch in the set of branches to be manually reviewed, combined with the recognition results of the preset model for the remaining branches.

[0065] Understandably, by obtaining the pruning category labels and confidence levels of each branch from the preset model, branches with confidence levels below the preset threshold are extracted separately and displayed in a differentiated labeling manner, guiding the review and confirmation of branches with low confidence levels. This achieves an organic combination of automatic model recognition and manual review, effectively compensating for the problem of high uncertainty in the recognition of the preset model in complex branch structure scenarios while retaining the efficiency of automatic recognition, and improving the recognition accuracy of the set of branches to be pruned.

[0066] In one embodiment, because pruning alters the original canopy structure and branch spatial distribution of trees, some branches are directly exposed to the prevailing wind direction after pruning due to the removal of obstructing branches. The wind pressure they experience may be significantly increased compared to before pruning. If the wind load distribution after pruning is not verified, some branches may experience wind pressure exceeding the structural safety threshold under strong wind conditions, posing a safety risk of branch breakage or tree collapse. Therefore, after obtaining the three-dimensional prediction model after pruning and the updated total trunk height, the set of branch deflection angles at each level, the size sequence of branches at each level, and the canopy projection area, the method further includes: calculating the wind pressure distribution of each branch layer after pruning based on the updated canopy projection area and the set of branch deflection angles at each level, combined with wind speed and direction data in the target area, to obtain the wind load distribution result after pruning; based on the wind load distribution result after pruning, detecting situations where the wind pressure on some branches exceeds a preset safety threshold, marking the corresponding branches as supplementary pruning candidates, and updating the visualized pruning scheme to obtain a comprehensive pruning scheme.

[0067] Among them, wind pressure distribution refers to the magnitude of the wind load per unit projected area of ​​each branch layer of a tree under given wind speed and direction conditions and its spatial distribution. Its magnitude is related to the windward projected area of ​​the branch layer, the branch deflection angle, and the local wind speed amplification factor. The wind load distribution result after pruning refers to the summary result of the wind pressure value of each branch layer and its spatial distribution calculated based on the three-dimensional prediction model after pruning. The preset safety threshold refers to the upper limit of wind pressure that a branch can withstand, determined based on the mechanical properties of the target tree species' branch materials and the branch diameter. Branches exceeding this threshold have a risk of structural damage. Supplementary pruning candidate branches refer to branches that need to be further included in the pruning evaluation after the initial pruning plan is implemented because the wind load distribution verification shows that the wind pressure they receive exceeds the preset safety threshold.

[0068] Specifically, based on the updated canopy projection area and the set of branch deflection angles at each level, and combined with the prevailing wind direction, wind speed, and wind direction data obtained from the meteorological data platform of the target area, the windward projection area of ​​each branch layer is calculated layer by layer from top to bottom. Based on the windward projection area of ​​each branch layer and the wind speed amplification factor of the corresponding level, the wind pressure value of each branch layer is obtained using the quasi-static wind pressure calculation method. The wind pressure values ​​and their spatial distribution of each branch layer are summarized to obtain the wind load distribution results after pruning. The wind load distribution results after pruning are traversed, and the wind pressure value of each branch layer is compared with the preset safety threshold matched by the fitting result of the corresponding branch diameter cylinder. Branches with wind pressure exceeding the preset safety threshold are detected and marked as supplementary pruning candidate branches. The location of the supplementary pruning candidate branches and the wind pressure value exceeding the threshold are superimposed and displayed in the visual pruning scheme in a differentiated labeling manner, and the comprehensive pruning scheme is updated to obtain the comprehensive pruning scheme.

[0069] Furthermore, in some possible embodiments, considering that trees in gardens also serve a function of providing shade and shelter, refer to Figure 2 The formulation of a comprehensive pruning plan also needs to consider the safety protection and shading requirements of surrounding structures. During the wind load distribution verification stage, the safety distance constraints of structures and the canopy shading coverage constraints should be introduced simultaneously. Specifically, the spatial coordinate range of the pavilion-type structure should be extracted, and the shortest horizontal distance from the end node of each branch of the target tree to the boundary of the pavilion-type structure should be calculated. Branches with a shortest horizontal distance less than the preset safety distance threshold of the structure should be marked as near-dangerous branches of the structure. The near-dangerous branches of the structure should be evaluated in combination with the wind load distribution results. For branches that exceed the preset safety threshold of wind pressure and are within the safety distance threshold of the structure, their pruning priority should be increased in the comprehensive pruning plan to meet the dual requirements of branch structural safety and protection of surrounding structures under strong wind conditions. For branches that only meet the near-dangerous condition of the structure but whose wind pressure does not exceed the preset safety threshold, they should be marked independently in the visualization interface of the comprehensive pruning plan so that operators can decide whether to include them in the pruning operation based on the actual situation.

[0070] Simultaneously, the outline of the pavilion-style structure's top surface is projected onto a horizontal plane to obtain the projected area of ​​the structure's top surface; the spatial overlap area between the canopy projection outline of the 3D prediction model after pruning and the projected area of ​​the structure's top surface is calculated to obtain the current canopy shading coverage area of ​​the pavilion-style structure; the ratio of the shading coverage area to the total area of ​​the projected area of ​​the structure's top surface is defined as the canopy shading coverage rate; it is then checked whether the canopy shading coverage rate is lower than a preset shading coverage rate threshold. If it is lower than the preset shading coverage rate threshold, then during the formulation of the comprehensive pruning plan, the side facing the pavilion-style structure, and when... In the pre-pruning plan, the branches marked as to be pruned are evaluated for their shading contribution. The overlapping area between the projected outline of each branch and the projected area of ​​the top surface of the structure is calculated as the shading contribution value of the branch. For branches whose shading contribution value exceeds the preset shading contribution threshold, their pruning priority is reduced or their pruning mark is adjusted to be retained in the comprehensive pruning plan. This is to prioritize the retention of branches that contribute more to the shading coverage of the pavilion-type structure while ensuring the structural safety of the branches and the protection needs of the structure, so as to achieve a comprehensive balance between the safe pruning of garden trees and the maintenance of the shading and protection function.

[0071] The system in the embodiments of this invention is described below from the perspective of hardware processing. Please refer to [link / reference needed]. Figure 3 This is a schematic diagram of the physical device structure of a system for constructing a tree model to simulate the environmental impact on tree growth, provided in an embodiment of this application.

[0072] It should be noted that, Figure 3 The structure of the system shown is merely an example and should not impose any limitations on the functionality and scope of use of the embodiments of the present invention.

[0073] like Figure 3 As shown, the system includes a CPU 301, which can perform various appropriate actions and processes according to a program stored in ROM 302 or a program loaded from storage section 308 into RAM 303, such as executing the methods described in the above embodiments. RAM 303 also stores various programs and data required for system operation. The CPU 301, ROM 302, and RAM 303 are interconnected via bus 304. I / O interface 305 is also connected to bus 304.

[0074] The following components are connected to I / O interface 305: input section 306 including a camera, infrared sensor, etc.; output section 307 including a liquid crystal display (LCD) and speakers, etc.; storage section 308 including a hard disk, etc.; and communication section 309 including a network interface card such as a LAN (Local Area Network) card and a modem, etc. Communication section 309 performs communication processing via a network such as the Internet. Drive 310 is also connected to I / O interface 305 as needed. Removable media 311, such as a disk, optical disk, magneto-optical disk, semiconductor memory, etc., are installed on drive 310 as needed so that computer programs read from them can be installed into storage section 308 as needed.

[0075] In particular, according to embodiments of the present invention, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments of the present invention include a computer program product comprising a computer program carried on a computer-readable medium, the computer program containing computer programs for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via communication section 309, and / or installed from removable medium 311. When the computer program is executed by CPU 301, it performs the various functions defined in the present invention.

[0076] It should be noted that the computer-readable medium shown in the embodiments of the present invention can be a computer-readable signal medium or a computer-readable storage medium, or any combination thereof. A computer-readable storage medium can be, for example,—but not limited to—an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of a computer-readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM), flash memory, optical fiber, portable compact disc read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination thereof. In the present invention, a computer-readable storage medium can be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device. In the present invention, a computer-readable signal medium can include a data signal propagated in baseband or as part of a carrier wave, wherein a computer-readable computer program is carried. The transmitted data signal can take many forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof.

[0077] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of the present invention. Each block in a flowchart or block diagram may represent a module, segment, or portion of code, which contains one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in a block diagram or flowchart, and combinations of blocks in a block diagram or flowchart, may be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0078] In another aspect, the present invention also provides a computer-readable storage medium, which may be included in the system described in the above embodiments; or it may exist independently and not assembled into the system. The storage medium carries one or more computer programs that, when executed by a processor of a system, cause the system to implement the methods provided in the above embodiments.

[0079] The above-described embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit it. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of this application.

[0080] As used in the above embodiments, depending on the context, the term "when..." can be interpreted as "if...", "after...", "in response to determining...", or "in response to detecting...". Similarly, depending on the context, the phrase "when determining..." or "if (the stated condition or event) is interpreted as "if determining...", "in response to determining...", "when (the stated condition or event) is detected", or "in response to detecting (the stated condition or event)".

[0081] In the above embodiments, implementation can be achieved entirely or partially through software, hardware, firmware, or any combination thereof. When implemented using software, it can be implemented entirely or partially in the form of a computer program product. The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, all or part of the processes or functions described in the embodiments of this application are generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired (e.g., coaxial cable, fiber optic, digital subscriber line) or wireless (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that integrates one or more available media. The available medium can be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium (e.g., solid-state drive), etc.

[0082] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. This program can be stored in a computer-readable storage medium, and when executed, it can include the processes described in the above method embodiments. The aforementioned storage medium includes various media capable of storing program code, such as ROM or random access memory (RAM), magnetic disks, or optical disks.

Claims

1. A method for constructing tree models to simulate the environmental impact on tree growth, characterized in that, include: A 3D point cloud scan of the above-ground part of the tree is performed to obtain a 3D geometric model of the above-ground part of the tree containing information on the spatial position and orientation of each branch. Based on the aforementioned three-dimensional geometric model, the hierarchical membership of each branch is analyzed, and the hierarchical topology of each branch with the trunk as the root node is determined. Based on the branch hierarchy topology, the total height of the trunk, the set of branch deflection angles at each level, the size sequence of branches at each level, and the canopy projection area are extracted, and the environmental parameters of the target area are obtained. Based on the set of branch deflection angles at each level, the size sequence of branches at each level, and the subordinate topology of branches at each level, a preset model trained with tree pruning samples is input to identify the crossing branches, inner branches, and vigorous branches in the three-dimensional geometric model, thereby obtaining a set of branches to be pruned. Based on the set of branches to be pruned, the spatial coordinates of each pruning node are located in the three-dimensional geometric model, and the pruning scheme for each pruning position in the corresponding physical tree is determined. According to the pruning scheme, the removal of the branches to be pruned is simulated in the three-dimensional geometric model. Combined with the environmental parameters, the three-dimensional prediction model after pruning and the updated total height of the main trunk, the set of deflection angles of branches at all levels, the size sequence of branches at all levels and the canopy projection area are obtained. Based on the three-dimensional geometric model and the three-dimensional prediction model after pruning, a comparison model before and after pruning is generated to obtain a visual pruning scheme.

2. The method according to claim 1, characterized in that, Based on the aforementioned three-dimensional geometric model, the hierarchical membership of each branch is analyzed to determine the hierarchical topology of branches at each level with the trunk as the root node. Specifically, this includes: The skeleton of the three-dimensional geometric model is extracted to obtain the initial skeleton lines and spatial connection relationships of each branch; Based on the initial skeleton line and spatial connection relationship, the inner branch regions where local point cloud data is missing due to the occlusion of outer branches are detected, and the set of missing branches is obtained. Based on the spatial position and deflection direction of the adjacent known branches of each missing branch in the set of missing branches, the shape of the missing branches is completed by interpolation algorithm to obtain a complete skeleton line; Based on the complete skeleton line, the hierarchical membership relationship of each branch is analyzed step by step with the main trunk as the root node to determine the subordinate topology of each branch.

3. The method according to claim 1, characterized in that, Based on the set of branches to be pruned, the spatial coordinates of each pruning node are located in the three-dimensional geometric model, and the pruning scheme for each pruning position in the corresponding physical tree is determined, specifically including: Based on the branch hierarchy topology, traverse the set of branches to be pruned, and detect cases in the set of branches to be pruned where the parent branch and its corresponding child branch on the same hierarchical path are simultaneously marked as to be pruned, thus obtaining a set of conflicting branch groups. Based on the set of conflicting branch groups, retain the pruning mark of the mother branch in each conflicting branch group, remove the pruning mark of the corresponding subordinate branch, and obtain the set of branches to be pruned after conflict removal. Based on the set of branches to be pruned after conflict resolution, the spatial coordinates of each pruning node are located in the three-dimensional geometric model, and the pruning scheme for each pruning position is determined.

4. The method according to claim 3, characterized in that, The step of locating the spatial coordinates of each pruning node in the three-dimensional geometric model based on the conflict-free set of branches to be pruned, and determining the pruning scheme for each pruning position, specifically includes: Traverse the set of branches to be pruned after conflict resolution, calculate the spatial distance between each pruning node and the surrounding retained branches, and detect cases where the spatial distance between the pruning node and the surrounding retained branches is less than a preset safe distance threshold to obtain a set of risky pruning nodes; Based on the set of risk pruning nodes, the pruning positions corresponding to the risk pruning nodes are highlighted in the three-dimensional geometric model, and the pruning nodes are shifted to the end along the extension direction of the branches to be pruned until the spatial distance between them and the surrounding retained branches meets the preset safe distance threshold, thus obtaining the adjusted spatial coordinates of the pruning nodes. Based on the adjusted spatial coordinates of the trimming nodes and the spatial coordinates of the remaining unadjusted trimming nodes, the trimming scheme for each trimming position is determined.

5. The method according to claim 1, characterized in that, The process involves inputting a preset model trained on tree pruning samples, based on the set of branch deflection angles at each level, the size sequence of branches at each level, and the subordinate topology of branches at each level, to identify crossing branches, inner branches, and vigorous shoots in the three-dimensional geometric model, thereby obtaining a set of branches to be pruned. Specifically, this includes: Input the set of branch deflection angles at each level, the sequence of branch size at each level, and the branch subordination topology at each level into the preset model to obtain the pruning category label and corresponding confidence level for each branch; If a branch is detected with a confidence level lower than a preset confidence threshold, a set of low-confidence branches is obtained. Based on the set of low-confidence branches, the set of low-confidence branches is displayed in the three-dimensional geometric model using a distinguishing annotation method to obtain the set of branches to be manually reviewed; Based on the confirmation results of the pruning categories of each branch in the set of branches to be manually reviewed, and combined with the identification results of the remaining branches by the preset model, a complete set of branches to be pruned is obtained.

6. The method according to claim 1, characterized in that, After obtaining the pruned three-dimensional prediction model and the updated total trunk height, the set of branch deflection angles at all levels, the branch size sequence at all levels, and the canopy projected area, the method further includes: Based on the updated canopy projection area and the set of branch deflection angles at all levels, combined with the wind speed and wind direction data of the target area, the wind pressure distribution of each branch layer of the pruned tree is calculated, and the wind load distribution results after pruning are obtained. Based on the wind load distribution results after pruning, it is detected that there are local branches where the wind pressure exceeds the preset safety threshold. The corresponding branches are marked as supplementary pruning candidates and the visualized pruning scheme is updated to obtain a comprehensive pruning scheme.

7. A system for constructing tree models to simulate the environmental impact on tree growth, characterized in that the system comprises: One or more processors and a memory; the memory is coupled to the one or more processors, the memory being used to store computer program code, the computer program code including computer instructions, the one or more processors invoking the computer instructions to cause the system to perform the method as described in any one of claims 1-6.

8. A computer-readable storage medium comprising instructions, characterized in that, When the instructions are executed on the system, the system performs the method as described in any one of claims 1-6.

9. A computer program product, characterized in that, When the computer program product is run on the system, the system performs the method as described in any one of claims 1-6.

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