Data-driven ancient tree support scheme generation methods, systems, equipment and media

Abstract

This invention discloses a data-driven method, system, device, and medium for generating ancient tree support schemes, relating to the field of digital ancient tree protection technology. The method includes the following steps: acquiring the original point cloud of the ancient tree; reconstructing the original point cloud into a QSM structural model composed of a continuous chain of cylinders, extracting the centerlines of each cylinder, removing branches and twigs with structural differences exceeding a preset range, and reconstructing the QSM structural model into a complete mesh model of the tree's trunk skeleton; importing the complete mesh model into finite element analysis software for deformation analysis under self-weight load, locating easily deformable branches, and simulating support results at different fixed points based on these branches; quantitatively evaluating the support scheme by comparing the deformation response at each point, and generating the optimal reinforcement point decision basis. This invention transforms the QSM from a parameter table into a computable mesh model, solving the discontinuity problem between point cloud data and structural mechanics analysis.

Description

Technical Field

[0001] This invention relates to the field of digital ancient tree protection technology, and in particular to a data-driven method, system, equipment and medium for generating ancient tree support schemes. Background Technology

[0002] Ancient and famous trees, as important natural and cultural heritage, have long growth periods and complex structures. They are subject to various factors such as wind loads, unbalanced self-weight, pests and diseases, and human interference, resulting in structural safety hazards such as falling and breaking. Currently, in the practice of protecting ancient trees, the setting of support systems still mainly relies on manual on-site observation and experience. It is usually determined whether to set up supports and the location of supports by visually inspecting the degree of trunk tilt and the distribution of branches.

[0003] The development of 3D laser scanning, point cloud modeling, and structural simulation technologies has provided new technical conditions for the digital representation and mechanical analysis of ancient tree structures. However, existing technologies mostly remain at the level of tree shape modeling or simple parameter statistics. Tree structures are usually represented by simplified models, and there is a lack of effective connection between point cloud data and structural mechanical analysis. This makes it difficult to truly reflect the branching structure and internal force path, resulting in low data utilization and difficulty in predicting the mechanical response and potential risks of ancient trees under different load conditions. Summary of the Invention

[0004] The purpose of this invention is to address the shortcomings of the prior art by providing a data-driven ancient tree support scheme generation method, system, device, and medium to solve the problems in the prior art.

[0005] The present invention specifically provides the following technical solution: A data-driven method for generating ancient tree support schemes includes: Collect original point clouds of ancient trees; The original point cloud of the ancient tree is reconstructed into a QSM structure model composed of a continuous chain of cylinders. The centerline of each cylinder is extracted, and branches and twigs with structural differences greater than a preset range are removed. The QSM structure model is then reconstructed into a complete mesh model of the tree trunk skeleton. The complete mesh model is imported into finite element analysis software to perform deformation analysis under self-weight load, locate easily deformable branches, and simulate the support results at different fixed points based on the easily deformable branches. The maximum displacement in the simulation results is used as the minimization optimization objective to obtain the support scheme with the minimum deformation at the support position. The optimal reinforcement point decision is generated based on the support scheme.

[0006] Preferably, the step of extracting the centerline of each cylinder, removing branches and twigs with structural differences exceeding a preset range, and reconstructing the QSM structural model into a complete mesh model of the tree trunk skeleton specifically involves: Remove geometric continuity disruptors, extract the centerline of the cylinder to obtain the first and last two points of the centerline, and obtain a series of points as the geometric path of the cylinder chain in space; the geometric continuity disruptors include isolated cylinders and noisy branches with length and thickness below the threshold. Remove topological continuity disruptors, establish effective parent-child connections between branches, and obtain several complete skeleton lines. Determine the trunk level based on whether these lines are connected end to end. The topological continuity disruptors are branches that are not connected to the trunk. By retaining cylinders that simultaneously satisfy geometric and topological continuity, a continuous basal skeleton without breaks or redundancy is obtained, which provides a structurally continuous skeletal foundation. This structurally continuous skeletal foundation is then abstracted into a continuous, complete mesh model.

[0007] Preferably, the deformation analysis under self-weight load to locate easily deformable branches specifically involves: Based on the geometric parameters and material properties of the complete mesh model, the stress distribution of each branch unit under its own weight is obtained; the geometric parameters include the mesh size, and the material properties include wood density and elastic modulus. By visualizing the displacement field and combining it with structural safety thresholds, easily deformable branches are identified. The easily deformable branches include risky branches and weak branches. The risky branches refer to branches whose maximum displacement exceeds the first preset displacement threshold, indicating that they have preliminary deformation anomalies under their own weight. The weak branches refer to branches that simultaneously satisfy the condition that the maximum displacement exceeds the first preset displacement threshold and the deformation energy exceeds the second preset deformation energy threshold, indicating that their structural performance has deteriorated significantly. Output the spatial location and quantitative indicators of the easily deformable branch, including the maximum displacement and deformation energy.

[0008] Preferably, the support results based on the simulation of different fixed points using easily deformable branches are as follows: On tree trunks with a grade higher than the threshold, a continuous candidate support section is set along the height direction; Traverse all support locations within the continuous candidate support section, apply rigid constraints to each location, and reacquire the displacement field under self-weight load. Based on the set structural safety threshold, support positions that meet the conditions are selected; the conditions include: the maximum displacement does not exceed the first preset displacement threshold, and the deformation energy does not exceed the second preset deformation energy threshold.

[0009] Preferably, the collection of the original point cloud of the ancient tree specifically includes: In areas where the flatness of the terrain around the ancient tree is within the threshold range, gridded ground control points are set up, and RTK-GNSS equipment is used to measure the three-dimensional coordinates of the GCP. Moving at a constant speed along the planned route, the ancient tree is scanned in a circular motion, with the diameter of the circle increasing from small to large, to obtain the original point cloud of the ancient tree.

[0010] Preferably, after collecting the original point cloud of the ancient tree, the method further includes: The original point cloud of the ancient tree is filtered and denoised to remove outlier noise caused by air dust or edge scattering during the scanning process, and point cloud data in LAS format is generated. The LAS format point cloud data is cropped to remove interfering factors, resulting in a single-tree point cloud dataset, which serves as the base dataset for reconstructing the QSM structural model; the interfering factors include fences and original supports.

[0011] Preferably, after reconstructing the original point cloud of the ancient tree into a QSM structure model composed of a continuous chain of cylinders, the method further includes: The QSM structural model is corrected to generate a CSV file containing the parameters of each cylinder and a PLY file containing the set of cylinders.

[0012] This invention provides a data-driven ancient tree support scheme generation system, comprising: The point cloud acquisition module is used to collect the original point cloud of ancient trees; The mesh model construction module is used to reconstruct the original point cloud of the ancient tree into a QSM structure model composed of a continuous chain of cylinders, extract the centerline of each cylinder, remove branches and twigs with structural differences greater than a preset range, and reconstruct the QSM structure model into a complete mesh model of the tree trunk skeleton. The generation module is used to import the complete mesh model into finite element analysis software, perform deformation analysis under self-weight load, locate easily deformable branches, and simulate the support results at different fixed points based on the easily deformable branches. The maximum displacement in the simulation results is used as the minimization optimization objective to obtain the support scheme with the minimum deformation at the support position. The optimal reinforcement point decision basis is generated based on the support scheme.

[0013] The present invention provides a computer device, including a memory and a processor. The memory stores a program, and when the program is executed by the processor, the processor performs the steps of the above-described data-driven ancient tree support scheme generation method.

[0014] The present invention provides a storage medium storing a computer program thereon, wherein the computer program, when executed by a processor, implements the steps of the above-described data-driven ancient tree support scheme generation method.

[0015] Compared with the prior art, the present invention has the following significant advantages: This invention reconstructs point clouds into a continuous cylindrical chain-like QSM structural model, extracts the centerline, and removes abnormal branches. This allows the final skeletal mesh model to accurately reflect the load-bearing path of ancient trees. In finite element analysis, it can accurately locate easily deformable branches and quantitatively compare the displacement results of different support points. Ultimately, it automatically generates a unique optimal reinforcement point scheme with the goal of "minimizing the maximum displacement," completely replacing traditional manual experience-based judgment. This enables scientific and quantitative decision-making for support settings and allows for virtual comparison of dozens of schemes in a single computer session, significantly improving the scientific rigor and success rate of support schemes. Attached Figure Description

[0016] Figure 1 A flowchart illustrating the overall process of digital modeling and structural safety assessment of ancient trees provided in this embodiment of the invention; Figure 2 This is a result image of ground point cloud separated by the CSF algorithm provided in an embodiment of the present invention; Figure 3 The resulting quantitative structure model (QSM) diagram generated in this embodiment of the invention; Figure 4 This is a displacement field cloud diagram for finite element deformation analysis in an embodiment of the present invention after applying a self-weight load; Figure 5 This is a schematic diagram of candidate points for the simulated support reinforcement scheme in an embodiment of the present invention; Figure 6 The flowchart illustrates a data-driven ancient tree support scheme generation method provided by this invention. Detailed Implementation

[0017] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0018] like Figure 1 and Figure 6 As shown in the figure, a data-driven ancient tree support scheme generation method in this embodiment includes the following steps: Step 1: Collect the original point cloud of the ancient tree.

[0019] Conduct on-site surveys of the ancient tree's growth environment to confirm the feasibility of the scanning path, and identify obstructions, sources of electromagnetic interference, and safety restricted areas. Establish gridded ground control points (GCPs) in flat and open areas around the ancient tree, with no fewer than 6 GCPs evenly distributed across the survey area. Use RTK-GNSS equipment (elevation accuracy ±3cm) to measure the three-dimensional coordinates of the GCPs, ensuring that the point's planar error is ≤±2cm.

[0020] Dynamic scanning data acquisition: An Oslei R8+ backpack system equipped with a 16-line LiDAR, integrating an IMU inertial navigation module and a 24-megapixel true-color camera, was used. Scanning parameters were set as follows: scan rate ≥ 600,000 points / second, point cloud density ≥ 1000 points / m², and ranging accuracy ±3mm@100m. Operators moved at a constant speed (0.5–1m / s) along the planned path, avoiding sudden stops and sharp turns. A scanning strategy of circling small loops followed by larger loops was adopted for areas under the trees. Scanning was paused and the equipment rotated to avoid interference sources such as tourists or vehicles, resuming acquisition only after the interference subsided. During the peak season, periods with sparse vegetation (such as early morning) were selected to ensure laser beam penetration ≥ 85%. The designed scanning path was a closed loop around the ancient tree (starting point S and ending point E coincided), improving the overall accuracy of the point cloud data.

[0021] Refined processing of point cloud data: such as Figure 2 As shown, the ground point cloud was separated using a cloth simulation filtering (CSFFilter) algorithm executed by point cloud processing software. Statistical filtering was then used to remove outliers generated by airborne dust or edge scattering during the scanning process, completing the initial point cloud cleaning. Point clouds of non-ancient tree structures such as fences and supports were manually removed using a cropping tool to ensure that the proportion of noise points was ≤0.5%. The processed point cloud data was converted to Recap format; data accuracy was verified: planar error ≤±8mm, elevation error ≤±12mm, and feature point spacing error ≤±5mm. A high-precision single-tree point cloud dataset was output, including LAS format point clouds, RCP format point clouds, and JPG panoramic imagery.

[0022] Step 2: Reconstruct the original point cloud of the ancient tree into a QSM structure model composed of a continuous chain of cylinders, extract the centerline of each cylinder, remove branches and twigs with structural differences greater than the preset range, and reconstruct the QSM structure model into a complete mesh model of the tree trunk skeleton.

[0023] Through skeleton extraction and cylinder fitting algorithms, a QSM structure model composed of continuous cylindrical chains is reconstructed, such as... Figure 3 As shown, the Spherefollowing tool in SimpleForest software was used for reconstruction, with the data of the sphere search radius as input. The tree structure was extracted using the sphere following algorithm, generating and saving a CSV file containing the parameters of each cylinder (Cylinder, Branch, DBH, Height, Volume, Projection area) and a PLY file containing the set of cylinders. The basal skeleton model was extracted, specifically as follows: By removing geometric continuity disruptors (such as isolated cylinders, excessively thin or short noisy branches) using the radius method, the centerline of each cylinder is extracted, obtaining the first and last points of the centerline to form a series of points, ensuring that the cylinder chain forms a continuous geometric path in space. By removing topological continuity disruptors (such as branches unconnected to the trunk) using the branchOrder method, effective parent-child connections are established between branches, resulting in several complete skeletal lines. The trunk level is determined based on whether these lines are connected end-to-end (specifically divided into three levels). Cylinders that simultaneously satisfy geometric and topological continuity are retained, forming a continuous, unbroken, and non-redundant basal skeleton, providing a structurally continuous skeletal foundation for subsequent mesh model construction. The basal skeleton is then abstracted into a continuous mesh model.

[0024] Step 3: Import the complete mesh model into the finite element analysis software, perform deformation analysis under self-weight load, locate the easily deformable branches, and simulate the support results at different fixed points based on the easily deformable branches. Use the maximum displacement in the simulation results as the minimization optimization objective to obtain the support scheme with the minimum deformation at the support position. Use the support scheme to generate the decision basis for the optimal reinforcement point.

[0025] like Figure 4 As shown, deformation analysis was performed by applying self-weight load: based on the geometric parameters (mesh size) and material properties (wood density, elastic modulus) of the mesh model, the stress distribution of each branch element under self-weight was calculated; through displacement field visualization and combined with structural safety thresholds, the following two types of key branches were identified: Risky branches: These are branches whose maximum displacement exceeds the first preset displacement threshold, indicating that they exhibit preliminary deformation anomalies under their own weight and should be included in the scope of key attention. Weak branches: These are branches that simultaneously meet the condition that their maximum displacement exceeds the first preset displacement threshold and their deformation energy exceeds the second preset deformation energy threshold, indicating that their structural performance has significantly deteriorated and that they have a high risk of instability or fracture. The spatial location and quantitative indicators (including maximum displacement and deformation energy) of risky and weak branches are output as the core basis for subsequent support scheme simulation and reinforcement decision-making.

[0026] Simulate the self-weight deformation effect at different fixed points: Set a continuous candidate support section (e.g., a height range of 2.0m to 3.0m) along the height direction on tree trunks of grades higher than the threshold (i.e., second and third grade tree trunks); traverse all possible support positions within this range, apply rigid constraints to each position, and recalculate the displacement field under self-weight load; based on the set structural safety threshold, select support positions that meet the following conditions: the maximum displacement does not exceed the first preset displacement threshold, and the deformation energy does not exceed the second preset deformation energy threshold; define the set of continuous support positions that meet the above conditions as the effective support section, which serves as the recommended height range for the installation of the support structure.

[0027] Generate quantitative evaluation results for the support reinforcement scheme: The optimization objectives are minimizing maximum displacement and deformation energy; output the optimal support locations and their corresponding quantitative indicators (maximum displacement), forming an executable support reinforcement scheme, such as... Figure 5 As shown.

[0028] This invention utilizes a complete technical chain—cylindrical chain → centerline extraction → anomaly pruning → skeletal mesh—to transform discrete point clouds into finite element mesh models with geometric continuity and topological consistency. This model fully preserves the skeletal structure and spatial force transmission paths of the tree trunk and branches, enabling finite element analysis software to accurately load self-weight and calculate the stress, strain, and displacement of each node. For identified easily deformable branches, the system supports the rapid setting of multiple candidate support points within the finite element model. By simulating the overall deformation response of each point after applying ideal rigid support, the maximum displacement reduction, stress redistribution, and cascading effects on adjacent branches under each scheme can be calculated. Finally, a unique optimal reinforcement point decision is generated based on a multi-objective comprehensive score, considering "optimal deformation control effect, minimal damage to the tree, and highest construction feasibility." Compared to traditional methods of "selecting points based on experience" or "on-site trial and error," this method allows for a virtual comparison of dozens of schemes in a single computer session, significantly improving the scientific rigor and success rate of the support scheme.

[0029] Based on the above method, this invention proposes a data-driven ancient tree support scheme generation system, comprising: The module includes a point cloud acquisition module for collecting original point clouds of ancient trees; a mesh model construction module for reconstructing the original point cloud of ancient trees into a QSM structure model composed of a continuous chain of cylinders, extracting the centerlines of each cylinder, removing branches and twigs with structural differences exceeding a preset range, and reconstructing the QSM structure model into a complete mesh model of the tree trunk skeleton; and a generation module for importing the complete mesh model into finite element analysis software for deformation analysis under self-weight load, locating easily deformable branches, simulating the support results at different fixed points based on the easily deformable branches, using the maximum displacement in the simulation results as the minimization optimization objective, obtaining the support scheme with the minimum deformation at the support location, and generating the optimal reinforcement point decision basis based on the support scheme.

[0030] The present invention also provides a computer device, including a memory and a processor, wherein the memory stores a program, and when the program is executed by the processor, the processor performs the steps of a data-driven ancient tree support scheme generation method.

[0031] According to the disclosed embodiments, the computer device can communicate with one or more external devices (e.g., keyboard, pointing device, Bluetooth communication, etc.) or with any device that enables the computing device to communicate with one or more other computing devices (e.g., router, demodulator, etc.).

[0032] The present invention also provides a storage medium storing a computer program thereon, characterized in that the computer program, when executed by a processor, implements the steps of a data-driven ancient tree support scheme generation method.

[0033] According to the disclosed embodiments, the storage medium can be a non-volatile computer-readable storage medium, such as, but not limited to: portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof. In this invention, the 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.

[0034] The above description, in conjunction with specific preferred embodiments, provides a more detailed explanation of the present invention. For those skilled in the art, various simple deductions or substitutions can be made without departing from the concept of the present invention, and all such deductions or substitutions should be considered to fall within the scope of protection of the present invention.

Claims

1. A data-driven ancient tree support scheme generation method, characterized by, Includes the following steps: Collect original point clouds of ancient trees; The original point cloud of the ancient tree is reconstructed into a QSM structure model composed of a continuous chain of cylinders. The centerline of each cylinder is extracted, and branches and twigs with structural differences greater than a preset range are removed. The QSM structure model is then reconstructed into a complete mesh model of the tree trunk skeleton. The complete mesh model is imported into finite element analysis software to perform deformation analysis under self-weight load, locate easily deformable branches, and simulate the support results at different fixed points based on the easily deformable branches. The maximum displacement in the simulation results is used as the minimization optimization objective to obtain the support scheme with the minimum deformation at the support position. The optimal reinforcement point decision is generated based on the support scheme.

2. The data-driven ancient tree support scheme generation method of claim 1, wherein, The process involves extracting the centerlines of each cylinder, removing branches and twigs with structural differences exceeding a preset range, and reconstructing the QSM structural model into a complete mesh model of the tree trunk skeleton. Specifically: Remove geometric continuity disruptors, extract the centerline of the cylinder to obtain the first and last two points of the centerline, and obtain a series of points as the geometric path of the cylinder chain in space; the geometric continuity disruptors include isolated cylinders and noisy branches with length and thickness below the threshold. Remove topological continuity disruptors, establish effective parent-child connections between branches, and obtain several complete skeleton lines. Determine the trunk level based on whether these lines are connected end to end. The topological continuity disruptors are branches that are not connected to the trunk. By retaining cylinders that simultaneously satisfy geometric and topological continuity, a continuous basal skeleton without breaks or redundancy is obtained, which provides a structurally continuous skeletal foundation. This structurally continuous skeletal foundation is then abstracted into a continuous, complete mesh model.

3. The data-driven ancient tree support scheme generation method of claim 1, wherein, The deformation analysis under self-weight load, and the location of easily deformable branches, specifically involves: Based on the geometric parameters and material properties of the complete mesh model, the stress distribution of each branch unit under its own weight is obtained; the geometric parameters include the mesh size, and the material properties include wood density and elastic modulus. By visualizing the displacement field and combining it with structural safety thresholds, easily deformable branches are identified. The easily deformable branches include risky branches and weak branches. The risky branches refer to branches whose maximum displacement exceeds the first preset displacement threshold, indicating that they have preliminary deformation anomalies under their own weight. The weak branches refer to branches that simultaneously satisfy the condition that the maximum displacement exceeds the first preset displacement threshold and the deformation energy exceeds the second preset deformation energy threshold, indicating that their structural performance has deteriorated significantly. Output the spatial location and quantitative indicators of the easily deformable branch, including the maximum displacement and deformation energy.

4. The data-driven ancient tree support scheme generation method of claim 3, wherein, The support results based on the simulation of easily deformable branches at different fixed points are as follows: On tree trunks with a grade higher than the threshold, a continuous candidate support section is set along the height direction; Traverse all support locations within the continuous candidate support section, apply rigid constraints to each location, and reacquire the displacement field under self-weight load. Based on the set structural safety threshold, support positions that meet the conditions are selected; the conditions include: the maximum displacement does not exceed the first preset displacement threshold, and the deformation energy does not exceed the second preset deformation energy threshold.

5. The data-driven ancient tree support scheme generation method of claim 1, wherein, The collection of original point clouds of ancient trees specifically includes: In areas where the flatness of the terrain around the ancient tree is within the threshold range, gridded ground control points are set up, and RTK-GNSS equipment is used to measure the three-dimensional coordinates of the GCP. Moving at a constant speed along the planned route, the ancient tree is scanned in a circular motion, with the diameter of the circle increasing from small to large, to obtain the original point cloud of the ancient tree.

6. The data-driven ancient tree support scheme generation method as described in claim 1, characterized in that, After collecting the original point cloud data of the ancient tree, the process also includes: The original point cloud of the ancient tree is filtered and denoised to remove outlier noise caused by air dust or edge scattering during the scanning process, and point cloud data in LAS format is generated. The LAS format point cloud data is cropped to remove interfering factors, resulting in a single-tree point cloud dataset, which serves as the base dataset for reconstructing the QSM structural model; the interfering factors include fences and original supports.

7. The data-driven ancient tree support scheme generation method as described in claim 1, characterized in that, After reconstructing the original point cloud of the ancient tree into a QSM structure model composed of a continuous chain of cylinders, the method further includes: The QSM structural model is corrected to generate a CSV file containing the parameters of each cylinder and a PLY file containing the set of cylinders.

8. A data-driven ancient tree support scheme generation system, characterized in that, include: The point cloud acquisition module is used to collect the original point cloud of ancient trees; The mesh model construction module is used to reconstruct the original point cloud of the ancient tree into a QSM structure model composed of a continuous chain of cylinders, extract the centerline of each cylinder, remove branches and twigs with structural differences greater than a preset range, and reconstruct the QSM structure model into a complete mesh model of the tree trunk skeleton. The generation module is used to import the complete mesh model into finite element analysis software, perform deformation analysis under self-weight load, locate easily deformable branches, and simulate the support results at different fixed points based on the easily deformable branches. The maximum displacement in the simulation results is used as the minimization optimization objective to obtain the support scheme with the minimum deformation at the support position. The optimal reinforcement point decision basis is generated based on the support scheme.

9. A computer device, characterized in that, The system includes a memory and a processor, wherein the memory stores a program that, when executed by the processor, causes the processor to perform the steps of a data-driven ancient tree support scheme generation method as described in any one of claims 1 to 7.

10. A storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the steps of the data-driven ancient tree support scheme generation method according to any one of claims 1 to 7.

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