A root modeling method for landslide model test considering root morphological characteristics

Abstract

The application provides a landslide model test root system modeling method considering root system morphological characteristics, relates to the field of three-dimensional modeling, and the method comprises the following steps: acquiring key characteristic parameters of a target vegetation root system prototype; performing scale conversion on the basis of geometric similarity ratios of landslide model tests and in combination with the key characteristic parameters to obtain model scale parameters; taking the model scale parameters and preset model control parameters as inputs, generating three-dimensional structure data of the root system through a parameterization algorithm; importing the three-dimensional structure data into three-dimensional modeling software to construct a three-dimensional digital root system model; and manufacturing a physical root system model for a landslide physical model test through a 3D printing process. The technical scheme of the application converts complex natural root systems from qualitative description into calculable and controllable quantitative parameter inputs, and provides a unified and accurate parameter basis for the programmed generation of three-dimensional root system structures.

Description

Technical Field

[0001] This application relates to the field of three-dimensional modeling, and in particular to a root system modeling method for landslide model experiments that takes into account root system morphology characteristics. Background Technology

[0002] Under extreme rainfall conditions, the impact of vegetation roots on slope stability is dual: on the one hand, the dominant flow channels formed by roots in the soil can exacerbate rainfall infiltration, locally altering the seepage field and pore water pressure distribution; on the other hand, roots can transmit dynamic forces such as wind loads, directly affecting the stress transmission and distribution mechanism within the slope. To further reveal the formation mechanism of landslides in this hydrogeological environment, physical model experiments are an important means of studying the interaction between vegetation, rainfall, and soil. Among these, the simulation accuracy of the root system model directly affects the reliability of the understanding of the disaster mechanism.

[0003] During natural growth, vegetation root systems are influenced by environmental factors such as gravity, sunlight, soil moisture, and slope, often resulting in highly spatially heterogeneous distribution patterns. Particularly in sloping habitats, root architecture exhibits significant asymmetry, specifically in root length, inclination angle, secondary lateral root density, and spatial distribution. This asymmetric root distribution not only creates localized dominant flow channels, significantly altering the path and spatial distribution of rainfall infiltration, but also regulates the direction of stress transmission within the soil, thus impacting slope stability. Therefore, in physical model experiments, accurately reproducing the spatial asymmetry of root morphology and its distribution range is crucial for evaluating the effectiveness of root system models.

[0004] Existing landslide physical model tests often use materials such as wood, steel, rubber, or chemical fibers to simulate vegetation root systems. While these methods are easy to implement, they have certain limitations: Firstly, they may result in distortions in morphology and topology. Simplified linear materials are difficult to reproduce the multi-level branching structure of natural roots, leading to differences in the frictional effects at the root-soil interface compared to actual conditions. Therefore, they still face challenges in accurately simulating root mechanical behavior. Secondly, they often exhibit a tendency towards homogeneity in spatial distribution. These materials are often arranged uniformly or randomly, failing to fully reflect the spatial heterogeneity of root growth in natural habitats.

[0005] With the development of additive manufacturing technology, digital root system modeling methods based on fractal theory or L-systems have gradually been introduced into model experiments. However, when applied to slope vegetation research, existing technologies still have shortcomings: First, the model configuration is relatively idealized, and most algorithms follow the strict self-similarity principle, resulting in root structures that tend to be isotropic or regularly symmetrical, making it difficult to accurately characterize the asymmetric root development phenomena caused by environmental factors such as gravity and light in slope environments; Second, the controllability of key parameters still needs improvement, and current methods still face difficulties in independently and accurately parameterizing root characteristic parameters such as root azimuth and root inclination. In rainfall-induced landslide research, these configuration parameters directly affect the guiding role of the root system on the slope seepage path and its anisotropic influence on stress transmission. The limitations of existing models make it difficult for experiments to fully reveal the complex coupling mechanism between vegetation, hydrology, and mechanics. Summary of the Invention

[0006] The purpose of this invention is to provide a root system modeling method for landslide model tests that considers root system morphology characteristics, in order to solve the problem that the root system model in traditional landslide model tests is too simplified and cannot truly reflect the complex spatial morphological characteristics of vegetation roots.

[0007] The above-mentioned objective of this application is achieved through the following technical solution: S1. Obtain key feature parameters of the target vegetation root system prototype; S2. Based on the geometric similarity ratio of the landslide model test, and combined with the key characteristic parameters, scale conversion is performed to obtain the model scale parameters; S3. Using the model scale parameters and preset model control parameters as input, the three-dimensional structure data of the root system is generated through a parametric algorithm. S4. Import the three-dimensional structural data into the three-dimensional modeling software to construct a three-dimensional digital model of the root system. S5. Using 3D printing technology, a physical root system model is made from the three-dimensional digital model of the root system for landslide physical model testing.

[0008] Optionally, step S1 includes: The key characteristic parameters include: vegetation type, root system architecture category, taproot length, diameter of different levels of root system, and effective root domain range; The effective root domain range is determined by a boundary circle constructed with the starting position of the principal root as the center and the projection length of the selected longest effective lateral root on the horizontal plane as the radius.

[0009] Optionally, step S2 includes: From the key feature parameters, the root system prototype feature parameters are obtained, which include: the length of the main root and the effective root domain range; The length parameter at the model scale is obtained from the root system prototype feature parameters; The scale conversion formula is:

[0010] in, This refers to the length parameter at the model scale. This refers to the length parameter at the prototype scale. It represents the geometric similarity ratio.

[0011] Optionally, step S3 includes: The model control parameters include: total model length limit, number of lateral roots at each level, root diameter taper ratio, and root azimuth and root inclination constraints. The parameterized algorithm performs hierarchical recursive operations, including S31: Generate the principal root and determine its starting point coordinates, ending point coordinates, inclination angle, and azimuth angle in three-dimensional space; Establish a rectangular coordinate system in three-dimensional space, with the root tip as the origin, the X and Y axes in the horizontal plane, and the Z axis pointing downwards. Then the coordinates of the starting point of the principal root are:

[0012] The coordinates of the endpoint of the principal root are:

[0013] in: The Z-coordinate of the location on the Earth's surface; The length of the principal root; The inclination angle of the main root is fixed at 90°, the azimuth angle is fixed at 0°, and the root tip diameter and root apex diameter are determined according to the preset root diameter taper ratio. S32: Within the effective branching interval of the main root, generate first-order lateral roots and control their spatial distribution; Avoid the apical region length on the main root and tip length Determine the length of the effective branch interval. :

[0014] Let the number of first-order lateral roots be Then the first Depth of the growth origin of a primary lateral root for:

[0015] Target length of first-order lateral root The attenuation is calculated based on its relative depth position:

[0016] in: The maximum horizontal distribution radius of the root system. To normalize the depth position parameters, The length attenuation coefficient, Used as a reference tilt angle; Azimuth of the first-order lateral root It is generated using a partitioned random method to form an asymmetric distribution on the horizontal plane; its tilt angle The spatial direction vector of the lateral roots is randomly determined within a preset range based on the root system configuration category. for:

[0017] If the predicted endpoint depth If the value exceeds a preset threshold, the final length of the lateral root is corrected as follows:

[0018] in This is a limit on the maximum depth of the model; Finally, the coordinates of the first-order lateral root endpoint were determined. ,as follows:

[0019] in: Let the coordinates be the starting coordinates of the first-order lateral root. ; S33: On the primary lateral roots, secondary lateral roots are generated based on a depth weighting allocation strategy, and their spatial distribution is controlled; the depth weighting allocation strategy refers to: calculating the weight value of the primary lateral root based on its starting depth, and distributing the total number of secondary lateral roots non-uniformly to each primary lateral root according to the weight value. Let the number of first-order lateral roots be Total number of secondary lateral roots Randomly determined from the following range:

[0020] in , This is a preset proportional coefficient; According to the Depth of origin of a first-order lateral root Calculate its weight value:

[0021] in It is a depth-weighted index; The number of secondary lateral roots is allocated to each primary lateral root according to the weight ratio:

[0022] in Indicates the first The number of secondary lateral roots assigned to a primary lateral root; Secondary lateral roots are generated by randomly selecting growth locations along the length of each primary lateral root; the azimuth angle of the secondary lateral roots... Its parent azimuth angle Based on the superimposed disturbance angle :

[0023] The length of secondary lateral roots is determined proportionally to the length of the primary root and is also corrected by depth constraints. S34: On the secondary lateral roots, generate tertiary lateral roots in the same manner as in step S33; S35: Stores the generated three-dimensional structural data of the primary root and lateral roots at all levels.

[0024] Optionally, step S4 includes: S41: Generate root geometry one by one based on the starting point coordinates, ending point coordinates, and diameter parameters in the structured data; The main root uses a cylinder as the basic geometric unit, and the lateral roots at all levels are constructed using cylinders or Bézier curves. The length of all roots is determined by the spatial distance between the start and end points of the root segment. The root tip diameter and root apex diameter are determined by the reference diameter and the root diameter taper ratio; The growth direction of the root segment is determined by the starting and ending coordinates of the root segment. The geometry of the daughter-level lateral root is spatially rotated and positioned so that its axis is consistent with the growth direction of the parent-level root segment. The corresponding diameter parameters are selected for the root system of different root order levels. S42: Introduce natural bending simulation treatment for lateral roots at all levels except the main root; S43: Perform topological integration and structural optimization on the overall three-dimensional digital model of the root system; Based on the parent-child relationship identifier in the three-dimensional structural data, a smooth transition is performed on the connection between the lateral root and the main root. S44: Export the three-dimensional digital model of the root system into a universal three-dimensional data format.

[0025] Optionally, step S5 includes: The 3D printing process employs fused deposition modeling technology, and the printing material is a resin or plastic material that simulates the mechanical properties of root systems.

[0026] An electronic device includes a processor, a memory, a user interface, and a network interface. The memory is used to store instructions, the user interface and the network interface are used to communicate with other devices, and the processor is used to execute the instructions stored in the memory to enable the electronic device to perform a root system modeling method for landslide model experiments that considers root system morphology characteristics.

[0027] A computer-readable storage medium storing instructions that, when executed, perform a root system modeling method for a landslide model experiment that takes into account root system morphology characteristics.

[0028] The beneficial effects of the technical solution provided in this application are: 1. High-fidelity root structure simulation and quantifiable reproducibility: This invention uses a parametric characterization method to efficiently construct a three-dimensional root system model with realistic spatial configuration, multi-level branching characteristics and asymmetric distribution, and transform it into a physical model suitable for landslide physical model tests, achieving high-fidelity and quantifiable reproducibility of the root system structure in terms of geometric morphology.

[0029] 2. Adaptive modeling capability for root systems of various vegetation types: This invention can adapt to the root system characteristics of different vegetation such as herbaceous plants, shrubs and trees by adjusting the root system configuration type and related parameters, which can meet the simulation needs of different vegetation types in landslide model tests and has good versatility.

[0030] 3. Capability for studying root-slope interaction mechanisms: The root model constructed using this invention can be used to systematically study the influence of roots on slope stability, deformation response and anti-slip capacity in indoor model tests, providing a reliable means for analyzing the root interaction mechanism during landslide instability.

[0031] 4. Parameter-driven flexibility and extended application capability: This invention supports parameterized control of root geometric features, spatial distribution and generation rules, which facilitates the rapid construction of root models with different morphological features. At the same time, it can be combined with physical model experiments and numerical analysis, and has good scalability and application potential. Attached Figure Description

[0032] The present application will be further described below with reference to the accompanying drawings and embodiments. In the accompanying drawings: Figure 1 This is a step diagram of an embodiment of this application; Figure 2 This is a flowchart of the parameterized algorithm for generating three-dimensional root structure data in the embodiments of this application; Figure 3 This is a schematic diagram of the lateral root azimuth angle and asymmetric characteristics in the root system model of this application embodiment; Figure 4 This is a schematic diagram of the lateral root inclination angle in the root system model of this application embodiment; Figure 5 This is an example diagram of a complete 3D root system model generated by the 3D modeling software in the embodiments of this application; Figure 6 This is an example diagram of a solid root system model generated by the 3D printing process in the embodiments of this application; Figure 7 This is a schematic diagram of the electronic device structure in the embodiments of this application. Detailed Implementation

[0033] To provide a clearer understanding of the technical features, objectives, and effects of this application, the specific embodiments of this application will now be described in detail with reference to the accompanying drawings.

[0034] The embodiments of this application provide a root system modeling method for landslide model experiments that takes into account root morphological characteristics.

[0035] Please refer to Figure 1 , Figure 1 This is a flowchart illustrating the steps of a landslide modeling method considering root morphology characteristics in an embodiment of this application, including: S1. Obtain key feature parameters of the target vegetation root system prototype; S2. Based on the geometric similarity ratio of the landslide model test, and combined with the key characteristic parameters, scale conversion is performed to obtain the model scale parameters; S3. Using the model scale parameters and preset model control parameters as input, the three-dimensional structure data of the root system is generated through a parametric algorithm. S4. Import the three-dimensional structural data into the three-dimensional modeling software to construct a three-dimensional digital model of the root system. S5. Using 3D printing technology, a physical root system model is made from the three-dimensional digital model of the root system for landslide physical model testing.

[0036] As one example, by using parametric design, key morphological features of real root systems are quantitatively characterized, and high-fidelity, repeatable physical root system models are prepared using 3D modeling and 3D printing technologies. The core of this method lies in establishing a digital conversion and generation process from real root system feature parameters to a 3D physical model.

[0037] The technical effects of this application are as follows: 1. By constructing a root morphology parameterization representation system covering root geometric parameters, root configuration types, multi-level branch topology, root inclination angle, root azimuth angle, and horizontal distribution asymmetry, the complex natural root system is transformed from a qualitative description into a calculable and controllable quantitative parameter input, providing a unified and accurate parameter basis for the programmed generation of root three-dimensional structures.

[0038] 2. By introducing a weighted allocation mechanism based on root growth depth, non-uniform distribution control is achieved in the multi-level lateral root number allocation process, thereby effectively simulating the real distribution law of natural roots in vertical space and avoiding the structural distortion problem caused by traditional uniform allocation.

[0039] 3. By adopting a partitioned random azimuth generation strategy for primary lateral roots and applying finite random perturbations to multi-level lateral roots based on the parent growth direction, the asymmetrical distribution of the root system in the horizontal plane is constructed, while maintaining the continuity of local growth direction and the consistency of overall configuration, thereby improving the naturalness and stability of the generated root system structure.

[0040] 4. By introducing a depth constraint function during the root length generation process, the termination position of each level of the root system is dynamically constrained, ensuring that the generated three-dimensional root structure is strictly limited to the preset model space, effectively preventing the root system from growing beyond its boundaries.

[0041] Step S1 includes: The key characteristic parameters include: vegetation type, root system architecture category, taproot length, diameter of different levels of root system, and effective root domain range; The effective root domain range is determined by a boundary circle constructed with the starting position of the principal root as the center and the projection length of the selected longest effective lateral root on the horizontal plane as the radius.

[0042] As one embodiment, the length of the taproot and the diameter of the root system at different levels of the target vegetation are determined by actual measurement or by consulting relevant literature, thereby determining the effective root domain range of the root system in the horizontal plane; wherein, with the root tip position (i.e., the starting position of the taproot) as the center and the projection length of the selected longest effective lateral root on the horizontal plane as the radius, a boundary circle of the effective root domain of the root system in the horizontal plane is constructed, thereby determining the spatial occupancy range of the target vegetation root system model in the horizontal plane.

[0043] Step S2 includes: From the key feature parameters, the root system prototype feature parameters are obtained, which include: the length of the main root and the effective root domain range; The length parameter at the model scale is obtained from the root system prototype feature parameters; The scale conversion formula is:

[0044] in, This refers to the length parameter at the model scale. This refers to the length parameter at the prototype scale. It represents the geometric similarity ratio.

[0045] Step S3 includes: In one embodiment, the model scale parameters after similarity ratio conversion are used as input parameters for the parametric generation program. Combined with preset model control parameters, a hierarchical recursive parametric algorithm automatically generates three-dimensional root system structure data with realistic spatial morphological characteristics. The parametric algorithm realizes the transformation of root system structure from parameters to three-dimensional geometry based on root topological growth rules and spatial constraints.

[0046] The model control parameters include: total model length limit, number of lateral roots at each level, root diameter taper ratio, and root azimuth and root inclination constraints. The parameterized algorithm performs hierarchical recursive operations, including S31: Generate the principal root and determine its starting point coordinates, ending point coordinates, inclination angle, and azimuth angle in three-dimensional space; Establish a rectangular coordinate system in three-dimensional space, with the root tip as the origin, the X and Y axes in the horizontal plane, and the Z axis pointing downwards. Then the coordinates of the starting point of the principal root are:

[0047] The coordinates of the endpoint of the principal root are:

[0048] in: The Z-coordinate of the location on the Earth's surface; The length of the principal root; The inclination angle of the main root is fixed at 90°, the azimuth angle is fixed at 0°, and the root tip diameter and root apex diameter are determined according to the preset root diameter taper ratio. S32: Within the effective branching interval of the main root, generate first-order lateral roots and control their spatial distribution; Avoid the apical region length on the main root and tip length Determine the length of the effective branch interval. :

[0049] Let the number of first-order lateral roots be Then the first Depth of the growth origin of a primary lateral root for:

[0050] Target length of first-order lateral root The attenuation is calculated based on its relative depth position:

[0051] in: The maximum horizontal distribution radius of the root system. To normalize the depth position parameters, The length attenuation coefficient, Used as a reference tilt angle; Azimuth of the first-order lateral root It is generated using a partitioned random method to form an asymmetric distribution on the horizontal plane; its tilt angle The spatial direction vector of the lateral roots is randomly determined within a preset range based on the root system configuration category. for:

[0052] If the predicted endpoint depth If the value exceeds a preset threshold, the final length of the lateral root is corrected as follows:

[0053] in This is a limit on the maximum depth of the model; Finally, the coordinates of the first-order lateral root endpoint were determined. ,as follows:

[0054] in: Let the coordinates be the starting coordinates of the first-order lateral root. ; As one embodiment, controlling its spatial distribution includes: calculating the target length of the first-order lateral root based on the normalized depth position, generating the azimuth angle of the first-order lateral root in a partitioned random manner to form an asymmetric distribution on the horizontal plane, and randomly determining its inclination angle within a preset inclination angle range.

[0055] S33: On the primary lateral roots, secondary lateral roots are generated based on a depth weighting allocation strategy, and their spatial distribution is controlled; the depth weighting allocation strategy refers to: calculating the weight value of the primary lateral root based on its starting depth, and distributing the total number of secondary lateral roots non-uniformly to each primary lateral root according to the weight value. Let the number of first-order lateral roots be Total number of secondary lateral roots Randomly determined from the following range:

[0056] in , This is a preset proportional coefficient; According to the Depth of origin of a first-order lateral root Calculate its weight value:

[0057] in It is a depth-weighted index; The number of secondary lateral roots is allocated to each primary lateral root according to the weight ratio:

[0058] in Indicates the first The number of secondary lateral roots assigned to a primary lateral root; Secondary lateral roots are generated by randomly selecting growth locations along the length of each primary lateral root; the azimuth angle of the secondary lateral roots... Its parent azimuth angle Based on the superimposed disturbance angle :

[0059] The length of secondary lateral roots is determined proportionally to the length of the primary root and is also corrected by depth constraints. S34: On the secondary lateral roots, generate tertiary lateral roots in the same manner as in step S33; S35: Stores the generated three-dimensional structural data of the primary root and lateral roots at all levels.

[0060] As one example, the generated primary root and lateral roots at all levels are uniformly stored as structured data, including their numbers, hierarchical relationships, spatial coordinates, lengths, azimuths, inclinations, and root diameters, for use in 3D modeling and subsequent 3D printing.

[0061] Step S4 includes: As one example, the spatial coordinates, topological relationships, and morphological parameters of the root system are input into professional 3D modeling software. By utilizing its procedural modeling capabilities, the abstract parametric root system data is converted into a high-fidelity 3D geometric model. Furthermore, natural curvature and connection optimization processes are introduced to significantly enhance the realism of the model, thereby constructing a 3D root system model with a reasonable structure and realistic morphology.

[0062] S41: Generate root geometry one by one based on the starting point coordinates, ending point coordinates, and diameter parameters in the structured data; As one example, the three-dimensional structural data is imported into the three-dimensional modeling software, including the starting coordinates, ending coordinates, diameter parameters, root order level, and topological parent-child relationship information of each root system, and the root system geometry is generated one by one.

[0063] The main root uses a cylinder as the basic geometric unit, and the lateral roots at all levels are constructed using cylinders or Bézier curves. The length of all roots is determined by the spatial distance between the start and end points of the root segment. The root tip diameter and root apex diameter are determined by the reference diameter and the root diameter taper ratio; As one embodiment, the root tip diameter and root apex diameter are determined by the reference diameter and root diameter taper ratio to simulate the geometric characteristics of the root system gradually tapering along the growth direction.

[0064] The growth direction of the root segment is determined by the starting and ending coordinates of the root segment. The geometry of the daughter-level lateral root is spatially rotated and positioned so that its axis is consistent with the growth direction of the parent-level root segment. The corresponding diameter parameters are selected for the root system of different root order levels. As one example, the diameter parameters are selected for roots of different root order levels to achieve hierarchical differentiation modeling of the main root and lateral roots at each level.

[0065] S42: Introduce natural bending simulation treatment for lateral roots at all levels except the main root; As one example, after generating the root geometry, in order to improve the morphological realism of the model, a controllable natural bending simulation is introduced for the lateral roots at all levels except the main root. Without changing the overall growth trend of the root segment, random perturbation is applied to the root growth path.

[0066] S43: Perform topological integration and structural optimization on the overall three-dimensional digital model of the root system; Based on the parent-child relationship identifier in the three-dimensional structural data, a smooth transition is performed on the connection between the lateral root and the main root. As one example, the overall root system model undergoes topological integration and structural optimization to maintain realistic morphology while ensuring good geometric continuity and computational efficiency. Based on the parent-child relationship identifiers in the root system parameters, the connection points between lateral roots and the main root are smoothly transitioned to avoid geometric breaks and ensure the integrity and reliability of the root system's topological structure. Simultaneously, the generated 3D mesh is simplified and optimized as necessary, reasonably reducing the number of polygons while ensuring accurate representation of root diameter variations and spatial morphology. This improves the model's rendering and export processing efficiency. Visual verification is used to confirm whether the root system's spatial distribution, branching structure, and morphological characteristics are consistent with the defined parameter constraints.

[0067] S44: Export the three-dimensional digital model of the root system into a universal three-dimensional data format.

[0068] As one example, the complete three-dimensional root system model is exported into a general three-dimensional data format to meet the application requirements of subsequent physical model making. The exported model fully retains the spatial geometric information, branch hierarchy relationship and morphological features of the root system, providing a standardized and directly usable three-dimensional root system data foundation for entity modeling in subsequent steps.

[0069] Step S5 includes: The 3D printing process employs fused deposition modeling technology, and the printing material is a resin or plastic material that simulates the mechanical properties of root systems.

[0070] As one example, based on the generated three-dimensional digital root model, the digital root model is transformed into a physical root model that can be directly used for landslide physical model tests using 3D printing technology. By performing pre-printing processing, parametric slicing, and manufacturing control on the three-dimensional root model, and combining appropriate printing materials and process parameters, a high-precision physical reproduction of the spatial structure, hierarchical characteristics, and asymmetric morphology of real vegetation roots is achieved, thereby obtaining a physical root model that meets the requirements of model tests.

[0071] The specific steps of 3D printing include: S51. Perform 3D printing preparation work. Check the integrity and printability of the complete root system 3D model exported in step S4 to ensure that there are no broken surfaces or overlapping issues in all root geometry. For any suspended branches or slender structures in the root model, perform support structure analysis and optimization configuration in the printing software. Add support structures automatically or manually as needed. While ensuring the stability of printing, minimize the contact area between the support and the main body of the model to reduce post-processing difficulty and minimize the impact on the geometric accuracy of the root system.

[0072] S52. After importing the root system model file into the 3D printing software, the printing materials and key process parameters are systematically configured according to the requirements of the landslide model test for the mechanical properties and geometric accuracy of the root system. The printing materials are selected based on the root system simulation target to make the printed product as close as possible to the actual root system mechanical properties in terms of stiffness, flexibility, or toughness. At the same time, core parameters such as layer height, joint position, infill density, printing speed, and support parameters are finely set. By balancing and optimizing printing accuracy, structural strength, and manufacturing efficiency, the printed root system model is ensured to meet the requirements of the model test in terms of dimensional accuracy, structural integrity, and mechanical response. Finally, a file containing complete printing path and process information is generated as the printing execution instruction.

[0073] S53. After inputting the printing instruction file generated by slicing into the 3D printing equipment, complete the equipment initialization and printing parameter calibration, including printing platform level correction, nozzle height setting and material extrusion temperature control, and start the printing task; during the printing process, monitor the key stages in real time, focusing on the first layer adhesion quality, the forming stability of slender root segments and the effectiveness of the support structure. For root models with long printing times or complex structures, check the printing status regularly to prevent printing failures caused by material stringing, deformation or local detachment, thereby ensuring the continuous forming quality of the entire root model.

[0074] S54. After the root system model is printed, perform necessary post-processing operations on the model, including removing the support structure and repairing the connection parts to eliminate the influence of support residue on the root system geometry. At the same time, grind or clean the model surface according to the characteristics of the printing material.

[0075] In one embodiment, this example uses the common arborescent pine (Pinus massoniana) of Fujian Province in southeastern China as the target vegetation, referring to the process... Figure 1 The root system physical model required for landslide model testing is prepared using the method described in this invention. The specific implementation steps are as follows: Key characteristic parameters of the target vegetation root system prototype were obtained. Masson pine, a typical tree species in Fujian Province, was selected as the target vegetation, and its root system architecture was determined to be VH type (vertical-horizontal hybrid type) based on its biological characteristics. Prototype root system data for this vegetation were obtained through literature review, determining its taproot length, diameter of lateral roots at all levels, and effective root domain range in the horizontal plane (see Table 1). This data served as the foundation for subsequent model construction.

[0076] Table 1. Key characteristic parameters of the prototype root system of Masson pine.

[0077] The key characteristic parameters were converted to a different model scale. Based on the similarity ratio design requirements of the landslide model test, the geometric similarity ratio λ of the root system was set to 1:25, and the obtained prototype parameters were converted accordingly. The key characteristic parameters at the model scale after conversion are shown in Table 2.

[0078] Table 2. Values ​​of key characteristic parameters at the model scale of Masson pine root system.

[0079] The root system 3D structure data is generated parametrically. The determined model parameters are input into the parametric algorithm, and the model control parameters shown in Table 3 are also added. Figure 2 The flowchart shown demonstrates how root system 3D structure data is automatically generated through hierarchical recursion.

[0080] Table 3 Model Control Parameters

[0081] An asymmetric azimuth generation algorithm is introduced to divide the horizontal circle into dense and sparse regions. Figure 3 The perturbation angle is superimposed on the reference angle to randomly generate the azimuth angle of the first-order lateral root, and the asymmetry of the actual root system lateral root distribution is guaranteed to be reflected.

[0082] refer to Figure 4 The inclination angle of the primary lateral roots is randomly distributed within a preset inclination angle range [10°, 80°] in accordance with the characteristics of the VH type root system, to ensure that the primary lateral roots are not too horizontal or vertical.

[0083] A total quantity control and depth-weighted strategy is used to generate secondary lateral roots. First, the global target total number range for secondary lateral roots is calculated. Based on a set multiplier factor of 1.25~2.0, the total number range is calculated, resulting in a range of [10, 16] for the total number of secondary lateral roots. The algorithm randomly selects integers. Then, a depth-weighted formula is introduced to assign weights to the primary lateral roots: Based on this, randomly generated secondary lateral roots are non-uniformly distributed to primary lateral roots with higher weights in deeper layers.

[0084] A parent-level guiding constraint mechanism is introduced to limit the random deflection of the secondary lateral root azimuth angle within the range of the parent azimuth angle superimposed on the disturbance angle: The total number of tertiary lateral root targets is determined based on the actual total number of secondary lateral roots generated and the set minimum multiplier factor.

[0085] Continuing with the depth-weighted logic, the depth weights of secondary lateral roots are calculated, and the number of tertiary lateral roots is preferentially allocated to the deeper secondary lateral roots. Furthermore, since the diameter of tertiary lateral roots is relatively small, they are not subject to tip tapering.

[0086] The program ultimately stores all generated principal roots and lateral roots at all levels as structured data, including their numbers, hierarchical relationships, spatial coordinates, and diameter parameters.

[0087] The generated structured data is imported into professional 3D modeling software to construct a 3D model of the root system. Figure 5 The complete three-dimensional root system model is exported as a three-dimensional data format STL.

[0088] Import the exported 3D digital model of the root system into the 3D printing slicing software for printing, specifically including: Analysis of suspended sections and configuration of supporting structures were conducted, with easily removable supporting structures added for lateral roots with large inclination angles. PLA material was selected based on the mechanical properties of Masson pine roots, and printing parameters were set. Equipment initialization and printing parameter calibration were completed, and the fused deposition modeling 3D printing equipment was started to complete the manufacturing process. After printing, the model was cleaned, underwent secondary curing, and the supports were removed. The connection points were then trimmed and polished, ultimately yielding a high-fidelity physical model of Masson pine roots that meets the requirements of landslide model testing. Figure 6 As shown.

[0089] This application also discloses an electronic device. (See reference...) Figure 7 , Figure 7 This is a schematic diagram of the structure of an electronic device disclosed in an embodiment of this application. The electronic device 500 may include: at least one processor 501, at least one network interface 504, a user interface 503, a memory 505, and at least one communication bus 502.

[0090] The communication bus 502 is used to enable communication between these components.

[0091] The user interface 503 may include a display screen, and optionally, the user interface 503 may also include a standard wired interface or a wireless interface.

[0092] The network interface 504 may optionally include a standard wired interface or a wireless interface (such as a Wi-Fi interface).

[0093] This application also discloses a computer-readable storage medium storing a plurality of instructions adapted for loading by a processor to execute the above-described root modeling method for landslide model tests considering root morphology characteristics.

[0094] The above are merely exemplary embodiments of this disclosure and should not be construed as limiting the scope of this disclosure. Any equivalent changes and modifications made in accordance with the teachings of this disclosure shall still fall within the scope of this disclosure.

[0095] This application is intended to cover any variations, uses, or adaptations of this disclosure that follow the general principles of this disclosure and include common knowledge or customary techniques in the art not described in this disclosure. The specification and embodiments are to be considered exemplary only, and the scope and spirit of this disclosure are defined by the claims.

Claims

1. A root system modeling method for landslide model experiments considering root morphology characteristics, characterized in that, The method includes the following steps: S1. Obtain key feature parameters of the target vegetation root system prototype; S2. Based on the geometric similarity ratio of the landslide model test, and combined with the key characteristic parameters, scale conversion is performed to obtain the model scale parameters; S3. Using the model scale parameters and preset model control parameters as input, the three-dimensional structure data of the root system is generated through a parametric algorithm. S4. Import the three-dimensional structural data into the three-dimensional modeling software to construct a three-dimensional digital model of the root system. S5. Using 3D printing technology, a physical root system model is made from the three-dimensional digital model of the root system for landslide physical model testing.

2. The root system modeling method for landslide model experiments considering root morphology characteristics as described in claim 1, characterized in that, Step S1 includes: The key characteristic parameters include: vegetation type, root system architecture category, taproot length, diameter of different levels of root system, and effective root domain range; The effective root domain range is determined by a boundary circle constructed with the starting position of the principal root as the center and the projection length of the selected longest effective lateral root on the horizontal plane as the radius.

3. The root system modeling method for landslide model experiments considering root morphology characteristics as described in claim 1, characterized in that, Step S2 includes: From the key feature parameters, the root system prototype feature parameters are obtained, which include: the length of the main root and the effective root domain range; The length parameter at the model scale is obtained from the root system prototype feature parameters; The scale conversion formula is: in, This refers to the length parameter at the model scale. This refers to the length parameter at the prototype scale. It represents the geometric similarity ratio.

4. The root system modeling method for landslide model experiments considering root morphology characteristics as described in claim 1, characterized in that, Step S3 includes: The model control parameters include: total model length limit, number of lateral roots at each level, root diameter taper ratio, and root azimuth and root inclination constraints. The parameterized algorithm performs hierarchical recursive operations, including S31: Generate the principal root and determine its starting point coordinates, ending point coordinates, inclination angle, and azimuth angle in three-dimensional space; Establish a rectangular coordinate system in three-dimensional space, with the root tip as the origin, the X and Y axes in the horizontal plane, and the Z axis pointing downwards. Then the coordinates of the starting point of the principal root are: The coordinates of the endpoint of the principal root are: in: The Z-coordinate of the location on the Earth's surface; The length of the principal root; The inclination angle of the main root is fixed at 90°, the azimuth angle is fixed at 0°, and the root tip diameter and root apex diameter are determined according to the preset root diameter taper ratio. S32: Within the effective branching interval of the main root, generate first-order lateral roots and control their spatial distribution; Avoid the apical region length on the main root and tip length Determine the length of the effective branch interval. : Let the number of first-order lateral roots be Then the first Depth of the growth origin of a primary lateral root for: Target length of first-order lateral root The attenuation is calculated based on its relative depth position: in: The maximum horizontal distribution radius of the root system. To normalize the depth position parameters, The length attenuation coefficient, Used as a reference tilt angle; Azimuth of the first-order lateral root It is generated using a partitioned random method to form an asymmetric distribution on the horizontal plane; its tilt angle The spatial direction vector of the lateral roots is randomly determined within a preset range based on the root system configuration category. for: If the predicted endpoint depth If the value exceeds a preset threshold, the final length of the lateral root is corrected as follows: in This is a limit on the maximum depth of the model; Finally, the coordinates of the first-order lateral root endpoint were determined. ,as follows: in: Let the coordinates be the starting coordinates of the first-order lateral root. ; S33: On the primary lateral roots, secondary lateral roots are generated based on a depth weighting allocation strategy, and their spatial distribution is controlled; the depth weighting allocation strategy refers to: calculating the weight value of the primary lateral root based on its starting depth, and distributing the total number of secondary lateral roots non-uniformly to each primary lateral root according to the weight value. Let the number of first-order lateral roots be Total number of secondary lateral roots Randomly determined from the following range: in , This is a preset proportional coefficient; According to the Depth of origin of a first-order lateral root Calculate its weight value: in It is a depth-weighted index; The number of secondary lateral roots is allocated to each primary lateral root according to the weight ratio: in Indicates the first The number of secondary lateral roots assigned to a primary lateral root; Secondary lateral roots are generated by randomly selecting growth locations along the length of each primary lateral root; the azimuth angle of the secondary lateral roots... Its parent azimuth angle Based on the superimposed disturbance angle : The length of secondary lateral roots is determined proportionally to the length of the primary root and is also corrected by depth constraints. S34: On the secondary lateral roots, generate tertiary lateral roots in the same manner as in step S33; S35: Stores the generated three-dimensional structural data of the primary root and lateral roots at all levels.

5. The root system modeling method for landslide model experiments considering root morphology characteristics as described in claim 1, characterized in that, Step S4 includes: S41: Generate root geometry one by one based on the starting point coordinates, ending point coordinates, and diameter parameters in the structured data; The main root uses a cylinder as the basic geometric unit, and the lateral roots at all levels are constructed using cylinders or Bézier curves. The length of all roots is determined by the spatial distance between the start and end points of the root segment. The root tip diameter and root apex diameter are determined by the reference diameter and the root diameter taper ratio; The growth direction of the root segment is determined by the starting and ending coordinates of the root segment. The geometry of the daughter-level lateral root is spatially rotated and positioned so that its axis is consistent with the growth direction of the parent-level root segment. The corresponding diameter parameters are selected for the root system of different root order levels. S42: Introduce natural bending simulation treatment for lateral roots at all levels except the main root; S43: Perform topological integration and structural optimization on the overall three-dimensional digital model of the root system; Based on the parent-child relationship identifier in the three-dimensional structural data, a smooth transition is performed on the connection between the lateral root and the main root. S44: Export the three-dimensional digital model of the root system into a universal three-dimensional data format.

6. The root system modeling method for landslide model experiments considering root morphology characteristics as described in claim 1, characterized in that, Step S5 includes: The 3D printing process employs fused deposition modeling technology, and the printing material is a resin or plastic material that simulates the mechanical properties of root systems.

7. An electronic device, characterized in that, The device includes a processor, a memory, a user interface, and a network interface. The memory is used to store instructions, the user interface and the network interface are used to communicate with other devices, and the processor is used to execute the instructions stored in the memory to enable the electronic device to perform the root system modeling method for landslide model tests considering root morphology characteristics as described in any one of claims 1-6.

8. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores instructions that, when executed by a computer, perform the root system modeling method for landslide model tests considering root morphology characteristics as described in any one of claims 1-6.

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