A tree crown wind field evaluation method and system based on wind environment simulation

Abstract

The embodiment of the present application relates to the technical field of wind field simulation, and discloses a tree crown wind field evaluation method based on wind environment simulation, comprising: obtaining a three-dimensional data model of a target environment to be evaluated; performing fluid dynamics simulation to simulate wind field distribution when wind flows through the three-dimensional data model, and a tree model of at least one tree is configured as a porous medium model in the wind field simulation simulation; obtaining a result based on the wind field simulation simulation; and determining a risk evaluation result for the corresponding tree according to a wind load response index. The wind field evaluation method in the embodiment of the present application calculates at least one wind load response index related to the corresponding tree, such as wind speed, pressure, wind load, etc., according to the result of the wind field simulation simulation. These quantitative indexes can intuitively reflect the wind force acting on the tree in the wind field, which helps to deeply understand the mechanical response of the tree under different wind environments and provides specific numerical basis for evaluating the wind resistance performance of the tree.

Description

Technical Field

[0001] This invention relates to the field of wind field simulation technology, specifically to a method and system for assessing tree canopy wind field based on wind environment simulation. Background Technology

[0002] Currently, trees play a vital role in urban landscapes, serving not only as an important component of urban greening but also as a key element in urban ecological balance. South China is frequently affected by typhoons, with an average of 5 to 8 typhoons making landfall annually. Urban trees are susceptible to risks such as broken branches and fallen trunks under the influence of typhoons or extreme weather, resulting in serious personal safety and economic losses.

[0003] Current methods for managing large trees in parks generally rely on human experience for assessment and management. For example, before and after typhoons, managers assess trees based on their historical experience and then reinforce them. However, this approach is somewhat outdated and cannot provide efficient protection. Therefore, designing an efficient tree management solution has become a pressing technical problem for those skilled in the art. Summary of the Invention

[0004] To address the aforementioned shortcomings, this invention discloses a tree canopy wind field assessment method based on wind environment simulation, which can achieve efficient wind field assessment in specific environments.

[0005] The first aspect of this invention discloses a method for assessing tree canopy wind field based on wind environment simulation, comprising:

[0006] Obtain a three-dimensional data model of the target environment to be evaluated, wherein the three-dimensional data model includes a tree model of at least one tree in the target environment to be evaluated;

[0007] Perform fluid dynamics simulation to simulate the wind field distribution when wind flows through the three-dimensional data model, wherein the tree model of the at least one tree is configured as a porous medium model in the wind field simulation.

[0008] Obtain the results based on wind field simulation and calculate at least one wind load response index related to the corresponding trees.

[0009] The risk assessment results for the corresponding trees are determined based on the wind load response index.

[0010] As an optional implementation, in a first aspect of the present invention, performing fluid dynamics simulation to simulate the wind field distribution when wind flows through the three-dimensional data model includes:

[0011] Multiple wind speed levels and multiple wind directions are determined, and computational fluid dynamics simulations are performed respectively to simulate the wind field distribution when the wind flows through the three-dimensional data model under different wind conditions;

[0012] The determination of the risk assessment results for the corresponding trees based on the wind load response index includes:

[0013] Based on the calculated wind load response index, a dynamic risk level is established for each tree for different wind speed levels and wind directions. The dynamic risk level is associated with a preset wind force threshold and the corresponding tree status.

[0014] A graded protection strategy is generated based on the status of each tree and the dynamic risk level associated with each tree, and is matched with the wind speed level and wind direction.

[0015] As an optional implementation, in the first aspect of the present invention, the establishment of dynamic risk levels further includes:

[0016] Identify the formation of localized high-wind-speed areas and regional extreme wind areas;

[0017] Increase the risk level of trees located in the aforementioned high-wind-speed areas;

[0018] Obtain tree status information associated with the regional extreme wind zone, determine the corresponding risk level based on the regional extreme wind zone and tree status information, and raise the risk level of trees located in the regional extreme wind zone that belong to shallow root systems or weak growth species to the highest risk level.

[0019] As an optional implementation, in a first aspect of the present invention, generating a hierarchical protection strategy includes:

[0020] For the risks at the first wind speed level, a first-class strategy is generated, which mainly involves monitoring and routine pruning.

[0021] For the risks under the second wind speed level, a second type of strategy is generated, which mainly focuses on targeted reinforcement and emergency pruning;

[0022] For risks at the third wind speed level, a third type of strategy is generated, which mainly involves delineating danger zones, implementing personnel evacuation, and initiating temporary reinforcement projects.

[0023] As an optional implementation, in the first aspect of the present invention, the evaluation method further includes:

[0024] Obtain plant model parameters, including model type, leaf area density, leaf area index, and drag force coefficient;

[0025] The corresponding leaf area index and drag coefficient are determined based on the model type.

[0026] The viscous drag coefficient and inertial drag coefficient of the porous media model are determined based on the leaf area density and drag force coefficient of the corresponding tree. The calculated viscous drag coefficient and inertial drag coefficient are then used to update the porous media model of the target plant in the fluid dynamics simulation.

[0027] As an optional implementation, in the first aspect of the present invention, the evaluation method further includes:

[0028] A three-dimensional mesh model of the tree surface is determined, where each mesh cell is defined as a canopy mesh cell;

[0029] Acquire wind field data generated by computational fluid dynamics simulations, where the wind field data is distributed across spatial grid points;

[0030] The wind field data is mapped onto the canopy grid cells;

[0031] Based on the mapped data, an influence coefficient is calculated for each canopy grid cell. The influence coefficient is determined based on the wind pressure value at the grid cell. The calculation of the influence coefficient is also based on the wind pressure gradient between the canopy grid cell and its adjacent grid cells, and the calculation of the influence coefficient is also based on the angle between the wind vector direction at the canopy grid cell and the surface normal vector direction.

[0032] Identify high-risk cell clusters composed of canopy grid cells whose influence coefficients exceed a preset threshold;

[0033] Output the spatial location information of the high-risk unit cluster;

[0034] Each identified high-risk cell cluster is assigned a unique identifier and ranked for risk based on the average impact coefficient of its internal grid cells.

[0035] As an optional implementation, in a first aspect of the present invention, determining the risk assessment result for the corresponding tree based on the wind load response index includes:

[0036] Obtain the wind pressure value on each grid cell of the tree surface.

[0037] For each surface grid cell, the wind force acting on that grid cell is calculated according to the wind force calculation formula, which is: ,in, The wind force acting on the corresponding grid cell. The wind pressure value on the grid cell. The surface area of ​​the corresponding grid cell;

[0038] Define a three-dimensional spatial coordinate system. For each surface mesh element, determine the spatial coordinates of its center point. Calculate the lever arm of each wind force acting on the tree trunk root, where the lever arm size is: ,in, As the lever arm, and Let x and y be the x and y coordinates of the i-th grid cell;

[0039] Each force is determined based on the bending moment calculation formula. The bending moment generated at the base of the tree trunk; the formula for calculating the bending moment is: ,in, For vector cross product, For strength The bending moment generated at the base of the tree trunk;

[0040] right Perform vector decomposition to decompose it into bending moment components rotating about the X-axis and bending moment components rotating about the Y-axis;

[0041] The bending moment components about the X-axis and about the Y-axis of all mesh elements are summed to obtain the X and Y components of the total bending moment at the root.

[0042] Calculate the total combined bending moment at the base of the tree trunk, and determine the corresponding dynamic risk level and lodging state based on the total combined bending moment, the section modulus of the tree cross section, and the bending strength of the wood.

[0043] As an optional implementation, in the first aspect of the present invention, the evaluation method further includes:

[0044] The dynamic risk level and main risk types are determined based on the simulated wind pressure on the canopy surface, bending moment at the trunk roots, and tree status information; the tree status information includes void ratio, tilt, and root system status.

[0045] Based on the main risk type, stress parameters, influence coefficients of each grid cell, and the three-dimensional tree model, corresponding pruning parameters are generated; the pruning parameters include specifying the branch to be pruned and / or the percentage of leaf area to be reduced;

[0046] The tree model is trimmed according to the trimming parameters, and fluid simulation is performed again based on the updated parameters to compare the trimming status.

[0047] And / or, the execution of fluid dynamics simulation to simulate the wind field distribution as wind flows through the three-dimensional data model includes:

[0048] Obtain wind environment simulation parameters, which include wind speed information and wind direction information;

[0049] The simulation area for wind field calculation is determined based on a three-dimensional data model of the target environment to be evaluated; the three-dimensional data model includes a tree model of at least one tree in the target environment to be evaluated.

[0050] The simulated region is divided into discrete grid cells to form a computational grid;

[0051] Based on the measured data of meteorological stations in the target environment, the inlet boundary conditions for fluid dynamics simulation are configured, and the inlet boundary conditions are set as gradient wind.

[0052] The wind field simulation is performed in the fluid dynamics simulation; the turbulence model is used in the fluid dynamics simulation, and iterative calculations are performed until the solution residuals are reduced to the set requirements.

[0053] A second aspect of this invention discloses a tree canopy wind field assessment system based on wind environment simulation, comprising:

[0054] Acquisition module: used to acquire a three-dimensional data model of the target environment to be evaluated, wherein the three-dimensional data model includes a tree model of at least one tree in the target environment to be evaluated;

[0055] Simulation module: used to perform fluid dynamics simulation to simulate the wind field distribution when wind flows through the three-dimensional data model, wherein the tree model of the at least one tree is configured as a porous medium model in the wind field simulation.

[0056] Calculation module: used to obtain the results based on wind field simulation and calculate at least one wind load response index related to the corresponding tree.

[0057] Assessment module: Used to determine the risk assessment results for the corresponding trees based on the wind load response index.

[0058] A third aspect of the present invention discloses an electronic device, comprising: a memory storing executable program code; a processor coupled to the memory; the processor calling the executable program code stored in the memory to execute the tree canopy wind field assessment method based on wind environment simulation disclosed in the first aspect of the present invention.

[0059] A fourth aspect of the present invention discloses a computer-readable storage medium storing a computer program, wherein the computer program causes a computer to execute the tree canopy wind field assessment method based on wind environment simulation disclosed in the first aspect of the present invention.

[0060] Compared with the prior art, the embodiments of the present invention have the following beneficial effects:

[0061] The wind field assessment method in this embodiment of the invention calculates at least one wind load response index related to the corresponding tree, such as wind speed, pressure, and wind load, based on the results of wind field simulation. These quantitative indicators can intuitively reflect the wind force acting on the tree in the wind field, which helps to understand the mechanical response of the tree in different wind environments and provides specific numerical basis for assessing the wind resistance performance of the tree. Attached Figure Description

[0062] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0063] Figure 1 This is a flowchart illustrating the tree canopy wind field assessment method based on wind environment simulation disclosed in an embodiment of the present invention.

[0064] Figure 2 This is a schematic diagram of the process for grid risk identification disclosed in an embodiment of the present invention;

[0065] Figure 3 This is a schematic diagram of the dynamic risk calculation process disclosed in an embodiment of the present invention;

[0066] Figure 4 This is a wind speed diagram of ancient and old trees at a height of 10m in a northwest wind under a wind speed of 17.2m / s, as disclosed in an embodiment of the present invention.

[0067] Figure 5 This is a wind pressure diagram of ancient and old trees at a height of 10m in a northwest wind at a wind speed of 17.2m / s, as disclosed in an embodiment of the present invention.

[0068] Figure 6 This is a schematic diagram of the structure of a tree canopy wind field assessment system based on wind environment simulation provided in an embodiment of the present invention;

[0069] Figure 7 This is a schematic diagram of the structure of an electronic device provided in an embodiment of the present invention. Detailed Implementation

[0070] 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 embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0071] It should be noted that the terms first, second, third, fourth, etc., in the specification and claims of this invention are used to distinguish different objects, not to describe a specific order. The terms used in the embodiments of this invention include and have, and any variations thereof, are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or device that includes a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to these processes, methods, products, or devices.

[0072] Example 1

[0073] Please see Figure 1 , Figure 1 This is a flowchart illustrating the tree canopy wind field assessment method based on wind environment simulation disclosed in this invention. The execution entity of the method described in this invention is an execution entity composed of software and / or hardware. This execution entity can receive relevant information via wired and / or wireless means and can send certain instructions. It may also have certain processing and storage functions. This execution entity can control multiple devices, such as remote physical servers or cloud servers and related software, or local hosts or servers and related software that perform related operations on devices located in a certain location. In some scenarios, it can also control multiple storage devices, which may be placed in the same location as the devices or in different locations. Figure 1 As shown, the tree canopy wind field assessment method based on wind environment simulation includes the following steps:

[0074] S101: Obtain a three-dimensional data model of the target environment to be evaluated, wherein the three-dimensional data model includes a tree model of at least one tree in the target environment to be evaluated;

[0075] S102: Perform fluid dynamics simulation to simulate the wind field distribution when wind flows through the three-dimensional data model, wherein the tree model of the at least one tree is configured as a porous medium model in the wind field simulation.

[0076] S103: Obtain the results based on wind field simulation and calculate at least one wind load response index related to the corresponding tree.

[0077] S104: Determine the risk assessment results for the corresponding trees based on the wind load response index.

[0078] Specifically, the solution of this invention acquires a three-dimensional data model of the target environment to be evaluated and configures the tree model as a porous medium model for fluid dynamics simulation, which can more accurately simulate the wind field distribution when wind flows through the tree canopy. This simulation method takes into account the actual shape, structure, and interaction of the trees with the surrounding environment. Compared with traditional simple estimation methods, it can more realistically reflect the flow of wind in the tree canopy and provide a more reliable data foundation for subsequent analysis.

[0079] This method can calculate at least one wind load response index related to a given tree, such as wind speed, pressure, and wind load, based on the results of wind field simulation. These quantified indicators can intuitively reflect the wind force acting on the tree in the wind field, helping to gain a deeper understanding of the tree's mechanical response under different wind environments. This provides specific numerical basis for assessing the wind resistance of trees and provides specific data for subsequent pruning.

[0080] Determining risk assessment results for specific trees based on wind load response indices helps relevant personnel understand the potential risks to trees in windy environments. For example, it allows for the prediction of which trees are more susceptible to damage in strong winds, enabling the implementation of appropriate protective measures such as pruning branches and reinforcing trunks to reduce wind damage and improve tree survival and safety.

[0081] Compared to traditional methods such as field measurements or wind tunnel experiments, the tree canopy wind field assessment method based on wind environment simulation has the advantages of low cost and high efficiency. It does not require extensive field investigations and complex experimental setups; results can be obtained quickly through computer simulation, greatly saving time, human and material resources. Furthermore, it can be simulated multiple times under different assumptions, facilitating researchers to analyze and optimize various factors.

[0082] More preferably, the step of performing fluid dynamics simulation to simulate the wind field distribution when wind flows through the three-dimensional data model includes:

[0083] Multiple wind speed levels and multiple wind directions are determined, and computational fluid dynamics simulations are performed respectively to simulate the wind field distribution when the wind flows through the three-dimensional data model under different wind conditions;

[0084] The determination of the risk assessment results for the corresponding trees based on the wind load response index includes:

[0085] Based on the calculated wind load response index, a dynamic risk level is established for each tree for different wind speed levels and wind directions. The dynamic risk level is associated with a preset wind force threshold and the corresponding tree status.

[0086] A graded protection strategy is generated based on the status of each tree and the dynamic risk level associated with each tree, and is matched with the wind speed level and wind direction.

[0087] The solution in this invention overcomes the limitation of the original method, which may only simulate a single wind condition, by determining multiple set wind speed levels and multiple wind directions and performing computational fluid dynamics (CFD) simulations for each. This achieves coverage of wind conditions across the entire target environment.

[0088] The above methods can accurately reproduce the wind field distribution under different natural wind conditions (such as wind speed levels like light breeze, strong wind, and typhoon, as well as different wind directions like prevailing wind, crosswind, and gusts). In particular, they can capture localized abrupt changes in the wind field of the tree canopy under corresponding wind direction and high wind speed combinations (such as eddies at building corners and the funneling effect in narrow passages). These methods enable comprehensive wind field assessments. The simulation results are no longer static, single data points, but rather form a three-dimensional correlated database of wind speed, wind direction, and wind field distribution. This provides more realistic data for subsequent calculations of tree wind load response and risk level determination, avoiding assessment biases caused by incomplete consideration of wind conditions.

[0089] The solution of this invention establishes a dynamic risk level for each tree based on different wind speeds and directions, and associates it with preset wind thresholds and tree conditions, making risk assessment both individualized and dynamically adaptable.

[0090] Breaking away from the traditional, extensive model of uniform risk levels: Based on the tree's own condition (such as age, diameter at breast height, crown width, root health, and whether there are pests / branches) and preset wind thresholds (such as the differences in wind resistance thresholds between young trees, old trees, and healthy trees), a corresponding relationship between wind speed, wind direction, and risk can be customized for each tree (for example, the same old tree is low risk under a level 5 easterly wind, but high risk under a level 8 westerly wind).

[0091] The dynamic risk level can match wind condition changes in real time. When the actual wind conditions (wind speed / wind direction) change, there is no need to re-execute the full simulation. Just call the pre-calculated risk level under the corresponding wind speed and wind direction combination to quickly locate high-risk trees and buy time for emergency response.

[0092] By generating tiered protection strategies tailored to specific wind conditions based on tree condition and dynamic risk level, the assessment results are transformed into directly actionable management plans, addressing the pain point of traditional assessments that only identify risks without providing solutions.

[0093] The protection strategy is adaptable to wind conditions, and outputs differentiated strategies for different wind speed levels and wind direction combinations (for example, for a level 6 northerly wind, priority is given to trunk reinforcement and canopy pruning for high-risk old trees, only dead branches need to be cleared for medium-risk mature trees, and no intervention is required for low-risk saplings).

[0094] The protection strategy takes into account the individual differences of trees, avoiding wasteful one-size-fits-all protection (such as not needing to over-reinforce healthy and strong trees), and can also accurately focus on high-risk objects (such as prioritizing the protection of high-risk trees near buildings / roads to reduce the risk of falling trees causing injury and traffic disruption), thus achieving on-demand protection and resource optimization.

[0095] The solutions provided by this invention offer clear guidelines for management: whether it is routine maintenance (such as developing regular pruning plans based on risk levels under normal wind direction and moderate wind speed) or emergency disaster prevention (such as deploying reinforcement priorities based on typhoon warning wind direction and risk levels under high wind speed), the work can be carried out directly based on the graded strategy, thereby improving the efficiency and scientific nature of tree management.

[0096] The wind speed, wind direction, dynamic risk level, and classification strategy database generated by the scheme in this embodiment of the invention can serve as a digital archive for long-term tree management in the target environment.

[0097] In the future, wind thresholds and risk levels can be dynamically adjusted by combining updates on tree growth status (such as annual diameter at breast height growth and crown width changes) and environmental changes (such as the impact of new buildings on the wind field), so as to achieve dynamic risk monitoring of trees throughout their entire life cycle.

[0098] It can also provide a basis for subsequent new tree planting planning (such as selecting more suitable tree species and determining reasonable planting density based on the risk distribution of wind speed and wind direction in different areas, and avoiding planting tree species with weak wind resistance in high-risk wind field areas), thereby reducing the risk of wind disasters to trees from the source.

[0099] More preferably, the establishment of dynamic risk levels further includes:

[0100] Identify the formation of localized high-wind-speed areas and regional extreme wind areas;

[0101] Increase the risk level of trees located in the aforementioned high-wind-speed areas;

[0102] Obtain tree status information associated with the regional extreme wind zone, determine the corresponding risk level based on the regional extreme wind zone and tree status information, and raise the risk level of trees located in the regional extreme wind zone that belong to shallow root systems or weak growth species to the highest risk level.

[0103] The solution in this invention overcomes the limitations of isolated individual assessments of tree risk by identifying local high-wind-speed areas and regional extreme wind areas under corresponding wind conditions, and incorporates the spatial heterogeneity of the wind field into the risk assessment system.

[0104] Local high wind speed areas (such as the narrow tube effect area of ​​building gaps and the turbulence acceleration area of ​​tree canopy gaps) and regional extreme wind areas (such as the area of ​​persistent strong winds formed by specific terrain / building groups) are often risk blind spots that are easily overlooked in traditional assessments, but their wind speeds may far exceed those of the surrounding environment, and their destructive force on trees in the area increases non-linearly.

[0105] By proactively identifying these special wind zones, the environmental risks of the wind field can be combined with the wind resistance of the trees themselves, avoiding misjudgments caused by relying solely on the individual condition of the trees or the average wind speed (for example, a healthy tree in a local high wind speed area may actually be at higher risk than a tree in poor condition in a normal wind zone).

[0106] Improving the risk level of trees in localized high-wind-speed areas allows for rapid focus on environmentally stress-driven risks: even trees in good condition (e.g., mature, healthy trees) require increased vigilance due to the harsh wind environment, avoiding underestimating their potential for damage from strong winds (e.g., broken branches, loosened roots) simply because they are robust. For trees in regional extreme wind zones, a grading system is implemented based on tree condition information, directly elevating shallow-rooted or weakly growing species to the highest risk level, achieving a combined assessment of environmental risk and species / physiological deficiencies.

[0107] Trees with shallow root systems (weak ability to resist wind and stabilize soil) and weak tree species (such as diseased and pest-infested trees, and trees with declining vigor) have low wind resistance thresholds and are considered extremely high-risk in extreme wind areas. Targeted upgrading of their levels can prevent the hidden dangers of priority collapse in extreme wind disasters from being covered up, and provide a clear priority basis for emergency response.

[0108] Specifically, by accurately identifying the risks of special wind zones and the trees within them, the areas and trees most likely to be at risk during wind disasters can be identified in advance: for trees in local high-wind-speed areas, targeted protection measures can be developed in advance (such as increasing trunk support and selectively pruning the windward side of the canopy to reduce the wind-exposed area); for shallow-rooted / vulnerable tree species in regional extreme wind zones, more aggressive preventive measures can be taken (such as transplanting to low-risk areas when necessary, high-intensity reinforcement, or early thinning), reducing the risk of fallen trees and broken branches under extreme wind conditions from the source, and reducing the threat to surrounding people, buildings, and transportation.

[0109] By differentiating risk levels, limited protective resources can be precisely targeted to truly high-risk trees and areas: excessive resources should not be allocated to healthy trees in ordinary wind zones, avoiding over-protection; priority protection should be given to trees in localized high-wind-speed areas and vulnerable tree species in extreme wind zones, avoiding insufficient protection and ensuring that resources are used effectively, thus improving the economy and effectiveness of wind disaster prevention and control. The identification and risk labeling of localized high-wind-speed areas and regional extreme wind zones can create a database linking specific wind field characteristics, tree species / status, and risk levels, providing a foundation for future research.

[0110] The influence of different terrains / building complexes on wind fields;

[0111] Response mechanisms of specific tree species (especially shallow-rooted and vulnerable species) under extreme wind conditions;

[0112] The construction of more accurate tree wind resistance threshold models provides empirical data, promoting the deepening of tree wind damage assessment from experience-based judgment to data-driven approaches.

[0113] More preferably, a tiered protection strategy is generated, including:

[0114] For the risks at the first wind speed level, a first-class strategy is generated, which mainly involves monitoring and routine pruning.

[0115] For the risks under the second wind speed level, a second type of strategy is generated, which mainly focuses on targeted reinforcement and emergency pruning;

[0116] For risks at the third wind speed level, a third type of strategy is generated, which mainly involves delineating danger zones, implementing personnel evacuation, and initiating temporary reinforcement projects.

[0117] The solution in this invention directly links the protection strategy to the wind speed level (first, second, and third levels), forming a clear correspondence between wind speed, risk, and measures:

[0118] The first type of strategy (first wind speed level, low to medium risk): focuses on monitoring and routine pruning, avoids taking high-intensity measures for low-risk wind conditions (such as not needing to blindly reinforce), reduces ineffective investment of manpower and materials, and conforms to the economic principle of daily maintenance.

[0119] The second type of strategy (second wind speed level, medium to high risk): through targeted reinforcement (such as trunk support and root reinforcement) and emergency pruning (thinning the windward side of the canopy to reduce wind load), focus on key points where the possibility of wind damage increases, reduce the risk of tree falling and branch breaking in advance, and avoid the risk accumulating to an extreme state.

[0120] The third category of strategy (third wind speed level, high to extreme risk): focuses on delineating danger zones, evacuating people, and implementing temporary reinforcement projects. It directly addresses the core threat under high wind speeds (prioritizing personnel safety) and minimizes casualties and property damage caused by wind disasters through proactive risk avoidance and enhanced engineering measures. This tiered approach ensures that each strategy is neither redundant nor lacking, and that resource investment is proportional to the level of risk.

[0121] The tiered strategy provides a standardized and procedural action guide for tree management entities (such as landscaping departments and scenic area operation and maintenance teams):

[0122] There is no need to rely on the experience and judgment of management personnel. The corresponding strategy can be directly called according to the real-time or forecast wind speed level (for example, when a second wind speed level warning is received, the list of trees that need to be reinforced and the key parts to be pruned can be quickly determined).

[0123] The strategies are specific (such as routine pruning, targeted reinforcement, and personnel evacuation), making them easy for frontline staff to understand and implement, reducing communication costs and execution deviations, and are especially suitable for rapid response in emergency scenarios.

[0124] Different wind speed levels correspond to fundamentally different levels of risk and hazard. The tiered strategy achieves an orderly prioritization of safety by clearly defining action priorities: at low wind speeds, the focus is on prevention and maintenance (monitoring and routine pruning), reducing the wind damage risks to trees themselves through long-term management (such as removing dead branches and maintaining crown balance); at medium and high wind speeds, the focus is on proactive risk mitigation (reinforcement and emergency pruning), directly improving the wind resistance of trees through engineering measures to prevent the risk from escalating; at extreme wind speeds, the focus is on life safety (danger zone delineation and personnel evacuation), taking the avoidance of casualties as the core objective, while supplementing it with temporary reinforcement to reduce property damage, which is in line with the basic principle of prioritizing life in disaster response.

[0125] More preferably, the evaluation method further includes:

[0126] Obtain plant model parameters, including model type, leaf area density, leaf area index, and drag force coefficient;

[0127] The corresponding leaf area index and drag coefficient are determined based on the model type.

[0128] The viscous drag coefficient and inertial drag coefficient of the porous media model are determined based on the leaf area density and drag force coefficient of the corresponding tree. The calculated viscous drag coefficient and inertial drag coefficient are then used to update the porous media model of the target plant in the fluid dynamics simulation.

[0129] In this embodiment of the invention, a porous media model is used to better reflect the physical characteristics of trees, thereby improving the accuracy of wind field simulation. The wind resistance effect of the tree canopy (such as airflow deceleration and turbulence formation) is mainly related to biophysical characteristics such as leaf morphology and distribution density. By introducing plant model parameters such as leaf area density (LAD), leaf area index (LAI), and drag coefficient, and converting them into the viscous drag coefficient and inertial drag coefficient of the porous media model, the realism of the wind field simulation is improved.

[0130] The solution of this invention realizes the transformation from empirical porous media settings to biophysical parameter-driven approaches: traditional porous media models may use a uniform drag coefficient and ignore the morphological differences of different trees; while the solution of this invention directly relates the drag coefficient to the leaf distribution density (LAD) of the tree and the airflow drag capacity (drag coefficient), so that the model can truly reflect the physical law that the denser the leaves, the greater the drag force and the stronger the wind resistance.

[0131] Accurately characterize the interaction between airflow and tree canopy: For example, the drag differences formed by conifers (lower drag coefficient) and broad-leaved trees (higher drag coefficient), and dense-canopy trees (high LAD) and sparse-canopy trees (low LAD) in the wind field can be accurately simulated, avoiding the distortion of wind field distribution caused by parameter homogenization (such as underestimating the wind-blocking effect of dense-canopy trees or overestimating the wind resistance of sparse-canopy trees).

[0132] By determining the leaf area index (LAI) and drag coefficient based on the model type, the wind field response of different tree species can be distinguished: the model type can correspond to a specific tree species (such as poplar, pine, banyan, etc.) or tree type (such as evergreen trees, deciduous trees, shrubs). Each type is preset with LAI and drag coefficient that match its biological characteristics (for example, the LAI of deciduous trees decreases in winter, and the drag coefficient is adjusted accordingly to simulate wind resistance changes in different seasons).

[0133] Calculating drag coefficients based on tree species-specific parameters can accurately capture the differences in wind loads borne by different tree species under the same wind conditions. For example, tree species with shallow root systems and high LAI (such as some fast-growing broad-leaved trees) experience significantly higher wind loads in strong winds than tree species with deep root systems and low LAI (such as conifers), providing a more accurate mechanical basis for subsequent risk level determination (such as prioritizing the marking of high-risk tree species).

[0134] By updating the drag coefficient based on the tree's current leaf area density and drag coefficient, the model can adapt to tree growth or environmental changes: During tree growth, LAD and crown width will gradually change (such as the LAI growth from sapling to mature tree), and regular parameter updates can ensure that the model always reflects the actual state of the tree; In response to temporary scene changes (such as crown thinning after pruning, leaf drop caused by pests and diseases), the porous media model can be quickly updated by adjusting LAD or drag coefficient, avoiding the use of static models to evaluate the dynamic tree wind field response (such as the wind load should be adjusted accordingly after the wind resistance of pruned trees is reduced, otherwise the risk will be overestimated).

[0135] This scheme establishes a complete correlation chain between plant biophysical parameters, porous media drag coefficient, wind field distribution, and wind load response. The accumulated parameter data (such as the correspondence between drag force coefficients of different tree species and LAI) can be used for:

[0136] Optimize the plant model parameter library to improve the parameter matching accuracy for different tree species and different growth stages;

[0137] Verify and improve the transformation logic of porous media models (such as the quantitative relationship between viscous drag coefficient and LAD), promote the development of wind field simulation from semi-empirical simulation to mechanism-driven simulation, and provide methodological reference for a wider range of ecological wind environment research (such as the ventilation effect of urban green space and the windbreak and sand fixation function of forest).

[0138] Due to the improved accuracy of wind field simulations (based on real plant parameters), subsequent calculations of wind load response indices (such as the pressure on trees and wind speed gradients) are closer to reality, and thus:

[0139] This makes the determination of dynamic risk levels more accurate (avoiding misjudgment of risk due to deviations in wind load calculations).

[0140] Ensuring the targeted nature of tiered protection strategies (such as the intensity of emergency pruning for high LAI tree species and reinforcement methods for tree species with specific drag coefficients, which can be formulated based on more realistic wind load data) ultimately improves the scientific rigor and effectiveness of wind disaster prevention and control for trees. Combining the above parameters enables more precise pruning adjustments.

[0141] More preferably, such as Figure 2 As shown, the evaluation method further includes:

[0142] S100a: Define a three-dimensional mesh model of the tree surface, where each mesh cell is defined as a canopy mesh cell;

[0143] S100b: Acquires wind field data generated by computational fluid dynamics simulations, where the wind field data is distributed on spatial grid points;

[0144] S100c: Map the wind field data onto the canopy grid cells;

[0145] S100d: Based on the mapped data, calculate the influence coefficient for each canopy grid cell. The influence coefficient is determined based on the wind pressure value at the grid cell. The calculation of the influence coefficient is also based on the wind pressure gradient between the canopy grid cell and its adjacent grid cells. The calculation of the influence coefficient is also based on the angle between the wind vector direction at the canopy grid cell and its surface normal vector direction.

[0146] S100e: Identify high-risk cell clusters composed of canopy grid cells whose influence coefficients exceed a preset threshold;

[0147] S100f: Output the spatial location information of the high-risk unit cluster;

[0148] S100g: Assign a unique identifier to each identified high-risk cell cluster and rank them by risk based on the average impact coefficient of its internal grid cells.

[0149] Traditional assessments often focus on the overall risk of trees (such as the risk of the entire tree falling over), while this approach, through canopy grid division and wind field data mapping, focuses risk assessment on local micro-regions within the tree canopy.

[0150] Step S100a divides the tree surface into discrete canopy grid units (such as subdivided grids of dense branches and leaves) to enable independent analysis of different parts of the canopy (such as the top of the windward side, the middle of the leeward side, and the branch connection).

[0151] Steps S100b and S100c accurately map the wind field data (wind speed and wind pressure at spatial grid points) simulated by CFD to the canopy grid cells, solving the problem of mismatch between wind field data and tree morphology, and ensuring that the wind environment parameters (such as local wind pressure and wind speed direction) of each grid cell truly reflect its actual stress state.

[0152] This fine-grained scale analysis can capture local weak areas that are ignored by traditional methods (such as the windward side of a thick branch may break due to excessive wind pressure, or the branch fork may be at risk of tearing due to excessive wind pressure gradient), thus accurately locating the risk to the specific location.

[0153] In step S100d, the influence coefficient integrates three key factors: wind pressure value, wind pressure gradient, and the angle between the wind vector and the surface normal vector, thus overcoming the limitations of a single wind pressure index.

[0154] The wind pressure value directly reflects the static pressure borne by the grid cell (the greater the pressure, the higher the risk of local damage).

[0155] The wind pressure gradient reflects the pressure difference between adjacent grid cells (the larger the gradient, the stronger the local airflow disturbance in the canopy, which can easily cause branch tremors and fatigue damage).

[0156] The angle between the wind vector and the surface normal vector reflects the impact direction of the wind (for example, wind blowing perpendicularly to the blade surface produces a greater impact force than wind blowing at an angle; the smaller the angle, the more significant the actual force).

[0157] The combination of these three factors enables the influence coefficient to comprehensively quantify the local stress intensity, dynamic changes in stress, and the effect of stress direction, thus more scientifically assessing the actual risk of the canopy grid unit (for example, even if a grid unit has a moderate wind pressure value, its risk may be higher than that of a unit with a high wind pressure value but a small gradient and a slanted wind direction because of the large wind pressure gradient and the wind direction being perpendicular to the surface).

[0158] Steps S100e and S100f identify high-risk clusters of trees with influence coefficients exceeding a threshold and output their spatial locations, thus addressing the practical pain point of knowing that trees are at risk but not knowing exactly where to protect against them.

[0159] High-risk unit clusters (such as multiple consecutive grid units reaching high influence coefficients) often correspond to weak areas of the canopy (such as concentrated areas of diseased and weak branches, and windward protrusions on the outer edge of the canopy). Their spatial location information (such as the dense leaf area on the northwest side of the top of the canopy of the eastern branch cluster 5 meters above the ground) can directly guide maintenance operations.

[0160] Compared to whole-tree reinforcement or indiscriminate pruning, protection based on high-risk unit clusters (such as local support for specific branches and directional thinning of high-risk leaf clusters) can minimize interference with tree growth, while precisely reducing local wind loads and improving protection efficiency.

[0161] Step S100g uses unique identifiers and average impact coefficients to sort high-risk areas, making the management of these areas more orderly and traceable.

[0162] Risk prioritization can clearly define the urgency of different high-risk unit clusters (e.g., the cluster with the highest average impact coefficient should be dealt with first), and when resources are limited (e.g., emergency reinforcement before a typhoon), it can ensure that the most dangerous local areas are dealt with first, reducing the probability of small risks turning into major accidents.

[0163] Unique identifiers can bind high-risk clusters to individual trees and specific locations, forming digital archives of risk areas. This facilitates subsequent tracking of changes (such as whether the risk has decreased after pruning or whether new high-risk clusters have emerged during the growth process), providing historical data support for long-term maintenance.

[0164] More preferably, such as Figure 3 As shown, determining the risk assessment result for the corresponding tree based on the wind load response index includes:

[0165] S1041: Obtain the wind pressure value on each grid cell of the tree surface.

[0166] S1042: For each surface grid cell, calculate the wind force acting on the corresponding grid cell according to the wind force calculation formula, which is: ,in, The wind force acting on the corresponding grid cell. The wind pressure value on the grid cell. The surface area of ​​the corresponding grid cell;

[0167] S1043: Define a three-dimensional spatial coordinate system. For each surface mesh element, determine the spatial coordinates of its center point. Calculate the lever arm of each wind force acting on the tree trunk root, where the lever arm size is: ,in, As the lever arm, and Let x and y be the x and y coordinates of the i-th grid cell;

[0168] S1044: Determine each force according to the bending moment calculation formula. The bending moment generated at the base of the tree trunk; the formula for calculating the bending moment is: ,in, For vector cross product, For strength The bending moment generated at the base of the tree trunk;

[0169] S1045: Yes Perform vector decomposition to decompose it into bending moment components rotating about the X-axis and bending moment components rotating about the Y-axis;

[0170] S1046: Summate the bending moment components about the X-axis and the bending moment components about the Y-axis of all mesh elements to obtain the X and Y components of the total bending moment at the root.

[0171] S1047: Calculate the total combined bending moment at the base of the tree trunk, and determine the corresponding dynamic risk level and lodging state based on the total combined bending moment, the section modulus of the tree cross section, and the bending strength of the wood.

[0172] Traditional risk assessments often rely on empirical judgments (such as subjective ratings based on tree condition), while the solution in this invention establishes a mechanical quantitative model of microscopic wind pressure, local wind force, root bending moment, and overall risk through steps S1041 and S1047.

[0173] Local wind force calculation directly correlates the wind pressure of each grid cell with the surface area to obtain the unit-level wind force, avoiding errors caused by estimating the average wind pressure of the whole tree (such as the wind pressure difference in different parts of the canopy being masked).

[0174] The lever arm and bending moment calculation uses the coordinates of the center point of the grid cell to calculate the lever arm and uses the vector cross product to solve for the bending moment at the root, accurately depicting the influence of the height and horizontal distance of the wind force's point of action on the torque at the root (for example, the bending moment generated by the wind force at the top of the tree crown is much greater than that at the bottom of the trunk due to the longer lever arm).

[0175] The total composite bending moment is obtained by decomposing the bending moment components around the X and Y axes and summing them, thus obtaining the total bending moment borne by the roots, which fully reflects the overall stress state of the tree under the three-dimensional wind field.

[0176] This kind of mechanical transfer calculation from the microscopic to the macroscopic level upgrades risk assessment from qualitative description to quantitative value, providing a solid mechanical basis for subsequent risk level classification.

[0177] Step S1047 correlates the total combined bending moment with the cross-sectional modulus of the tree and the wood's bending strength, overcoming the one-sidedness of relying solely on external wind loads and achieving a balanced assessment of external loads and internal resistance.

[0178] The cross-sectional modulus reflects the tree trunk's ability to resist bending deformation (e.g., a thicker trunk has a higher modulus and stronger bending resistance); the bending strength of wood is the mechanical limit of the wood material itself (e.g., healthy wood has higher strength than rotten wood); by comparing the total combined bending moment with the critical bending moment based on the cross-sectional modulus and bending strength, it can be directly determined whether the tree faces the risk of bending fracture or root overturning (if the total bending moment exceeds the critical value, the risk level is significantly increased). The critical bending moment can be estimated using the material mechanics formula: M = σ * S, where σ is the bending strength of the wood, which can be obtained from a table, and S is the cross-sectional modulus of the trunk.

[0179] This assessment method fully considers individual differences in trees (such as tree diameter, health status, and timber characteristics), avoiding the irrationality of uniformly rating different trees under the same wind conditions (for example, under the same total bending moment, young trees may be at higher risk due to their smaller section modulus).

[0180] By calculating and decomposing the total combined bending moment, the potential direction of tree collapse and the weak points under stress can be further analyzed:

[0181] The magnitudes of the X and Y components of the total bending moment can reflect the differences in the forces acting on the tree in different horizontal directions (e.g., if the X component is much larger than the Y component, it indicates that the tree is more likely to tilt in the X-axis direction).

[0182] By combining the characteristics of tree root distribution (such as shallow-rooted trees being more prone to overall collapse under horizontal bending moment, while deep-rooted trees may first experience trunk bending and breakage), the specific collapse pattern (overall collapse or trunk breakage) can be predicted.

[0183] This forward-looking prediction can provide precise guidance for protective measures (such as adding diagonal supports on the side with high bending moment components in the X-axis direction; and locally reinforcing tree trunk sections with insufficient cross-sectional modulus), minimizing the probability of collapse.

[0184] The wind pressure, wind force, bending moment, and risk calculation system established by the embodiments of this invention can output clear quantitative indicators (such as total combined bending moment value and critical safety threshold), providing a standardized basis for the following scenarios: Pruning scheme optimization: By simulating the change in wind pressure distribution of the canopy grid after pruning, the reduction of the total bending moment by pruning is calculated (such as thinning the windward side of the canopy can reduce the total bending moment by 30%), and the pruning effect is quantified to optimize the scheme; Reinforcement engineering design: The reinforcement strength (such as the additional resistance that needs to be borne) is determined according to the magnitude of the total bending moment to avoid insufficient or excessive reinforcement.

[0185] The entire calculation process is based on explicit mechanical formulas (wind force, lever arm, bending moment calculation) and measurable parameters (wind pressure, grid area, coordinates, section modulus, wood strength), avoiding the drawbacks of relying on subjective experience in traditional assessments: different assessors can obtain consistent total bending moment and risk level using the same data, improving the reliability of assessment results; the risk assessment can be dynamically updated by adjusting parameters (such as updating the tree section modulus to reflect growth or decay), ensuring that the results always match the actual condition of the trees.

[0186] The solution in this invention, through multiple iterations of simulation, pruning, and re-simulation, finds an optimal pruning scheme that can significantly reduce the overall wind pressure and overturning moment of the tree canopy under the target wind direction, while maximizing the preservation of the tree's landscape value and photosynthetic area.

[0187] The final output of the embodiments of this invention is not a qualitative suggestion, but a quantitative guidance. For example, it suggests prioritizing the pruning of 15% to 20% of the canopy on the southeast side, specifically targeting branch groups numbered Branch 01 to Branch 05. A protection strategy database is established. When the system identifies a tree as high-risk, it can automatically recommend the optimal combination of protection schemes. For example, if the system detects that the wind pressure on the canopy of ancient tree A exceeds the limit under southeast winds, the recommended scheme is: precise thinning of branches on the southeast side of the canopy (suggesting the removal of 30% of the leaf area) and installation of a flexible cable system running southeast and northwest.

[0188] When setting up the support structure, the support rod itself is not rigid, but includes an adjustable damping hydraulic or pneumatic buffer mechanism. This mechanism allows the branches to sway normally in a light breeze (which is beneficial to tree growth), but provides sufficient damping force to dissipate wind energy and prevent the branches from breaking due to sudden overload when strong winds arrive.

[0189] More preferably, the evaluation method further includes:

[0190] The dynamic risk level and main risk types are determined based on the simulated wind pressure on the canopy surface, bending moment at the trunk roots, and tree status information; the tree status information includes void ratio, tilt, and root system status.

[0191] Based on the main risk type, stress parameters, influence coefficients of each grid cell, and the three-dimensional tree model, corresponding pruning parameters are generated; the pruning parameters include specifying the branch to be pruned and / or the percentage of leaf area to be reduced;

[0192] The tree model is trimmed according to the trimming parameters, and fluid simulation is performed again based on the updated parameters to compare the trimming status.

[0193] The solution in this invention determines the dynamic risk level and main risk types by using wind pressure on the tree canopy surface, bending moment at the tree trunk roots, and tree condition information (void ratio, tilt, root system status). This breaks through the single assessment logic that relies solely on wind load and achieves coupled risk identification of external forces and internal defects.

[0194] For example: a tree may not have reached the critical value of root bending moment, but it has a high trunk cavity rate (internal structure is damaged), and its main risk type is trunk breakage; another tree has a poor root system (shallow roots, rot), and even if the wind pressure is moderate, its main risk type is overall toppling.

[0195] Identifying the main risk types allows for direct identification of the core objectives of pruning (e.g., to reduce the windward area of ​​the canopy to lower the bending moment for the risk of trunk breakage, and to prioritize pruning high branches to lower the center of gravity for the risk of lodging), thus avoiding misalignment between pruning measures and the root causes of risks.

[0196] Based on the main risk types, stress parameters, grid cell influence coefficients, and tree 3D models, specific pruning parameters (such as the identification of branches to be pruned and the percentage of leaf area to be reduced) are generated, solving the problem of the extensive nature of traditional pruning that relies on experience-based estimation.

[0197] Pressure parameters and influence coefficients guide: branches corresponding to grid units with high wind pressure and high influence coefficients (such as protruding branches on the windward side and high-risk unit clusters at the outer edge of the canopy) are accurately marked as pruning targets to ensure that pruning directly affects the parts that contribute the most to the wind load;

[0198] Quantitative indicators support the calculation of the percentage of leaf area to be reduced based on the wind load reduction requirements (e.g., simulations show that a 20% reduction in root bending moment is required, which corresponds to a 15% reduction in leaf area). This helps to avoid over-pruning affecting tree growth or under-pruning failing to reduce risks.

[0199] 3D model association: Pruning parameters are bound to the 3D model of the tree (such as pruning first-level branches numbered 3 and 5), providing visual guidance for on-site operations and reducing human identification errors.

[0200] By implementing a closed-loop process of pruning tree models, re-simulating fluidity, and comparing the states before and after pruning, the scientific validity and dynamic optimization of the pruning plan were achieved. The re-simulation can directly output the wind pressure distribution, root bending moment changes (such as a 30% reduction in total bending moment after pruning), and risk level reduction after pruning, clarifying the actual effect of the pruning measures and avoiding blind operation that is ineffective.

[0201] If the risk still does not meet the standard after the first trimming (e.g., the total bending moment is still higher than the safety threshold), the trimming parameters can be adjusted based on the new simulation results (e.g., increasing the number of pruned branches or the percentage of leaf area) until the risk is reduced to an acceptable range. Multiple combinations of trimming parameters can be generated (e.g., different pruning locations, different leaf area reduction ratios). By comparing the wind load reduction effect and tree growth impact of each scheme through multiple simulations (e.g., excessive pruning may lead to insufficient photosynthesis), the scheme with the optimal balance between risk reduction and ecological protection can be selected.

[0202] The solution of this invention, through precise pruning parameters and effect verification, can minimize the pruning range (e.g., only targeting high-risk branches and retaining the canopy in low-risk areas), and reduce the impact on tree growth, landscape value, and ecological functions (such as shading and carbon sequestration).

[0203] Compared to large-scale pruning or indiscriminate reinforcement, targeted pruning of high-risk areas can reduce manpower and time investment, while simulation verification can avoid repetitive operations and improve maintenance efficiency. Targeted pruning can reduce the continuous damage of wind loads to weak parts of trees (such as hollow trunks and shallow root systems), reduce the risk of short-term lodging or breakage, and extend the healthy growth life of trees.

[0204] And / or, the execution of fluid dynamics simulation to simulate the wind field distribution as wind flows through the three-dimensional data model includes:

[0205] Obtain wind environment simulation parameters, which include wind speed information and wind direction information;

[0206] The simulation area for wind field calculation is determined based on a three-dimensional data model of the target environment to be evaluated; the three-dimensional data model includes a tree model of at least one tree in the target environment to be evaluated.

[0207] The simulated region is divided into discrete grid cells to form a computational grid;

[0208] Based on the measured data of meteorological stations in the target environment, the inlet boundary conditions for fluid dynamics simulation are configured, and the inlet boundary conditions are set as gradient wind.

[0209] A wind field simulation is performed within the fluid dynamics simulation; a turbulence model is used in the fluid dynamics simulation, and iterative calculations continue until the solution residuals are reduced to a set requirement. This set requirement is specifically 10. -4 , magnitude.

[0210] The solution of this invention is based on a three-dimensional data model (including tree model) of the target environment to be evaluated to delineate the simulation area, which avoids the waste of computing resources due to the area being too large or the neglect of the influence of the surrounding environment on the wind field (such as the bypass effect of surrounding buildings and terrain on wind flow) due to the area being too small. It ensures that the simulation area can cover the core influence range of the tree canopy wind field and reasonably control the scale of calculation.

[0211] The simulation area is divided into discrete grid cells, and the grid density can be dynamically adjusted according to the complexity of key areas such as tree canopy (e.g., dense grid is used around trees, and sparse grid is used in areas far away). While ensuring the accuracy of wind field calculation (e.g., turbulence, wind speed gradient) near the canopy, the overall computational efficiency is balanced, and resource redundancy or insufficient local accuracy caused by uniform grid across the entire area is avoided.

[0212] Wind speed varies with altitude in a gradient distribution (for example, wind speed near the ground is less affected by surface friction, while wind speed at high altitudes is higher). Setting the inlet boundary condition as gradient wind breaks through the simplistic assumption of uniform wind speed inlet and is more in line with the wind field characteristics of the actual atmospheric boundary layer.

[0213] By configuring boundary conditions based on measured data within the target environment (such as wind speed profiles at different altitudes and prevailing wind directions), the initial wind field parameters (the variation of wind speed and direction with altitude) in the simulation are highly consistent with the real environment. This avoids overall wind field deviations caused by empirical boundary conditions (such as overestimating or underestimating the actual wind intensity of the tree canopy), and provides reliable basic wind field data for subsequent tree wind load calculations.

[0214] Because the wind field in the tree canopy exhibits strong turbulent effects (such as airflow around branches and leaves, and vortices), a turbulence model can accurately capture these complex flow characteristics (such as the distribution of Reynolds stress and turbulent kinetic energy), avoiding the neglect of turbulence effects by laminar flow models. This allows for a more realistic simulation of the interaction between wind and the tree canopy (such as local wind pressure fluctuations caused by turbulence). The scheme in this embodiment of the invention has strict convergence criteria (residual ≤ 10). -4 Iterative calculations continue until the residual decreases to 10. -4 The magnitude is crucial to ensure that the numerical solution of the wind field simulation reaches a stable convergence state—excessively high residuals can lead to fluctuations in the calculation results (such as repeated changes in wind pressure values ​​at the same location), while 10 -4 The magnitude of the data ensures that the numerical error is within an acceptable range, making the simulated wind speed and wind pressure distribution repeatable and reliable, and providing stable data support for subsequent risk assessment.

[0215] The solution in this embodiment of the invention can also obtain a visualized cloud map, specifically as follows: Figure 4 and Figure 5 As shown, the visualized cloud map includes wind speed cloud map, wind pressure cloud map, and streamline map.

[0216] The wind field assessment method in this embodiment of the invention calculates at least one wind load response index related to the corresponding tree, such as wind speed, pressure, and wind load, based on the results of wind field simulation. These quantitative indicators can intuitively reflect the wind force acting on the tree in the wind field, which helps to understand the mechanical response of the tree in different wind environments and provides specific numerical basis for assessing the wind resistance performance of the tree.

[0217] Example 2

[0218] Please see Figure 6 , Figure 6 This is a schematic diagram of the tree canopy wind field assessment system based on wind environment simulation disclosed in an embodiment of the present invention. Figure 6 As shown, the tree canopy wind field assessment system based on wind environment simulation may include:

[0219] Acquisition module 21: used to acquire a three-dimensional data model of the target environment to be evaluated, wherein the three-dimensional data model includes a tree model of at least one tree in the target environment to be evaluated;

[0220] Simulation module 22: used to perform fluid dynamics simulation to simulate the wind field distribution when wind flows through the three-dimensional data model, wherein the tree model of the at least one tree is configured as a porous medium model in the wind field simulation.

[0221] Calculation module 23: used to obtain the results based on wind field simulation and calculate at least one wind load response index related to the corresponding tree.

[0222] Assessment module 24: used to determine the risk assessment results for the corresponding trees based on the wind load response index.

[0223] The wind field assessment method in this embodiment of the invention calculates at least one wind load response index related to the corresponding tree, such as wind speed, pressure, and wind load, based on the results of wind field simulation. These quantitative indicators can intuitively reflect the wind force acting on the tree in the wind field, which helps to understand the mechanical response of the tree in different wind environments and provides specific numerical basis for assessing the wind resistance performance of the tree.

[0224] Example 3

[0225] Please see Figure 7 , Figure 7 This is a schematic diagram of the structure of an electronic device disclosed in an embodiment of the present invention. The electronic device can be a computer, a server, etc. Of course, in certain cases, it can also be a mobile phone, tablet computer, monitoring terminal, or other smart device, as well as an image acquisition device with processing capabilities. Figure 7 As shown, the electronic device may include:

[0226] Memory 510 storing executable program code;

[0227] Processor 520 coupled to memory 510;

[0228] The processor 520 calls the executable program code stored in the memory 510 to execute some or all of the steps in the tree canopy wind field assessment method based on wind environment simulation in Embodiment 1.

[0229] This invention discloses a computer-readable storage medium storing a computer program that causes a computer to perform some or all of the steps in the tree canopy wind field assessment method based on wind environment simulation in Embodiment 1.

[0230] This invention also discloses a computer program product, wherein when the computer program product is run on a computer, the computer performs some or all of the steps in the tree canopy wind field assessment method based on wind environment simulation in Embodiment 1.

[0231] This invention also discloses an application publishing platform, which is used to publish computer program products. When the computer program products are run on a computer, the computer performs some or all of the steps in the tree canopy wind field assessment method based on wind environment simulation in Embodiment 1.

[0232] In various embodiments of the present invention, it should be understood that the sequence number of each process does not necessarily imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present invention.

[0233] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; they can be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0234] Furthermore, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0235] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-accessible memory. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a memory and includes several requests to cause a computer device (which can be a personal computer, server, or network device, specifically a processor in the computer device) to execute some or all of the steps of the methods described in the various embodiments of the present invention.

[0236] In the embodiments provided by this invention, it should be understood that B corresponding to A means that B is associated with A, and B can be determined based on A. However, it should also be understood that determining B based on A does not mean determining B solely based on A; B can also be determined based on A and / or other information.

[0237] Those skilled in the art will understand that some or all of the steps in the various methods of the embodiments described can be implemented by a program instructing related hardware. This program can be stored in a computer-readable storage medium, including read-only memory (ROM), random access memory (RAM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), one-time programmable read-only memory (OTPROM), electronically erasable programmable read-only memory (EEPROM), compact disc read-only memory (CD, ROM) or other optical disc storage, disk storage, magnetic tape storage, or any other computer-readable medium capable of carrying or storing data.

[0238] The foregoing has provided a detailed description of the tree canopy wind field assessment method, system, electronic device, and storage medium based on wind environment simulation disclosed in the embodiments of the present invention. Specific examples have been used to illustrate the principles and implementation methods of the present invention. The descriptions of the above embodiments are only for the purpose of helping to understand the method and core ideas of the present invention. At the same time, for those skilled in the art, there will be changes in the specific implementation methods and application scope based on the ideas of the present invention. Therefore, the content of this specification should not be construed as a limitation of the present invention.

Claims

1. A method for assessing the wind field of tree canopy based on wind environment simulation, characterized in that, include: Obtain a three-dimensional data model of the target environment to be evaluated, wherein the three-dimensional data model includes a tree model of at least one tree in the target environment to be evaluated; Performing fluid dynamics simulation to simulate the wind field distribution when wind flows through the three-dimensional data model, wherein the tree model of at least one tree is configured as a porous medium model in the wind field simulation; the performing fluid dynamics simulation to simulate the wind field distribution when wind flows through the three-dimensional data model includes: Multiple wind speed levels and multiple wind directions are determined, and computational fluid dynamics simulations are performed respectively to simulate the wind field distribution when the wind flows through the three-dimensional data model under different wind conditions; Obtain the results based on wind field simulation and calculate at least one wind load response index related to the corresponding trees. The risk assessment results for the corresponding trees are determined based on the wind load response index; the determination of the risk assessment results for the corresponding trees based on the wind load response index includes: Based on the calculated wind load response index, a dynamic risk level is established for each tree for different wind speed levels and wind directions. The dynamic risk level is associated with a preset wind force threshold and the corresponding tree status. A graded protection strategy matching wind speed and wind direction is generated based on the status of each tree and the dynamic risk level associated with each tree. The evaluation method further includes: A three-dimensional mesh model of the tree surface is determined, where each mesh cell is defined as a canopy mesh cell; Acquire wind field data generated by computational fluid dynamics simulations, where the wind field data is distributed across spatial grid points; The wind field data is mapped onto the canopy grid cells; Based on the mapped data, an influence coefficient is calculated for each canopy grid cell. The influence coefficient is determined based on the wind pressure value at the grid cell. The calculation of the influence coefficient is also based on the wind pressure gradient between the canopy grid cell and its adjacent grid cells, and the calculation of the influence coefficient is also based on the angle between the wind vector direction at the canopy grid cell and its surface normal vector direction. Identify high-risk cell clusters composed of canopy grid cells whose influence coefficients exceed a preset threshold; Output the spatial location information of the high-risk unit cluster; Each identified high-risk cell cluster is assigned a unique identifier and ranked for risk based on the average impact coefficient of its internal grid cells.

2. The tree canopy wind field assessment method based on wind environment simulation as described in claim 1, characterized in that, The establishment of dynamic risk levels also includes: Identify the formation of localized high-wind-speed areas and regional extreme wind areas; Increase the risk level of trees located in the aforementioned high-wind-speed areas; Obtain tree status information associated with the regional extreme wind zone, determine the corresponding risk level based on the regional extreme wind zone and tree status information, and raise the risk level of trees located in the regional extreme wind zone that belong to shallow root systems or weak growth species to the highest risk level.

3. The tree canopy wind field assessment method based on wind environment simulation as described in claim 1, characterized in that, Generate a tiered protection strategy, including: For the risks at the first wind speed level, a first-class strategy is generated, which mainly involves monitoring and routine pruning. For the risks under the second wind speed level, a second type of strategy is generated, which mainly focuses on targeted reinforcement and emergency pruning; For risks at the third wind speed level, a third type of strategy is generated, which mainly involves delineating danger zones, implementing personnel evacuation, and initiating temporary reinforcement projects.

4. The tree canopy wind field assessment method based on wind environment simulation as described in claim 1, characterized in that, The evaluation method further includes: Obtain plant model parameters, including model type, leaf area density, leaf area index, and drag force coefficient; The corresponding leaf area index and drag coefficient are determined based on the model type. The viscous drag coefficient and inertial drag coefficient of the porous media model are determined based on the leaf area density and drag force coefficient of the corresponding tree. The calculated viscous drag coefficient and inertial drag coefficient are then used to update the porous media model of the target plant in the fluid dynamics simulation.

5. The tree canopy wind field assessment method based on wind environment simulation as described in claim 1, characterized in that, The determination of the risk assessment results for the corresponding trees based on the wind load response index includes: Obtain the wind pressure value on each grid cell of the tree surface; For each surface grid cell, the wind force acting on that grid cell is calculated according to the wind force calculation formula, which is: ,in, The wind force acting on the corresponding grid cell. The wind pressure value on the grid cell. The surface area of ​​the corresponding grid cell; Define a three-dimensional spatial coordinate system. For each surface mesh element, determine the spatial coordinates of its center point. Calculate the lever arm of each wind force acting on the tree trunk root, where the lever arm size is: ,in, As the lever arm, and Let x and y be the x and y coordinates of the i-th grid cell; Each force is determined based on the bending moment calculation formula. The bending moment generated at the base of the tree trunk; the formula for calculating the bending moment is: ,in, For vector cross product, For strength The bending moment generated at the base of the tree trunk; right Perform vector decomposition to decompose it into bending moment components rotating about the X-axis and bending moment components rotating about the Y-axis; The bending moment components about the X-axis and about the Y-axis of all mesh elements are summed to obtain the X and Y components of the total bending moment at the root. Calculate the total combined bending moment at the base of the tree trunk, and determine the corresponding dynamic risk level and lodging state based on the total combined bending moment, the section modulus of the tree cross section, and the bending strength of the wood.

6. The tree canopy wind field assessment method based on wind environment simulation as described in claim 5, characterized in that, The evaluation method further includes: The dynamic risk level and main risk types are determined based on the simulated wind pressure on the canopy surface, bending moment at the trunk roots, and tree status information; the tree status information includes void ratio, tilt, and root system status. Based on the main risk type, stress parameters, influence coefficients of each grid cell, and the three-dimensional tree model, corresponding pruning parameters are generated; the pruning parameters include specifying the branch to be pruned and / or the percentage of leaf area to be reduced. The tree model is trimmed according to the trimming parameters, and fluid simulation is performed again based on the updated parameters to compare the trimming status. And / or, the execution of fluid dynamics simulation to simulate the wind field distribution as wind flows through the three-dimensional data model includes: Obtain wind environment simulation parameters, which include wind speed information and wind direction information; The simulation area for wind field calculation is determined based on a three-dimensional data model of the target environment to be evaluated; the three-dimensional data model includes a tree model of at least one tree in the target environment to be evaluated. The simulated region is divided into discrete grid cells to form a computational grid; Based on the measured data from meteorological stations within the target environment, the inlet boundary conditions for fluid dynamics simulation are configured, and the inlet boundary conditions are set as gradient wind. The wind field simulation is performed in the fluid dynamics simulation; the turbulence model is used in the fluid dynamics simulation, and iterative calculations are performed until the solution residuals are reduced to the set requirements.

7. A tree canopy wind field assessment system based on wind environment simulation, characterized in that, include: Acquisition module: used to acquire a three-dimensional data model of the target environment to be evaluated, wherein the three-dimensional data model includes a tree model of at least one tree in the target environment to be evaluated; Simulation module: used to perform fluid dynamics simulation to simulate the wind field distribution when wind flows through the three-dimensional data model, wherein the tree model of at least one tree is configured as a porous medium model in the wind field simulation; the execution of fluid dynamics simulation to simulate the wind field distribution when wind flows through the three-dimensional data model includes: Multiple wind speed levels and multiple wind directions are determined, and computational fluid dynamics simulations are performed respectively to simulate the wind field distribution when the wind flows through the three-dimensional data model under different wind conditions; Calculation module: used to obtain the results based on wind field simulation and calculate at least one wind load response index related to the corresponding tree. Assessment module: used to determine the risk assessment result for the corresponding tree based on the wind load response index; the determination of the risk assessment result for the corresponding tree based on the wind load response index includes: Based on the calculated wind load response index, a dynamic risk level is established for each tree for different wind speed levels and wind directions. The dynamic risk level is associated with a preset wind force threshold and the corresponding tree status. A graded protection strategy matching wind speed and wind direction is generated based on the status of each tree and the dynamic risk level associated with each tree. The evaluation system also includes: A three-dimensional mesh model of the tree surface is determined, where each mesh cell is defined as a canopy mesh cell; Acquire wind field data generated by computational fluid dynamics simulations, where the wind field data is distributed across spatial grid points; The wind field data is mapped onto the canopy grid cells; Based on the mapped data, an influence coefficient is calculated for each canopy grid cell. The influence coefficient is determined based on the wind pressure value at the grid cell. The calculation of the influence coefficient is also based on the wind pressure gradient between the canopy grid cell and its adjacent grid cells, and the calculation of the influence coefficient is also based on the angle between the wind vector direction at the canopy grid cell and its surface normal vector direction. Identify high-risk cell clusters composed of canopy grid cells whose influence coefficients exceed a preset threshold; Output the spatial location information of the high-risk unit cluster; Each identified high-risk cell cluster is assigned a unique identifier and ranked for risk based on the average impact coefficient of its internal grid cells.

8. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program, wherein the computer program causes a computer to perform the tree canopy wind field assessment method based on wind environment simulation as described in any one of claims 1 to 6.

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