Farmland protection forest system space configuration method based on Great Wall grain structure

Abstract

The invention discloses a farmland protection forest system space configuration method based on a Great Wall grain structure, and belongs to the technical field of agricultural ecological engineering. The method comprises the following steps: designing a unitized Great Wall grain-shaped grid as a forest network basic form; acquiring farmland and disaster characteristics of a target area, and determining the number of forest networks and great wall grain structure parameters according to a protection target; the Great Wall grain-shaped grids are arranged according to rules to form a forest network, and a final farmland protection forest system space configuration scheme is formed after optimization and adjustment. The system comprises a Great Wall pattern grid form design system, a forest network configuration and optimization module and a dynamic monitoring regulation and control system. By means of the innovative Great Wall grain-shaped grid structure, it is guaranteed that the farmland protection forest exerts protection benefits, meanwhile, the farmland protection forest is matched with a mechanical production means, the occupied area of the forest belt can be reduced, the land utilization efficiency is improved, and the ecological benefits, the production benefits and the regional coordination are achieved.

Description

A Spatial Configuration Method for Farmland Shelterbelt Systems Based on Great Wall Pattern Structure Technical Field

[0001] This invention belongs to the field of farmland ecological protection and agricultural spatial planning technology, and in particular, a method for spatial configuration of farmland shelterbelt system based on Great Wall pattern structure. Background Technology

[0002] Farmland shelterbelts are a key bioengineering measure for ensuring agricultural ecological security, mitigating wind and sand disasters, and improving field microclimates. The core of their protective effectiveness lies in the scientific spatial configuration of the shelterbelt network system. Traditional farmland shelterbelt configurations, especially the widely used "square" grid layout, while simple and intuitive in design, have increasingly revealed a series of technical bottlenecks that are incompatible with the needs of modern agricultural development.

[0003] First, traditional square-shaped forest networks occupy a large area and have a significant land-constraining effect. Given the increasing scarcity of land resources, farmers have low acceptance of them, leading to a decline in their willingness to build and maintain them, and resulting in fragmented and incomplete existing forest networks. Second, their regular grid pattern is poorly compatible with the operational needs of modern mechanized agriculture, especially large tractor units and agricultural drones. Conflicts exist between agricultural machinery access routes and forest belt space, resulting in frequent turning and U-turns within fields and making continuous operation across multiple fields difficult. This affects the efficiency of large-scale, contiguous production, creating a spatial contradiction between "ecological protection" and "agricultural production."

[0004] While existing technologies have made some attempts to improve this situation, they mostly focus on local optimization and fail to systematically resolve the fundamental contradiction. For example, some have proposed reducing the area of ​​land threatened by tree species replacement, but have not changed the basic spatial form of the forest network; the construction of multi-layered protection systems focuses on the macro-structure, lacking sufficient refinement and adaptability design for the internal farmland grids; and some have provided construction techniques for specific tree species, but have not addressed innovation in the grid form itself. None of these methods have fundamentally resolved the conflict between forest network land occupation and agricultural machinery operations from a spatial structural perspective.

[0005] Furthermore, existing methods for configuring shelterbelts are mostly static, one-off designs, generally lacking long-term dynamic monitoring and adaptive maintenance mechanisms based on actual environmental changes and the decline in protective effectiveness after the shelterbelts are built. Once the design is completed, the forest network structure is fixed and cannot cope with fluctuations in effectiveness caused by climate change, forest decline, or local structural breakage of the forest network, making it difficult to fully, continuously, and stably exert a protective effect.

[0006] Therefore, a novel spatial configuration system for farmland shelterbelts is urgently needed. This involves innovating the spatial unit morphology of the shelterbelt network, designing a new grid structure that occupies less land and is highly compatible with agricultural machinery operations, and constructing a standardized, end-to-end methodology from data collection and design layout to adjustment and optimization. Simultaneously, a long-term dynamic monitoring and intelligent maintenance system should be introduced to ensure the continuous and stable effectiveness of the shelterbelt. This system overcomes the shortcomings of traditional farmland shelterbelt construction techniques, achieving coordination and maximizing benefits between "ecological protection" and "agricultural production." Summary of the Invention

[0007] To address the aforementioned technical problems, this application provides a spatial configuration method for farmland shelterbelt systems based on Great Wall pattern structures.

[0008] The specific details of the invention are as follows:

[0009] A spatial configuration method for farmland shelterbelt systems based on Great Wall pattern structures includes the following steps:

[0010] (1) Design the Great Wall-patterned mesh shape to suit the target area and determine its structural parameters;

[0011] (2) Obtain baseline farmland data and disaster characteristics of the target area;

[0012] (3) Based on the disaster characteristics, plan the basic forest belt and match the Great Wall pattern grid type and parameters;

[0013] (4) Construct and optimize the forest network system within the framework of the main forest belt to form the final configuration scheme.

[0014] Preferably, in step (1), the Great Wall pattern grid morphology includes a common type suitable for general wind damage areas and an enhanced type suitable for strong wind damage areas; the structural parameters include the minimum length of the forest network unit (a), the length of the retained secondary forest belt (b), the width of the field passage (c), and the additional secondary forest belt length (d) unique to the enhanced grid.

[0015] Preferably, in step (2), the farmland baseline data includes at least the farmland spatial distribution and field boundary information obtained through remote sensing image interpretation; the disaster characteristics include at least the main wind direction and wind damage intensity information obtained through meteorological data analysis.

[0016] Preferably, step (2) further includes converting the preset protection target into a quantitative target range of forest network landscape connectivity index, forest network rate and forest network aggregation degree through a preset association model.

[0017] Preferably, step (3) specifically includes:

[0018] (3.1) Determine the dominant orientation of the basal forest belt based on the aforementioned main wind direction;

[0019] (3.2) Based on the field boundaries, determine the specific spatial location of the basal forest belt;

[0020] (3.3) Based on the wind damage intensity, select the appropriate grid type from the ordinary type and the enhanced type, and determine the specific values ​​of parameters a, b, c and d according to the agricultural machinery operation requirements and protection objectives.

[0021] Preferably, step (4) specifically includes:

[0022] (4.1) Using the designated basal forest belt as the framework, the forest network is arranged within the farmland area according to the selected grid type and parameters to generate a preliminary forest network system;

[0023] (4.2) Calculate the forest network landscape connectivity index, forest network rate, and forest network aggregation degree of the preliminary forest network system;

[0024] (4.3) Compare the calculation results with the quantization target range, and iteratively optimize by fine-tuning the grid parameters or the arrangement density until the requirements are met, and output the final configuration scheme.

[0025] Furthermore, in step (4.3), a computational fluid dynamics model is used to simulate and verify the windbreak effectiveness of the forest network during the optimization process.

[0026] Preferably, the method further includes the steps of monitoring the effectiveness and adaptively maintaining the established forest network system. Monitoring nodes are deployed within the forest network system to continuously collect environmental and tree growth data; the actual protective effectiveness of the system is periodically assessed based on the data; and when the assessment results continuously deviate from expectations, a maintenance prompt containing the specific location and adjustment type is generated.

[0027] Preferably, the maintenance prompts are generated by an auxiliary decision-making system; the auxiliary decision-making system diagnoses the causes of performance deviation based on historical optimization cases and a knowledge base of forest growth response, and outputs recommended adjustment strategies, including local grid type replacement, structural parameter adjustment, or replanting and replacement of trees.

[0028] The benefits of this invention are as follows: by introducing a "Great Wall pattern" structure that conforms to the principles of fluid mechanics to replace the traditional square green net, a fundamental innovation has been achieved in spatial configuration. This not only significantly reduces the land occupied by the forest belt and provides an adaptation channel for modern agricultural machinery operations, but also constructs a complete technical system from data-driven design and simulation optimization to dynamic monitoring and maintenance. This transforms the shelterbelt from a static "one-off project" into a "full life cycle system" that can be intelligently adjusted and continuously exert its effectiveness. Thus, while ensuring the core protective benefits, it achieves a synergistic improvement in land intensification, production adaptation, and ecological value enhancement. Attached Figure Description

[0029] Figure 1. Technical Flowchart;

[0030] Figure 2. Schematic diagram of the morphology and structural parameters of the Great Wall pattern farmland shelterbelt (thick black lines represent the main shelterbelts, and thin black lines represent the secondary shelterbelts).

[0031] Figure 3. Schematic diagram of spatial configuration steps of farmland shelterbelt system based on Great Wall pattern structure;

[0032] Figure 4. Spatial configuration diagram of the Great Wall-patterned farmland shelterbelt system in the case study area. Detailed Implementation

[0033] The following embodiments illustrate the present invention in detail. All raw materials and equipment used in the present invention are commercially available products and can be directly obtained through market purchase.

[0034] The present application will be further described in detail below with reference to embodiments, comparative examples and performance test results. These embodiments should not be construed as limiting the scope of protection claimed in this application.

[0035] In the following description of the embodiments, specific details such as particular system architectures and techniques are set forth for illustrative purposes and not for limitation, in order to provide a thorough understanding of the embodiments of this application. However, those skilled in the art will understand that this application may also be implemented in other embodiments without these specific details. In other instances, detailed descriptions of well-known systems, apparatuses, circuits, and methods are omitted so as not to obscure the description of this application with unnecessary detail.

[0036] It should be understood that, when used in this application specification and the appended claims, the term "comprising" indicates the presence of the described features, integrals, steps, operations, elements and / or components, but does not exclude the presence or addition of one or more other features, integrals, steps, operations, elements, components and / or a collection thereof.

[0037] It should also be understood that the term “and / or” as used in this application specification and the appended claims means any combination of one or more of the associated listed items and all possible combinations, and includes such combinations.

[0038] As used in this application specification and the appended claims, the term "if" may be interpreted, depending on the context, as "when," "once," "in response to determination," or "in response to detection." Similarly, the phrase "if determined" or "if detected [the described condition or event]" may be interpreted, depending on the context, as meaning "once determined," "in response to determination," "once detected [the described condition or event]," or "in response to detection [the described condition or event]."

[0039] Furthermore, in the description of this application and the appended claims, the terms "first," "second," "third," etc., are used only to distinguish descriptions and should not be construed as indicating or implying relative importance.

[0040] References to "one embodiment" or "some embodiments" as described in this specification mean that one or more embodiments of this application include a specific feature, structure, or characteristic described in connection with that embodiment. Therefore, the phrases "in one embodiment," "in some embodiments," "in other embodiments," "in still other embodiments," etc., appearing in different parts of this specification do not necessarily refer to the same embodiment, but rather mean "one or more, but not all, embodiments," unless otherwise specifically emphasized. The terms "comprising," "including," "having," and variations thereof mean "including but not limited to," unless otherwise specifically emphasized.

[0041] Technical Specifications

[0042] 1. Overall technical framework and process

[0043] The core of this invention lies in constructing a complete closed-loop technology system encompassing "design-configuration-optimization-monitoring-maintenance-enhancement." The process begins with a targeted, innovative design of the Great Wall-shaped forest network structure. Based on precise analysis of multi-source data, it utilizes parametric methods and simulation to achieve scientific configuration and optimization of the forest network. After completion, long-term, adaptive maintenance is achieved through an IoT monitoring network and an intelligent decision support system. The entire technical process, as shown in Figure 1, forms a cycle of sustainable improvement.

[0044] 2. Great Wall Pattern Grid Design System

[0045] This is the structural basis and source of innovation of the present invention. As shown in Figure 2, the "Great Wall pattern" is an optimized structure based on the principles of fluid mechanics with the goal of enhancing turbulence. Its "alternating concave and convex, continuous structure" morphology enhances the dissipation of surface wind by the forest belt.

[0046] Standard grid type: Suitable for areas with low wind damage intensity and high degree of agricultural intensification. Its structure uses staggered secondary forest belts to form a continuous "U"-shaped agricultural machinery passageway perpendicular to the direction of the main wind. Core parameters include:

[0047] a (Minimum length of forest network unit): The basic dimension for the longitudinal flexibility of the grid, typically ranging from 400-1200m. Its theoretical lower limit is determined by the effective protection distance; the calculation formula can be found here: (H is the average height of the forest belt during the mature stage, and the coefficient is an empirical value), while the upper limit is limited by the natural boundary of the field.

[0048] b (Retaining the length of the secondary forest belt): A key parameter defining the depth of the "fence," typically set to... Its value is the decision point for balancing protection and production: increasing the b value enhances lateral wind resistance, but will compress the effective width of the passage; decreasing the b value has the opposite effect.

[0049] c (field passage width): A rigid constraint to ensure efficient operation of large agricultural machinery; its minimum value is c. min The maximum turning radius R of the main agricultural machinery unit max Determined by the safety margin δ: (Usually δ≥20 m). In practice, c is generally set to 150-250 m.

[0050] Reinforced Grid: An additional secondary forest belt is added inside the standard U-shaped channel. It enhances airflow disturbance and avoids local airflow acceleration, making it suitable for areas severely affected by wind and sand disasters. Parameters The determination of wind damage intensity is positively correlated with the severity of the damage, and a preliminary estimate can be made using the following formula: Where V is the design wind speed (m / s), V t To enable the enhanced wind speed threshold (e.g., 6.5 m / s), D represents the number of dust storm days per year. t The corresponding number of days is the threshold value, and k1 and k2 are the regional wind and sand activity correction coefficients. The initial estimate needs to be substituted into the computational fluid dynamics (CFD) model for final optimization and verification.

[0051] 3. Data-driven precision configuration of forest networks

[0052] The steps for constructing a forest network based on the "Great Wall pattern" grid are shown in Figure 3.

[0053] Multi-source data fusion acquisition:

[0054] Farmland baseline data: Using satellite remote sensing images such as Gaofen series and Sentinel-2, high-precision classification of farmland and non-agricultural land is carried out using semantic segmentation models based on deep learning (such as U-Net variants), and vectorized field boundaries are extracted by combining edge detection algorithms (such as Canny operator) to generate a "field spatial database" with topological relationships.

[0055] Disaster characteristic data: Integrating time-series data of over 30 years from the regional meteorological station network, the design wind speed V for different return periods is fitted using either the Gumbel distribution or the generalized extreme value distribution (GEV). d The main hazard wind direction angle θ is determined by using the wind direction frequency rose diagram.

[0056] Quantitative mapping of protection objectives to structural indicators: This is a key innovative step connecting macro-level needs with micro-level design. This invention establishes a universal quantitative correlation model between "protection efficiency" and "landscape pattern index." Based on long-term observations of multiple typical shelterbelt areas across the country and extensive CFD simulation data, nonlinear regression analysis reveals a stable logarithmic function relationship between the regional average wind speed reduction rate (ղ) and the forest network landscape connectivity index (L) (see Table 1 for data). Where α and β are fitting parameters that depend on the regional climate and underlying surface characteristics. This represents the random error term. For example, in the case of the black soil region, the fitted result is... , Therefore, when given a protection target When the target landscape connectivity index is 15%, it can be derived by inverse calculation. Furthermore, through big data statistics (see Table 2), a system was established... The joint probability distribution of forest network formation rate F and forest network aggregation degree C is used to determine the relationship with L. target A reasonable target range for matching F and C [F] min F max ] and [C min C max This transforms abstract objectives into computable and optimizable spatial pattern constraints.

[0057] Parameter matching and computational fluid dynamics (CFD) optimization validation:

[0058] Main forest belt layout: Based on the prevailing wind direction angle θ, the orientation of the main forest belt is set to θ±90°. The initial main forest belt distribution is automatically generated by spatially overlaying the "field spatial database" with the preset forest belt width buffer.

[0059] Iterative optimization of grid parameters: Using the basal forest belt as the framework, parameterized Great Wall pattern grid cells are embedded. A multi-objective optimization algorithm (such as NSGA-II) is used for iterative solution. The decision variables are the set of grid parameters {a, b, c, d} (d is 0 in the ordinary type). The objective function is: 1) Maximize protective benefits: Maximize(ղ simulated 2) Minimize land occupation: Minimize(Total) length The constraints include: c ≥ C min The calculated values ​​of L, F, and C must fall within their respective target ranges.

[0060] CFD simulation verification: Each candidate solution generated in each iteration must be evaluated for effectiveness through CFD simulation. Using the RANS equations and the standard k-ε turbulence model, the forest belt region is modeled as a porous medium domain. Its inertial drag coefficient C2 and viscous drag coefficient 1 / α are determined by experimental formulas based on the forest belt permeability φ. (A and B are empirical constants) are dynamically assigned. The wind speed field of the entire watershed is simulated and calculated, the average wind speed of the farmland area is extracted, and the wind speed is calculated. simulated .

[0061] 4. Dynamic monitoring and intelligent maintenance system

[0062] Construction of intelligent monitoring network:

[0063] In the completed Great Wall-patterned forest network, IoT monitoring nodes are deployed according to the principle of "combining key structural points with random sampling." Key points include: the intersection of forest belts with different orientations, the deepest part of the "barrier," and the middle of the "d" segment of the reinforced grid. Each node integrates an ultrasonic anemometer (measurement range 0-60 m / s, accuracy ±0.3 m / s), a soil moisture / conductivity sensor, a tree diameter growth measurement ring (accuracy ±0.1 mm), and a camera. Data is transmitted to the edge gateway via a low-power wide-area network (LPWAN, such as LoRaWAN), and then aggregated to the cloud data center. The sampling frequency can be dynamically adjusted according to the season and weather events (normally once per hour, once every 5 minutes in windy weather).

[0064] Performance assessment and degradation early warning model:

[0065] The system defines and calculates the Actual Protection Index (PEI) in real time. The calculation formula is as follows: .in: , These represent the average wind speed at monitoring points within the forest network and the wind speed at the upwind control point, respectively, at time t.

[0066] ∆BAI t ∆BAI represents the increase in the basal area at breast height of trees within period t. expected This represents the expected growth for the same period (calculated based on the tree species growth curve and local hydrothermal conditions). w1 and w2 are weighting coefficients. Typically, w1 = 0.7 and w2 = 0.3. The system sets an early warning threshold PEI. threshold (85% of the initial stable period average PEI). When the moving average PEI of a certain management zone (a Great Wall stripe unit within a field) is lower than the PEI for three consecutive assessment periods. threshold The system will automatically trigger a yellow alert; if an abnormal increase in forest mortality or an insect pest image recognition alarm is detected at the same time, a red alert will be triggered.

[0067] Intelligent Assisted Decision Engine:

[0068] Once the warning is triggered, the intelligent auxiliary decision-making engine starts, and its workflow is as follows:

[0069] Multi-dimensional diagnostic analysis: The engine first performs multi-source data fusion diagnosis on the warning area.

[0070] Wind field pattern analysis: Time-series data from all wind speed sensors in the area are retrieved and compared with the expected wind field simulated by CFD. If the data shows that the wind speed at a specific "notch" is consistently higher than the simulated value, it may be diagnosed as "local wind gap expansion".

[0071] Vegetation stress analysis: This involves analyzing the correlation between soil moisture, electrical conductivity data, and tree growth data. If growth is stagnant but soil moisture is sufficient, combined with pigment analysis of leaf images (via camera multispectral analysis), it may be diagnosed as "salt stress" or "pest and disease infestation."

[0072] Structural integrity analysis: Combined with regular drone inspections, detect whether there are gaps or breaks in the forest belt.

[0073] Case-Based Reasoning (CBR) Strategy Retrieval and Matching: The core of the engine is a continuously growing "scenario-strategy-effect" case library. Each case includes: a problem description vector P (containing features such as wind speed deviation, soil data, and growth deviation), a strategy vector S ("change the grid type from ordinary to strong", "increase parameter b by 50 m", "replant 3 salt-tolerant tamarisk trees per gap"), and an effect vector E (PEI recovery rate, cost, and time consumption).

[0074] When the new problem P new When it appears, the system calculates its comparison with all historical problems in the case library. Weighted Euclidean distance similarity: Among them, w j The weights for each diagnostic feature are initialized by the expert system and optimized through machine learning. The top k (e.g., k=5) historical cases with the highest similarity are retrieved.

[0075] Strategy generation and effect deduction: For the retrieved candidate strategies {S1, S2, ..., S...} k The engine does not directly copy the existing strategy, but uses it as a foundation, adapting it to the specific constraints of the current region (such as budget and construction season) to form a new strategy S. new Subsequently, a lightweight version of the CFD model and a growth prediction model were invoked to analyze S. new Conduct rapid simulations to predict the recovery curve of PEI, costs, and potential impact on agricultural machinery operations after implementation.

[0076] Multi-criteria decision-making and recommendation output: The system will ultimately output 2-3 optimal policy options, each accompanied by detailed deduction results. The recommendation ranking is based on a multi-criteria utility function: Among them, PEI gainThe expected PEI gain is represented by Cost, the estimated cost is represented by Durability, and λ is the adjustable preference weight. Finally, the recommended strategy and simulation results are provided to managers for decision-making in the form of a visual report.

[0077] Table 1 Correlation Curve between Protection Efficiency and Forest Network Landscape Structure Index

[0078]

[0079] Table 2. Value Range of Forest Network Landscape Structure Index

[0080]

[0081] Note: Forest network connectivity and forest network aggregation are dimensionless indices with values ​​in the range [0,1]; forest network coverage rate is the length of the forest belt per unit area of ​​farmland, in units of 10. 2 m / km 2 .

[0082] Example 1: Spatial Configuration of Common Great Wall Pattern Farmland Shelterbelt System in Northeast Black Soil Region

[0083] This case study is located in farmland in a black soil region of Northeast China, with a total area of ​​9.23 km². 2 The region is flat with contiguous farmland. The main disaster is strong winds in spring, which threaten the topsoil and seedlings during the sowing season. Agricultural production is highly mechanized, creating a clear need for access for large agricultural machinery.

[0084] (1) Design of Great Wall pattern grid morphology and determination of parameter system

[0085] Referring to Figures 2 and 3, and considering the region's characteristics of "moderate wind damage intensity and priority for production needs," a standard Great Wall pattern grid was selected. Parameters a (minimum length of the forest network unit), b (length of the retained secondary forest belt), and c (width of the field passage) were initially determined as variables to be optimized, and their values ​​must simultaneously meet both protection and production needs.

[0086] (2) Data-driven precise configuration of forest networks

[0087] Farmland and disaster feature extraction: Sentinel-2 remote sensing imagery was used and interpreted through the U-Net deep learning model to obtain a high-precision farmland distribution vector map (Figures 4A and 4B). Analysis of 30 years of meteorological data determined that the dominant wind direction was southwest, with a 10-year return period design wind speed of 18 m / s.

[0088] Target Quantification: The protection target is set as reducing the average wind speed in the field by 15%. The required forest network landscape connectivity index L is calculated by using the "Protection Efficiency - Forest Network Landscape Connectivity" correlation model (Table 1). target The value is 0.199. Referring to Table 2, the target range for the corresponding forest network coverage rate is determined to be [1.74, 2.75] × 10⁻⁶. 2 m / km 2 The forest network density ranges from [0.70, 1.57].

[0089] Layout and parameter optimization of the main forest belt:

[0090] Based on the prevailing wind direction (southwest), the orientation of all basal forest belts is determined to be northeast-southwest (perpendicular to the prevailing wind). The locations of the basal forest belts are delineated using farmland boundaries and main roads as a framework (see Appendix 4C). (L) target Minimum turning radius of agricultural machinery (set c) min With a constraint of 200 m, the NSGA-II algorithm was used to perform multi-objective optimization of parameters a and b (objectives: maximizing windbreak benefit and minimizing the total length of the forest belt). After CFD wind field simulation verification, the final optimized parameters were determined to be: a = 800 m, b = 400 m, and c = 200 m. Under these parameters, the preliminary forest network landscape connectivity index was calculated to be 0.178, falling within the target range.

[0091] Forest network construction and adjustment: Using the basal forest belts as the framework, a standard Great Wall-patterned grid with parameters (800, 400, 200) was automatically arranged in the GIS platform to generate a preliminary forest network system (see Figure 4D). The calculated forest network coverage rate is 2.1 × 10⁻⁶. 2 m / km 2 This satisfies the objective. The final configuration scheme is then formed (see attached Figure 4E).

[0092] (3) Implementation of dynamic monitoring and intelligent maintenance system

[0093] Monitoring network deployment: After the forest network is completed, IoT monitoring nodes will be deployed deep in the 10 key "blocks" and in the middle of the 5 main forest belts to continuously monitor wind speed.

[0094] Intelligent Assisted Decision Triggering and Execution: After three years of system operation, monitoring showed that the PEI index at a location near the regional wind front had continuously decreased from 0.82 to 0.68, triggering a red alert. The intelligent assisted decision engine was activated.

[0095] Diagnosis: Data analysis revealed an abnormal increase in wind speed inside the "barrier" (section b=400 m).

[0096] Case retrieval and strategy generation: The engine retrieved similar cases ("increased wind speed") from the case library, and the strategy with the highest matching degree was "a gap appeared in the forest belt, replant and adjust the local structure of the forest network". Combining local conditions, the engine generated a new strategy: "Replant at both ends of the 'crenellations' of the Great Wall pattern in plot No. 7".

[0097] Effect projection and output: Local CFD simulation results show that replanting will improve the local windbreak capacity of the forest network, causing the PEI to rise back to 0.75 within one year. This strategy report was output to the managers for implementation.

[0098] Example 2: Spatial Configuration of Enhanced Great Wall-Patterned Farmland Shelterbelt System in the Western Liaoning Sandy Area

[0099] This case study is located in farmland on the edge of the Horqin Sandy Land in western Liaoning Province, where wind and sand activity is intense, with an average of more than 20 days of sandstorms per year, making ecological protection extremely urgent.

[0100] (1) Morphological design and parameter determination

[0101] Referring to Figure 3, this embodiment focuses on the enhanced version (B).

[0102] Due to the high intensity of wind damage, a reinforced Great Wall pattern grid was directly selected. Based on the core parameters a, b, and c, the key parameter d (extra subforest belt length) was added.

[0103] (2) Forest network configuration and simulation optimization

[0104] Feature analysis and target setting: Remote sensing interpretation shows an intermingling distribution of farmland and sandy land. Meteorological analysis determined the dominant wind direction to be northwest, with a designed instantaneous gust speed of 25 m / s. The protection target was set to reduce wind speed by 30% and block sand transport.

[0105] Enhanced parameter optimization: The basal forest belt is arranged perpendicular to the northwest wind. During optimization, parameter d becomes the key variable. An initial estimate was made using empirical formulas, followed by fine optimization using CFD simulations. The CFD simulations specifically considered aeolian two-phase flow to evaluate the sand accumulation distribution under different d values. The final optimized parameters were determined to be: a = 600 m, b = 300 m, c = 180 m, d = 150 m. Under this configuration, the reinforced mesh effectively created a secondary wind shadow zone within the mesh.

[0106] Forest network construction: Reinforced grids are prioritized for deployment at the windward edge, gradually transitioning inwards. The constructed forest network system has been verified through simulation, demonstrating that its windbreak effectiveness meets standards.

[0107] (3) Intelligent maintenance for wind and sand environments

[0108] Sandstorm monitoring: Sand flux sensors have been added to the monitoring nodes to focus on monitoring the sand accumulation before and after the forest belt in section "d".

[0109] Specific Decision-Making for Sandstorms: When the system detects, through monitoring and drone imagery, that the protective effect of a certain "d" segment of the forest belt is reduced due to excessive sand accumulation, the intelligent auxiliary decision-making engine will initiate a special case search for sandstorms. Possible recommended strategies include: "adding temporary mechanical sand barriers upwind of the 'd' segment of the forest belt" and "cutting off and rejuvenating shrubs in areas with excessive sand accumulation to promote growth and enhance sand-fixing capacity." The engine will use a sandstorm flow model to deduce changes in sand accumulation morphology under different measures to assist in decision-making.

[0110] Using the traditional square forest network in Figure 4F as a control, the technical effect is illustrated. In the area of ​​Example 1, a traditional square forest network was simultaneously constructed as a control. Statistical and simulation results show that, while achieving the same windbreak effect (reducing wind speed by more than 15%), the total length of the forest belt is reduced by 20%, and the length of the secondary forest belt is reduced by 35%, providing continuous operating space for agricultural machinery.

[0111] The embodiments described above are merely illustrative of the technical solutions of the present invention and are not intended to limit them. In particular, the specific values ​​of the associated model forms and structural parameters (a, b, c, d) mentioned in the embodiments are preferred implementations for specific case areas. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features (including but not limited to the correction of model calculation coefficients, adaptive adjustments to the parameters of the Great Wall-patterned mesh, or the selection and replacement of hardware equipment), based on the natural conditions, disaster characteristics, farmland spatial distribution, and protection targets of the target area. All such modifications, equivalent substitutions, or improvements that do not depart from the spirit and essence of the present invention should be included within the scope of protection of the present invention.

Claims

1. A method for spatial configuration of farmland shelterbelt systems based on Great Wall pattern structures, characterized in that, Includes the following steps: (1) The design includes both standard and reinforced Great Wall-patterned grid shapes; (2) Obtain baseline farmland data and disaster characteristics of the target area; (3) Based on the disaster characteristics, plan the basic forest belt, select the matching Great Wall pattern grid type and determine the structural parameters; (4) Construct the forest network system with the basic forest belt as the skeleton and optimize and iterate to form the final configuration scheme.

2. The spatial configuration method for a farmland shelterbelt system based on the Great Wall pattern structure according to claim 1, characterized in that, In step (1), the Great Wall pattern grid morphology includes a common type suitable for general wind damage areas and an enhanced type suitable for strong wind damage areas; the structural parameters include the minimum length of the forest network unit (a), the length of the retained secondary forest belt (b), the width of the field passage (c), and the additional secondary forest belt length (d) unique to the enhanced grid.

3. The spatial configuration method for a farmland shelterbelt system based on the Great Wall pattern structure according to claim 2, characterized in that, In step (2), the farmland baseline data includes at least the farmland spatial distribution and field boundary information obtained through remote sensing image interpretation; the disaster characteristics include at least the main wind direction and wind damage intensity information obtained through meteorological data analysis.

4. The spatial configuration method for a farmland shelterbelt system based on the Great Wall pattern structure according to claim 2, characterized in that, Step (2) also includes: converting the preset protection targets into quantitative target ranges of forest network landscape connectivity index, forest network rate and forest network aggregation degree through a preset association model.

5. The spatial configuration method for a farmland shelterbelt system based on the Great Wall pattern structure according to claim 2, characterized in that, Step (3) specifically includes: (3.1) determining the dominant direction of the main forest belt according to the main wind direction; (3.2) delineating the specific spatial location of the main forest belt in conjunction with the field boundary; (3.3) selecting the appropriate grid type from the ordinary and enhanced types according to the wind damage intensity, and determining the specific values ​​of parameters a, b, c, and d according to the needs of agricultural machinery operations and protection objectives.

6. The spatial configuration method for a farmland shelterbelt system based on the Great Wall pattern structure according to claim 2, characterized in that, Step (4) specifically includes: (4.1) Using the designated basal forest belt as the framework, arranging the forest network within the farmland area according to the selected grid type and parameters to generate a preliminary forest network system; (4.2) Calculating the forest network landscape connectivity index, forest network rate and forest network aggregation degree of the preliminary forest network system; (4.3) Comparing the calculation results with the quantitative target range, iteratively optimizing by fine-tuning the grid parameters or arrangement density until the requirements are met, and outputting the final configuration scheme.

7. The spatial configuration method for a farmland shelterbelt system based on the Great Wall pattern structure according to claim 6, characterized in that, In step (4.3), a computational fluid dynamics model is used to simulate and verify the windbreak effectiveness of the forest network during the optimization process.

8. A method for spatial configuration of farmland shelterbelt systems based on Great Wall pattern structures according to any one of claims 1 to 7, characterized in that, The method also includes the steps of monitoring the effectiveness and adaptively maintaining the established forest network system: setting up monitoring nodes in the forest network system to continuously collect environmental wind field and tree growth data; periodically evaluating the actual protective effectiveness of the system based on the data; and generating maintenance prompts containing specific locations and adjustment types when the evaluation results continuously deviate from expectations.

9. A spatial configuration method for a farmland shelterbelt system based on a Great Wall pattern structure according to claim 8, characterized in that, The maintenance prompts are generated by an auxiliary decision-making system; the auxiliary decision-making system diagnoses the reasons for the reduced windbreak effectiveness and outputs recommended adjustment strategies, including local grid type replacement, structural parameter adjustment, or replanting and replacement of trees.

10. A spatial configuration system for farmland shelterbelts based on the Great Wall pattern structure, characterized in that, include: (1) The Great Wall pattern grid morphology design system is used to construct a common and enhanced Great Wall pattern grid topology library adapted to mechanized operations, and to define a set of structural parameters including the length of forest network unit, the length of secondary forest belt, the width of agricultural machinery passage and the length of additional secondary forest belt; (2) The forest network configuration and optimization module is used to integrate farmland baseline data and disaster characteristics, convert the protection target into the quantitative constraints of landscape connectivity index, forest network rate and forest network aggregation degree, and use this as the objective function to iteratively optimize and simulate the set of structural parameters in the backbone forest belt skeleton to generate a configuration scheme; (3) The dynamic monitoring and intelligent maintenance system is used to dynamically evaluate the actual windbreak performance based on the real-time environmental and tree growth data fed back by the monitoring nodes, and to generate adjustment strategies and maintenance prompts based on auxiliary decision rules when the performance deviates from expectations.

Images