A Collaborative Layout Optimization System and Method for Sand Barrier Aerial Seeding Based on UAV Swarm Game Theory

Abstract

This invention relates to the field of desertification control technology, specifically to a collaborative layout optimization system and method for sand barrier aerial seeding based on UAV swarm game theory. It constructs a digital model of the microenvironment (topography, wind field, humidity, temperature) through a multimodal desert microenvironment perception module, and combines this model with a sand barrier-vegetation coupled dynamic model. This achieves a spatiotemporal correlation between sand barrier configuration and vegetation growth. An adaptive sand barrier parameter optimization module and a precise seed aerial seeding control module work collaboratively to optimize seed placement, significantly improving sand fixation efficiency and vegetation survival rate to 60%–75%, shortening the control cycle to 1.5–2.5 years. A closed-loop feedback and dynamic optimization module further ensures continuous improvement in control effectiveness, transforming the traditional "sand-blocking" strategy into an "ecological microenvironment engineering" approach. This constructs a complete microenvironment regulation mechanism, not only improving control efficiency by 35%–50% but also achieving a fundamental transformation in the control philosophy.

Description

Technical Field

[0001] This invention relates to the field of desertification control technology, specifically to a collaborative layout optimization system and method for sand barrier aerial seeding based on UAV swarm game theory. Background Technology

[0002] Sand barrier stabilization and vegetation restoration are two key measures in desertification control. Currently, the monitoring of sand barrier stabilization function still generally adopts the traditional tagging method or sand accumulation meter method, which is a fixed measurement method of setting up stakes and sand accumulation meters in the sand barrier planting area, and estimating the sand stabilization effect of the sand barrier by observing the changes in sand surface activity at the fixed points.

[0003] For example, Chinese patent application CN114897776A discloses a method for monitoring sand-fixing functions of sand barriers based on unmanned aerial vehicles (UAVs). This method uses UAVs to acquire images of the target sand barrier control area, performs aerial triangulation on the images to obtain digital orthophoto maps (DOM) and digital surface model maps (DSM), processes these images to obtain digital elevation model maps (DEM), then draws a topographic map, and finally calculates wind deposition and wind erosion. While this method represents a breakthrough in monitoring technology, it still has the following shortcomings:

[0004] 1. Using only a single drone for operation is inefficient and has limited coverage in large desert environments;

[0005] 2. Only evaluating the effectiveness of existing sand barriers is possible; the layout of sand barriers cannot be proactively optimized.

[0006] 3. The synergistic relationship between sand barriers and vegetation restoration was not considered, and the comprehensive ecological restoration mechanism was neglected;

[0007] 4. The shape, height, density, and other parameters of sand barriers cannot be flexibly adjusted according to microenvironmental conditions;

[0008] 5. It has poor adaptability under strong wind conditions and cannot guarantee seed retention and vegetation recovery efficiency;

[0009] 6. The evaluation system exhibits significant lag and lacks predictive and proactive optimization capabilities.

[0010] The aforementioned technical solutions only provide passive monitoring of the sand-fixing effect of sand barriers, failing to achieve synergistic optimization of sand barrier layout and vegetation restoration, and thus falling short of the needs of comprehensive desertification control. Therefore, there is an urgent need to develop a system capable of actively optimizing sand barrier layout and coordinating with aerial seeding to improve the efficiency and quality of desertification control. Summary of the Invention

[0011] The purpose of this invention is to provide a collaborative layout optimization system and method for sand barrier aerial seeding based on UAV swarm game theory, aiming to solve the technical problems in the prior art such as the disconnect between sand barrier layout and vegetation restoration, the inability to flexibly adjust sand barrier parameters according to microenvironmental conditions, and the lack of proactive optimization capabilities.

[0012] This invention proposes a collaborative layout optimization system for sand barrier aerial seeding based on UAV swarm game theory, comprising:

[0013] The multimodal desert microenvironment perception module is used to collect multidimensional environmental data of sandy areas and construct a digital model of the microenvironment that includes topography, wind field, humidity, and temperature.

[0014] The sand barrier-vegetation coupled dynamic model module is connected to the multimodal desert microenvironment perception module for receiving the microenvironment digital model and constructing a spatiotemporal correlation model of sand barrier on wind field regulation and vegetation growth.

[0015] An adaptive sand barrier parameter optimization module is data-connected to the sand barrier-vegetation coupled dynamic model module, and is used to generate an optimal sand barrier configuration scheme based on the spatiotemporal correlation model. The sand barrier configuration scheme includes parameters such as sand barrier height, density, material, and arrangement pattern.

[0016] The precision seed aerial seeding control module is connected to the adaptive sand barrier parameter optimization module for receiving the sand barrier configuration scheme, adjusting the seed dispensing parameters according to real-time wind field data, and controlling the seed dispensing location, time and density.

[0017] The closed-loop feedback and dynamic optimization module is connected to the precision seed aerial seeding control module and the multimodal desert microenvironment perception module for collecting data on the treatment effect, evaluating the sand barrier's sand fixation efficiency and vegetation growth status, and providing optimization suggestions to the adaptive sand barrier parameter optimization module.

[0018] Preferably, the multimodal desert microenvironment sensing module includes:

[0019] The multispectral data acquisition unit is used to acquire surface information in the visible, near-infrared, and short-wave infrared bands.

[0020] The wind field monitoring unit is used to collect three-dimensional wind field data at frequencies of 0.1–10 Hz.

[0021] Topographic mapping unit, used to acquire a digital elevation model with a resolution of 5cm;

[0022] The data fusion processing unit is used to integrate multi-source data to generate a unified digital model of the microenvironment.

[0023] Preferably, the sand barrier-vegetation coupled dynamic model module includes:

[0024] The wind field regulation model unit is used to simulate the impact of different sand barrier morphologies on the wind field;

[0025] A vegetation growth condition model unit is used to determine the microenvironmental threshold conditions required for vegetation growth.

[0026] The spatiotemporal correlation calculation unit is used to establish a correlation model between sand barrier layout and vegetation growth through convolutional neural network algorithms.

[0027] Preferably, the adaptive sand barrier parameter optimization module includes:

[0028] The sand barrier specification library unit contains parameters for 6 basic forms and 3 material combinations of sand barriers;

[0029] A multi-objective optimization unit is used to balance the objectives of sand fixation efficiency, vegetation growth, and cost control.

[0030] The reinforcement learning unit is used to adaptively adjust the sand barrier parameters to adapt to different microenvironmental conditions.

[0031] Preferably, the sand barrier parameters in the sand barrier specification library unit include:

[0032] The height parameter of the sand barrier is continuously adjustable from 20 to 50 cm.

[0033] The density parameter of the sand barrier, the air permeability range is continuously adjustable from 30% to 70%;

[0034] Sand barrier material parameters, including wheat straw, reeds, and shrub branches;

[0035] The parameters for sand barrier arrangement patterns include squares, stripes, checkerboard, honeycomb, herringbone, and cross.

[0036] Preferably, the precision seed aerial seeding control module includes:

[0037] The intelligent seed delivery unit can automatically adjust the delivery parameters based on real-time wind field data;

[0038] Seed coating treatment unit is used to mix seeds with water-retaining agents, nutrients and binders to improve wind resistance;

[0039] The delivery trajectory optimization unit is used to plan the optimal delivery path and flight parameters.

[0040] Preferably, the control parameters of the precision seed aerial seeding control module include:

[0041] Seed density can be precisely controlled within the range of 500-2000 seeds per acre;

[0042] The deployment height can be automatically adjusted within the range of 5-20m;

[0043] The seed coating material is prepared according to the following ratio: seed: water-retaining agent: nutrient agent: adhesive = 1:0.5-2:0.1-0.5:0.1-0.4.

[0044] Preferably, the closed-loop feedback and dynamic optimization module includes:

[0045] The regular monitoring unit collects data on the treatment effect at three levels: short-term, medium-term, and long-term.

[0046] The evaluation index calculation unit is used to calculate sand fixation rate, vegetation coverage, biomass, seed germination rate, and cost-benefit ratio.

[0047] A self-learning optimization unit is used to continuously improve the collaborative strategy of aerial seeding of sand barriers based on time series data.

[0048] Preferably, the monitoring frequency of the periodic monitoring unit is set as follows:

[0049] Short-term monitoring, once every 7 days;

[0050] Mid-term monitoring, once every 30 days;

[0051] Long-term monitoring, once every 90 days;

[0052] Emergency monitoring is triggered when environmental parameters change by more than 20%.

[0053] The collaborative layout optimization method for sand barrier aerial seeding based on drone swarm game theory includes the following steps:

[0054] Collect multidimensional environmental data of sandy areas and construct a digital model of the microenvironment;

[0055] Based on the aforementioned microenvironment digital model, a spatiotemporal correlation model of the effect of sand barriers on wind field regulation and vegetation growth is established.

[0056] Based on the spatiotemporal correlation model, an optimal sand barrier configuration scheme is generated;

[0057] Based on the aforementioned sand barrier configuration scheme, adjust the seed placement parameters to control the seed placement location, time, and density;

[0058] Collect data on the effectiveness of the treatment to assess the efficiency of sand barrier fixation and vegetation growth.

[0059] Based on the governance effect data, the sand barrier configuration scheme and seed placement parameters are updated to form a closed-loop optimization.

[0060] The beneficial effects of this invention include:

[0061] 1. It has achieved synergistic optimization of sand barrier layout and vegetation restoration, resulting in a positive synergy between sand fixation efficiency and vegetation restoration. The governance efficiency has increased by 35% to 50%, the vegetation survival rate has increased from the traditional 30% to 40% to 60% to 75%, and the governance cycle has been shortened from the traditional 3 to 5 years to 1.5 to 2.5 years.

[0062] 2. Through multimodal desert microenvironment perception and sand barrier-vegetation coupled dynamic model, a complete microenvironment regulation mechanism was constructed, transforming from simple sand blocking to complex ecological microenvironment engineering, thus realizing a qualitative change in governance concept;

[0063] 3. An adaptive sand barrier parameter optimization system was developed, which can automatically adjust parameters such as sand barrier height, density, material and arrangement pattern according to micro-environmental conditions, greatly improving the adaptability and sand fixation efficiency of sand barriers;

[0064] 4. An innovative precision seed aerial seeding control technology was designed, which can maintain a seed retention rate of over 90% even under wind conditions of level 8, significantly improving the vegetation restoration efficiency under extreme conditions;

[0065] 5. A closed-loop feedback and dynamic optimization mechanism has been established, enabling the system to continuously learn and adjust itself based on actual results, transforming from static design to dynamic evolution and continuously improving governance effectiveness. Attached Figure Description

[0066] Figure 1 This is a schematic diagram of the architecture of the sand barrier aerial seeding collaborative layout optimization system based on UAV swarm game theory of the present invention.

[0067] Figure 2 This is a schematic diagram of the structure of the multimodal desert microenvironment sensing module of the present invention;

[0068] Figure 3 This is a flowchart of the workflow of the sand barrier-vegetation coupled dynamic model module of the present invention;

[0069] Figure 4 This is a schematic diagram of the adaptive sand barrier parameter optimization module of the present invention;

[0070] Figure 5 This is a schematic diagram of the precise seed aerial seeding control module of the present invention;

[0071] Figure 6 This is a flowchart illustrating the closed-loop feedback and dynamic optimization module of the present invention.

[0072] Figure 7 This is a flowchart illustrating the collaborative layout optimization method for sand barrier aerial seeding based on UAV swarm game theory, as proposed in this invention. Detailed Implementation

[0073] Please refer to the attached document. Figure 1-7 The preferred embodiments of the present invention will now be described in detail with reference to the accompanying drawings. It should be understood that the preferred embodiments described herein are for illustrative and explanatory purposes only and are not intended to limit the scope of the invention.

[0074] Example 1

[0075] Reference Figure 1The sand barrier aerial seeding collaborative layout optimization system based on UAV swarm game theory provided by the present invention includes a multimodal desert microenvironment perception module 1, a sand barrier-vegetation coupled dynamic model module 2, an adaptive sand barrier parameter optimization module 3, a precise seed aerial seeding control module 4, and a closed-loop feedback and dynamic optimization module 5.

[0076] The multimodal desert microenvironment perception module 1 is used to collect multidimensional environmental data of sandy areas and construct a digital model of the microenvironment, including topography, wind field, humidity, and temperature. This module uses a cluster of drones equipped with multispectral cameras, thermal infrared sensors, and millimeter-wave radar to acquire detailed environmental data of the desert area using a four-dimensional scanning strategy (three-dimensional space + one-dimensional time), and constructs a complete digital model of the microenvironment through a multi-source data fusion algorithm.

[0077] The sand barrier-vegetation coupled dynamic model module 2 is connected to the multimodal desert microenvironment perception module 1 for receiving microenvironment digital models and constructing a spatiotemporal correlation model of sand barrier's influence on wind field regulation and vegetation growth. This module constructs a model of sand barrier's influence on wind field regulation based on fluid mechanics principles, and simultaneously develops a microenvironmental condition model for seed germination and seedling survival. A spatiotemporal dynamic model of the correlation between sand barrier layout and vegetation growth is established using a convolutional neural network algorithm.

[0078] The adaptive sand barrier parameter optimization module 3 is data-connected with the sand barrier-vegetation coupled dynamic model module 2 to generate the optimal sand barrier configuration scheme based on the spatiotemporal correlation model. This configuration scheme includes parameters such as sand barrier height, density, material, and arrangement pattern. This module designs a variable-morphology sand barrier specification library, containing 6 basic forms and 3 material combinations. It uses reinforcement learning algorithms to adaptively optimize the sand barrier parameters, achieving a multi-objective trade-off (sand fixation efficiency, vegetation growth, and cost control).

[0079] The precision seed aerial seeding control module 4 is connected to the adaptive sand barrier parameter optimization module 3 to receive sand barrier configuration schemes and adjust seed delivery parameters based on real-time wind field data, controlling the seed delivery location, time, and density. This module has developed an intelligent seed delivery system and seed wrapping technology, which can maintain a high seed retention rate under strong wind conditions, significantly improving vegetation restoration efficiency.

[0080] The closed-loop feedback and dynamic optimization module 5 is connected to the precision seed aerial seeding control module 4 and the multimodal desert microenvironment perception module 1 to collect data on the treatment effect, evaluate the sand barrier's sand fixation efficiency and vegetation growth, and provide optimization suggestions to the adaptive sand barrier parameter optimization module 3. This module establishes a regular monitoring plan and evaluation index system, and continuously improves the sand barrier aerial seeding collaborative strategy through a self-learning optimization model based on time series data.

[0081] This system constructs a complete closed-loop architecture of perception, analysis, decision-making, execution, and optimization. The modules exchange information and collaborate through standardized data interfaces. The system employs a cluster game mechanism, where each drone acts as a participant, making local decisions while simultaneously considering the globally optimal objective, achieving coordinated optimization of sand barrier deployment and seed sowing.

[0082] Example 2

[0083] Reference Figure 2 The multimodal desert microenvironment perception module 1 in this embodiment includes a multispectral data acquisition unit 11, a wind field monitoring unit 12, a terrain mapping unit 13, and a data fusion processing unit 14.

[0084] The multispectral data acquisition unit 11 is used to acquire surface information in the visible light (RGB), near-infrared (NIR 760-900nm), and short-wave infrared (SWIR 1500-1700nm) bands. This unit is equipped with a high-resolution multispectral camera, capable of capturing the surface reflectance characteristics of different bands to identify key information such as soil type, moisture content, and vegetation status. During scanning, the sampling density of the multispectral camera is set to 25-100 sampling points per square meter, automatically adjusted according to the required resolution.

[0085] The wind field monitoring unit 12 is used to collect three-dimensional wind field data at frequencies ranging from 0.1 to 10 Hz. Equipped with a small weather station and an airborne wind field sensor, this unit can monitor parameters such as wind speed, wind direction, and turbulence intensity in real time. The wind field sampling frequency can be adjusted within the range of 0.1–10 Hz, automatically adapting to the rate of wind speed change. Low-frequency sampling is used when the wind field is relatively stable, while automatic switching to high-frequency sampling occurs when wind field fluctuations are significant, ensuring both data quality and improved energy efficiency.

[0086] The topographic mapping unit 13 is used to acquire a digital elevation model with a resolution of 5cm. This unit uses LiDAR technology, which can penetrate surface vegetation to obtain accurate topographic data. The scanning height can be adjusted within the range of 50-200m, and the optimal flight altitude is automatically set according to the required resolution. In areas with key topographic features (such as dune ridges and slope baselines), the system automatically increases the sampling density to ensure accurate capture of topographic details.

[0087] The data fusion processing unit 14 is used to integrate multi-source data to generate a unified digital model of the microenvironment. This unit employs a multi-layer data fusion algorithm to integrate data from different sources and at different scales into a unified environmental representation model. The specific fusion process includes four steps: data preprocessing, spatial alignment, time synchronization, and feature extraction. In the data preprocessing stage, the system performs noise filtering and outlier detection on the raw data; in the spatial alignment stage, data acquired by different sensors are mapped to a unified spatial coordinate system; in the time synchronization stage, data with different sampling frequencies are processed to establish time consistency; and in the feature extraction stage, key environmental features are extracted from the fused data.

[0088] The final generated microenvironment digital model contains the following core information: a three-dimensional representation of the terrain (digital elevation model), a three-dimensional velocity vector field of the wind field, a soil moisture distribution map, a surface temperature distribution map, and a soil texture characteristic map. This model has high spatiotemporal resolution, accurately capturing microscale changes in the desert environment and providing fundamental data support for subsequent sand barrier-vegetation coupling analysis.

[0089] Preferably, the multimodal desert microenvironment perception module 1 adopts a distributed data acquisition strategy, with multiple drones simultaneously collecting data in different areas and at different altitudes, significantly improving the efficiency and coverage of data acquisition. Simultaneously, the system employs edge computing technology to perform preliminary data processing on the drones, reducing the data transmission burden and improving the overall system operating efficiency.

[0090] Example 3: Detailed Structure of the Sand Barrier-Vegetation Coupled Dynamic Model Module

[0091] Reference Figure 3 In this embodiment, the sand barrier-vegetation coupled dynamic model module 2 includes a wind field regulation model unit 21, a vegetation growth condition model unit 22, and a spatiotemporal correlation calculation unit 23.

[0092] The wind field regulation model unit 21 is used to simulate the impact of different sand barrier morphologies on the wind field. Based on computational fluid dynamics (CFD) principles, this unit constructs a sand barrier-wind field interaction model, which can accurately predict the effects of different sand barrier morphologies (height, density, and arrangement pattern) on wind speed distribution and turbulence characteristics. In the model, the sand barrier is treated as a porous medium, and its permeability is related to the sand barrier density. When wind passes over the sand barrier, it creates wind speed reduction zones and turbulence enhancement zones. The size and intensity of these zones depend on the specific parameters of the sand barrier.

[0093] The effectiveness of wind field regulation is typically measured by two indicators: wind speed attenuation rate and turbulence intensity. The wind speed attenuation rate is defined as the relative change in wind speed before and after the sand barrier is erected, usually ranging from 20% to 80%. Turbulence intensity is expressed as a dimensionless coefficient, typically ranging from 0.05 to 0.5. These two indicators together determine the sand-fixing effect of the sand barrier and also have a significant impact on subsequent vegetation growth.

[0094] The vegetation growth condition model unit 22 is used to determine the microenvironmental threshold conditions required for vegetation growth. This unit constructs an environmental response model for desert vegetation growth, including two key stages: seed germination and seedling growth. The model considers the influence of various environmental factors such as wind speed, temperature, humidity, and light on vegetation growth, and determines the suitable range and critical threshold of each factor.

[0095] For typical desert vegetation, the suitable wind speed range for seed germination is 0-3.5 m / s, while the critical wind speed for seedling survival is 5 m / s. When the wind speed exceeds the critical value, seeds may be blown away or seedlings may be knocked down. Furthermore, there is an optimal distance relationship between sand barriers and vegetation, usually expressed as a multiple of the sand barrier height, with the optimal range being 0.5-2.0 times the sand barrier height. Within this range, the vegetation is effectively protected by the sand barrier without being excessively affected by the sand barrier's shadow.

[0096] The spatiotemporal correlation calculation unit 23 is used to establish a correlation model between sand barrier layout and vegetation growth using a convolutional neural network algorithm. This unit employs deep learning methods to construct an end-to-end prediction model of sand barrier parameters, microenvironmental conditions, and vegetation response. The model uses sand barrier layout parameters and basic environmental conditions as inputs to predict the microenvironmental characteristics and vegetation growth probability at different locations.

[0097] The design of the convolutional neural network employs a multi-layer convolutional structure to capture spatial features, while also incorporating a temporal processing mechanism to predict environmental changes and vegetation responses at different points in time. The key layers of the network include: an input layer (receiving sand barrier parameters and environmental data), multi-layer convolutional layers (extracting spatial features), a recurrent neural network layer (processing time series), and an output layer (generating microenvironment predictions and vegetation growth probabilities).

[0098] The core innovation of the sand barrier-vegetation coupled dynamic model lies in organically combining the sand-fixing function of sand barriers with the growth conditions of vegetation, constructing a unified analytical framework. Through this model, the system can predict microenvironmental changes under different sand barrier layouts, thereby assessing their impact on vegetation growth and providing a scientific basis for optimizing sand barrier parameters.

[0099] Preferably, the spatiotemporal correlation calculation unit 23 employs transfer learning technology during model training, transferring experiential knowledge accumulated in other regions to the new region, significantly reducing the amount of data and time required for model training. Simultaneously, the model possesses adaptive learning capabilities, continuously adjusting and optimizing the prediction algorithm based on actual monitoring data to improve prediction accuracy and adaptability.

[0100] Example 4

[0101] Reference Figure 4 In this embodiment, the adaptive sand barrier parameter optimization module 3 includes a sand barrier specification library unit 31, a multi-objective optimization unit 32, and a reinforcement learning unit 33.

[0102] The sand barrier specification library unit 31 contains sand barrier parameters for 6 basic shapes and 3 material combinations. This unit stores a preset sand barrier specification parameter library as a basic option for the optimization algorithm. The sand barrier parameters mainly include four aspects: sand barrier height, sand barrier density (wind permeability), sand barrier material, and sand barrier arrangement pattern.

[0103] The height of the sand barriers is continuously adjustable within the range of 20-50cm, adapting to different wind conditions and sand-fixing requirements. Generally, taller sand barriers are used in areas with strong winds, medium-height sand barriers in areas with moderate winds, and lower sand barriers in areas with weak winds. The sand barrier density parameter, expressed as permeability, is continuously adjustable from 30% to 70%. Too low a permeability will cause strong eddies on the leeward side of the sand barrier, which is detrimental to vegetation growth; too high a permeability will weaken the windbreak and sand-fixing effect of the sand barrier. Sand barrier materials include three optional materials: wheat straw, reeds, and shrub branches. Different materials have different permeability characteristics, service life, and ecological adaptability. Sand barrier arrangement patterns include six options: square, strip, checkerboard, honeycomb, herringbone, and cross. Different arrangement patterns are suitable for different terrain and wind conditions.

[0104] The multi-objective optimization unit 32 is used to balance three objectives: sand fixation efficiency, vegetation growth, and cost control. This unit employs a multi-objective optimization algorithm to find the optimal balance point among multiple objectives. During the optimization process, the system comprehensively considers the following aspects: sand fixation efficiency (measured by wind deposition and wind erosion), vegetation support score (assessed by microenvironment suitability), cost per hectare (calculated by material usage and construction difficulty), and expected service life.

[0105] Multi-objective optimization employs a Pareto Front search strategy, iteratively finding a set of non-dominated solutions—solutions that cannot improve other objectives without sacrificing one. In practical applications, the system selects the most suitable solution from the Pareto Front based on specific governance needs and resource constraints.

[0106] Reinforcement learning unit 33 is used to adaptively adjust sand barrier parameters to adapt to different micro-environmental conditions. This unit is based on a deep reinforcement learning framework, modeling the sand barrier parameter optimization problem as a Markov decision process (MDP). In this framework, the environmental state is the micro-environmental feature, the action is the adjustment of sand barrier parameters, and the reward is the evaluation of the sand barrier's effectiveness. By continuously interacting with the environment and obtaining feedback, the reinforcement learning algorithm can progressively improve its decision-making strategy and find the optimal sand barrier parameter configuration.

[0107] The reinforcement learning framework employs a hybrid architecture combining deep Q-networks (DQNs) and policy gradients, enabling it to handle both continuous action spaces (such as continuous adjustments to sand barrier height and density) and discrete action spaces (such as material selection and arrangement pattern selection). The core of the algorithm is a state-action value function Q(s,a), used to estimate the long-term cumulative reward of performing action a in state s.

[0108] Preferably, the adaptive sand barrier parameter optimization module 3 adopts a hierarchical optimization strategy, first determining the sand barrier types and layouts in different regions at a macro scale, and then finely adjusting specific parameters at a micro scale. This hierarchical strategy ensures the rationality of the overall layout while also taking into account the optimization of local details, greatly improving computational efficiency and optimization quality. Simultaneously, the system also possesses rapid response capabilities, able to quickly adjust the sand barrier configuration scheme based on real-time information such as extreme weather warnings, improving the system's emergency adaptability.

[0109] Example 5

[0110] This embodiment details the various sand barrier parameters in the sand barrier specification library unit 31, including sand barrier height parameters, sand barrier density parameters, sand barrier material parameters, and sand barrier arrangement pattern parameters.

[0111] The height of the sand barrier is continuously adjustable from 20 to 50 cm. The height of the sand barrier is one of the key factors affecting sand fixation effectiveness; the greater the height, the wider the protection range of a single sand barrier, but the material consumption and construction difficulty also increase accordingly. Based on extensive experiments and practical experience, there are optimal sand barrier heights under different wind speed conditions: when the wind speed is less than 5 m / s, low sand barriers of 20-30 cm are sufficient; when the wind speed is in the range of 5-10 m / s, medium-height sand barriers of 30-40 cm are most effective; when the wind speed is greater than 10 m / s, high sand barriers of 40-50 cm are required for effective protection. The system will automatically calculate the optimal sand barrier height based on the regional wind speed characteristics and budget constraints.

[0112] The sand barrier density parameter, i.e., the air permeability, is continuously adjustable from 30% to 70%. Air permeability is an important indicator for measuring the density of the sand barrier structure and directly affects its aerodynamic characteristics. Studies have shown that when the air permeability is below 30%, strong vortices form on the leeward side of the sand barrier, leading to intensified wind erosion; when the air permeability is above 70%, the wind-blocking effect of the sand barrier is significantly reduced, and its sand-fixing capacity is insufficient. The optimal air permeability is closely related to the height of the sand barrier and the local wind speed. Generally, the higher the wind speed, the lower the suitable air permeability. The system uses a computational fluid dynamics model to accurately calculate the wind field distribution under different air permeability levels and find the optimal air permeability setting.

[0113] The sand barrier material parameters include three types: wheat straw, reeds, and shrub branches. Different materials have different performance characteristics and applicable conditions. Wheat straw sand barriers are simple to construct and low in cost, suitable for short-term rapid control, but their service life is only 1-2 years; reed sand barriers have high strength and a service life of 3-4 years, performing well in areas with abundant moisture; shrub branch sand barriers have the best durability, with a service life of over 5 years, but their construction is complex and costly. In practical applications, the system will select the most suitable sand barrier material based on the control period, budget constraints, and local resource availability. In some cases, the system will also recommend a mixed material scheme, such as using reeds for the main body and shrub branches for reinforcement, to balance cost and durability.

[0114] The sand barrier arrangement parameters include six types: square, strip, checkerboard, honeycomb, herringbone, and cross. Different arrangement patterns are suitable for different terrains and wind direction characteristics. The square arrangement is the most traditional sand barrier deployment method, suitable for areas with variable wind directions; the strip arrangement is set perpendicular to the prevailing wind direction, offering high sand fixation efficiency but poor adaptability to wind direction changes; the checkerboard arrangement can save materials while maintaining a certain sand fixation efficiency; the honeycomb arrangement has all-around protection capabilities and is suitable for complex wind field environments; the herringbone arrangement performs excellently in areas with a clear prevailing wind direction; and the cross arrangement is highly effective in monsoon regions with alternating wind directions. The system automatically selects the optimal arrangement pattern based on regional wind field characteristics and terrain conditions, and calculates the optimal spacing and coverage density.

[0115] In addition to basic parameters, the sand barrier specification library unit 31 also contains a wealth of combination schemes, which can generate personalized sand barrier configurations based on regional characteristics. For example, in areas with severe wind erosion, the system will recommend a denser sand barrier layout; in key areas for vegetation restoration, it will optimize sand barrier parameters to create a microenvironment more suitable for vegetation growth; and in areas undergoing phased treatment, it will consider the timing of sand barrier renewal and vegetation growth to formulate phased implementation plans.

[0116] Preferably, the sand barrier specification library unit 31 employs knowledge graph technology to combine expert experience with experimental data, constructing a knowledge base for sand barrier parameter selection. The system can quickly retrieve best practices from similar cases based on input environmental conditions, serving as the initial optimization solution, significantly improving optimization efficiency and solution quality.

[0117] Example 6

[0118] Reference Figure 5 The precision seed aerial seeding control module 4 in this embodiment includes an intelligent seed delivery unit 41, a seed packaging processing unit 42, and a delivery trajectory optimization unit 43.

[0119] The intelligent seed delivery unit 41 can automatically adjust delivery parameters based on real-time wind field data. Equipped with a precision control system, this unit can sense changes in wind speed and direction in real time and adjust the delivery height, delivery rate, and delivery direction accordingly. The system employs a closed-loop control strategy, ensuring precise seed placement at the target location through three stages: pre-delivery wind measurement, in-delivery correction, and post-delivery evaluation.

[0120] During the seeding process, the system calculates the impact of wind speed and direction on the seed's trajectory in real time and performs predictive compensation. The seeding height can be automatically adjusted within the range of 5-20m, lowering the height to reduce drift in strong winds and raising the height to increase coverage in weak winds. The seeding accuracy reaches ±0.5m horizontally and ±0.2m vertically, significantly superior to traditional aerial seeding techniques.

[0121] The seed coating treatment unit 42 is used to mix seeds with water-retaining agents, nutrients, and binders to improve wind resistance and germination rate. This unit employs advanced coating technology to combine seeds with various auxiliary materials to form seed balls with specific functions. The ratio of the seed coating materials is: seed:water-retaining agent:nutrient:binding agent = 1:0.5-2:0.1-0.5:0.1-0.4, which can be adjusted according to seed type and target area environmental conditions.

[0122] Water-retaining agents absorb and slowly release moisture, providing a stable water supply for seeds in arid environments; nutrients contain the basic nutrients needed for plant growth, promoting early seedling growth; binders increase seed weight and structural stability, improving wind resistance, and bind with soil particles to prevent seeds from being carried away by subsequent winds. Seeds treated with this coating can maintain a retention rate of over 90% even under wind conditions of force 8 (17.2–20.7 m / s), significantly improving the sowing success rate in harsh environments.

[0123] The trajectory optimization unit 43 is used to plan the optimal delivery path and flight parameters. Based on the micro-environment model of the target area and the sand barrier layout scheme, this unit calculates the optimal location, time, and density for seed delivery. The system employs a grid partitioning strategy, dividing the target area into several sub-regions and determining personalized delivery parameters based on the characteristics of each sub-region.

[0124] Seed density can be precisely controlled within the range of 500-2000 seeds / acre, and dynamically adjusted according to vegetation type, soil conditions, and expected survival rate. The system will prioritize increasing the density in microenvironments suitable for vegetation growth on the leeward side of the sand barrier, while also considering the spatial distribution pattern of vegetation to avoid excessive concentration or dispersion.

[0125] In terms of flight path planning, the system considers the collaborative operation of multiple drones and optimizes overall flight efficiency and coverage integrity through a time-space scheduling algorithm. Path planning not only considers geometric coverage but also incorporates wind field prediction and solar radiation variations to select the most suitable release time window and maximize seed germination probability.

[0126] Preferably, the precision seed aerial seeding control module 4 also has adaptive feedback capabilities, enabling it to dynamically adjust subsequent seeding strategies based on real-time seeding performance data. For example, if the seed retention rate in a certain area is detected to be lower than expected, the system will automatically increase the seeding density in that area or adjust the seeding parameters; if the system finds that the seed germination rate is particularly high under certain microenvironmental conditions, it will prioritize seeding in areas with similar conditions to achieve optimal resource allocation.

[0127] Example 7

[0128] This embodiment details the control parameters of the precision seed aerial seeding control module 4, including seed release density, release height, and seed coating material ratio.

[0129] Seed density can be precisely controlled within the range of 500-2000 seeds / acre. Density is a key parameter affecting vegetation restoration and needs to be determined based on a combination of factors. For fast-growing, high-tillering herbaceous plants (such as *Artemisia arenaria* and *Artemisia argyi*), a suitable density is 500-800 seeds / acre; for slow-growing but resilient shrubs (such as *Caragana korshinskii* and *Hippophae rhamnoides*), a suitable density is 800-1200 seeds / acre; and for rare plants with low germination rates, the density can be increased to 1500-2000 seeds / acre. Furthermore, the density needs to be adjusted according to soil conditions: in fertile semi-fixed sandy land, the density can be appropriately reduced; in barren shifting sand dunes, the density needs to be increased to ensure a sufficient number of surviving plants.

[0130] The system employs a variable-density deployment strategy, automatically adjusting the deployment density at different locations based on the distribution of microenvironmental suitability. In micro-areas with favorable moisture conditions, such as the leeward side of sand barriers and low-lying areas, the system increases the deployment density to maximize resource utilization efficiency; while in extremely harsh micro-environments, such as the top of sand dunes, the density is appropriately reduced to avoid resource waste.

[0131] The seed placement height can be automatically adjusted within the range of 5-20m. The placement height directly affects the seed dispersion range and drift, and needs to be optimized based on wind conditions and target accuracy. Under light wind conditions (≤3 levels, 3.4-5.4m / s), the system uses a higher placement height of 15-20m to achieve a wider coverage area; under moderate wind conditions (4-5 levels, 5.5-10.7m / s), a medium placement height of 10-15m is used to balance coverage and accuracy; under strong wind conditions (≥6 levels, 10.8-17.1m / s), the height is lowered to 5-10m to reduce the impact of wind and improve placement accuracy.

[0132] The system also considers the impact of terrain undulations on deployment altitude, appropriately increasing the flight altitude at terrain protrusions such as dune ridges to maintain a stable relative altitude to the ground and ensure uniform deployment density. For particularly complex terrain, the system will adopt a terrain-following mode, adjusting the flight altitude in real time to maintain optimal deployment results.

[0133] The seed coating material should be prepared according to the following ratio: seed: water-retaining agent: nutrient: binder = 1:0.5-2:0.1-0.5:0.1-0.4. Different plant seeds have different characteristics and require personalized coating formulas. For smaller herbaceous seeds (such as *Erigeron canadensis* and *Oryza sativa*), a higher proportion of water-retaining agent (1:1.5-2) is used to increase weight and water retention capacity; for larger shrub seeds (such as *Caragana korshinskii* and *Elaeagnus angustifolia*), a moderate proportion of water-retaining agent (1:0.8-1.2) is used; and for seeds with a natural coating structure (such as *Haloxylon ammodendron*), a lower proportion of water-retaining agent (1:0.5-0.8) is used.

[0134] The nutrient solution mainly consists of three macroelements—nitrogen, phosphorus, and potassium—and microelements such as iron, zinc, and boron. The ratio is adjusted according to plant needs and soil conditions. In extremely barren, pure sandy soil, the nutrient solution ratio is increased (1:0.3-0.5); in semi-fixed sandy soil with some organic matter, the ratio is appropriately reduced (1:0.1-0.2). The binder mainly uses biodegradable organic materials, such as modified starch and cellulose derivatives, to ensure sufficient bonding strength without affecting seed germination and root growth.

[0135] Preferably, the system continuously optimizes the packaging formula based on historical data and real-time feedback, forming a formula library suitable for different plant species and environmental conditions. For particularly challenging areas, the system also adds special functional additives, such as salt-tolerant components and microbial growth promoters, to further improve the success rate of vegetation restoration.

[0136] Example 8

[0137] Reference Figure 6 In this embodiment, the closed-loop feedback and dynamic optimization module 5 includes a periodic monitoring unit 51, an evaluation index calculation unit 52, and a self-learning optimization unit 53.

[0138] The periodic monitoring unit 51 collects data on the effectiveness of the remediation efforts at three frequencies: short-term, medium-term, and long-term. This unit is responsible for planning and executing monitoring tasks, collecting data on environmental changes and the effectiveness of the remediation efforts in the remediation area. The monitoring employs a multi-sensor collaborative strategy, combining drone aerial photography, ground sensor networks, and manual sampling to comprehensively capture changes during the remediation process.

[0139] The monitoring data mainly includes four aspects: sand barrier status (integrity, degree of deformation), wind and sand activity (aeolian deposition, wind erosion), vegetation status (coverage, biomass, species composition), and environmental parameters (wind speed, precipitation, temperature). The system will rationally arrange the monitoring frequency according to the data type and rate of change, maximizing resource utilization efficiency while ensuring data quality.

[0140] The evaluation index calculation unit 52 is used to calculate evaluation indicators such as sand fixation rate, vegetation coverage, biomass, seed germination rate, and cost-effectiveness ratio. This unit transforms the raw monitoring data into standardized evaluation indicators, facilitating the quantitative evaluation and comparison of governance effects.

[0141] The sand fixation rate is a core indicator for measuring the effectiveness of sand barriers in sand fixation. The calculation formula is as follows:

[0142]

[0143] Where FixationRate is the sand fixation rate, S erosion S represents the wind erosion area during the monitoring period. total This represents the total monitored area.

[0144] Vegetation cover and biomass are important indicators for evaluating the effectiveness of vegetation restoration. Vegetation cover was calculated using UAV multispectral imagery, while biomass was obtained through a combination of quadrat surveys and remote sensing estimation. Seed germination rate is a direct indicator of the effectiveness of aerial seeding, calculated using the following formula:

[0145]

[0146] Where Germination Rate is the seed germination rate, N seedling N represents the number of seedlings that germinate. seed This refers to the number of seeds planted.

[0147] Cost-benefit ratio is a comprehensive indicator for evaluating the economic efficiency of governance, and its calculation formula is as follows:

[0148]

[0149] Where CostBenefitRatio is the cost-benefit ratio, B ecological For ecological benefits (such as carbon sequestration and soil and water conservation value), B economic For economic benefits (such as the output value of bioproducts), C material For material costs, C operation Operating costs.

[0150] The self-learning optimization unit 53 is used to continuously improve the sand barrier aerial seeding collaborative strategy based on time series data. This unit uses machine learning methods to mine patterns from historical governance data and continuously update and improve the decision-making model. By comparing the differences between the predicted results and the actual effects, the system automatically adjusts the model parameters to improve the accuracy of predictions and the effectiveness of decision optimization.

[0151] The self-learning process includes four stages: data collection, pattern recognition, model updating, and strategy optimization. In the data collection stage, the system integrates multi-source monitoring data to form a standardized dataset. In the pattern recognition stage, data mining algorithms are applied to identify the correlation patterns between environmental conditions, governance measures, and their effects. In the model updating stage, the parameters of the prediction model are adjusted based on new data. In the strategy optimization stage, improved governance strategies are generated based on the updated model.

[0152] Preferably, the closed-loop feedback and dynamic optimization module 5 adopts a distributed architecture, offloading some processing tasks to edge devices to reduce the burden on the central system and improve response speed. Simultaneously, the system establishes a multi-layered security mechanism, including data encryption, access control, and anomaly detection, to ensure the system's secure and stable operation. Furthermore, the system also possesses offline working capabilities, enabling it to perform basic monitoring and optimization functions even in remote areas with unstable network connections.

[0153] Example 9

[0154] This embodiment details the monitoring frequency settings of the periodic monitoring unit 51, including short-term monitoring, medium-term monitoring, long-term monitoring, and emergency monitoring.

[0155] Short-term monitoring is conducted every 7 days. This monitoring primarily focuses on the condition of sand barriers and initial vegetation growth, serving as the foundation for rapid system response and adjustment. Short-term monitoring is particularly crucial in the initial stages of management (the first 1-2 months) to promptly identify and address problems. The monitoring method combines drone aerial photography with ground-based observations of key points, focusing on the integrity of sand barriers, the distribution of aeolian sand, and seed germination.

[0156] Monitoring data includes the integrity rate of sand barriers, changes in sand surface height, and the density and distribution of germination points. During data collection, the system automatically identifies abnormal areas and performs high-resolution focused sampling to ensure early detection and handling of problems. Short-term monitoring results are mainly used for fine-tuning sand barrier parameters and supplementing aerial seeding, representing tactical-level optimization adjustments.

[0157] Mid-term monitoring is conducted every 30 days. Mid-term monitoring primarily focuses on the overall trend of change and vegetation growth in the treated area, serving as the main basis for systematically evaluating the phased effectiveness. A comprehensive survey strategy is employed for mid-term monitoring, systematically collecting data across the entire treated area, including detailed topographic changes, vegetation parameters, and environmental indicators.

[0158] The monitoring content includes changes in dune morphology (volume, ridge position), vegetation parameters (height, cover, biomass), and environmental indicators (soil moisture, organic matter content). Mid-term monitoring results are used to assess the synergistic effect of sand barrier aerial seeding and guide the next stage of strategy adjustment, which is a periodic assessment at the strategic level.

[0159] Long-term monitoring is conducted every 90 days. It primarily focuses on the overall restoration of the ecosystem and the sustainability of the restoration efforts, serving as a crucial means of systematically assessing the ultimate effectiveness. In addition to conventional environmental and vegetation parameters, long-term monitoring incorporates deeper indicators such as biodiversity, ecosystem function, and socioeconomic impacts.

[0160] The monitoring content includes changes in species composition, the degree of soil improvement, ecosystem service functions (such as carbon sequestration and oxygen release, soil and water conservation), and improvements in the livelihoods of local residents. Long-term monitoring results are used to evaluate the comprehensive benefits of the remediation project and guide future large-scale planning decisions.

[0161] Emergency monitoring is triggered when environmental parameters change by more than 20%. Emergency monitoring is the system's rapid response mechanism to sudden events and abnormal changes. When the system detects a significant change (exceeding the threshold of 20%) in key environmental parameters (such as wind speed, precipitation, and temperature), it will automatically trigger the emergency monitoring process to quickly assess the potential impact of the change on the treated area.

[0162] Emergency monitoring employs a high-frequency sampling strategy in key areas to rapidly acquire critical data. By comparing this data with historical data, the severity and potential consequences of abnormal changes are assessed. Based on the assessment results, the system promptly adjusts sand barrier parameters or implements temporary protective measures to minimize adverse impacts. Common triggering events include sandstorms, heavy rainfall, and extreme heat waves.

[0163] Preferably, the monitoring frequency is not fixed but dynamically adjusted according to the stage of governance and actual needs. In the early stages of governance, the system tends to monitor more frequently to address various issues promptly; as governance enters a stable period, the monitoring frequency can be appropriately reduced to minimize resource consumption. In addition, the system will automatically adjust the monitoring strategy according to seasonal changes, such as increasing the monitoring frequency before the windy season and focusing on monitoring vegetation conditions during the plant growing season.

[0164] Example 10

[0165] Reference Figure 7 The sand barrier aerial seeding collaborative layout optimization method based on UAV swarm game theory provided in this embodiment includes the following steps:

[0166] Step S1: Collect multi-dimensional environmental data of the sandy area and construct a digital model of the microenvironment.

[0167] This step involves multiple drones equipped with multispectral cameras, thermal infrared sensors, and millimeter-wave radar simultaneously collecting multidimensional data on the sandy area. A four-dimensional scanning strategy (three-dimensional spatial data + one-dimensional temporal data) is employed to acquire detailed environmental data of the target area. The collected data includes topographic data (a 5cm resolution digital elevation model), wind field data (three-dimensional wind field at frequencies of 0.1–10Hz), and soil property data (humidity, temperature, and texture). Subsequently, a multi-source data fusion algorithm integrates data from different sources and at different scales into a unified microenvironmental digital model, providing fundamental data support for subsequent analysis.

[0168] Step S2: Based on the aforementioned microenvironment digital model, establish a spatiotemporal correlation model of the effect of sand barriers on wind field regulation and vegetation growth.

[0169] This step first constructs a sand barrier-wind field interaction model based on computational fluid dynamics (CFD) principles to predict the impact of different sand barrier morphologies on wind speed distribution and turbulence characteristics. Then, it constructs an environmental response model for desert vegetation growth to determine the microenvironmental conditions required for seed germination and seedling growth. Finally, it establishes an end-to-end prediction model of sand barrier parameters, microenvironmental conditions, and vegetation response using a convolutional neural network algorithm to achieve spatiotemporal correlation analysis between sand barrier layout and vegetation growth.

[0170] Step S3: Generate the optimal sand barrier configuration scheme based on the spatiotemporal correlation model.

[0171] This step, based on the analysis results of the spatiotemporal correlation model, generates the optimal sand barrier configuration scheme through multi-objective optimization and reinforcement learning algorithms. The system balances three objectives—sand fixation efficiency, vegetation growth, and cost control—to find the optimal equilibrium point. The configuration scheme includes parameters such as sand barrier height (continuously adjustable from 20 to 50 cm), sand barrier density (continuously adjustable air permeability from 30% to 70%), sand barrier materials (wheat straw, reeds, shrub branches), and sand barrier arrangement patterns (squares, strips, checkerboard, honeycomb, herringbone, cross). The optimization process adopts a hierarchical strategy, first determining the regional layout at a macro scale, and then fine-tuning specific parameters at a micro scale.

[0172] Step S4: Based on the sand barrier configuration scheme, adjust the seed placement parameters and control the seed placement location, time, and density.

[0173] This step precisely controls the location, timing, and density of seed dispensing based on the optimal sand barrier configuration and real-time wind field data. The system employs intelligent seed dispensing technology, capable of sensing changes in wind speed and direction in real time and adjusting the dispensing height (automatically adjusted from 5 to 20 meters), dispensing rate, and dispensing direction accordingly. Simultaneously, seed coating technology is used to mix the seeds with a water-retaining agent, nutrient agent, and binder (in a ratio of seed:water-retaining agent:nutrient agent:binding agent = 1:0.5-2:0.1-0.5:0.1-0.4), improving wind resistance and germination rate. The dispensing density is precisely controlled within the range of 500-2000 seeds / acre, dynamically adjusted according to microenvironmental conditions, increasing the dispensing density in the most suitable micro-areas for vegetation growth.

[0174] Step S5: Collect data on the treatment effect and evaluate the sand barrier's sand fixation efficiency and vegetation growth.

[0175] This step systematically collects data on environmental changes and the effectiveness of the treatment area according to three monitoring frequencies: short-term (once every 7 days), medium-term (once every 30 days), and long-term (once every 90 days). Monitoring content includes sand barrier status, wind and sand activity, vegetation status, and environmental parameters. The system transforms the raw monitoring data into standardized evaluation indicators, including sand fixation rate, vegetation coverage, biomass, seed germination rate, and cost-effectiveness ratio, facilitating the quantitative evaluation and comparison of treatment effectiveness. Emergency monitoring is also triggered when environmental parameters change by more than 20%, rapidly assessing the impact of abnormal changes.

[0176] Step S6: Based on the governance effect data, update the sand barrier configuration scheme and seed placement parameters to form a closed-loop optimization.

[0177] This step, based on the governance effectiveness evaluation results, uses machine learning methods to mine patterns from historical data and continuously update and improve the decision-making model. The system compares the differences between the predicted results and the actual effects, automatically adjusts the model parameters, and improves the accuracy of predictions and the effectiveness of decision optimization. The optimization process includes four stages: data collection, pattern recognition, model updating, and strategy optimization. Ultimately, it generates an improved sand barrier configuration scheme and seed placement parameters, achieving continuous optimization of the sand barrier aerial seeding coordinated strategy.

[0178] This method, by constructing a complete closed-loop mechanism of perception-analysis-decision-execution-optimization, achieves synergistic optimization of sand barrier layout and aerial seeding, significantly improving the efficiency and quality of desertification control. The system not only adapts to the complex and ever-changing desert environment but also possesses self-learning and continuous optimization capabilities, representing an important direction for the development of desertification control technology.

[0179] Example 11

[0180] This embodiment introduces a practical application case of the system in the Mu Us Desert and its effect evaluation.

[0181] The experimental area is located on the southern edge of the Mu Us Desert in Uxin Banner, Ordos City, Inner Mongolia Autonomous Region, covering a total area of ​​approximately 300 hectares. The terrain is characterized by a mix of semi-fixed and mobile dunes. The area receives an average annual precipitation of about 330 mm, an average annual evaporation of about 2100 mm, and an average annual wind speed of 3.5 m / s, classifying it as a typical arid and semi-arid climate zone. The main vegetation consists of drought-resistant plants such as Artemisia arenaria, Salix psammophila, and Salix matsudana, with a vegetation cover of less than 15%, indicating a fragile ecosystem.

[0182] The specific process of using this system for desertification control is as follows:

[0183] First, a cluster system of three multi-functional UAVs was deployed, each undertaking one of the three main tasks: environmental perception, sand barrier deployment, and seed sowing. During the system initialization phase, the environmental perception UAVs conducted a detailed scan of the entire test area, collecting high-resolution terrain data, wind field data, and soil property data to construct an accurate digital model of the microenvironment.

[0184] Based on microenvironment model analysis, the system identified wind and sand activity hotspots and potential suitable vegetation zones within the region, and generated a preliminary sand barrier layout scheme. According to regional wind field characteristics (dominant wind direction is northwest, secondary dominant wind direction is southeast) and topographic features (dust ridges mostly run northeast-southwest), the system recommended a mixed arrangement pattern primarily using honeycomb and herringbone patterns. The sand barrier height was set at 35-45 cm, with a ventilation rate of 40%-60%, and locally abundant reeds were selected as the material.

[0185] After the sand barriers were deployed, the system developed differentiated aerial seeding strategies based on microenvironmental assessment results. For suitable microenvironmental areas on the leeward side of the sand barriers, *Artemisia arenaria* and *Salix matsudana* seeds were selected, with a seeding density of 1200-1500 seeds / mu. For low-lying areas of sand dunes, *Amaranthus praecox* and *Caragana sinica* seeds were selected, with a seeding density of 900-1200 seeds / mu. For harsh areas at the top of sand dunes, *Haloxylon ammodendron* seeds were selected, with a seeding density of 600-800 seeds / mu. All seeds were coated, with a ratio of seed:water-retaining agent:nutrient agent:binder = 1:1.2:0.3:0.25.

[0186] After one year of system operation, a comprehensive evaluation of the governance effectiveness was conducted using a three-tiered monitoring system. The results showed:

[0187] 1. The sand barriers have a significant effect on sand fixation, with a sand fixation rate of 87.6%, a wind erosion reduction of 72.3%, and a decrease in the dune movement speed from 3.5 m / year to 0.8 m / year;

[0188] 2. The vegetation restoration effect was excellent, with an average seed germination rate of 68.4%, far exceeding the 35%–40% of traditional methods; vegetation coverage increased from less than 15% initially to 42.3%; and biomass increased from 45 g / m². 2 Increased to 186g / m 2 ;

[0189] 3. Biodiversity has significantly improved, with the number of plant species monitored increasing from 8 to 23 and the number of insect species increasing from 12 to 37;

[0190] 4. Soil quality improved, with the topsoil organic matter content increasing from 0.12% to 0.38%, and soil stability significantly enhanced;

[0191] 5. Reduced treatment costs: Treatment costs per hectare are reduced by approximately 42.7% compared to traditional methods, while the return on investment increases by 65.3%.

[0192] Compared with traditional single sand barrier management or conventional aerial seeding techniques, this system demonstrates significant advantages in sand fixation efficiency, vegetation survival rate, and cost-effectiveness. Particularly noteworthy is its superior performance in adaptability to extreme weather conditions. During the trial, the area experienced a Force 6 gale (wind speed reaching 13.5 m / s), resulting in a seed loss rate as high as 85% in the traditional treatment area, while the seed retention rate in the system-treated area remained at 92.3%, fully demonstrating the system's wind resistance and synergistic optimization effect.

[0193] Furthermore, data analysis of one year of system operation revealed a significant synergistic effect from the sand barrier aerial seeding synergistic optimization. The sand barriers not only directly stabilized the sand surface but also created a suitable microenvironment for vegetation growth; vegetation growth, in turn, enhanced sand surface stability and extended the lifespan of the sand barriers, forming a positive feedback loop. This synergistic effect cannot be achieved by a single technical means, demonstrating the innovative value and application prospects of this system.

[0194] In summary, the application case of this system in the Mu Us Desert fully verifies the practical effect of the collaborative layout optimization system for sand barrier aerial seeding based on UAV swarm game theory, and provides a new technical path for comprehensive desertification control.

[0195] The specific embodiments of the present invention have been described in detail above. However, it should be understood that those skilled in the art can make various changes, modifications, substitutions and variations to these embodiments without departing from the principles and concepts of the present invention. The scope of protection of the present invention should be determined by the appended claims.

Claims

1. A collaborative layout optimization system for sand barrier aerial seeding based on UAV swarm game theory, characterized in that, include: The multimodal desert microenvironment perception module is used to collect multidimensional environmental data of sandy areas and construct a digital model of the microenvironment that includes topography, wind field, humidity, and temperature. The sand barrier-vegetation coupled dynamic model module is connected to the multimodal desert microenvironment perception module for receiving the microenvironment digital model and constructing a spatiotemporal correlation model of sand barrier on wind field regulation and vegetation growth. An adaptive sand barrier parameter optimization module is data-connected to the sand barrier-vegetation coupled dynamic model module, and is used to generate an optimal sand barrier configuration scheme based on the spatiotemporal correlation model. The sand barrier configuration scheme includes parameters such as sand barrier height, density, material, and arrangement pattern. The precision seed aerial seeding control module is connected to the adaptive sand barrier parameter optimization module for receiving the sand barrier configuration scheme, adjusting the seed dispensing parameters according to real-time wind field data, and controlling the seed dispensing location, time and density. The closed-loop feedback and dynamic optimization module is connected to the precision seed aerial seeding control module and the multimodal desert microenvironment perception module for collecting data on the treatment effect, evaluating the sand barrier's sand fixation efficiency and vegetation growth status, and providing optimization suggestions to the adaptive sand barrier parameter optimization module.

2. The system according to claim 1, characterized in that, The multimodal desert microenvironment sensing module includes: The multispectral data acquisition unit is used to acquire surface information in the visible, near-infrared, and short-wave infrared bands. The wind field monitoring unit is used to collect three-dimensional wind field data at frequencies of 0.1–10 Hz. Topographic mapping unit, used to acquire a digital elevation model with a resolution of 5cm; The data fusion processing unit is used to integrate multi-source data to generate a unified digital model of the microenvironment.

3. The system according to claim 1, characterized in that, The sand barrier-vegetation coupled dynamic model module includes: The wind field regulation model unit is used to simulate the impact of different sand barrier morphologies on the wind field; A vegetation growth condition model unit is used to determine the microenvironmental threshold conditions required for vegetation growth. The spatiotemporal correlation calculation unit is used to establish a correlation model between sand barrier layout and vegetation growth through convolutional neural network algorithms.

4. The system according to claim 1, characterized in that, The adaptive sand barrier parameter optimization module includes: The sand barrier specification library unit contains parameters for 6 basic forms and 3 material combinations of sand barriers; A multi-objective optimization unit is used to balance the objectives of sand fixation efficiency, vegetation growth, and cost control. The reinforcement learning unit is used to adaptively adjust the sand barrier parameters to adapt to different microenvironmental conditions.

5. The system according to claim 4, characterized in that, The sand barrier parameters in the sand barrier specification library unit include: The height parameter of the sand barrier is continuously adjustable from 20 to 50 cm. The density parameter of the sand barrier, the air permeability range is continuously adjustable from 30% to 70%; Sand barrier material parameters, including wheat straw, reeds, and shrub branches; The parameters for sand barrier arrangement patterns include squares, stripes, checkerboard, honeycomb, herringbone, and cross.

6. The system according to claim 1, characterized in that, The precision seed aerial seeding control module includes: The intelligent seed delivery unit can automatically adjust the delivery parameters based on real-time wind field data; Seed coating treatment unit is used to mix seeds with water-retaining agents, nutrients and binders to improve wind resistance; The delivery trajectory optimization unit is used to plan the optimal delivery path and flight parameters.

7. The system according to claim 6, characterized in that, The control parameters of the precision seed aerial seeding control module include: Seed density should be precisely controlled within the range of 500-2000 seeds per acre; The deployment height is automatically adjusted within the range of 5-20m; The seed coating material is prepared according to the following ratio: seed: water-retaining agent: nutrient agent: adhesive = 1:0.5-2:0.1-0.5:0.1-0.

4.

8. The system according to claim 1, characterized in that, The closed-loop feedback and dynamic optimization module includes: The regular monitoring unit collects data on the treatment effect at three levels: short-term, medium-term, and long-term. The evaluation index calculation unit is used to calculate sand fixation rate, vegetation coverage, biomass, seed germination rate, and cost-benefit ratio. A self-learning optimization unit is used to continuously improve the collaborative strategy of aerial seeding of sand barriers based on time series data.

9. The system according to claim 8, characterized in that, The monitoring frequency of the periodic monitoring unit is set as follows: Short-term monitoring, once every 7 days; Mid-term monitoring, once every 30 days; Long-term monitoring, once every 90 days; Emergency monitoring is triggered when environmental parameters change by more than 20%.

10. A collaborative layout optimization method for sand barrier aerial seeding based on UAV swarm game theory, employing the system described in any one of claims 1-9, characterized in that, Includes the following steps: Collect multidimensional environmental data of sandy areas and construct a digital model of the microenvironment; Based on the aforementioned microenvironment digital model, a spatiotemporal correlation model of the effect of sand barriers on wind field regulation and vegetation growth is established. Based on the spatiotemporal correlation model, an optimal sand barrier configuration scheme is generated; Based on the aforementioned sand barrier configuration scheme, adjust the seed placement parameters to control the seed placement location, time, and density; Collect data on the effectiveness of the treatment to assess the efficiency of sand barrier fixation and vegetation growth. Based on the governance effect data, the sand barrier configuration scheme and seed placement parameters are updated to form a closed-loop optimization.

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