Grassland desertification full-life-cycle intelligent management and emergency response method

Abstract

The invention discloses a grassland desertification full-life-cycle intelligent management and emergency response method. The method comprises the following steps: acquiring multi-source data through a space-ground-air integrated monitoring network, and constructing a space-time knowledge graph fused with ecological background information; carrying out desertification risk early recognition and early warning on the atlas by utilizing a dynamic risk assessment model of a fusion mechanism and data; when the early warning exceeds a threshold value, an emergency response mechanism is started, a multi-target genetic algorithm is adopted to generate an optimal resource scheduling scheme with the shortest resource scheduling time and the lowest transportation cost, and an instruction is automatically issued; after emergency actions, vegetation growth, soil water and salt migration and wind erosion processes are simulated based on digital twinborn bodies, long-term ecological and economic benefit prediction and quantitative evaluation are carried out on the treatment effect, the result is fed back to the risk evaluation model, and a management closed loop of monitoring, evaluation, decision making, treatment and re-monitoring is formed; the grassland desertification management system realizes the whole-process intelligent, precise and closed-loop management of grassland desertification from sensing, early warning, decision making to governance and evaluation.

Description

Technical Field

[0001] This invention belongs to the field of information technology for ecological protection and restoration, specifically involving a smart management and emergency response method for the entire life cycle of grassland desertification that combines integrated air-ground-space monitoring, spatiotemporal knowledge graphs, artificial intelligence risk assessment, and digital twin technology. Background Technology

[0002] Grasslands, as an important terrestrial ecosystem, play a crucial role in preventing desertification, maintaining biodiversity, and ensuring the sustainable development of animal husbandry. However, due to the combined effects of climate change and human activities, grassland degradation and desertification are becoming increasingly serious worldwide. Timely and accurate monitoring, assessment, and management of grassland desertification have become an urgent challenge.

[0003] Currently, the monitoring and management of grassland desertification mainly relies on manual ground surveys and satellite remote sensing interpretation. Traditional methods primarily depend on professionals conducting regular field surveys, combined with visual interpretation or simple index analysis of periodic satellite remote sensing imagery. This approach is time-consuming and labor-intensive, has limited coverage, and is insensitive to early and subtle changes in the desertification process, exhibiting significant time lag. Isolated model predictions exist, with some existing technologies using statistical models based on historical data or simple mechanistic models to assess land degradation risk. These models rely on single data sources, failing to effectively integrate multi-dimensional, real-time dynamic data from space, air, and ground, and have long update cycles, making accurate prediction difficult. The system fails to reflect the dynamic changes in desertification risk, resulting in insufficient accuracy and timeliness in prediction. Its passive emergency response often leads to desertification being discovered only in its later stages, at which point remediation plans rely heavily on expert experience and lack scientific resource optimization and scheduling decision support. This results in inefficient emergency response, high remediation costs, and difficulty in quantifying and evaluating effectiveness. Furthermore, the management process is fragmented, with existing monitoring, assessment, decision-making, and remediation processes operating independently. Data flow and business flow fail to form an effective closed loop. After implementation, there is a lack of continuous and accurate effect evaluation and feedback mechanisms, hindering dynamic optimization and adjustment of remediation strategies and making it difficult to achieve adaptive management throughout the entire lifecycle.

[0004] Therefore, there is an urgent need in this field for a grassland desertification management technology solution that can integrate multi-source data, achieve early and accurate warning, provide intelligent decision support, and dynamically simulate and provide feedback on governance effects, in order to overcome the shortcomings of existing technologies such as fragmentation, lag and passivity. Summary of the Invention

[0005] The purpose of this invention is to overcome the problems of lag, passivity, and disconnect in management in existing grassland desertification monitoring and control technologies, and to provide a smart management and emergency response method for the entire life cycle of grassland desertification. It aims to achieve intelligent, precise, and closed-loop management throughout the entire process, from data perception, early risk warning, intelligent decision-making to effect evaluation and feedback, thereby improving the efficiency and scientific nature of grassland desertification prevention and control. This invention can achieve its purpose through the following technical solutions:

[0006] To achieve the above-mentioned objectives, the present invention adopts the following technical solution:

[0007] A smart management and emergency response method for the entire life cycle of grassland desertification includes the following steps:

[0008] S1: Collect multi-source grassland data through an integrated air-ground-space monitoring network, and construct a spatiotemporal knowledge graph that integrates grassland ecological baseline information, including vegetation type, soil physicochemical properties, topography and geomorphology and historical meteorological elements.

[0009] S2: Based on the spatiotemporal knowledge graph, use the dynamic risk assessment model to calculate the risk level of grassland desertification and perform early identification and warning of the desertification process;

[0010] S3: When the warning level exceeds the preset threshold, the emergency response mechanism is activated, and the optimal emergency resource scheduling plan and action instructions are generated based on the operations research optimization algorithm;

[0011] S4: After the emergency response is carried out, based on continuous monitoring data, the governance effect is simulated and evaluated using a digital twin, and the evaluation results are fed back to the dynamic risk assessment model to form a management closed loop of monitoring, evaluation, decision-making, governance, and re-monitoring.

[0012] A smart management and emergency response method for the entire life cycle of grassland desertification; acquiring large-scale vegetation cover index and surface temperature data through satellite remote sensing; conducting high-precision patrols of key areas using drones equipped with multispectral sensors to acquire data on vegetation community structure and bare sand distribution; and collecting real-time data on soil moisture, wind speed, and soil pH through a network of IoT sensor nodes deployed on the ground.

[0013] The spatiotemporal knowledge graph uses multi-source data as entities and attributes, and models the spatiotemporal and ecological causal relationships between entities as relations to form semantic associations; specifically including:

[0014] The grassland multi-source data is spatiotemporally registered and standardized; the processed data is instantiated into entities in a knowledge graph; semantic rules and relation extraction algorithms are then used to establish spatial relationships between entities (located or adjacent), temporal relationships (occurring or preceding), and causal relationships (inhibiting, aggravating, or affecting the ecology); thus forming a spatiotemporal knowledge graph that integrates static background features and dynamic logical reasoning, and supports semantic querying and logical reasoning.

[0015] Furthermore, the ground-based IoT sensor node performs edge computing, specifically including:

[0016] Local preprocessing and outlier detection are performed on the collected raw data;

[0017] The high-frequency acquisition mode is only activated and the data is uploaded when there is data anomaly or when instructions are received from the cloud, in order to reduce overall power consumption and communication load.

[0018] Preferably, the dynamic risk assessment model in step S2 is an AI model that integrates mechanisms and data, which performs the following operations;

[0019] The fusion mechanism and data refer to the following: the model uses a data-driven temporal deep learning network as its core to make a preliminary prediction of the probability of desertification risk, while coupling a grassland ecological mechanism model to verify the rationality and provide a physical interpretation of the preliminary prediction results.

[0020] Specifically, it performs the following operations:

[0021] Using the spatiotemporal knowledge graph as input, feature sequences related to desertification are extracted;

[0022] The feature sequence is input into a long short-term memory network pre-trained based on long sequence multi-source samples from grasslands, and the weights are updated online through incremental learning to predict the probability of desertification risk within a specific time window in the future.

[0023] By combining the grassland ecological mechanism model, the desertification risk probability is compared with the results of the grassland ecological mechanism model in parallel. The dominant driving factors are identified through feature importance analysis, and an interpretable risk level is formed. The grassland ecological mechanism model is based on the energy balance equation of soil, vegetation and atmosphere. The critical threshold of desertification is calculated simultaneously and used for cross-validation with the desertification risk probability predicted by the time-series deep learning network.

[0024] Preferably, the emergency response mechanism in step S3 specifically includes:

[0025] S301: Based on the warning level and the geographical location of the desertified patches, determine the types and quantities of resources that need to be dispatched, including grass seeds, sand barriers, watering trucks, and workers.

[0026] S302: Using a multi-objective genetic algorithm, a fitness function is constructed with the dual objectives of minimizing resource scheduling time and minimizing transportation cost, to calculate the optimal delivery path and scheduling scheme for resources from the warehouse to each desertified patch;

[0027] S303: Automatically distribute the scheduling scheme and action instructions to the corresponding mobile terminals and track the execution status in real time;

[0028] Furthermore, step S302 specifically includes:

[0029] An optimization model is constructed with the core objective of minimizing total transportation time and total transportation cost. The total transportation time includes the vehicle's travel time and the operation dwell time at each desertification patch, while the total transportation cost includes the vehicle's fixed cost, variable transportation cost, and penalty cost caused by time window delays. A chromosome is constructed using a two-segment integer coding method. The first segment code represents the order in which all desertification patches are visited, and the second segment code represents the resource loading allocation scheme for each transport vehicle.

[0030] Based on the allocation scheme, a fitness function is designed with transportation time efficiency and transportation cost economy as the core evaluation indicators. The weight of each indicator is dynamically adjusted according to the warning level. The higher the warning level, the greater the weight of the time efficiency indicator. During the fitness evaluation process, the compliance of the resource demand satisfaction of each desertification spot, the maximum load limit of each vehicle, and the emergency operation time window requirements is verified simultaneously.

[0031] Based on the fitness function, a population of feasible solutions that satisfy all constraints is initialized. During the iterative evolution process, individual selection based on the roulette wheel strategy, sequential crossover operation that preserves key gene segments, and random two-point inversion mutation operation are executed in sequence. When the evolution reaches the preset maximum number of iterations or the optimal solution satisfies the convergence condition, the calculation is terminated and the scheme with the best comprehensive evaluation value is selected from the Pareto non-dominated solution set as the final delivery path and scheduling scheme.

[0032] Furthermore, step S4, which involves simulating and evaluating the governance effect using a digital twin, includes:

[0033] S401: Based on the aforementioned spatiotemporal knowledge graph and grassland ecological process model, construct a digital twin capable of simulating vegetation growth, soil water and salt transport, and wind erosion processes;

[0034] S402: In the digital twin, different governance schemes are simulated and deduced to predict their long-term ecological and economic benefits;

[0035] S403: Based on the comparison between actual monitoring data and simulated data after treatment, quantitatively evaluate the treatment effect, and use the comparison results to dynamically optimize the model parameters of the digital twin;

[0036] Furthermore, the prediction of its long-term ecological and economic benefits in S402 includes:

[0037] Based on the simulation and deduction of the digital twin, key ecological and economic benefit indicators within the future time scale are quantitatively output.

[0038] Among them, ecological benefit indicators include at least: the increase in vegetation coverage, the accumulation of soil organic carbon storage, and the rate of decrease in wind erosion modulus.

[0039] Economic benefit indicators should include at least: the expected revenue from carbon trading, the direct income from increased forage production, and the future cost savings from curbing desertification.

[0040] By comprehensively comparing the indicators under different governance schemes, a forward-looking assessment and selection of their long-term comprehensive benefits can be achieved.

[0041] Preferably, the method is executed based on a collaborative architecture of cloud central node, edge computing node, mobile terminal and sensor node, wherein:

[0042] The cloud-based central node is responsible for global data storage, knowledge graph construction, and large-scale simulation.

[0043] Edge computing nodes are deployed at regional management stations and are responsible for real-time data processing, lightweight model inference, and rapid issuance of emergency commands within their respective regions.

[0044] Mobile terminals and sensor nodes act as execution ends, responsible for data acquisition and command reception.

[0045] The beneficial effects of this invention are as follows:

[0046] This invention achieves deep fusion and semantic association of multi-source grassland ecological data by constructing an integrated air-ground-space monitoring network and spatiotemporal knowledge graph, significantly improving data utilization efficiency and the comprehensiveness of state perception. Based on the fusion mechanism and dynamic risk assessment model of the data, it enables early and accurate identification and warning of grassland desertification risks, effectively overcoming the lag and passivity of traditional methods. By introducing operations research optimization algorithms to generate emergency resource scheduling schemes, it greatly improves the efficiency of emergency response and reduces governance costs. Furthermore, by using digital twins to dynamically simulate and evaluate the governance effects and feeding the results back to the risk assessment model, it forms an intelligent management closed loop of monitoring, assessment, decision-making, governance, and re-monitoring, achieving adaptive and precise management of the entire life cycle of grassland desertification. In addition, by linking ecological value with green finance, it innovatively transforms governance results into economic benefits, providing endogenous impetus for the sustainability of grassland protection. Finally, through the collaborative architecture of cloud, edge, and mobile terminals, this invention ensures the efficient, stable, and scalable operation of the system, providing a complete, efficient, and implementable intelligent management solution for grassland desertification prevention and control. Attached Figure Description

[0047] To better understand and implement this application, the technical solution is described in detail below with reference to the accompanying drawings.

[0048] Fig. 1 A method diagram illustrating a smart management and emergency response approach for the entire lifecycle of grassland desertification;

[0049] Fig. 2 A front view of a smart management and emergency response method for the entire life cycle of grassland desertification;

[0050] Fig. 3 This is a flowchart of a smart management and emergency response method for the entire life cycle of grassland desertification. Detailed Implementation

[0051] To further illustrate the technical means and effects adopted by the present invention to achieve its intended purpose, exemplary embodiments will be described in detail below, examples of which are illustrated in the accompanying drawings. In the following description relating to the drawings, unless otherwise indicated, the same numbers in different drawings represent the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with this application. Rather, they are merely examples of methods and systems consistent with some aspects of this application as detailed in the appended claims.

[0052] The terminology used in this application is for the purpose of describing particular embodiments only and is not intended to be limiting of the application. The singular forms “a,” “the,” and “the” used in this application and the appended claims are also intended to include the plural forms unless the context clearly indicates otherwise. It should also be understood that the term “and / or” as used herein refers to and includes any or all possible combinations of one or more of the associated listed items.

[0053] The following detailed description of the specific implementation methods, features, and effects of the present invention, in conjunction with the accompanying drawings and preferred embodiments, is provided in detail.

[0054] Example 1

[0055] Please see Figs. 1-3 This embodiment provides a specific implementation method for a smart management and emergency response system for the entire life cycle of grassland desertification.

[0056] This embodiment executes the method through a collaborative architecture comprising a cloud central platform, multiple regional edge computing nodes, and mobile terminals and IoT sensor networks deployed in the field.

[0057] A smart management and emergency response method for the entire life cycle of grassland desertification includes the following steps:

[0058] S1. Collect multi-source grassland data through an integrated air-ground-space monitoring network, and construct a spatiotemporal knowledge graph that integrates grassland ecological baseline information, including vegetation type, soil physicochemical properties, topography and geomorphology and historical meteorological elements.

[0059] Furthermore, the integrated air-ground-space monitoring network utilizes Landsat-8 / 9 and Sentinel-2 multi-source satellite imagery to acquire long-term time-series vegetation cover index (NDVI) data at 30-meter resolution and surface temperature data at 10-meter high spatial resolution. For key inspection areas, DJI Matrice 350 RTK drones equipped with P1 multispectral sensors are used to perform automated strip patrols at a flight altitude of 100 meters to generate centimeter-level accuracy maps of vegetation community structure and bare sand distribution. Simultaneously, a network of IoT sensor nodes based on the LoRaWAN communication protocol is deployed on the ground, with a node density of one per square kilometer. This network integrates a YL-69 soil moisture sensor with an accuracy of ±3%, an anemometer with a range of 0-60 m / s, and a pH sensor with an accuracy of ±0.1%, enabling real-time acquisition and low-power transmission of soil moisture, wind speed, and pH values.

[0060] S2. Based on the spatiotemporal knowledge graph, use the dynamic risk assessment model to calculate the risk level of grassland desertification, and perform early identification and warning of the desertification process;

[0061] The Neo4j graph database is used as the storage engine, and the ontology schema is defined based on the Apache Jena framework. The construction process first performs spatiotemporal registration and standardization on multi-source data from air, land, and space. Then, vegetation patches, soil nodes, weather stations, etc. are abstracted into entities, and dynamic attributes such as NDVI value, soil moisture, and precipitation are added to them. Subsequently, through preset semantic rules and relation extraction algorithms, spatial relationships, temporal relationships, and ecological causal relationships such as influencing factors are established between entities, ultimately forming a reasonable knowledge graph that integrates static background features and dynamic logical reasoning. S3, when the warning level exceeds the preset threshold, the emergency response mechanism is activated, and the optimal emergency resource scheduling plan and action instructions are generated based on the operations research optimization algorithm.

[0062] Furthermore, a multi-objective genetic algorithm is employed, with a population size of 100, an evolutionary cycle of 500 generations, a crossover probability of 0.8, and a mutation probability of 0.1. A weighted fitness function is constructed with the dual objectives of minimizing resource scheduling time and minimizing transportation cost. T represents resource scheduling time, and C represents transportation cost. In the route planning, three types of realistic constraints are strictly followed: the maximum load of each transport vehicle is 5 tons, each desertified spot must complete the service within a 4-hour emergency time window, and the vehicle speed is dynamically calibrated according to different road grades such as asphalt roads and dirt roads. Finally, the Pareto optimal delivery route solution in terms of time and cost is selected through non-dominated sorting.

[0063] S4. After the emergency response is carried out, based on continuous monitoring data, the governance effect is simulated and evaluated using a digital twin, and the evaluation results are fed back to the dynamic risk assessment model to form a management closed loop of monitoring, evaluation, decision-making, governance, and re-monitoring.

[0064] Furthermore, step S1 specifically includes: acquiring large-scale vegetation cover index and surface temperature data through satellite remote sensing;

[0065] By using drones equipped with multispectral sensors, high-precision surveys can be conducted in key areas to obtain data on vegetation community structure and the distribution of bare sandy land.

[0066] The system collects real-time data on soil moisture, wind speed, and soil pH by deploying a network of IoT sensor nodes on the ground.

[0067] The spatiotemporal knowledge graph uses multi-source data as entities and attributes, and models the spatiotemporal and ecological causal relationships between entities as relations to form semantic associations. Specifically, this includes: performing spatiotemporal registration and standardization on the grassland multi-source data; instantiating the processed data into entities in the knowledge graph; and then using semantic rules and relation extraction algorithms to establish spatial relationships (location, adjacency), temporal relationships (occurrence, precedence), and ecological causal relationships (inhibition, aggravation, influence) between entities; thus forming a spatiotemporal knowledge graph that integrates static background features and dynamic logical reasoning, supporting semantic queries and logical reasoning. Furthermore, the edge computing performed by the ground-based IoT sensor nodes specifically includes: performing local preprocessing and outlier detection on the collected raw data; and only activating a high-frequency acquisition mode and uploading data when data is abnormal or when cloud instructions are received, in order to reduce overall power consumption and communication load.

[0068] Further, step S2 specifically includes:

[0069] Using the spatiotemporal knowledge graph as input, feature sequences related to desertification are extracted; the feature sequences are then input into a temporal deep learning network that is pre-trained using long-sequence multi-source samples of grassland history and can be incrementally learned and updated online to predict the probability of desertification risk within a specific time window in the future; combined with a grassland ecological mechanism model, the prediction results of the AI ​​model are validated and explained for rationality, and the risk level and main driving factors are output.

[0070] The extraction of feature sequences related to desertification was achieved through multi-source data from a spatiotemporal knowledge graph. First, entities and attributes closely related to the desertification process were screened from the knowledge graph, including vegetation cover index, soil moisture, soil pH, wind speed, surface temperature, and historical meteorological data on precipitation and evaporation. The data was processed into time series format, and redundant information was removed using feature engineering methods such as sliding window analysis, principal component analysis, and correlation screening, retaining key desertification driving factors. Finally, a set of multidimensional feature sequences was formed, which included the current state, integrated historical trends and spatial correlations, providing structured input for subsequent risk assessment.

[0071] The probability of desertification risk within a specific time window is predicted using a temporal deep learning network. The network employs a long short-term memory architecture and is pre-trained using multi-source samples from long-term grassland historical sequences to learn the temporal patterns of desertification evolution. After inputting the feature sequence, the network calculates the probability of desertification occurring within the next 30 days of a specific time window through forward propagation. The model supports online incremental learning and can dynamically update parameters by combining real-time monitoring data to improve prediction accuracy and adaptability, outputting a probability value between 0 and 1, representing the likelihood of desertification occurring.

[0072] The risk level is output by fusing AI prediction results with grassland ecological mechanism models. The desertification risk probability predicted by the time-series deep learning network is compared and verified with the simulation results of the mechanism model to ensure the rationality of the prediction. The risk level is divided according to the probability value: low risk is below 0.3; medium risk is between 0.3 and 0.6; and high risk is above 0.6. The model identifies the main driving factors through feature importance analysis. The risk level and key factors are output to provide an interpretable basis for early warning and decision-making.

[0073] Specifically, the model uses a three-layer LSTM network as the core temporal prediction unit, with 128 hidden units per layer, followed by a dropout layer with a dropout rate of 0.2 and a Sigmoid activation output layer. It uses multi-source historical data covering at least 10 years of grassland desertification evolution cycles, pre-trained with mean squared error as the loss function and an Adam optimizer with an initial learning rate of 0.001, a batch size of 32, and a training cycle of 100 rounds. After deployment, the model supports online incremental learning. When the error between the real-time monitoring data and the prediction results continuously exceeds a 15% threshold, a parameter fine-tuning mechanism is automatically triggered. By freezing the bottom layer of the LSTM network and only updating the weights of the last two fully connected layers, the model's capabilities are dynamically evolved and the prediction accuracy is continuously optimized.

[0074] Furthermore, in a method for intelligent management and emergency response of grassland desertification throughout its entire life cycle, the emergency response mechanism in step S3 specifically includes:

[0075] S301: Based on the warning level and the geographical location of the desertified patches, determine the types and quantities of resources that need to be dispatched, including grass seeds, sand barriers, watering trucks, and workers.

[0076] S302: Using a multi-objective genetic algorithm, a fitness function is constructed with the dual objectives of minimizing resource scheduling time and minimizing transportation cost, to calculate the optimal delivery path and scheduling scheme for resources from the warehouse to each desertified patch;

[0077] S303: Automatically distribute the scheduling scheme and action instructions to the corresponding mobile terminals and track the execution status in real time;

[0078] Furthermore, step S302 specifically includes:

[0079] An optimization model is constructed with the core objective of minimizing total transportation time and total transportation cost. The total transportation time includes the vehicle's travel time and the operation dwell time at each desertification patch, while the total transportation cost includes the vehicle's fixed cost, variable transportation cost, and penalty cost caused by time window delays. A chromosome is constructed using a two-segment integer coding method. The first segment code represents the order in which all desertification patches are visited, and the second segment code represents the resource loading allocation scheme for each transport vehicle.

[0080] Based on the allocation scheme, a fitness function is designed with transportation time efficiency and transportation cost economy as the core evaluation indicators. The weight of each indicator is dynamically adjusted according to the warning level. The higher the warning level, the greater the weight of the time efficiency indicator. During the fitness evaluation process, the compliance of the resource demand satisfaction of each desertification spot, the maximum load limit of each vehicle, and the emergency operation time window requirements is verified simultaneously.

[0081] Based on the fitness function, a population of feasible solutions that satisfy all constraints is initialized. During the iterative evolution process, individual selection based on the roulette wheel strategy, sequential crossover operation that preserves key gene fragments, and random two-point inversion mutation operation are executed in sequence. When the evolution reaches the preset maximum number of iterations or the optimal solution satisfies the convergence condition, the calculation is terminated and the scheme with the best comprehensive evaluation value is selected from the Pareto non-dominated solution set as the final delivery path and scheduling scheme.

[0082] Specifically, through the optimized design of a multi-objective genetic algorithm, an optimal balance between time efficiency and economic cost was achieved in the emergency resource scheduling scheme. Under the premise of ensuring that the resource needs of each desertification spot, vehicle load limits, and emergency time windows are met, the optimal scheduling scheme that comprehensively considers transportation routes, resource allocation, and operation sequence can be quickly generated. Through an adaptive weight adjustment mechanism based on the warning level, the optimization strategy is intelligently adjusted according to the urgency of the event. When facing a high-risk warning, priority is given to ensuring response speed, while under normal circumstances, cost control is emphasized. The final scheme based on the Pareto optimal solution set screening not only ensures the timeliness of emergency response but also significantly reduces the transportation cost of resource allocation, providing scientific and efficient decision support for emergency management of grassland desertification.

[0083] Further, step S4 specifically includes: simulating and evaluating the governance effect using a digital twin, including:

[0084] S401: Based on the aforementioned spatiotemporal knowledge graph and grassland ecological process model, construct a digital twin capable of simulating vegetation growth, soil water and salt transport, and wind erosion processes;

[0085] S402: In the digital twin, different governance schemes are simulated and deduced to predict their long-term ecological and economic benefits;

[0086] S403: Based on the comparison between actual monitoring data and simulated data after treatment, quantitatively evaluate the treatment effect, and use the comparison results to dynamically optimize the model parameters of the digital twin;

[0087] Furthermore, in the digital twin, the prediction of long-term ecological and economic benefits is achieved by using governance schemes, such as planting different grass species and the density and layout of sand barriers, as input parameters to drive the built-in grassland ecological process model to perform dynamic simulation. The model simulates the vegetation growth, soil water and salt transport, and wind erosion changes over the next few years or even decades, and quantifies key ecological benefit indicators, such as the increase in vegetation coverage, soil carbon sequestration, and wind erosion reduction. Based on these ecological indicators, combined with market data and cost models, the corresponding economic benefits are further calculated, such as carbon sink trading revenue, the benefits from increased pasture production, and the governance costs saved due to the reduction of desertification. This allows for a forward-looking assessment and comparison of the long-term comprehensive benefits of different schemes.

[0088] Specifically, a high-fidelity ecological process model is built on the cloud, integrating the BEPS model to simulate vegetation photosynthetic productivity, the HYDRUS-1D model to solve the soil water and salt transport equation, and the WEPS wind erosion prediction model to calculate sand transport flux. Administrators can set sand barrier layouts, such as 1.5m×1.5m grass squares, and grass species ratios, such as Caragana korshinskii:Artemisia argyi = 6:4, through drag-and-drop components on the interactive interface. The system automatically drives multi-model coupling calculations to predict changes in vegetation cover, soil carbon sequestration, and wind erosion modulus over the next 5-10 years, and generates real-time dynamic 3D desertification evolution animations and radar charts comparing governance benefits. Simultaneously, through data assimilation technology, actual monitoring data after governance is fused and corrected with simulation results to continuously optimize model parameters and improve prediction fidelity.

[0089] Furthermore, a method for intelligent management and emergency response to grassland desertification throughout its entire life cycle also includes steps for realizing ecological value:

[0090] Based on the vegetation carbon sequestration capacity and ecosystem health status dynamically reflected by the spatiotemporal knowledge graph, ecological value is calculated and realized; based on the vegetation biomass data in the spatiotemporal knowledge graph, grassland carbon sink is calculated and a verification report that meets carbon trading standards is generated; grassland health status is linked to green financial products, providing preferential loan interest rates or insurance premiums for herders with good grassland conditions, so as to promote sustainable governance through economic incentives.

[0091] First, based on the dynamically updated vegetation biomass and community structure data in the spatiotemporal knowledge graph, the photosynthetic efficiency model and soil carbon flux monitoring data are used to achieve accurate calculation of grassland carbon storage by combining millimeter-wave remote sensing inversion with ground verification.

[0092] Then, the certified carbon sink assets are stored on the blockchain through smart contracts, and a digital audit report that meets international certification standards is automatically generated. At the same time, a dynamic mapping relationship between the grassland health index and the financial risk control model is constructed. When the system detects that the grassland ecological parameters are continuously better than the set threshold, the pre-credit process is triggered to the cooperating financial institution through the API interface, and a differentiated credit plan is automatically generated for the corresponding herder. The changes in ecological indicators are also bound to the insurance floating rate algorithm to form a dynamic premium adjustment mechanism based on IoT data.

[0093] Furthermore, the prediction of its long-term ecological and economic benefits in S402 includes:

[0094] Based on the simulation and deduction of the digital twin, key ecological and economic benefit indicators within the future time scale are quantitatively output.

[0095] Among them, ecological benefit indicators include at least: the increase in vegetation coverage, the accumulation of soil organic carbon storage, and the rate of decrease in wind erosion modulus.

[0096] Economic benefit indicators should include at least: the expected revenue from carbon trading, the direct income from increased forage production, and the future cost savings from curbing desertification.

[0097] By comprehensively comparing the indicators under different governance schemes, a forward-looking assessment and selection of their long-term comprehensive benefits can be achieved;

[0098] Furthermore, a smart management and emergency response method for the entire lifecycle of grassland desertification is proposed. This method is based on a collaborative architecture of cloud-based central nodes, edge computing nodes, and mobile terminals. It employs the MQTT protocol for low-latency data synchronization, with edge nodes incorporating lightweight TensorFlow Lite models for real-time inference. Mobile terminals utilize 4G and 5G multi-mode transmission to ensure reliable command delivery. Specifically, the cloud-based central node is responsible for global data storage, knowledge graph construction, and large-scale simulation; edge computing nodes are deployed at regional management stations, responsible for real-time data processing, lightweight model inference, and rapid issuance of emergency commands within their respective regions; and mobile terminals and sensor nodes serve as execution ends, responsible for data acquisition and command reception.

[0099] In the aforementioned collaborative architecture, the cloud-based central node is primarily responsible for establishing a unified grassland ecological data lake, integrating, archiving, and storing multi-source monitoring data across the entire region for long-term purposes. Relying on its powerful computing capabilities, it constructs and maintains a global spatiotemporal knowledge graph, performs knowledge reasoning and relationship mining, and undertakes large-scale, high-fidelity digital twin simulation operations, such as ecological process extrapolation over decades and comparison of the benefits of multiple governance schemes.

[0100] Edge computing nodes are deployed at regional management stations close to the monitoring site. Their core responsibilities include: receiving, cleaning, fusing, and extracting features from real-time sensor data streams flowing into the area; running lightweight AI models, such as TensorFlow Lite, pre-trained in the cloud, to achieve millisecond-level real-time inference and early warning of desertification risks in the region; and maintaining basic risk assessment and emergency command relay capabilities by relying on locally stored lightweight knowledge graph subsets and rule bases when the network is unstable or disconnected from the cloud, ensuring the continuity of critical business operations.

[0101] The system consists of mobile terminals and sensor nodes. Sensor nodes are responsible for collecting raw ecological data such as soil moisture and wind speed. Mobile terminals, such as handheld devices used by operators, have two-way communication capabilities. On the one hand, they receive detailed task instructions and optimized path navigation from the edge or cloud. On the other hand, they upload on-site data, task execution progress, and status feedback, such as photo confirmation, in real time.

[0102] This embodiment constructs a smart grassland desertification management system based on a collaborative architecture of cloud-based central nodes, edge computing nodes, and mobile terminals. It builds a spatiotemporal knowledge graph through an integrated air-ground-space monitoring network, uses dynamic models that combine fusion mechanisms and AI to achieve early risk warning, generates optimal emergency dispatch plans with the help of multi-objective genetic algorithms, and simulates and evaluates the governance effect through digital twin technology. Finally, it forms a closed-loop management system for the entire process from monitoring, early warning, decision-making to governance and evaluation, realizing intelligent, precise, and sustainable grassland desertification prevention and control.

[0103] Example 2

[0104] This embodiment provides a specific implementation of a smart desertification management system for alpine grassland regions. Based on Embodiment 1, the system has undergone comprehensive technical adaptation and optimization for the special geographical and climatic conditions of alpine grasslands, forming a complete solution with regional characteristics.

[0105] At the data acquisition layer, the integrated air-ground-space monitoring network has been specially enhanced; in addition to the original Landsat-8 / 9 and Sentinel-2 satellite data sources, high-frequency observation data from the Fengyun-4FY-4A meteorological satellite has been added. By utilizing its high temporal resolution infrared and visible light data, the interference of frequent cloud cover in high-altitude and cold regions on surface temperature retrieval has been effectively overcome.

[0106] In the drone patrol segment, a DJI Mavic 3E Enterprise Edition equipped with a multispectral camera was selected, with the flight altitude optimized to 80 meters. Data analysis was conducted using the MSAVI2 modified soil-adjusted vegetation index 2, optimized for low vegetation cover areas, which significantly improved the identification accuracy of sparse vegetation community structure in alpine meadows. The ground-based IoT sensing network underwent a cold-resistant enhancement design, with the node density increased to 2 per square kilometer. It was encapsulated in a fully sealed, freeze-proof shell and integrated an S-SMC-M005 high-precision soil moisture sensor with an accuracy of ±2%, a PT1000 ground temperature probe with a range of -40℃ to +60℃, and an ultrasonic snow depth sensor. This enabled simultaneous real-time monitoring of multiple parameters such as soil moisture, temperature, freeze-thaw status, and snow depth, providing a crucial data foundation for analyzing the impact of freeze-thaw cycles on soil structural stability.

[0107] At the knowledge construction layer, the ontology model of the spatiotemporal knowledge graph expands the specific entities and relationships of alpine ecosystems; new entities such as permafrost, seasonal snow cover, and rodent burrows are added, and dynamic attributes such as permafrost depth, snow cover rate, and rodent burrow density are added to them; through the relationship extraction algorithm, new ecological causal relationship chains such as permafrost degradation, its causes, soil moisture infiltration, aggravation, desertification, as well as snow cover, inhibition, and wind erosion are established, enabling the knowledge graph to more accurately depict the unique driving mechanism of desertification in alpine grasslands;

[0108] In the core algorithm model layer, the dynamic risk assessment model has undergone structural adjustments to adapt to the data characteristics of the high-altitude and cold environment. It adopts a computationally more efficient two-layer LSTM network with 64 hidden layer units, and uses nearly 15 years of desertification evolution samples collected from typical high-altitude grassland areas such as the Qinghai-Tibet Plateau and Altai Mountains for transfer learning and pre-training. The model's prediction time window has been adjusted to the next 45 days to better match the pace of desertification in high-altitude and cold regions. At the same time, the risk level classification threshold has been specifically calibrated based on local ecological sensitivity: a probability value below 0.25 is considered low risk, 0.25 to 0.55 is medium risk, and above 0.55 is high risk, thereby enabling earlier detection of high-risk areas.

[0109] The emergency response mechanism has undergone a dual upgrade in resources and algorithms to address the operational challenges in high-altitude and cold environments. In addition to conventional grass seeds and sand barriers, the emergency resource list includes newly added cold-resistant and drought-tolerant grass species such as Kentucky bluegrass and Leymus chinensis, bio-based antifreeze and water-retaining agents, and lightweight fuel-powered snow melting equipment. The path optimization algorithm incorporates real-time road icing and snow coefficients to dynamically adjust vehicle speeds under different road conditions, making the planning results more realistic. The scheduling model employs an improved NSGA-II non-dominated sorting genetic algorithm II, minimizing scheduling time and transportation costs while using route safety as a third optimization objective. Through the Pareto optimal solution set, it provides decision-makers with multiple optimal solutions that balance time, cost, and safety.

[0110] In the effect simulation and evaluation layer, the digital twin is coupled with a specialized alpine ecological process model. Its core integrates a freeze-thaw simulation module based on the Stefan equation, a CoupModel that can finely quantify the soil water-heat coupled transport process, and a wind erosion prediction model WEPS with localized parameter calibration. In the digital twin's interactive interface, management personnel can set specific plans such as laying biodegradable fiber blankets and arranging sand barriers at different intervals. Through multi-model coupling calculations, the system can deduce the response process of vegetation restoration, soil moisture dynamics, permafrost changes, and wind erosion modulus in the next 5-10 years, and visualize the comparison of long-term ecological benefits of different plans.

[0111] In the ecological value realization stage, the system innovatively uses snow cover rate as a dynamic correction factor for carbon sink accounting and incorporates it into the grassland carbon storage calculation model, making the accounting results more scientific. Based on blockchain technology, an automatically executed smart contract has been developed, which can verify carbon sink volume according to monitoring data and realize the automatic quarterly accounting of carbon sink revenue and accurate distribution to contracted herders, thus constructing a transparent and efficient new ecological compensation mechanism for high-altitude pastoral areas.

[0112] An enhanced air-ground-space monitoring network continuously collects multi-source data from the plateau. The Fengyun-4 satellite provides high-frequency surface temperature observations, UAVs use the MSAVI2 index for vegetation monitoring, and a cold-resistant sensor network collects real-time data on soil temperature and humidity, freeze-thaw status, and snow depth. Edge nodes perform real-time data fusion processing and update a spatiotemporal knowledge graph containing characteristics such as permafrost depth and snow cover. Based on the expanded knowledge graph, a two-layer LSTM risk assessment model is used to predict desertification risk for the next 45 days, and an emergency mechanism is activated when the risk probability exceeds 0.55. An improved NSGA-II algorithm is used to generate a three-dimensional optimization scheme that takes into account time, cost, and safety, and to allocate specialized resources such as cold-resistant grass seeds, water-retaining agents, and snow-melting equipment. After treatment, the 5-10 year treatment effect is simulated by a digital twin that couples the Stefan equation and CoupModel, and the actual monitoring data is fed back to the model for parameter optimization. At the same time, based on blockchain smart contracts, accurate accounting and automatic quarterly allocation of carbon sink assets are realized, forming a closed-loop management of the entire life cycle from monitoring and early warning to ecological value transformation.

[0113] This embodiment significantly improves the system's monitoring sensitivity, model prediction accuracy, emergency response reliability, and ecological value transformation precision under complex conditions such as low temperature, freeze-thaw, and snow accumulation through a series of in-depth technical customizations for high-altitude and harsh environments. It provides a practical and efficient smart management model for the prevention and control of desertification in the fragile ecosystem of alpine grasslands.

[0114] Example 3

[0115] An innovative implementation method for a smart management system for desertification in agro-pastoral ecotones is provided. Based on the technical framework of the previous embodiments, it is upgraded to address the complex human-land relationship and diverse desertification driving forces in agro-pastoral ecotones.

[0116] At the data acquisition level, a four-in-one monitoring system integrating land, air, and human resources has been constructed: at the satellite data level, the PlanetScope constellation has been introduced to achieve daily full coverage monitoring with a resolution of 3 meters, while SkySat satellites have been deployed to provide sub-meter imagery of 0.5 meters in key areas; the UAV array is equipped with a hyperspectral imager in the 400-1000nm band and lidar to achieve accurate inversion of pasture biomass and three-dimensional reconstruction of micro-topography; the ground IoT network innovatively adopts a hybrid power supply of solar and wind power nodes, integrating multi-channel soil respiration sensors, livestock hoof pressure sensors, and smart fence status monitors, with a node density of 4 per square kilometer;

[0117] A dedicated app for herders was developed, using a blockchain incentive mechanism to encourage real-time uploading of grazing tracks, grassland conditions, and infrastructure damage, forming an all-weather, three-dimensional perception network. At the knowledge construction level, a breakthrough was made in establishing a knowledge graph driven by both nature and society, adding social entities such as grazing intensity index, farming system, and settlement distribution, and constructing 26 cross-domain causal relationship chains, including overgrazing, vegetation cover decline, soil crust damage, wind erosion, water-saving irrigation, soil moisture improvement, and vegetation restoration. An uncertainty reasoning mechanism was also introduced to address the ambiguity of farmers' and herders' behavior data.

[0118] At the core algorithm level, a pioneering desertification control decision-making system based on multi-agent deep reinforcement learning (MADRL) is developed. Different pasture units are modeled as interacting agents, and a state space with multiple dimensions including vegetation index, soil parameters, and economic benefits is designed. A composite action space including rotational grazing scheme adjustment, irrigation strategy optimization, and reseeding timing selection is defined. Cross-pasture collaborative governance is achieved through a centralized training-decentralized execution framework. The model adopts a competitive network architecture to handle the trade-offs between multiple governance objectives. Through a curriculum learning strategy, it is gradually trained from simple scenarios to complex situations, ultimately forming an intelligent decision-making capability that can adapt to different climate years and socio-economic conditions.

[0119] The emergency response mechanism has developed a contingency plan simulation platform based on digital twins, integrating the multi-agent path planning (MAPF) algorithm to coordinate various emergency resources, and innovatively designing a dynamic resource priority assessment model that comprehensively considers factors such as the expansion rate of desertification patches, ecological sensitivity, and the urgency of governance. Simultaneously, it has established a cross-regional emergency resource sharing mechanism, enabling automatic leasing and settlement of machinery and equipment between adjacent pastures through smart contracts. The digital twin has also made a breakthrough by integrating a social-ecological coupled system (SES) model, embedding a herder decision-making model based on behavioral economics on the basis of ecological process simulation, enabling simulation of herders' production decisions and their ecological consequences under different policy scenarios.

[0120] Multi-scenario simulation capabilities have been developed to support managers in comparing and analyzing the long-term effects of policies such as grazing bans, ecological compensation, and industrial restructuring. In the ecological value realization phase, a virtuous cycle mechanism has been established that integrates desertification control, carbon sequestration growth, and increased income for herders. An innovative automated ecological performance evaluation system based on IoT data has been designed, automatically distributing ecological rewards via smart contracts when continuous vegetation improvement is detected. Simultaneously, an index-based grassland insurance product has been developed, directly linking insurance payouts to objective meteorological indicators such as wind speed and precipitation, enabling automatic and rapid post-disaster claims processing.

[0121] A comprehensive monitoring system integrating land, air, space, and human resources continuously collects multi-source data. Satellites provide daily 3-meter resolution images of the entire region and 0.5-meter high-resolution images of key areas. Unmanned aerial vehicle (UAV) arrays periodically acquire hyperspectral and lidar data. Ground-based IoT networks transmit parameters such as soil respiration and livestock hoof pressure in real time, while herders' apps simultaneously upload grazing behavior data. Edge computing nodes fuse multi-source data to drive dynamic updates of a dual-driven natural-social knowledge graph, analyzing 26 cross-domain causal chains through an uncertainty reasoning mechanism. Based on the updated knowledge graph, a multi-agent deep reinforcement learning system uses multi-dimensional data such as vegetation index, soil parameters, and economic benefits as state input. Through a competitive network architecture, it outputs composite actions such as adjusting rotational grazing plans and optimizing irrigation strategies, forming a cross-pasture collaborative governance strategy. When the system identifies desertification risk exceeding a threshold... During the implementation phase, a digital twin-based contingency planning platform is activated, employing a multi-agent path planning algorithm to coordinate emergency resources and generating optimal scheduling schemes using a dynamic priority assessment model. Simultaneously, a cross-regional resource sharing mechanism is activated via smart contracts. During governance implementation, a socially and ecologically coupled digital twin simulates ecological responses under different policy scenarios in real time, evaluating long-term governance effects through multi-scenario simulations. In the later stages of governance, an automated ecological performance evaluation system based on IoT data continuously monitors vegetation restoration. When predetermined ecological indicators are reached, ecological rewards are automatically distributed via smart contracts, while indexed grassland insurance triggers automatic claims based on real-time meteorological data. Finally, blockchain technology is used to store and verify the entire governance process data on the blockchain, forming a complete closed loop of monitoring, early warning, decision-making, governance, evaluation, and incentives, achieving coordinated development of desertification control and improvement of herders' livelihoods.

[0122] The above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention in any way. Although the present invention has been disclosed above with reference to preferred embodiments, it is not intended to limit the present invention. Any person skilled in the art can make some modifications or alterations to the above-disclosed technical content to create equivalent embodiments without departing from the scope of the present invention. Any simple modifications, equivalent changes and alterations made to the above embodiments based on the technical essence of the present invention without departing from the scope of the present invention shall still fall within the scope of the present invention.

Claims

1. A method for intelligent management and emergency response to grassland desertification throughout its entire life cycle, characterized in that, Includes the following steps: S1: Collect multi-source grassland data through an integrated air-ground-space monitoring network, and construct a spatiotemporal knowledge graph that integrates grassland ecological baseline information, including vegetation type, soil physicochemical properties, topography and geomorphology and historical meteorological elements. S2: Based on the spatiotemporal knowledge graph, use the dynamic risk assessment model to calculate the risk level of grassland desertification and perform early identification and warning of the desertification process; S3: When the warning level exceeds the preset threshold, the emergency response mechanism is activated, and the optimal emergency resource scheduling plan and action instructions are generated based on the operations research optimization algorithm; S4: After the emergency response is carried out, based on continuous monitoring data, the governance effect is simulated and evaluated using a digital twin, and the evaluation results are fed back to the dynamic risk assessment model to form a management closed loop of monitoring, evaluation, decision-making, governance, and re-monitoring.

2. The method according to claim 1, characterized in that, Step S1 specifically includes: Large-scale vegetation cover index and surface temperature data are obtained through satellite remote sensing; By using drones equipped with multispectral sensors, high-precision surveys can be conducted in key areas to obtain data on vegetation community structure and the distribution of bare sandy land. The system collects real-time data on soil moisture, wind speed, and soil pH by deploying a network of IoT sensor nodes on the ground.

3. The method according to claim 1, characterized in that, The spatiotemporal knowledge graph uses the multi-source data as entities and attributes, and models the spatiotemporal and ecological causal relationships between entities as relationships to form semantic associations. Specifically, it includes: The grassland multi-source data is spatiotemporally registered and standardized; the processed data is instantiated into entities in a knowledge graph; semantic rules and relation extraction algorithms are then used to establish spatial relationships between entities (located in or adjacent to each other), temporal relationships (occurring before or preceding each other), and causal relationships (inhibiting, aggravating, or affecting the ecology); thus forming a spatiotemporal knowledge graph that integrates static background features and dynamic logical reasoning, and supports semantic querying and logical reasoning.

4. The method according to claim 2, characterized in that, In step S1, the ground-based IoT sensor node performs edge computing, specifically including: Local preprocessing and outlier detection are performed on the collected raw data; The high-frequency acquisition mode is only activated and the data is uploaded when there is data anomaly or when instructions are received from the cloud, in order to reduce overall power consumption and communication load.

5. The method according to claim 1, characterized in that, The dynamic risk assessment model in step S2 is an AI model that integrates mechanisms and data. The integration of mechanisms and data means that the model uses a data-driven time-series deep learning network as its core to make a preliminary prediction of the probability of desertification risk, while coupling a grassland ecological mechanism model to verify the rationality and provide a physical interpretation of the preliminary prediction results. Specifically, it performs the following operations: Using the spatiotemporal knowledge graph as input, feature sequences related to desertification are extracted; The feature sequence is input into a long short-term memory network pre-trained based on long sequence multi-source samples from grasslands, and the weights are updated online through incremental learning to predict the probability of desertification risk within a specific time window in the future. By comparing the probability of desertification risk with the results of grassland ecological mechanism models in parallel, and identifying the dominant driving factors through feature importance analysis, an interpretable risk level can be formed. Among them, the grassland ecological mechanism model is based on the energy balance equation of soil, vegetation and atmosphere, and simultaneously calculates the critical threshold of desertification, which is used for cross-validation with the desertification risk probability predicted by the time-series deep learning network.

6. The method according to claim 1, characterized in that, The emergency response mechanism in step S3 specifically includes: S301: Based on the warning level and the geographical location of the desertified patches, determine the types and quantities of resources that need to be dispatched, including grass seeds, sand barriers, watering trucks, and workers. S302: Using a multi-objective genetic algorithm, a fitness function is constructed with the dual objectives of minimizing resource scheduling time and minimizing transportation cost, to calculate the optimal delivery path and scheduling scheme for resources from the warehouse to each desertified patch; S303: Automatically distribute the scheduling scheme and action instructions to the corresponding mobile terminals and track the execution status in real time.

7. The method according to claim 1, characterized in that, Step S302 specifically includes: An optimization model is constructed with the core objective of minimizing total transportation time and total transportation cost. The total transportation time includes the vehicle's travel time and the operation dwell time at each desertification patch, while the total transportation cost includes the vehicle's fixed cost, variable transportation cost, and penalty cost caused by time window delays. A chromosome is constructed using a two-segment integer coding method. The first segment code represents the order in which all desertification patches are visited, and the second segment code represents the resource loading allocation scheme for each transport vehicle. Based on the allocation scheme, a fitness function is designed with transportation time efficiency and transportation cost economy as the core evaluation indicators. The weight of each indicator is dynamically adjusted according to the warning level. The higher the warning level, the greater the weight of the time efficiency indicator. During the fitness evaluation process, the compliance of the resource demand satisfaction of each desertification spot, the maximum load limit of each vehicle, and the emergency operation time window requirements is verified simultaneously. Based on the fitness function, a population of feasible solutions that satisfy all constraints is initialized. During the iterative evolution process, individual selection based on the roulette wheel strategy, sequential crossover operation that preserves key gene fragments, and random two-point inversion mutation operation are executed in sequence. When the evolution reaches the preset maximum number of iterations or the optimal solution satisfies the convergence condition, the calculation is terminated and the scheme with the best comprehensive evaluation value is selected from the Pareto non-dominated solution set as the final delivery path and scheduling scheme.

8. The method according to claim 1, characterized in that, Step S4, which involves simulating and evaluating the governance effect using a digital twin, includes: S401: Based on the aforementioned spatiotemporal knowledge graph and grassland ecological process model, construct a digital twin capable of simulating vegetation growth, soil water and salt transport, and wind erosion processes; S402: In the digital twin, different governance schemes are simulated and deduced to predict their long-term ecological and economic benefits; S403: Based on the comparison between actual monitoring data and simulated data after treatment, quantitatively evaluate the treatment effect, and use the comparison results to dynamically optimize the model parameters of the digital twin.

9. The method according to claim 8, characterized in that, The prediction of its long-term ecological and economic benefits in S402 includes: Based on the simulation and deduction of the digital twin, key ecological and economic benefit indicators within the future time scale are quantitatively output. Among them, ecological benefit indicators include at least: the increase in vegetation coverage, the accumulation of soil organic carbon storage, and the rate of decrease in wind erosion modulus. Economic benefit indicators should include at least: the expected revenue from carbon trading, the direct income from increased forage production, and the future cost savings from curbing desertification. By comprehensively comparing the indicators under different governance schemes, a forward-looking assessment and selection of their long-term comprehensive benefits can be achieved.

10. The method according to claim 1, characterized in that, The method is executed based on a collaborative architecture of cloud central node, edge computing node, mobile terminal and sensor node, wherein: The cloud-based central node is responsible for global data storage, knowledge graph construction, and large-scale simulation. Edge computing nodes are deployed at regional management stations and are responsible for real-time data processing, lightweight model inference, and rapid issuance of emergency commands within their respective regions. Mobile terminals and sensor nodes act as execution ends, responsible for data acquisition and command reception.

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