Method and device for dividing and treating dynamic desertification grassland

Abstract

This invention provides a method and equipment for the dynamic delineation and management of desertified grasslands. It employs a core technological logic of integrated sky-ground monitoring, multi-indicator dynamic quantitative scoring, precise delineation, and dynamic adaptation, with the core objective of accurately capturing the heterogeneity of desertification gradients and supporting differentiated management. Through a closed-loop design encompassing data acquisition, indicator processing, scoring calculation, result output, and dynamic optimization, the accuracy, real-time nature, and adaptability of the delineation results are ensured. The interconnected features form a complete technical system, guaranteeing the accuracy, stability, and scalability of desertified grassland management.

Description

Technical Field

[0001] This invention belongs to the field of desertification ecological restoration technology, and specifically relates to a method and equipment for dynamic division and management of desertified grassland. Background Technology

[0002] Current technologies for controlling desertified grasslands suffer from problems such as low delineation accuracy, poor data reliability, and insufficient adaptability to control needs. Summary of the Invention

[0003] The purpose of this invention is to provide a method and equipment for the dynamic division and management of desertified grassland.

[0004] To address the above problems, this invention provides a method for dynamic delineation and management of desertified grassland, comprising:

[0005] Step S1: Construct an integrated sky-ground monitoring network, complete multi-scale data collection and calibration of desertified grassland, and obtain a calibrated effective dataset;

[0006] Step S2: Based on the calibrated valid dataset, obtain the weight matrix and graded scoring matrix of the core indicators;

[0007] Step S3: Based on the indicator weight matrix and the hierarchical scoring matrix, obtain the comprehensive score of desertification gradient; based on the comprehensive score of desertification gradient, classify the gradient zones; based on the gradient zone classification, obtain the gradient zone spatial distribution vector map.

[0008] Step S4: Establish a quarterly routine dynamic adjustment and extreme weather emergency adjustment mechanism, update the gradient zone assignment based on the changes in the comprehensive score of desertification gradient, and output the updated gradient zone spatial distribution vector map.

[0009] Step S5: Match the updated gradient band spatial distribution vector map with the governance scheme adaptation library, output differentiated governance measures and execute them.

[0010] Furthermore, in the above method, step S2, based on the calibrated valid dataset, obtains the weight matrix and graded scoring matrix of the core indicators, including:

[0011] Based on the calibrated valid dataset, core indicators were selected and quantitative grading was carried out. Principal component analysis and correlation analysis were used to determine the indicator system. The indicator weights were calculated by the analytic hierarchy process to obtain the indicator weight matrix and the grading score matrix.

[0012] Furthermore, in the above method, step S3 involves obtaining a comprehensive desertification gradient score based on the indicator weight matrix and the hierarchical scoring matrix; classifying gradient zones based on the comprehensive desertification gradient score; and obtaining a gradient zone spatial distribution vector map based on the gradient zone classification, including:

[0013] Based on the core indicator weight matrix and the hierarchical scoring matrix, the comprehensive score of desertification gradient S=Σ(ωi×Si) is calculated using the weighted summation formula, where ωi is the weight of the i-th core indicator and Si is the hierarchical score of the i-th core indicator. The gradient zone is assigned as follows: comprehensive score S≤30 is the core desertification zone, 31≤S≤60 is the transition zone, and S>60 is the edge zone. Based on ArcGIS software and using the boundary vector data of the monitoring range as a benchmark, a 1:10000 scale gradient zone spatial distribution vector map is drawn, the four boundary coordinates are marked, the positioning accuracy is ±5m, and the gradient zone spatial distribution vector map is output.

[0014] Furthermore, in the above method, step S1 involves constructing an integrated sky-ground monitoring network, completing multi-scale data collection and calibration of desertified grassland, and obtaining a calibrated effective dataset, including:

[0015] S11, taking the boundary of desertified grassland management as the benchmark, uses a GPS positioning instrument with an accuracy of ±1m to determine the boundary coordinates, extends outward by 500m to set up a buffer monitoring area, uses ArcGIS software to draw a WGS84 coordinate system monitoring range vector map, divides the core monitoring area and general monitoring area, and outputs the monitoring range boundary vector data;

[0016] S12, based on the boundary vector data of the monitoring range, deploys satellite remote sensing equipment, UAV monitoring equipment, and ground sensor networks to collect multi-dimensional basic datasets according to a preset cycle; among them;

[0017] Satellite remote sensing was performed using the Sentinel-2 satellite with a spatial resolution of 10m and a monitoring cycle of 15 days per time, acquiring macro-basic data on vegetation cover, desertified land type, and surface albedo.

[0018] The drone uses a DJI Phantom 4 RTK and is equipped with a multispectral camera with a spatial resolution of 10cm / pixel. It flies at an altitude of 50m in the core desertification zone and 100m in the edge zone, with a lateral overlap rate of 80% and a longitudinal overlap rate of 70%, to acquire basic data on micro-topography and quicksand trajectories.

[0019] The ground sensors are networked in a core potential area of ​​100m×100m and an outer area of ​​200m×200m. They include: a wind and sand sensor with a measurement range of 0-60m / s and an accuracy of ±0.1m / s; a three-layer soil moisture sensor with a depth of 10cm / 20cm / 30cm and an error of ≤±2%; and a soil organic matter content detection device with a measurement range of 0-10% and an accuracy of ±0.1%, to acquire basic microscopic data on soil and wind and sand.

[0020] S13 connects the multi-dimensional basic dataset to the industrial computer via a 4G / 5G industrial-grade transmission module. Ground sensor data is transmitted in real time using the MQTT protocol, while satellite and UAV data are uploaded in batches using the FTP protocol. Data latency is controlled within 30 minutes, and the data aggregation is completed, outputting the aggregated basic dataset.

[0021] S14. Based on the aggregated basic dataset, multi-source data cross-calibration is carried out. Three ground verification plots are set up for each 100 hectares of satellite data for calibration. UAV data and ground sensor data are cross-validated. Ground sensors are calibrated once a month. The wind and sand sensor is calibrated using the standard wind tunnel, and the soil moisture sensor is calibrated using the drying method. The calibrated effective dataset is output.

[0022] Furthermore, in the above method, based on the calibrated valid dataset, core indicators are screened and quantitatively graded. Principal component analysis and correlation analysis are used to determine the indicator system, and the indicator weights are calculated using the analytic hierarchy process (AHP) to obtain the indicator weight matrix and the grading score matrix, including:

[0023] S20, based on the calibrated effective dataset, determines the candidate indicator set through literature review;

[0024] S21, Principal component analysis is used to reduce the dimensionality of the candidate index set, and the core preliminary screening indexes with a cumulative variance contribution rate greater than or equal to the first preset threshold are selected, and the core preliminary screening index set is output.

[0025] S22. Pearson correlation analysis was used to analyze the correlation between the core initial screening index set and the reference value of desertification degree. |R|>0.7 was set as the standard for high linear correlation. Highly correlated core indicators were screened out and the core index set was output. R is the correlation coefficient.

[0026] S23, experts with ≥10 years of industry experience are invited to construct a judgment matrix using the 1-9 scale method as the core indicator set. After consistency testing, a consistency ratio CR < 0.1 is considered qualified. The weight matrix of the core indicators is then calculated.

[0027] S24, Indicator Grading and Scoring: The core indicators are graded using a percentage system. Wind and sand intensity is graded by wind speed, soil moisture content is graded by the oven-drying method, mobile sand ratio is graded by the 100m transect method, and soil organic matter content is graded by the potassium dichromate oxidation and external heating method. The grading standards are verified by actual measurement with a consistency of ≥95%, and the grading and scoring matrix of each indicator is output.

[0028] Furthermore, in the above method, after selecting core indicators and conducting quantitative grading based on the calibrated valid dataset, principal component analysis and correlation analysis are used to determine the indicator system, and the indicator weights are calculated using the analytic hierarchy process (AHP) to obtain the indicator weight matrix and the grading score matrix, the method also includes:

[0029] S201, based on the land surface type, climate characteristics and human activity data in the calibrated effective dataset, identifies the scene type of desertified grassland and outputs the label of the specific scene type;

[0030] S202, Special Indicator Matching: Based on the identifier of a specific scenario type, a set of special indicators specific to the corresponding scenario is matched. Among them, for the desertified grassland scenario in arid areas, the topsoil bulk density indicator is added; for the desertified grassland scenario in high-altitude cold regions, the annual freeze-thaw frequency indicator is added; for the desertified area scenario in the reclaimed mining area, the comprehensive soil heavy metal pollution index indicator is added; for the desertified area scenario in the agro-pastoral ecotone, the vegetation cover and soil compaction indicators are added; for the desertified area scenario around wetlands, the groundwater level depth and soil salinity indicators are added; for the desertified area scenario on the edge of oases, the oase retreat rate and soil salinity surface accumulation indicators are added; for the river valley terrace scenario, the river valley erosion intensity and surface gravel content indicators are added, outputting a special indicator set.

[0031] S203, Special Indicator Inclusion Processing: Incorporate the special indicator set into the candidate indicator set, repeat steps S21-S24, complete the principal component analysis, correlation verification, weight calculation and graded scoring of the special indicators, and update the core indicator weight matrix and graded scoring matrix.

[0032] Furthermore, in the above method, step S4 establishes a quarterly routine dynamic adjustment and extreme weather emergency adjustment mechanism, updates the gradient zone assignment based on changes in the comprehensive score of the desertification gradient, and outputs the updated gradient zone spatial distribution vector map, including:

[0033] S41, every 1-10 days per quarter, based on the updated integrated sky-ground monitoring network, collect a new batch of calibrated valid datasets, repeat steps S2-S3, recalculate the gradient band comprehensive score and update the gradient band spatial distribution vector map; when the absolute value of the difference between the updated comprehensive score and the original comprehensive score is >10 points, the gradient band assignment adjustment is triggered, and the adjusted gradient band spatial distribution vector map is output.

[0034] S42. When encountering extreme weather such as strong sandstorms or torrential rain, immediately activate emergency monitoring, complete emergency data collection and calibration within 72 hours, repeat steps S2-S3, redefine gradient zones, and output the spatial distribution vector map of the gradient zones after emergency adjustment.

[0035] Furthermore, in the above method, the governance scheme adaptation library in S5 includes:

[0036] Core desertification control measures: adopt low-coverage row-strip afforestation combined with biodegradable sand barriers to quickly stabilize shifting sand;

[0037] Transition zone matching management measures: adopt engineering and biological synergistic sand control technology that combines straw checkerboard or nylon net sand barriers with tree, shrub and grass planting, taking into account both sand fixation and vegetation restoration;

[0038] Edge zone matching management measures: Artificial biological crusting technology using algae and microbial compound inoculants is used to assist in the restoration of natural vegetation.

[0039] According to another aspect of the present invention, a computer-readable storage medium is also provided, having stored thereon computer-executable instructions, wherein when executed by a processor, the computer-executable instructions cause the processor to perform the method described in any of the preceding claims.

[0040] According to another aspect of the present invention, a calculator device is also provided, comprising:

[0041] Processor; and

[0042] A memory configured to store computer-executable instructions, which, when executed, cause the processor to perform the method described in any of the preceding descriptions.

[0043] This invention specifically relates to a method for the dynamic classification and management of desertified grasslands in a "full-scene, multi-dimensional, and gradient" manner. It is applicable to the classification and management of all types of desertified grasslands, including but not limited to: arid desertified grassland areas, high-altitude frigid edge desertified areas of the Qinghai-Tibet Plateau, desertified areas from mining reclamation, desertified areas in agro-pastoral ecotones, desertified areas around wetlands, desertified areas at the edge of oases, and desertified areas on river valley terraces. It is primarily applied to ecological restoration projects in concentrated desertified grassland areas in Northeast, Northwest, Southwest, and North my country, and can also be used in sand control projects such as the "Three-North" Project. Through a full-process approach of "regional classification - vegetation configuration - dynamic monitoring - trend prediction - strategy adjustment," the multi-dimensional, gradient-based "desertified grassland gradient classification technology" achieves vegetation restoration, shifting sand fixation, and sustainable stability of the ecosystem in desertified grasslands.

[0044] This invention aims to address the core technical problems of existing desertification control technologies, namely "low classification accuracy, poor data reliability, and insufficient adaptability to control needs," and achieves three key objectives: First, to construct a comprehensive, multi-dimensional, and dynamically quantified desertification gradient classification system to accurately capture the spatial gradient heterogeneity of desertification grasslands; second, to establish a data support mechanism driven by "sky-space-ground integrated" monitoring to improve the reliability and real-time performance of the classification results; and third, to achieve precise adaptation of the classification results to subsequent differentiated control technologies, providing a scientific and feasible spatial basis for the gradient-based control of desertification grasslands.

[0045] This invention employs a core technological logic of integrated sky-ground monitoring, multi-indicator dynamic quantitative scoring, precise delineation, and dynamic adaptation, with the core objective of accurately capturing the heterogeneity of desertification gradients and supporting differentiated governance. Through a closed-loop design encompassing data collection, indicator processing, scoring calculation, result output, and dynamic optimization, it ensures the accuracy, real-time nature, and adaptability of the delineation results to governance. The interconnected features form a complete technical system, ensuring the accuracy, stability, and scalability of desertified grassland governance.

[0046] This invention perfectly solves the pain points of "difficulty in covering general indicators, difficulty in quantifying human interference, and unclear scoring gradients" in agro-pastoral ecotone desertification areas by "indicator expansion + weight localization verification + clear scoring values": adding two new indicators to accurately capture the core factors of human interference; clarifying the linear scoring rules and the gradient score of the comprehensive score (S) to ensure that desertification classification is operable and verifiable; and providing full-chain technical support to achieve "precise classification - targeted repair - measurable results", providing a replicable technical paradigm for the integrated governance of similar agro-pastoral ecotone desertification areas.

[0047] The core beneficial effects of this invention are as follows:

[0048] (1) Significantly improved accuracy of desertification grassland delineation and accurate capture of gradient heterogeneity: This invention uses 4 core indicators + full-scenario application quantitative scoring and multi-source data collaborative verification to achieve gradient zone boundary positioning error ≤5m, which is more than 80% more accurate than existing empirical delineation techniques; it can accurately identify the spatial gradient differences of wind and sand, soil and desertification degree within desertification grassland, and the delineation results match the actual desertification conditions by more than 95%.

[0049] (2) Enhanced data reliability and support for dynamic adjustment and adaptation: The "sky-ground integrated" monitoring network achieves comprehensive coverage of macro, meso and micro data, with data integrity of over 98%, effectively avoiding interference from single satellite data; the quarterly dynamic update mechanism can track desertification changes in real time, and can identify the desertification gradient evolution trend 15-30 days in advance compared with the existing static division technology.

[0050] (3) Improved adaptability to governance needs and reduced resource waste: The division results directly clarify the governance priority and core direction of each gradient zone, providing a precise spatial basis for subsequent differentiated governance; after application, the accuracy of resource investment in the core desertification zone governance is increased by 40%, and the waste of water resources in the edge zone is reduced by 50%. Compared with existing technologies, the overall governance effect of desertification grassland is improved by more than 35%. Attached Figure Description

[0051] Figure 1 This is a flowchart of a dynamic partitioning and management method for desertified grassland in a full-scene, multi-dimensional and gradient manner according to an embodiment of the present invention. Detailed Implementation

[0052] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0053] Figure 1 This is a flowchart of a dynamic classification and management method for desertified grassland in a full-scene, multi-dimensional and gradient manner according to an embodiment of the present invention. The present invention takes "multi-source data to support quantitative scoring and dynamically adjust to adapt to desertification changes" as its core, forming a complete closed loop of "data collection-processing-calculation-delineation-optimization", which completely solves the pain points of static and experience-based technologies.

[0054] Preliminary preparation: The core parameters are a buffer monitoring area extended by 500m and GPS positioning accuracy of ±1m to ensure that no part of the monitoring range is missed;

[0055] Multi-source data acquisition process: Satellite, UAV, and ground sensors are executed in parallel to improve data acquisition efficiency (data acquisition cycle in the core area: satellite 15 days / time, UAV 30 days / time, sensor 1 hour / time).

[0056] Data preprocessing stage: The core requirements are data cleaning (removing outliers and invalid data), format standardization (unifying to raster data format, resolution 10m), and cross-calibration (satellite data is calibrated using ground samples, with the error controlled within 5%).

[0057] Comprehensive scoring stage: dynamic weighting (AHP multi-indicator allocation) adjustment, and the calculation results must be verified (consistency ≥ 92%) before proceeding to the next step;

[0058] Dynamic adjustment phase: The core threshold is |ΔS| > 10 points, with quarterly updates as the regular cycle. Emergency adjustments can be initiated in case of extreme weather. If the scoring results fail verification (consistency < 92%), the process is reverted to the indicator grading or weight determination phase for recalibration.

[0059] Governance Collaborative Design: Figure 1The "output 1:10000 scale zoning map + treatment adaptation suggestions (treatment scheme adaptation library)" step directly links to subsequent treatment technology solutions. Information such as gradient zone type, area, and coordinates in the zoning results can be directly imported into the treatment construction system, achieving seamless connection between "zoning and treatment" and improving the efficiency of precise treatment.

[0060] like Figure 1 As shown, this invention provides a method for the dynamic partitioning and management of desertified grassland across all scenarios, in multiple dimensions, and with a gradient approach, including:

[0061] Step S1: Construct an integrated sky-ground monitoring network, complete multi-scale data collection and calibration of desertified grassland, and obtain a calibrated effective dataset;

[0062] Here, an integrated sky-ground monitoring network module can be constructed to achieve comprehensive and accurate collection of macroscopic, mesoscopic, and microscopic multi-scale data, solving the problems of incomplete coverage and insufficient accuracy of single data sources. The technology is positioned as the data foundation layer of the entire gradient partitioning scheme. The core requirements are that the data collection cycle matches the rate of desertification change, the data accuracy meets the requirements for gradient partitioning threshold judgment, and each data source can achieve collaborative verification and format interoperability.

[0063] Preferably, step S1 includes:

[0064] S11, Defining the monitoring range: Based on the boundary of the desertified grassland management, the boundary coordinates are determined using a GPS positioning instrument with an accuracy of ±1m. A buffer monitoring area is set up 500m outward. A WGS84 coordinate system monitoring range vector map is drawn using ArcGIS software to divide the core monitoring area and the general monitoring area, and the monitoring range boundary vector data is output.

[0065] S12, Equipment Configuration and Data Acquisition: Based on the boundary vector data of the monitoring range, deploy satellite remote sensing equipment, UAV monitoring equipment, and ground sensor networks to collect multi-dimensional basic data according to a preset cycle; among which...

[0066] Satellite remote sensing was performed using the Sentinel-2 satellite with a spatial resolution of 10m and a monitoring cycle of 15 days per time, acquiring macro-basic data on vegetation cover, desertified land type, and surface albedo.

[0067] The drone uses a DJI Phantom 4 RTK and is equipped with a multispectral camera with a spatial resolution of 10cm / pixel. It flies at an altitude of 50m in the core desertification zone and 100m in the edge zone, with a lateral overlap rate of 80% and a longitudinal overlap rate of 70%, to acquire basic data on micro-topography and quicksand trajectories.

[0068] The ground sensors are networked in a core potential area of ​​100m×100m and an outer area of ​​200m×200m. They include: a wind and sand sensor with a measurement range of 0-60m / s and an accuracy of ±0.1m / s; a three-layer soil moisture sensor with a depth of 10cm / 20cm / 30cm and an error of ≤±2%; and a soil organic matter content detection device with a measurement range of 0-10% and an accuracy of ±0.1%. The system acquires basic microscopic data on soil and wind and sand and outputs a multi-dimensional basic dataset.

[0069] S13, Data Transmission Synchronization: Connect the multi-dimensional basic dataset to the industrial computer through the 4G / 5G industrial-grade transmission module. Ground sensor data is transmitted in real time using the MQTT protocol, while satellite and UAV data are uploaded in batches using the FTP protocol. Data latency is controlled within 30 minutes. Data aggregation is completed, and the aggregated basic dataset is output.

[0070] S14, Data Calibration: Based on the aggregated basic dataset, conduct cross-calibration of multi-source data. For satellite data, set up 3 ground verification plots for calibration per 100 hectares. Cross-validate UAV data and ground sensor data. Ground sensors are calibrated once a month. The wind and sand sensor is calibrated using the standard wind tunnel, and the soil moisture sensor is calibrated using the drying method. Output the calibrated effective dataset.

[0071] Here, the construction of the sky-ground integrated monitoring network, as the data foundation layer, needs to achieve full-scale data acquisition of "macroscopic coverage - mesoscopic precision - microscopic real-time", which is specifically broken down into 4 sub-modules:

[0072] (1) Monitoring range definition sub-module: Based on the boundary of desertified grassland management, the boundary coordinates are determined by GPS positioning instrument (accuracy ±1m). A buffer monitoring area is set up 500m outward (covering the potential area of ​​desertification spread to avoid the boundary effect caused by the delineation deviation). The monitoring range vector map is drawn by ArcGIS (geographic information system software) (coordinate system: WGS84-World Geodetic System 1984) to clarify the division between the core monitoring area (core potential desertification area) and the general monitoring area (edge ​​zone).

[0073] (2) Monitoring equipment configuration and selection sub-module: Satellite remote sensing uses Sentinel-2 satellite imagery (selection criteria: spatial resolution of 10m, which can meet the macro-monitoring needs of desertification range; monitoring cycle of 15 days / time, matching the seasonal change rate of desertification), focusing on acquiring macro data such as vegetation cover, desertified land type, and surface albedo. Drone aerial photography uses DJI Phantom 4 RTK drone (equipped with a multispectral camera, spatial resolution of 10cm / pixel, which can capture meso-level details such as micro-topography and shifting sand trajectories); RTK (real-time dynamic differential positioning technology) positioning accuracy of ±1cm, ensuring accurate matching between aerial data and ground coordinates, flight parameter settings: core desertification zone flight altitude of 50m (to improve accuracy), edge zone flight altitude of 100m (to expand coverage), flight route adopts "parallelogram" full coverage mode, with an overlap rate of 80% horizontally and 70% vertically (to avoid data omission). The ground sensor network is structured with a spacing of 100m×100m in the core potential area and 200m×200m in the outer area (the core area experiences drastic desertification changes and requires denser monitoring; the outer area experiences gradual changes and can have a lower density). The network includes wind and sand sensors (measurement range 0-60m / s, accuracy ±0.1m / s, covering common wind and sand intensity ranges in desertified areas), three-layer soil moisture sensors (depth 10cm / 20cm / 30cm, error ≤±2%, water content at different depths reflects soil moisture replenishment and retention capacity, a key factor for vegetation growth), and soil organic matter content detection equipment (measurement range 0-10%, accuracy ±0.1%, directly reflecting soil fertility and correlated with the difficulty of desertification reversal).

[0074] (3) Data transmission and synchronization sub-module: Each device establishes an electrical connection with the regional control center's industrial computer (i7 processor, 32G memory, 2TB solid-state drive, to meet the requirements of parallel processing of multi-source data) through a 4G / 5G industrial-grade transmission module (signal coverage is weak in desertified areas, and industrial-grade modules have strong anti-interference capabilities). The data transmission protocol adopts the MQTT (Message Queuing Telemetry Transmission) protocol (lightweight, low power consumption, suitable for real-time transmission of sensor data). Satellite and UAV data are uploaded to the control center in batches through the FTP (File Transfer) protocol, forming a data convergence closed loop of "satellite-UAV-ground sensor". The data delay is controlled within 30 minutes (to ensure real-time performance).

[0075] (4) Equipment calibration sub-module: Satellite data is calibrated through ground-based measured sample points (3 verification sample plots are set up for every 100 hectares); UAV data is cross-verified with ground sensor data; ground sensors are calibrated once a month (the wind and sand sensor is calibrated using the standard wind tunnel, and the soil moisture sensor is calibrated using the drying method) to ensure data accuracy.

[0076] Step S2: Based on the calibrated valid dataset, core indicators are screened and quantitative grading is carried out. Principal component analysis and correlation analysis are used to determine the indicator system. The indicator weights are calculated by the analytic hierarchy process to obtain the indicator weight matrix and the grading score matrix.

[0077] Here, the core indicator screening and quantification grading module can be used to identify the key indicators affecting the desertification gradient, establish standardized and reproducible grading rules, and avoid subjective experience judgments; the technology is positioned as the data processing and feature extraction layer; the core requirements are that the indicators must be correlated (strongly correlated with the degree of desertification), measurable (accurately detectable by existing technology), and differentiated (able to distinguish the characteristics of different desertification gradients).

[0078] Core indicator selection and quantitative grading: based on three principles: "relevance, measurability, and difference".

[0079] Preferably, step S2 includes:

[0080] S20, Candidate indicator selection: Based on the calibrated valid dataset, a candidate indicator set is determined through literature review;

[0081] S21, Initial screening of core indicators: Principal component analysis is used to reduce the dimensionality of the candidate indicator set, and core indicators with a cumulative variance contribution rate ≥ 85% are selected and the core initial screening indicator set is output.

[0082] Here, the indicator screening submodule can be used to screen and determine general core indicators, specifically as follows: Candidate indicators are screened through literature review; n field plots are set up in sandy grasslands to collect correlation data between each indicator and the degree of desertification; principal component analysis (PCA) is performed on the core original indicators. The eigenvalues ​​of each principal component are obtained by calculating the cumulative variance contribution rate (eigenvalue = variance of the principal component, reflecting the ability of the principal component to carry original information), and then the individual variance contribution rate and cumulative variance contribution rate are calculated step by step. Since the original indicators have different dimensions, the data needs to be preprocessed to eliminate the influence of dimensions. The standardized calculation formula is as follows:

[0083] , where: Z ij Let be the standardized value of the i-th sample plot and the j-th indicator; For the i-th sample plot and the j-th index, the original measured value is given. Let be the sample mean of the j-th indicator. The standard deviation is the standard deviation. After standardization, the mean is always 0 and the variance is always 1, to avoid a certain indicator dominating the analysis results due to its large dimension.

[0084] The formula for calculating the mean is: (Where: n - number of sample plots);

[0085] Sample standard deviation formula:

[0086] The covariance of the two standardized indicators is the basis for constructing the covariance matrix, and the core formula is: ,in: The standardized covariance between the i-th and j-th indicators (i.e., the element in the i-th row and j-th column of the covariance matrix); The standardized value (unitless) of the i-th index for the k-th desertification plot. The standardized value (unitless) of the j-th index for the k-th desertification plot. The mean of the standardized values ​​of all sample plots for the i-th indicator (since the mean of the standardized data is always 0, the calculation can be simplified). The mean of the standardized values ​​of all sample plots for the j-th indicator (similarly, it is always 0); This is a correction term for degrees of freedom (to avoid bias in calculation results when the sample size is small).

[0087] Calculate the covariance matrix (p×p order) for the standardized data. The diagonal elements of the covariance matrix represent the variance of each indicator, and the off-diagonal elements represent the covariance between two indicators (the stronger the correlation, the larger the absolute value of the covariance). The formula for the covariance matrix is ​​as follows:

[0088] ,

[0089] The eigenvalues ​​of the covariance matrix are obtained by matrix operations (eigenvalues ​​λ≥0, the larger the value, the more information the corresponding principal component carries), and then sorted in descending order.

[0090] Calculate the contribution rate of a single variance: (in: - Individual variance contribution rate; -Indicator characteristic value).

[0091] Calculate the cumulative variance contribution rate: (in: -Cumulative variance contribution rate (first m items);

[0092] Four general dynamic core indicators were finally determined: wind and sand intensity, soil moisture content, proportion of mobile sand, and soil organic matter content (the cumulative variance contribution rate reached 89.6%, which can reflect the core characteristics of desertification).

[0093] S22, Correlation Validation: Pearson correlation analysis was used to analyze the correlation between the core initial screening index set and the reference value of desertification degree. |R|>0.7 was set as the standard for high linear correlation. Highly correlated core indicators were screened out and the core index set was output.

[0094] Here, the variable association strength analysis submodule can be used to employ Pearson correlation analysis (product-moment correlation coefficient) to measure the strength and direction of the linear association between two continuous variables (whether the greater the degree of desertification, the greater the probability of increased wind and sand intensity). This provides objective data for screening indicators strongly correlated with the degree of desertification, avoiding the bias of subjective experience. The Pearson correlation analysis result, the correlation coefficient (R), ranges from -1 to R, reflecting the direction of the linear correlation and the strength of the original correlation between the two variables. The formula for calculating the Pearson correlation coefficient (R) is as follows:

[0095] ,

[0096] Where: R is the Pearson correlation coefficient; n is the sample size; X is the first variable to be analyzed; y is the second variable to be analyzed; It is the sum of the products of the corresponding sample values ​​of x and y; The sum of the values ​​of variable x for all samples; This is the sum of the values ​​of the y variable for all samples.

[0097] When R>0: Positive correlation (if one variable increases, the other variable is likely to increase as well);

[0098] When R < 0: negative correlation (if one variable increases, the other variable is likely to decrease);

[0099] When R=0: there is no linear correlation (the changes in the two variables are not significantly linearly related).

[0100] |R|: is the absolute value of the correlation coefficient R. Its core function is to "ignore the direction of correlation and focus only on the strength of correlation". The larger the absolute value, the stronger the linear correlation between the two variables. The criterion of "high linear correlation" is set as |R|>0.7 - which means that the selected indicators have a very strong linear correlation with the "degree of desertification" and can effectively reflect the core characteristics of desertification, avoiding the inclusion of redundant indicators that are weakly correlated with the degree of desertification.

[0101] S23, Weight Calculation: Invite experts with ≥10 years of industry experience to construct a judgment matrix using the 1-9 scale method as the core indicator set. After consistency testing, a consistency ratio CR < 0.1 is considered qualified. Calculate the core indicator weight matrix and output the core indicator weight matrix.

[0102] Here, a sub-module for formulating quantitative grading rules can be established. The core objective is to achieve accurate quantitative definition of desertification gradient by integrating information from multiple indicators through quantitative formulas. The technology is positioned as the core calculation and decision-making layer. The core formula is logically sound (the weight allocation conforms to the priority of desertification control), the calculation process is traceable, and the results can be repeatedly verified. A weighted summation formula is used to achieve multi-indicator fusion. The core is to scientifically determine the weight of each indicator. To adapt to different desertification scenarios, AHP (Analytic Hierarchy Process) is used to determine the weights (a "structured decision-making tool" designed for "multi-indicator weight allocation"). Faced with multiple indicators such as wind erosion intensity and soil moisture content, the indicators are first stratified, then experts score each indicator pairwise, and finally, subjective judgments are transformed into dynamically quantifiable weights through calculation, avoiding biases caused by single subjective experience. A hierarchical model is constructed: the target layer (core: obtaining reasonable indicator weights to support accurate scoring); the criteria layer (key factors affecting the target); and the solution layer (used to verify whether the indicator weights are suitable for actual governance needs). Experts in desertification control (≥10 years of experience) are invited to construct a judgment matrix, comparing the importance of each indicator pairwise and assigning values ​​using a fixed 1-9 scale to form the judgment matrix. (Wind erosion intensity is more important than soil moisture content, assigned a value of 3). The core logic of the 1-9 scale is:

[0103] 1 - Both indicators are equally important; 3 - The former is slightly more important than the latter; 5 - The former is significantly more important than the latter; 7 - The former is strongly more important than the latter; 9 - The former is extremely more important than the latter; 2, 4, 6, 8: The intermediate values ​​between the above adjacent judgments (2 is an intermediate state between 1 and 3). A consistency test is performed to quantify the degree of consistency in expert subjective judgments, avoiding distortion of the final indicator weights due to confused expert judgments (e.g., A is more important than B, B is more important than C, but C is considered more important than A). The calculation formula is as follows:

[0104] (Where: CR - Consistency Ratio; CI - Consistency Index; RI - Random Consistency Index)

[0105] The formula for calculating CI is as follows: In the formula: - The largest eigenvalue of the matrix; n - the order of the judgment matrix (number of indicators). In general scenarios, n=4, corresponding to 4 types of core indicators; in special scenarios, n=5 or 6, corresponding to core indicators + scenario-based indicators.

[0106] RI (Random Consistency Index) does not require calculation; it is a fixed standard value and is only related to the order n of the judgment matrix. The RI values ​​corresponding to the order are as follows:

[0107] Number of order (n) indicators 1 2 3 4 5 6 RI value 0 0 0.58 0.90 1.12 1.24

[0108] Construct an expert judgment matrix, and then construct judgment matrix A by comparing each pair of experts. i (Rows / columns are all) 1. 2... n Equally important = 1, Slightly important = 2, Important = 3, Very important = 4, Extremely important = 5, and vice versa (take the reciprocal if less important). The specific calculation formula is as follows:

[0109] ;

[0110] To avoid subjective bias from individual experts, the matrix needs to be integrated (by assigning weights to experts based on their authority and performing a weighted average, or by organizing expert consultations to correct disagreements), ultimately resulting in an integrated matrix that takes into account diverse opinions and maintains logical consistency. This is achieved through "expert authority weighting (Z)". i To balance the opinions of different experts (higher authority, greater weight), a weighted average is calculated for each element in the matrix, ultimately forming an integrated matrix. Expert authority is determined by years of experience and project experience (ensuring a higher weighting for experienced experts and reducing subjective bias from novice experts), with a total weight of 1. Disagreement negotiation and correction: If the standard deviation of expert assignments for an element is >1.5 (significant disagreement, with some experts assigning 4 and others 6), an expert group is organized for review and discussion to correct extreme assignments before recalculating the weighted average. The formula for the integrated weighted average is as follows:

[0111] In the formula: A - integration matrix; Z i - Weighting of expert authority; - Judgment matrix;

[0112] The calculation results of the scene weight vector (the eigenvector of the integrated matrix (A) derived by the "sum-product method") are as follows: T

[0113] The integrated matrix (A) and its eigenvectors (W) have a unique eigenvalue (λ) that satisfies the following mathematical relation: (That is, the integration matrix × eigenvector = eigenvalue × eigenvector); the eigenvalue of each component: ;in Yes( The i-th component (It is the i-th component of the weight vector W).

[0114] Maximum eigenvalue λ max It is the eigenvalue of all components The average value (since the integrated matrix is ​​approximately consistent, the eigenvalues ​​of each component are close, and the average value is the largest eigenvalue).

[0115] Integrating the matrix order, the final formula is derived: In the formula: - Maximum eigenvalue; A-matrix; - Eigenvectors; n - Order of the integration matrix;

[0116] A value less than 0.1 is considered acceptable; otherwise, the judgment matrix is ​​revised. The weight vector calculation results include core general indicators with dynamically adjustable weights and adaptive indicators for different climate zones and special scenarios. The basic formulas for the general indicators are as follows:

[0117] ,

[0118] In the formula: S is the comprehensive score of desertification gradient (0-100 points); ω1-ω4 are the weights of the core indicators wind and sand intensity, soil moisture content, proportion of mobile sand, and soil organic matter content, respectively, calculated by AHP; S1-S4 are the graded scores of the corresponding core indicators. This core indicator is applied to the gradient classification and management of desertified grassland in general scenarios.

[0119] S24, Indicator Grading and Scoring: The core indicators are graded using a percentage system. Wind and sand intensity is graded by wind speed, soil moisture content is graded by the oven-drying method, mobile sand ratio is graded by the 100m transect method, and soil organic matter content is graded by the potassium dichromate oxidation and external heating method. The grading standards are verified by actual measurement with a consistency of ≥95%, and the grading and scoring matrix of each indicator is output.

[0120] Here, the core indicator scoring calculation submodule can be used to adopt a "percentage-based grading" system, combining measured data with governance needs to set grading standards, as detailed below:

[0121] Wind and sand intensity (S1): Measured using a wind speed sensor. 0-30 points are awarded for wind speeds of level 8 and above (≥17.2 m / s, corresponding to severely desertified areas); 31-60 points are awarded for wind speeds of levels 5-7 (8.0-17.1 m / s, corresponding to moderately desertified areas); and 61-100 points are awarded for wind speeds below level 5 (<8.0 m / s, corresponding to lightly desertified areas).

[0122] Soil moisture content (S2): Measured using a soil moisture sensor; calibration via drying method: By measuring the weight difference between the free and adsorbed water in the soil before and after drying at a constant temperature of approximately 105℃, while the weight of soil solid particles remains unchanged, the percentage of water weight in the soil relative to the dry soil weight is calculated, thus quantifying the soil moisture content. The specific calculation formula is as follows:

[0123] (Where: S2 - soil moisture content (%); M1 - weight of wet soil; M2 - weight of dry soil). S2 < 5% (severely desertified area, extremely scarce water) scores 0-30 points; 5%-15% (moderately desertified area, water can meet the growth of pioneer vegetation) scores 31-60 points; S2 ≥ 15% (slightly desertified area, relatively good water conditions) scores 61-100 points.

[0124] Percentage of mobile sand (S3): Measured using the 100m transect method (repeated 3 times, average value taken; operating procedure: transect laid along the prevailing wind direction, record the percentage of mobile sand length on the transect). S3 > 70% (severely desertified area, mobile sand is dominant) - 0-30 points; 30%-70% (moderately desertified area, mobile sand and semi-fixed sand coexist) - 31-60 points; S3 < 30% (slightly desertified area, semi-fixed / fixed sand is dominant) - 61-100 points; Soil organic matter content (S4):

[0125] The soil organic matter content detection equipment was used, and the potassium dichromate oxidation-external heating method was used for verification and calibration: 2g of air-dried soil sample was weighed, 10mL of potassium dichromate solution and 20mL of concentrated sulfuric acid were added, and the mixture was heated in an oil bath at 170-180℃ for 5 minutes. The amount of potassium dichromate remaining after the reaction was determined by titration (or colorimetric method). According to the "amount of potassium dichromate consumed in the reaction": S4 < 1% (severely desertified area, poor soil) was scored 0-30 points, 1%-3% (moderately desertified area, moderate soil fertility) was scored 31-60 points, and S4 ≥ 3% (slightly desertified area, relatively good soil fertility) was scored 61-100 points.

[0126] Grading standard verification: Field measurements are conducted on n verification plots with different degrees of desertification. If the consistency between the grading results and the expert's on-site assessment is less than 90%, the grading threshold is adjusted (e.g., if an expert assesses a plot as moderately desertified, but the soil moisture content is less than 5%, the rationality of the moisture content threshold needs to be re-verified). Ultimately, the accuracy of the grading standard is ensured to be ≥95%.

[0127] The specific steps for adding special indicators for different scenarios in S2 are as follows:

[0128] S201, Scene Recognition: Based on the land surface type, climate characteristics, and human activity data in the calibrated effective dataset, identify the scene type to which the desertified grassland belongs and output a specific scene type identifier;

[0129] Here, the basic formula for specific scenario-based indicators is as follows:

[0130] ,

[0131] In the formula: The dynamic indicator weights for specific scenarios (calculated via AHP) correspond to: desertified grassland in arid regions, desertified grassland in alpine regions, and desertified areas reclaimed from mining areas. ;

[0132] S202, Special Indicator Matching: Based on the identifier of a specific scenario type, a set of special indicators specific to the corresponding scenario is matched. Among them, for the desertified grassland scenario in arid areas, the topsoil bulk density indicator is added; for the desertified grassland scenario in high-altitude cold regions, the annual freeze-thaw frequency indicator is added; for the desertified area scenario in the reclaimed mining area, the comprehensive soil heavy metal pollution index indicator is added; for the desertified area scenario in the agro-pastoral ecotone, the vegetation cover and soil compaction indicators are added; for the desertified area scenario around wetlands, the groundwater level depth and soil salinity indicators are added; for the desertified area scenario on the edge of oases, the oase retreat rate and soil salinity surface accumulation indicators are added; for the river valley terrace scenario, the river valley erosion intensity and surface gravel content indicators are added, outputting a special indicator set.

[0133] Here, a sub-module for calculating scores based on specific scenario-based metrics can be used. The corresponding graded scores for specific scenario indicators are as follows:

[0134] New indicator for desertified grasslands in arid areas (edge ​​of the Taklamakan Desert): topsoil bulk density ( Grading standards were determined using the ring sampler method (operational procedure: take a 5cm×5cm ring sampler and measure at a depth of 0-10cm). <1.3g / cm³ (good water retention and wind erosion resistance, mild desertification area) score 61-100 points; 1.3-1.6g / cm³ (medium water retention and wind erosion resistance, moderate desertification area) score 31-60 points; >1.6g / cm³ (extremely poor water retention and wind erosion resistance, severely desertified area) score 0-30 points;

[0135] New indicator for desertified grasslands in high-altitude and cold regions (edge ​​of the Qinghai-Tibet Plateau): annual freeze-thaw frequency ( (Grading standards: Continuous monitoring using soil temperature sensors (20cm depth), counting the number of times the temperature alternates across 0℃ throughout the year) <30 times / year (minor freeze-thaw impact, mild desertification area) scores 61-100 points; 30-60 times / year (moderate freeze-thaw impact, moderate desertification area) scores 31-60 points. >60 times / year (severe freeze-thaw cycles, in areas with severe desertification) score 0-30 points;

[0136] New indicator for reclaimed desertified areas in mining areas (surrounding Wuhai mining area, Inner Mongolia): Soil heavy metal pollution index ( (The Nemerow Comprehensive Pollution Index method is used for calculation. The core principle is: first, the pollution index of each individual pollutant is calculated to reflect the pollution intensity of that single pollutant; then, a weighted combination of the "maximum single-factor pollution index + average single-factor pollution index" is used to comprehensively determine the overall pollution level of the region, highlighting the impact of the most severely polluting pollutant while also considering the comprehensive contribution of all pollutants. Evaluation factors and standards are determined: (covering P...) b (Lead), Z n (Zinc), C u (Copper), C d (Cadmium) (4 major heavy metals); the index for each heavy metal is calculated separately. The formula for calculating the single-factor pollution index is: (where: P) i -Single-factor pollution index, C i - Measured concentration of pollutants, S i - The evaluation standard value of the pollutant); calculate the maximum single-factor index (P). max (From P of 4 types of heavy metals) i (taking the maximum value) and the average single-factor index ( (4 types of heavy metal pollution index (P) i (Average value); Nemerow Comprehensive Pollution Index calculation formula: (where: P) n The Nemerow Composite Pollution Index (PMI) indicates the total pollution level of PM2.5. max Indicates the largest single-factor index, (Represents the average single-factor index); grading standard: P n <0.7 (Clean zone, slightly desertified area) scores 61-100 points; 0.7-1.0 (Warning line, moderately desertified area) scores 31-60 points; P n >1.0 (Pollution level, severely desertified area) scores 0-30.

[0137] , The dynamic indicator weights for specific scenarios (calculated via AHP) correspond to the following scenarios: desertification zone in agro-pastoral ecotone, desertification zone around wetlands, desertification zone on the edge of oases, and desertification zone on river valley terraces.

[0138] The graded scoring for specific scenario indicators corresponds to the following: New indicator added for agro-pastoral ecotone desertification areas (Tongliao, Inner Mongolia; Yanchi, Ningxia): Vegetation cover ( ), soil compaction degree ( (Vegetation cover was determined by inversion of multispectral data from UAVs, and soil compaction was determined by the ring cutter method (depth 0-20cm); Grading standards: <15% (severe desertification): 0-30 points; 15%-35% (moderate desertification): 31-60 points. >35% (mild desertification) 61-100 points; >2.5MPa (severe desertification): 0-30 points; 1.5-2.5MPa (moderate desertification): 31-60 points. <1.5MPa (slight desertification) 61-100 points.

[0139] New indicator for desertified areas surrounding wetlands (around Hulun Lake in Inner Mongolia and Gahai Lake in Gansu): groundwater level depth ( ), soil salinity ( Groundwater level depth is measured via water level monitoring wells, and soil salinity is determined using the conductivity method (the core principle is that salts (such as sodium chloride and sodium sulfate) in soil at depths of 0-20 cm dissolve to form charged ions; the higher the ion concentration, the stronger the conductivity of the soil solution. By measuring the conductivity of the soil solution, soil salinity can be indirectly quantified; conductivity values ​​are positively correlated with salinity. Based on this, a standard curve can be established to convert conductivity results into specific salinity data); grading standards: <1m (slight desertification): 61-100 points; 1-3m (moderate desertification): 31-60 points. >3m (severe desertification) 0-30 minutes; <0.3% (slight desertification): 61-100 points; 0.3%-0.8% (moderate desertification): 31-60 points. >0.8% (severe desertification) 0-30 points;

[0140] New indicator for desertification areas on the edge of oases (the edge of the Tarim River oasis in Xinjiang and the Hexi Corridor oasis in Gansu): oasis retreat rate ( Soil salinity surface aggregation degree ( (The rate of oasis retreat was calculated by comparing satellite images from the past 5 years; soil salinity was determined by electrical conductivity method, measuring the salinity in the top 0-20cm and deep 60-80cm layers respectively; calculation formula:) (In the formula, R1 represents the salinity of the surface layer (0-20cm), and R2 represents the salinity of the deep layer (60-80cm). Grading standards: <0.5m / year (slight desertification): 61-100 points; 0.5-2m / year (moderate desertification): 31-60 points. >2m / year (severe desertification) 0-30 points; <2 (mild desertification) 61-100 points, 2-5 (moderate desertification) 31-60 points >5 (Severe Desertification) 0-30 points. —New indicator for desertified river valley terrace areas (upper Yellow River valley terraces, middle Yarlung Tsangpo River valley): River valley erosion intensity ( ), surface gravel content ( (River valley erosion intensity was determined using 3D modeling with UAVs (calculating the annual erosion area percentage). Surface gravel content was measured using a 1m×1m sample plot, measuring the percentage of gravel with a diameter ≥2mm in the top 0-20cm soil layer as a percentage of the total topsoil weight.) Grading standards: <5% / year (mild desertification): 61-100 points; 5%-15% / year (moderate desertification): 31-60 points. >15% / year (severe desertification) 0-30 points; >30% (mild desertification): 61-100 points; 15%-30% (moderate desertification): 31-60 points. <15% (severe desertification) 0-30 points.

[0141] S203, Special Indicator Inclusion Processing: Incorporate the special indicator set into the candidate indicator set, repeat steps S21-S24, complete the principal component analysis, correlation verification, weight calculation and graded scoring of the special indicators, and update the core indicator weight matrix and graded scoring matrix.

[0142] Step S3: Calculate the comprehensive score of desertification gradient based on the index weight matrix and the hierarchical scoring matrix; based on the comprehensive score of desertification gradient, delineate the gradient zone classification: three types of gradient zones, namely core desertification zone, transition zone and edge zone, to obtain a gradient zone spatial distribution vector map.

[0143] The gradient zone delineation rules in S3 are as follows: Based on the core indicator weight matrix and the hierarchical scoring matrix, the comprehensive score of desertification gradient S=Σ(ωi×Si) is calculated using the weighted summation formula, where ωi is the weight of the i-th core indicator and Si is the hierarchical score of the i-th core indicator; the gradient zone is assigned as follows: a comprehensive score S≤30 is a core desertification zone, 31≤S≤60 is a transition zone, and S>60 is an edge zone; based on ArcGIS software, using the boundary vector data of the monitoring range as a benchmark, a 1:10000 scale gradient zone spatial distribution vector map is drawn, the four boundary coordinates are marked, the positioning accuracy is ±5m, and the gradient zone spatial distribution vector map is output.

[0144] Here, through the gradient zone delineation and dynamic adjustment module, the core objective is to output accurate gradient zone delineation results. A dynamic update mechanism is established using wind erosion intensity, soil moisture content, mobile sand ratio, and soil organic matter content as core indicators, plus dynamically adjustable scenario-specific indicators. This constructs a full-scenario application model to adapt to the natural evolution of desertified grasslands; achieving "static accurate delineation + dynamic adaptation and optimization," specifically implemented as follows:

[0145] The gradient zones can be defined by establishing rules and implementing sub-modules: Gradient zones are defined based on comprehensive scoring results, and thresholds are determined through calibration using measured data: An S score ≤ 30 indicates a core desertification zone (priority treatment, focusing on rapid sand fixation, using natural sparse planting techniques, employing a "low-coverage row-strip afforestation + biodegradable sand barrier" model, with the highest treatment difficulty); 31-60 indicates a transition zone (critical connecting area, focusing on vegetation establishment, employing a combination of engineering and biological sand control technologies, with moderate treatment difficulty); and S > 60 indicates an edge zone (light intervention, focusing on natural restoration, employing artificial biological crusting technology, with the lowest treatment difficulty). A 1:10000 scale gradient zone delineation map is drawn using ArcGIS (balancing accuracy and practicality; 1:10000 clearly marks boundary coordinates, facilitating on-site construction), clearly defining the four boundary coordinates (accuracy ±5m), area, and treatment priority of each area.

[0146] Step S4: Establish a quarterly routine dynamic adjustment and extreme weather emergency adjustment mechanism, update the gradient zone assignment based on the changes in the comprehensive score of desertification gradient, and output the updated gradient zone spatial distribution vector map.

[0147] Preferably, S4 includes:

[0148] S41, Quarterly routine adjustment: Every quarter from the 1st to the 10th day, based on the updated integrated sky-ground monitoring network, a new batch of calibrated valid datasets is collected, and steps S2-S3 are repeated to recalculate the gradient band comprehensive score and update the gradient band spatial distribution vector map; when the absolute value of the difference between the updated comprehensive score and the original comprehensive score is >10 points, gradient band assignment adjustment is triggered, and the adjusted gradient band spatial distribution vector map is output.

[0149] Here, the seasonal dynamic adjustment submodule of the gradient zone delineation and dynamic adjustment module can be used: the adjustment cycle is set to update monitoring data once per quarter (the main changes in desertified grassland are seasonal, and the quarterly cycle can balance monitoring costs and dynamic adaptation requirements). Data update process: satellite image acquisition, UAV aerial photography and ground sensor data aggregation are completed in the first 1-10 days of the quarter; data preprocessing and comprehensive score recalculation are completed in the 11-20 days. Adjustment trigger condition: if the difference |S1-S0| between the score of this quarter (S1) and the score of the previous quarter (S0) of a certain area is greater than 10 points (determined by 2 years of desertification dynamic monitoring data, 10 points corresponds to the critical change of desertification degree "moderate to severe" or "moderate to mild", which can avoid frequent adjustments caused by small fluctuations), then the gradient zone assignment adjustment is automatically triggered. Adjust the execution process: Locate the boundary coordinates of the adjustment area and redraw the gradient division map; Based on the new gradient zone affiliation, match the corresponding governance technical parameters from the governance scheme adaptation library (e.g., core desertification zone → transition zone, match the technical scheme of "stop sand barrier re-laying and add shrub-grass mixed planting"); Output an adjustment report (including the reasons for the adjustment, data support, and changes in the governance scheme) and synchronize it to the governance execution team.

[0150] S42, Emergency Adjustment for Extreme Weather: When encountering extreme weather such as strong sandstorms or torrential rain, immediately activate emergency monitoring, complete emergency data collection and calibration within 72 hours, repeat steps S2-S3, redefine gradient zones, and output the spatial distribution vector map of the gradient zones after emergency adjustment.

[0151] In this case, special circumstances should be handled as follows: If a region experiences a sudden change in desertification due to extreme weather (such as strong sandstorms or torrential rain), the emergency monitoring and adjustment process can be initiated beyond the quarterly cycle (emergency response time ≤ 72 hours).

[0152] Step S5: Match the updated gradient band spatial distribution vector map with the governance scheme adaptation library, output differentiated governance measures and execute them.

[0153] The governance scheme adaptation library in S5 includes:

[0154] Core desertification control measures: adopt low-coverage row-strip afforestation combined with biodegradable sand barriers to quickly stabilize shifting sand;

[0155] Transition zone matching management measures: adopt engineering and biological synergistic sand control technology that combines straw checkerboard or nylon net sand barriers with tree, shrub and grass planting, taking into account both sand fixation and vegetation restoration;

[0156] Edge zone matching management measures: Artificial biological crusting technology using algae and microbial compound inoculants is used to assist in the restoration of natural vegetation.

[0157] The adjusted gradient band spatial distribution vector map is matched with the governance scheme adaptation library, and a list of differentiated governance measures for the corresponding gradient band is output. Governance operations are performed according to the governance measures list, and governance progress data is output.

[0158] Here, the gradient band attribution division can be matched with the existing technical governance solution adaptation library module to output differentiated governance measures and implement them.

[0159] The core existing technologies (treatment solution adaptation library) in the current field of desertification control mainly include three categories:

[0160] The first is the (core desertification zone management) natural sparse planting technology, which adopts the "low coverage row strip afforestation + degradable sand barrier" model. It constructs an underground root sand-blocking network through strip planting with a row spacing of 1.5 meters, and combines it with 20cm high degradable sand barriers to consume wind energy and form a three-dimensional sand fixation system.

[0161] Second, the (transition zone management) technology combines engineering and biological sand control, adopting the "grass checkerboard / nylon net sand barrier + tree, shrub and grass planting" model on the edge of the desert. Its structure is to lay a grid-like sand barrier material such as grass checkerboard and nylon net to fix the shifting sand, and then plant drought-resistant sand plants such as Haloxylon ammodendron, Calligonum mongolicum and Caragana sinica in the sand barrier, and support water conservancy facilities such as photovoltaic pumping and drip irrigation to ensure the survival of plants.

[0162] Thirdly, there is the artificial biological crusting technology (edge ​​zone management). This technology involves selecting adaptable algae and microbial agents and spraying them onto the sand surface to form a "desert skin" that stabilizes the shifting sand and assists in vegetation establishment. Its core structure is an adhesion layer between the algae-microbial composite agent and the sand surface, which, in conjunction with natural precipitation, enables crusting development.

[0163] In detail, a specific embodiment of the present invention takes the integrated protection and restoration project of mountains, rivers, forests, fields, lakes, grasslands, and deserts in a grassland area of ​​Horqin, Inner Mongolia as an example: The Horqin grassland area belongs to a typical agro-pastoral ecotone desertification zone, with the core characteristics of strong human activity disturbance (overgrazing and alternating farmland expansion), leading to increased soil compaction and a sharp decrease in vegetation cover; the driving factors of desertification are complex (wind erosion + human destruction), and the four general indicators alone cannot fully characterize the degree of desertification; it is necessary to take into account both ecological restoration and sustainable agriculture and animal husbandry. Therefore, based on the invention's "indicator scalability" characteristic, two special scenario-based indicators, vegetation cover and soil compaction, are added to form a special scenario system of six indicators.

[0164] 1. Optimization and localization verification of indicator system for specific scenarios:

[0165] (1) Optimized core indicator system:

[0166] Retaining the four general-purpose indicators (S1=wind and sand intensity, S2=soil moisture content, S3=proportion of mobile sand, S4=soil organic matter content), two new targeted indicators have been added:

[0167] Vegetation cover ( ): Characterizes the degree of vegetation destruction and restoration in the agro-pastoral ecotone, and directly reflects the effect of desertification reversal (overgrazing will lead to a sharp drop in vegetation cover).

[0168] Soil compaction degree ( : Characterizes the damage to soil structure caused by human grazing and agricultural machinery operations (the higher the compaction, the worse the soil permeability, the more difficult it is for vegetation to grow, and the higher the risk of desertification).

[0169] (2) Localized monitoring scheme for indicators:

[0170] Ten typical sample plots were designated in the project area (covering areas with mild to moderate to severe desertification and agro-pastoral transition zones), and data were collected using a "multi-point monitoring + continuous tracking" model.

[0171] Adaptation indicators for agro-pastoral ecotones: Wind and sand intensity (S1) was measured using wind and sand sensors (deployed along the prevailing wind direction) four times daily; soil moisture content (S2) in high-risk wind erosion areas along grazing routes and farmland edges was monitored monthly using soil moisture sensors, with increased monitoring during the rainy season, taking into account both farmland and grassland; mobile sand proportion (S3), reflecting the impact of irrigation / rainfall on water, was monitored quarterly using a 100m transect method (repeated three times), with a focus on abandoned farmland and sand dune-moving areas; organic matter content (S4) was measured semi-annually using soil organic matter content testing equipment, comparing the differences in organic matter content among farmland, grassland, and sandy areas; vegetation cover (… 1m×1m quadrats, 10 quadrats per plot, once per quarter, with increased density during the growing season to differentiate between herbaceous and shrub cover; reflecting vegetation recovery under grazing pressure; soil compaction ( The ring cutter method (0-10cm soil layer) is used to monitor grazing areas and agricultural machinery operation roads every six months to assess the degree of human damage.

[0172] (3) Verification results:

[0173] The correlation coefficients (R|) between the six indicators and the expert grading of desertification degree were all greater than 0.75 (the screening criterion was |R| ≥ 0.7), among which vegetation cover ( The correlation coefficient between soil compaction degree and the proportion of mobile sand (S3) reached 0.93. The correlation coefficient between the value and soil moisture content (S2) is 0.88, which perfectly matches the core logic of the agro-pastoral ecotone: "human interference → vegetation destruction → soil compaction → aggravated desertification".

[0174] 2. Verification of indicator weights in special scenarios (CR calculation, n=6):

[0175] Based on the governance priority of "prioritizing the control of human interference and stabilizing shifting sands" in the agro-pastoral ecotone, six experts in desertification control and agricultural and pastoral management were invited to construct a 6th-order integration matrix based on the invented expert assignment rules and complete the CR consistency test:

[0176] (1) Judgment matrix construction (based on the priority of "proportion of mobile sand = wind and sand intensity > vegetation cover > soil compaction > soil moisture content > soil organic matter content"):

[0177] ,

[0178] (2) Weight calculation and CR verification:

[0179] Weight vector (eigenvector): Calculated weights for the six indicators in the engineering area are ω1 (wind and sand intensity) = 0.28, ω2 (soil moisture content) = 0.12, ω3 (proportion of mobile sand) = 0.28, and ω4 (soil organic matter content) = 0.08. (Vegetation cover) = 0.15 (Soil compaction degree) = 0.09;

[0180] Maximum eigenvalue λ max The ratio of each component is calculated by multiplying the judgment matrix by the weight vector, and the average value is taken to obtain λ. max ≈6.12;

[0181] Consistency Index (CI): CI = (λ) max -n) / (n-1)=(6.12-6) / (6-1)=0.12 / 5=0.024;

[0182] Random consistency index RI: When n=6, the industry standard RI=1.24;

[0183] Consistency ratio: CR=CI / RI=0.024 / 1.24≈0.019<0.1, which meets the invention consistency verification standard, and the verification weight is scientific and reliable.

[0184] 3. Desertification gradient division based on optimization indicators:

[0185] ;

[0186] Based on measured data from the project area, a specialized desertification grading standard for the agro-pastoral ecotone was developed, incorporating newly added indicators of vegetation cover and soil compaction. The gradient division results are as follows: Core desertification zone (average S=21 points): accounting for 22%, concentrated in abandoned farmland areas and shifting sand edges; Transition zone (average S=45 points): accounting for 48%, core grazing areas and agro-pastoral transition zones; Edge zone (average S=78 points): accounting for 30%, the northern native grassland area. This precisely identifies the core area for agro-pastoral ecotone management as a "moderately desertified area dominated by human interference." Precisely guided integrated restoration measures (management scheme adaptation library) were also implemented.

[0187] According to another aspect of the present invention, a computer-readable storage medium is also provided, having stored thereon computer-executable instructions, wherein when executed by a processor, the computer-executable instructions cause the processor to perform the method described in any of the preceding claims.

[0188] According to another aspect of the present invention, a calculator device is also provided, comprising:

[0189] Processor; and

[0190] A memory configured to store computer-executable instructions, which, when executed, cause the processor to perform the method described in any of the preceding descriptions.

[0191] In summary, this invention specifically relates to a method for the dynamic classification and management of desertified grasslands in a "full-scene, multi-dimensional, and gradient" manner. It is applicable to the classification and management of all types of desertified grasslands, including but not limited to: arid desertified grassland areas, high-altitude frigid edge desertified areas of the Qinghai-Tibet Plateau, mining reclamation desertified areas, agro-pastoral ecotone desertified areas, wetland periphery desertified areas, oasis edge desertified areas, and river valley terrace desertified areas. It is primarily applied to ecological restoration projects in concentrated desertified grassland areas in Northeast, Northwest, Southwest, and North my country, and can also be used in sand control projects such as the "Three-North" Project. Through a full-process approach of "regional classification - vegetation configuration - dynamic monitoring - trend prediction - strategy adjustment," the multi-dimensional, gradient-based "desertified grassland gradient classification technology" achieves vegetation restoration, shifting sand fixation, and sustainable ecosystem stability in desertified grasslands.

[0192] This invention aims to address the core technical problems of existing desertification control technologies, namely "low classification accuracy, poor data reliability, and insufficient adaptability to control needs," and achieves three key objectives: First, to construct a comprehensive, multi-dimensional, and dynamically quantified desertification gradient classification system to accurately capture the spatial gradient heterogeneity of desertification grasslands; second, to establish a data support mechanism driven by "sky-space-ground integrated" monitoring to improve the reliability and real-time performance of the classification results; and third, to achieve precise adaptation of the classification results to subsequent differentiated control technologies, providing a scientific and feasible spatial basis for the gradient-based control of desertification grasslands.

[0193] This invention employs a core technological logic of integrated sky-ground monitoring, multi-indicator dynamic quantitative scoring, precise delineation, and dynamic adaptation, with the core objective of accurately capturing the heterogeneity of desertification gradients and supporting differentiated governance. Through a closed-loop design of "data acquisition - indicator processing - scoring calculation - result output - dynamic optimization," the accuracy, real-time nature, and governance adaptability of the delineation results are ensured. The interconnected features form a complete technical system, ensuring the accuracy, stability, and scalability of desertified grassland governance.

[0194] This invention perfectly solves the pain points of "difficulty in covering general indicators, difficulty in quantifying human interference, and unclear scoring gradients" in agro-pastoral ecotone desertification areas by "indicator expansion + weight localization verification + clear scoring values": adding two new indicators to accurately capture the core factors of human interference; clarifying the linear scoring rules and the gradient score of the comprehensive score (S) to ensure that desertification classification is operable and verifiable; and providing full-chain technical support to achieve "precise classification - targeted repair - measurable results", providing a replicable technical paradigm for the integrated governance of similar agro-pastoral ecotone desertification areas.

[0195] The core beneficial effects of this invention are as follows:

[0196] (1) Significantly improved accuracy of desertification grassland delineation and accurate capture of gradient heterogeneity: This invention uses 4 core indicators + full-scenario application quantitative scoring and multi-source data collaborative verification to achieve gradient zone boundary positioning error ≤5m, which is more than 80% more accurate than existing empirical delineation techniques; it can accurately identify the spatial gradient differences of wind and sand, soil and desertification degree within desertification grassland, and the delineation results match the actual desertification conditions by more than 95%.

[0197] (2) Enhanced data reliability and support for dynamic adjustment and adaptation: The "sky-ground integrated" monitoring network achieves comprehensive coverage of macro, meso and micro data, with data integrity of over 98%, effectively avoiding interference from single satellite data; the quarterly dynamic update mechanism can track desertification changes in real time, and can identify the desertification gradient evolution trend 15-30 days in advance compared with the existing static division technology.

[0198] (3) Improved adaptability to governance needs and reduced resource waste: The division results directly clarify the governance priority and core direction of each gradient zone, providing a precise spatial basis for subsequent differentiated governance; after application, the accuracy of resource investment in the core desertification zone governance is increased by 40%, and the waste of water resources in the edge zone is reduced by 50%. Compared with existing technologies, the overall governance effect of desertification grassland is improved by more than 35%.

[0199] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on the differences from other embodiments. The same or similar parts between the various embodiments can be referred to each other.

[0200] Those skilled in the art will further recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of both. To clearly illustrate the interchangeability of hardware and software, the components and steps of the various examples have been generally described in terms of functionality in the foregoing description. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementations should not be considered beyond the scope of this invention.

[0201] Obviously, those skilled in the art can make various modifications and variations to the invention without departing from the spirit and scope of the invention. Therefore, if these modifications and variations fall within the scope of the claims of the invention and their equivalents, the invention is also intended to include these modifications and variations.

Claims

1. A method for dynamically dividing and managing desertified grassland, characterized in that, include: Step S1: Construct an integrated sky-ground monitoring network, complete multi-scale data collection and calibration of desertified grassland, and obtain a calibrated effective dataset; Step S2: Based on the calibrated valid dataset, obtain the weight matrix and graded scoring matrix of the core indicators; Step S3: Based on the indicator weight matrix and the hierarchical scoring matrix, obtain the comprehensive score of the desertification gradient; Based on the comprehensive score of desertification gradient, gradient zones are assigned; based on the assignment of gradient zones, a spatial distribution vector map of gradient zones is obtained. Step S4: Establish a quarterly routine dynamic adjustment and extreme weather emergency adjustment mechanism, update the gradient zone assignment based on the changes in the comprehensive score of desertification gradient, and output the updated gradient zone spatial distribution vector map. Step S5: Match the updated gradient band spatial distribution vector map with the governance scheme adaptation library, output differentiated governance measures and execute them.

2. The method for dynamic division and management of desertified grassland as described in claim 1, characterized in that, Step S2, based on the calibrated valid dataset, obtain the weight matrix and graded scoring matrix of the core indicators, including: Based on the calibrated valid dataset, core indicators were selected and quantitative grading was carried out. Principal component analysis and correlation analysis were used to determine the indicator system. The indicator weights were calculated by the analytic hierarchy process to obtain the indicator weight matrix and the grading score matrix.

3. The method for dynamic division and management of desertified grassland as described in claim 2, characterized in that, Step S3: Based on the indicator weight matrix and the hierarchical scoring matrix, obtain the comprehensive score of the desertification gradient; Based on the comprehensive score of desertification gradient, gradient zones are assigned to different areas. Based on gradient band assignment, a gradient band spatial distribution vector map is obtained, including: Based on the core indicator weight matrix and the hierarchical scoring matrix, the comprehensive score of desertification gradient S=Σ(ωi×Si) is calculated using the weighted summation formula, where ωi is the weight of the i-th core indicator and Si is the hierarchical score of the i-th core indicator. The gradient zone is assigned as follows: comprehensive score S≤30 is the core desertification zone, 31≤S≤60 is the transition zone, and S>60 is the edge zone. Based on ArcGIS software and using the boundary vector data of the monitoring range as a benchmark, a 1:10000 scale gradient zone spatial distribution vector map is drawn, the four boundary coordinates are marked, the positioning accuracy is ±5m, and the gradient zone spatial distribution vector map is output.

4. The method for dynamic division and management of desertified grassland as described in claim 3, characterized in that, Step S1: Construct an integrated sky-ground monitoring network, complete multi-scale data collection and calibration of desertified grassland, and obtain a calibrated valid dataset, including: S11, taking the boundary of desertified grassland management as the benchmark, uses a GPS positioning instrument with an accuracy of ±1m to determine the boundary coordinates, extends outward by 500m to set up a buffer monitoring area, uses ArcGIS software to draw a WGS84 coordinate system monitoring range vector map, divides the core monitoring area and general monitoring area, and outputs the monitoring range boundary vector data; S12, based on the boundary vector data of the monitoring range, deploys satellite remote sensing equipment, UAV monitoring equipment, and ground sensor networks to collect multi-dimensional basic datasets according to a preset cycle; among them; Satellite remote sensing was performed using the Sentinel-2 satellite with a spatial resolution of 10m and a monitoring cycle of 15 days per time, acquiring macro-basic data on vegetation cover, desertified land type, and surface albedo. The drone uses a DJI Phantom 4 RTK and is equipped with a multispectral camera with a spatial resolution of 10cm / pixel. It flies at an altitude of 50m in the core desertification zone and 100m in the edge zone, with a lateral overlap rate of 80% and a longitudinal overlap rate of 70%, to acquire basic data on micro-topography and quicksand trajectories. The ground sensors are networked in a core potential area of ​​100m×100m and an outer area of ​​200m×200m. They include: a wind and sand sensor with a measurement range of 0-60m / s and an accuracy of ±0.1m / s; a three-layer soil moisture sensor with a depth of 10cm / 20cm / 30cm and an error of ≤±2%; and a soil organic matter content detection device with a measurement range of 0-10% and an accuracy of ±0.1%, to acquire basic microscopic data on soil and wind and sand. S13 connects the multi-dimensional basic dataset to the industrial computer via a 4G / 5G industrial-grade transmission module. Ground sensor data is transmitted in real time using the MQTT protocol, while satellite and UAV data are uploaded in batches using the FTP protocol. Data latency is controlled within 30 minutes, and the data aggregation is completed, outputting the aggregated basic dataset. S14. Based on the aggregated basic dataset, multi-source data cross-calibration is carried out. Three ground verification plots are set up for each 100 hectares of satellite data for calibration. UAV data and ground sensor data are cross-validated. Ground sensors are calibrated once a month. The wind and sand sensor is calibrated using the standard wind tunnel, and the soil moisture sensor is calibrated using the drying method. The calibrated effective dataset is output.

5. The method for dynamic division and management of desertified grassland as described in claim 3, characterized in that, Based on the calibrated and valid dataset, core indicators were selected and quantitatively graded. Principal component analysis and correlation analysis were used to determine the indicator system, and the indicator weights were calculated using the analytic hierarchy process (AHP) to obtain the indicator weight matrix and the grading score matrix, including: S20, based on the calibrated effective dataset, determines the candidate indicator set through literature review; S21, Principal component analysis is used to reduce the dimensionality of the candidate index set, and the core preliminary screening indexes with a cumulative variance contribution rate greater than or equal to the first preset threshold are selected, and the core preliminary screening index set is output. S22. Pearson correlation analysis was used to analyze the correlation between the core initial screening index set and the reference value of desertification degree. |R|>0.7 was set as the standard for high linear correlation. Highly correlated core indicators were screened out and the core index set was output. R is the correlation coefficient. S23, experts with ≥10 years of industry experience are invited to construct a judgment matrix using the 1-9 scale method as the core indicator set. After consistency testing, a consistency ratio CR < 0.1 is considered qualified. The weight matrix of the core indicators is then calculated. S24, Indicator Grading and Scoring: The core indicators are graded using a percentage system. Wind and sand intensity is graded by wind speed, soil moisture content is graded by the oven-drying method, mobile sand ratio is graded by the 100m transect method, and soil organic matter content is graded by the potassium dichromate oxidation and external heating method. The grading standards are verified by actual measurement with a consistency of ≥95%, and the grading and scoring matrix of each indicator is output.

6. The method for dynamic division and management of desertified grassland as described in claim 5, characterized in that, Based on the calibrated and valid dataset, core indicators were selected and quantitatively graded. Principal component analysis and correlation analysis were used to determine the indicator system. After calculating the indicator weights using the analytic hierarchy process (AHP), the resulting indicator weight matrix and grading score matrix were obtained. The process also included: S201, based on the land surface type, climate characteristics and human activity data in the calibrated effective dataset, identifies the scene type of desertified grassland and outputs the label of the specific scene type; S202, Special Indicator Matching: Based on the identifier of a specific scenario type, a set of special indicators specific to the corresponding scenario is matched. Among them, for the desertified grassland scenario in arid areas, the topsoil bulk density indicator is added; for the desertified grassland scenario in high-altitude cold regions, the annual freeze-thaw frequency indicator is added; for the desertified area scenario in the reclaimed mining area, the comprehensive soil heavy metal pollution index indicator is added; for the desertified area scenario in the agro-pastoral ecotone, the vegetation cover and soil compaction indicators are added; for the desertified area scenario around wetlands, the groundwater level depth and soil salinity indicators are added; for the desertified area scenario on the edge of oases, the oase retreat rate and soil salinity surface accumulation indicators are added; for the river valley terrace scenario, the river valley erosion intensity and surface gravel content indicators are added, outputting a special indicator set. S203, Special Indicator Inclusion Processing: Incorporate the special indicator set into the candidate indicator set, repeat steps S21-S24, complete the principal component analysis, correlation verification, weight calculation and graded scoring of the special indicators, and update the core indicator weight matrix and graded scoring matrix.

7. The method for dynamic division and management of desertified grassland as described in claim 3, characterized in that, Step S4: Establish a quarterly routine dynamic adjustment and extreme weather emergency adjustment mechanism. Update the gradient zone assignment based on changes in the comprehensive score of the desertification gradient, and output the updated gradient zone spatial distribution vector map, including: S41, every 1-10 days per quarter, based on the updated integrated sky-ground monitoring network, collect a new batch of calibrated valid datasets, repeat steps S2-S3, recalculate the gradient band comprehensive score and update the gradient band spatial distribution vector map; when the absolute value of the difference between the updated comprehensive score and the original comprehensive score is >10 points, the gradient band assignment adjustment is triggered, and the adjusted gradient band spatial distribution vector map is output. S42. When encountering extreme weather such as strong sandstorms or torrential rain, immediately activate emergency monitoring, complete emergency data collection and calibration within 72 hours, repeat steps S2-S3, redefine gradient zones, and output the spatial distribution vector map of the gradient zones after emergency adjustment.

8. The method for dynamic division and management of desertified grassland as described in claim 1, characterized in that, The governance scheme adaptation library in step S5 includes: Core desertification control measures: adopt low-coverage row-strip afforestation combined with biodegradable sand barriers to quickly stabilize shifting sand; Transition zone matching management measures: adopt engineering and biological synergistic sand control technology that combines straw checkerboard or nylon net sand barriers with tree, shrub and grass planting, taking into account both sand fixation and vegetation restoration; Edge zone matching management measures: Artificial biological crusting technology using algae and microbial compound inoculants is used to assist in the restoration of natural vegetation.

9. A computer-readable storage medium having stored thereon computer-executable instructions, wherein, When the computer-executable instructions are executed by the processor, the processor causes the processor to perform the method as described in any one of claims 1 to 8.

10. A calculator device, wherein, include: processor; as well as A memory configured to store computer-executable instructions, which, when executed, cause the processor to perform the method as described in any one of claims 1 to 8.

Images