A method for dynamic configuration of sand source control in the Kongdui wind-blown sand area

Abstract

This invention discloses a dynamic configuration method for sand source control in the Kongdui sandstorm area, relating to the field of wind and sand protection technology. The method includes the following steps: collecting meteorological data, wind and sand data, and historical sand source change data for the Kongdui sandstorm area; and using remote sensing technology and ground monitoring equipment to obtain geographical information and vegetation cover of the sand source area. This invention achieves real-time perception and precise analysis of sand source changes by monitoring key parameters such as wind speed, wind direction, and dust concentration in real time, combined with high-resolution ground image data obtained from UAV and satellite remote sensing technologies. This not only improves the efficiency and accuracy of data collection but also enables precise deployment of control measures tailored to specific sand source dynamics. By dynamically adjusting the layout and density of control facilities and optimizing the type and area of ​​vegetation cover, it can more effectively block wind and sand erosion, reduce the frequency and intensity of sandstorms, and significantly improve the efficiency and accuracy of sand source control.

Description

Technical Field

[0001] This invention relates to the field of wind and sand protection technology, specifically to a dynamic configuration method for sand source control in the Kongdui wind and sand area. Background Technology

[0002] The sand source control in the Kongdui sandstorm area mainly stems from the severe sandstorm control situation and ecological protection needs in the Kongdui area of ​​the Yellow River in Inner Mongolia Autonomous Region. The Kongdui area, especially the "Ten Kongdui", is located in the upper reaches of the Yellow River and has a complex topography, including loess hilly and gully areas and downstream plains. These areas have frequent sandstorm activity and serious soil erosion, which poses a threat to the water quality and ecological security of the Yellow River. Due to its special topography and climate, the Kongdui area has prominent sandstorm erosion problems. A large amount of sediment enters the Yellow River through Kongdui, increasing the sediment content of the Yellow River and causing serious impacts on water conservancy projects, agricultural production and ecological environment in the downstream areas.

[0003] In existing technologies, sand source control in the Kongdui wind-blown sand area focuses on static analysis and lacks the ability to dynamically and in real-time evaluate the effectiveness of sand source control. As a result, it is difficult for relevant personnel to adjust and optimize the configuration scheme in a timely manner. Furthermore, the dynamic changes of sand sources in the Kongdui wind-blown sand area are affected by a variety of factors, which increases the difficulty of sand source control in the Kongdui wind-blown sand area. Therefore, how to enhance the dynamic evaluation capability of sand source control in the Kongdui wind-blown sand area in order to optimize the configuration scheme is the problem we need to solve. To this end, a dynamic configuration method for sand source control in the Kongdui wind-blown sand area is proposed. Summary of the Invention

[0004] The purpose of this invention is to provide a dynamic configuration method for sand source control in the Kongdui wind-blown sand area, so as to solve the problems mentioned in the background art.

[0005] To solve the above-mentioned technical problems, the technical solution adopted by the present invention is as follows:

[0006] A method for dynamic configuration of sand source control in the Kongdui wind-blown sand area includes the following steps:

[0007] Step 1: Collect meteorological data, wind and sand data, and historical sand source change data of Kongdui wind and sand area, and use remote sensing technology and ground monitoring equipment to obtain geographical information and vegetation cover of sand source area, and monitor the dynamic changes of sand source. Among them, meteorological data and wind and sand data of Kongdui wind and sand area include real-time data and historical data.

[0008] Step 2: Preprocess the collected data, including cleaning, noise reduction, standardization and normalization, to prepare for subsequent analysis, and perform feature analysis to extract the characteristics of sand source change, namely sand source distribution range, movement speed and erosion intensity, and construct a feature dataset.

[0009] Step 3: Extract relevant data on sand source change characteristics from the feature dataset, construct a sand source dynamic assessment model based on the time series analysis model, comprehensively analyze and calculate the sand source dynamic assessment coefficient, and analyze the sand source change trend;

[0010] Step 4: In conjunction with IoT technology, deploy a sensor network in the Kongdui sandstorm area to monitor wind speed, wind direction, and sand source change indicators in real time, and use drones and satellite remote sensing technology to regularly acquire ground image data and update the sand source distribution map.

[0011] Step 5: Combine the real-time monitoring data with the analysis results of the sand source dynamic assessment model to conduct a comprehensive analysis, identify the hot spots and trends of sand source changes, and evaluate the effectiveness of the current sand source control plan.

[0012] Step 6: Based on the assessment results, identify existing problems and deficiencies, optimize the sand source control plan, adjust the layout and density of control facilities, increase the vegetation coverage area and types, track the dynamic changes of sand sources, and adjust the sand source control effect.

[0013] A further improvement to the technical solution of this invention lies in the following: In step 1, the process of collecting meteorological data, wind and sand data, and historical sand source change data of the Kongdui wind and sand area is as follows:

[0014] Step 101: Obtain real-time meteorological data through meteorological stations and automatic meteorological observation stations deployed in the Kongdui wind and sand area, including key meteorological parameters such as wind speed, wind direction, temperature, humidity, and precipitation; obtain historical meteorological data from meteorological departments and research institutions, including long-term meteorological monitoring data and statistical analysis results, covering the changing trends and statistical characteristics of meteorological parameters such as wind speed, wind direction, and precipitation over many years.

[0015] Step 102: Obtain real-time data on wind and sand through sandstorm monitoring stations and sandstorm early warning systems, including the time, intensity, and range of sandstorm occurrences; and obtain historical data on wind and sand from environmental protection departments, research institutions, and historical documents, including the frequency, duration, and impact range of sandstorms.

[0016] Step 103: Obtain historical sand source change data through remote sensing image analysis, ground surveys and historical document records, including changes in topography, vegetation cover and soil erosion in the sand source area;

[0017] Step 104: Use remote sensing technology and ground observation equipment to conduct real-time monitoring and obtain geographic information of the sand source area, including topography, vegetation cover and land use type;

[0018] Step 105: Deploy ground monitoring equipment in the sand source area, set up sand collectors, collect and measure the sand content and particle size distribution information in the air, and use spectrometers and vegetation coverage monitoring equipment carried by UAVs to monitor the changes in vegetation coverage in the sand source area.

[0019] Step 106: Establish a data warehouse, integrate the collected real-time and historical data, and associate data from different sources through key fields such as timestamps and geographic locations to form a data sequence table for the Kongdui wind and sand area.

[0020] A further improvement to the technical solution of this invention lies in the following: In step 2, the extraction process of sand source change characteristics is as follows:

[0021] Step 201 involves preprocessing the relevant data in the Kongdui wind and sand area data sequence table, including data cleaning, denoising, standardization, and normalization. Data cleaning removes duplicate, erroneous, incomplete, or abnormal data to ensure accuracy and reliability, reduce noise interference, and improve the signal-to-noise ratio. For meteorological data, filtering techniques (such as low-pass filtering and median filtering) are used to remove high-frequency noise. For remote sensing image data, image processing algorithms (such as edge detection and morphological filtering) are used to remove image noise. Standardization converts the data into a form with zero mean and unit variance. Normalization scales the data to the [0,1] interval, and the processing ensures that the distribution shape of the data is not changed.

[0022] Step 202: Extract features from relevant data in the preprocessed Kongdui wind-blown sand area data sequence table to obtain sand source change features, namely sand source distribution range, movement speed, and erosion intensity.

[0023] Step 203: The distribution range characteristics of sand sources are identified using remote sensing image data and image segmentation and classification algorithms. The geographical distribution of sand source areas is analyzed, and edge detection algorithms are used to extract the boundaries of sand source areas. The area, perimeter, and shape index of sand source areas are calculated. The movement speed characteristics are obtained by selecting remote sensing image data from multiple time points and comparing the locations of sand source areas at different time points using image registration technology. The movement distance and direction of sand source areas are calculated, and the movement speed of sand sources is obtained by dividing the movement distance by the time interval. The erosion intensity characteristics are obtained by analyzing the frequency, intensity, and duration of sandstorms using wind and sand data collected from ground monitoring equipment. Combined with soil erosion models (such as RUSLE and WEPP), the erosion intensity of sand source areas is estimated based on rainfall intensity, vegetation cover, and soil type factors.

[0024] Step 204: Integrate the extracted features into a unified dataset, ensuring that each feature has a corresponding time and space label, and encode the relevant feature data to obtain a feature dataset in tabular form. Each row represents an observation record at a time point, and each column corresponds to a feature. The feature dataset includes the feature name, data type, and data source.

[0025] A further improvement to the technical solution of this invention lies in the following: In step 3, the calculation process of the dynamic evaluation coefficient of the sand source is as follows:

[0026] Step 301: Extract the sand source distribution range features, movement speed features, and erosion intensity features related to sand source changes from the feature dataset, and traverse the feature dataset to obtain the relevant data for each feature;

[0027] Step 302: Arrange the extracted feature data in chronological order to form a time series, check the integrity of the time series to ensure that no time points are missing, and draw a time series graph to observe the trend and periodicity of sand source changes. Apply time series decomposition technology to decompose the time series into trend, seasonal and random components.

[0028] Step 303: Use the feature dataset to collect relevant data on the distribution range, movement speed, and erosion intensity of sand sources related to changes in sand sources, and design dynamic assessment indicators for sand sources, namely, the rate of change of sand source area, the rate of change of vegetation coverage, and the rate of change of soil moisture. Combine the autoregressive moving average model with the assessment indicators to construct a dynamic assessment model for sand sources, and comprehensively assess the dynamic changes of sand sources. The dynamic assessment indicators for sand sources can fully reflect the changes and trends of sand sources.

[0029] Step 304: Combine the output results of the sand source dynamic assessment model with the preset assessment indicators to calculate the sand source dynamic assessment coefficient and quantify the degree of change and trend direction of the sand source.

[0030] Step 305: Based on historical sand source change data and sand source dynamic assessment coefficient, preset the change level of the sand source change trend assessment, namely low change level, medium change level and high change level, assess the stability of sand source, and match the corresponding assessment threshold for each change level.

[0031] Step 306: Based on the dynamic assessment coefficient of the sand source and the changing trend of the assessment results, determine the change level of the sand source change trend in order to analyze the change trend and influencing factors of the sand source.

[0032] A further improvement to the technical solution of this invention is that the calculation formula for the dynamic evaluation coefficient of the sand source is:

[0033] ;

[0034] ;

[0035] in, This is the dynamic assessment coefficient for sand sources. This is a comprehensive rate of change function. For the first Rate of change of sand source area over time period For the first The rate of change in vegetation cover over a period of time. For the first Soil moisture change rate over time period This represents the maximum rate of change in the area of ​​the sand source. This represents the maximum value of the rate of change in vegetation cover. This represents the maximum value of the rate of change in soil moisture. For wind speed, This represents the average wind speed. This represents the change in rainfall. This represents the average rainfall. This represents the number of time periods in the time series. The value ranges from 0 to 1. An increase indicates a decrease in the stability of the sand source, while A decrease indicates that the stability of the sand source has increased.

[0036] A further improvement of the technical solution of the present invention is that: the multiple change levels correspond to multiple evaluation thresholds, wherein the evaluation thresholds include an upper limit threshold and a lower limit threshold;

[0037] The multiple change levels and the multiple evaluation thresholds satisfy the following relationship:

[0038] Low change level ;

[0039] Medium change level ;

[0040] High change level ;

[0041] in, This is the dynamic assessment coefficient for sand sources. These are the lower threshold corresponding to the medium change level and the upper threshold corresponding to the low change level. These are the lower threshold corresponding to high change levels and the upper threshold corresponding to medium change levels. , .

[0042] A further improvement to the technical solution of the present invention is that: in step 4, the process of updating the sand source distribution map is as follows:

[0043] Step 401: Analyze monitoring needs, determine the parameters to be monitored, including wind speed, wind direction, and dust concentration, and determine the scope and key areas of the monitoring area. Plan the layout of the sensor network to ensure comprehensive coverage of the monitoring area.

[0044] Step 402: Based on the determined monitoring parameters, select the corresponding sensor devices, namely wind speed sensor, wind direction sensor, and particulate matter concentration sensor. Install sensor nodes in the Kongdui sandstorm area, connect them to the data acquisition system, and configure a data acquisition device to collect sensor data. Ensure that the sensor devices can withstand the impact of harsh environments such as sandstorms and maintain long-term stable operation.

[0045] Step 403: Build an IoT platform to realize remote monitoring and analysis of data. Integrate the data collected by the sensors into the IoT platform through the gateway for real-time data processing and storage.

[0046] Step 404: Plan the UAV flight path to cover the entire Kongdui sandstorm area and obtain the latest ground image data. Select the satellite data source according to the satellite transit time and resolution, and use high-resolution satellite remote sensing data as a supplement to expand the area of ​​ground image data.

[0047] Step 405: Preprocess the image data acquired by UAVs and satellites, including operations such as correction, stitching and cropping, extract sand source distribution information, identify sand source types and distribution ranges through image classification and recognition technology, compare and analyze the processed image data with existing sand source distribution maps, and update the sand source distribution maps to reflect the latest sand source changes.

[0048] A further improvement to the technical solution of this invention lies in the following: In step 5, the process of identifying hotspot areas and trends of sand source changes is as follows:

[0049] Step 501: Collect real-time monitoring data on wind speed, wind direction, and dust concentration from the IoT sensor network, and obtain the latest ground image data from drones and satellite remote sensing platforms;

[0050] Step 502: Use the preprocessed real-time monitoring data and remote sensing image data as model inputs, run the sand source dynamic assessment model, and comprehensively analyze the influence of wind speed and wind direction on sand and dust diffusion, as well as the relationship between sand and dust concentration and sand source distribution.

[0051] Step 503: Combining the model analysis results and real-time monitoring data, spatial analysis is performed using a geographic information system to identify areas with significant changes in sand sources. Hotspot areas of sand source changes are identified through cluster analysis, and hotspot areas are marked on a map using GIS technology for easy visualization and analysis.

[0052] Step 504: Analyze the time series data of sand source changes to identify the long-term trend and short-term fluctuations of sand source changes;

[0053] Step 505: Based on the preset evaluation indicators (dust concentration reduction rate, vegetation coverage increase rate, and soil erosion rate reduction), and combined with historical data and current monitoring results, compare the real-time monitoring data with the data before and after the implementation of the control plan to evaluate the control effect, analyze whether the trend of sand source change has been effectively controlled or aggravated, and evaluate the actual effect of the control plan in reducing dust concentration, stabilizing sand sources, and improving the ecological environment.

[0054] A further improvement to the technical solution of the present invention is that: in step 6, the process of optimizing the sand source control scheme is as follows:

[0055] Step 601: Analyze and evaluate the results, identify the problems in the current sand source control plan, such as the sand and dust concentration in some areas not being significantly reduced, the vegetation coverage rate increasing slowly, etc., compare the actual monitoring data with the expected targets, and determine the gaps and deficiencies.

[0056] Step 602: Based on the dynamic changes of the sand source and the effectiveness of the existing control facilities, re-plan the layout of the control facilities, increase or decrease the density of the facilities, and form an effective protection network in key areas.

[0057] Step 603: Develop a detailed implementation plan, clarify the specific implementation steps, timetable, and responsible persons for each optimization measure, and ensure that all work is carried out in an orderly manner. During the implementation of optimization measures, continuously monitor the dynamic changes of sand sources and the effectiveness of control, collect data in a timely manner and conduct evaluations, and dynamically adjust the optimization plan based on the monitoring and evaluation results to ensure that the control effect achieves the expected goals. For measures with insignificant effects, analyze the reasons in a timely manner and take measures to improve them.

[0058] Due to the adoption of the above technical solution, the technical progress achieved by this invention compared to the prior art is as follows:

[0059] 1. This invention provides a dynamic configuration method for sand source control in the Kongdui sandstorm area. By real-time monitoring of key parameters such as wind speed, wind direction, and dust concentration, combined with high-resolution ground image data obtained by UAV and satellite remote sensing technologies, it achieves instant perception and precise analysis of sand source changes. This not only improves the efficiency and accuracy of data collection, but also enables the control measures to be precisely deployed according to the specific dynamics of the sand source. By dynamically adjusting the layout and density of control facilities, as well as optimizing the type and area of ​​vegetation cover, it can more effectively block wind and sand erosion, reduce the frequency and intensity of sandstorms, and significantly improve the efficiency and accuracy of sand source control.

[0060] 2. This invention provides a dynamic configuration method for sand source control in the Kongdui wind-blown sand area. By continuously monitoring and evaluating changes in sand sources, the control strategy can be adjusted and optimized in a timely manner, making the system more adaptable and flexible. When the distribution, movement speed, or diffusion range of sand sources changes, it can respond quickly. By adjusting the layout of control facilities, increasing vegetation coverage, or replacing vegetation with more adaptable species, it can effectively cope with new sand source threats. This not only improves the effectiveness of sand source control but also reduces the uncertainty and risks caused by changes in sand sources. Attached Figure Description

[0061] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in this invention. For those skilled in the art, other drawings can be obtained based on these drawings.

[0062] Figure 1 This is a flowchart of the method of the present invention;

[0063] Figure 2 This is a flowchart illustrating the calculation of the dynamic evaluation coefficient of sand sources in this invention.

[0064] Figure 3 This is a flowchart for updating the sand source distribution map according to the present invention. Detailed Implementation

[0065] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0066] Example 1, such as Figure 1 , Figure 2 As shown, the present invention provides a dynamic configuration method for sand source control in the Kongdui wind-blown sand area, comprising the following steps:

[0067] Step 1: Collect meteorological data, wind and sand data, and historical sand source change data for the Kongdui sandstorm area. Utilize remote sensing technology and ground monitoring equipment to obtain geographical information and vegetation cover of the sand source area, and monitor dynamic changes in the sand source. The meteorological and wind and sand data for the Kongdui sandstorm area include real-time and historical data. Real-time meteorological data, including key meteorological parameters such as wind speed, wind direction, temperature, humidity, and precipitation, is obtained from meteorological departments and research institutions. Historical meteorological data, including long-term meteorological monitoring data and statistical analysis results, covers the changing trends and statistical characteristics of meteorological parameters such as wind speed, wind direction, and precipitation over many years. Real-time wind and sand data, including the time, intensity, and extent of sandstorms, is obtained from sandstorm monitoring stations and sandstorm early warning systems. Data from environmental protection departments, research institutions, and historical records is also collected. Historical data on wind and sand was obtained, including the frequency, duration, and impact range of sandstorms. Historical data on sand source changes were acquired through remote sensing image analysis, ground surveys, and historical document records, including changes in topography, vegetation cover, and soil erosion in the sand source area. Real-time monitoring was conducted using remote sensing technology and ground observation equipment to obtain geographic information of the sand source area, including topography, vegetation cover, and land use types. Ground monitoring equipment was deployed in the sand source area, and sand collectors were arranged to collect and measure the dust content and particle size distribution information in the air. Spectrometers and vegetation cover monitoring equipment carried by UAVs were used to monitor changes in vegetation cover in the sand source area. A data warehouse was established to integrate the collected real-time and historical data, and data from different sources were linked through key fields such as timestamps and geographic locations to form a data sequence table for the Kongdui wind and sand area.

[0068] Step 2 involves preprocessing the collected data, including cleaning, denoising, standardization, and normalization, to prepare for subsequent analysis. Feature analysis is then performed to extract sand source change characteristics, namely, sand source distribution range, movement speed, and erosion intensity. A feature dataset is constructed. Relevant data in the Kongdui aeolian sandstorm area data sequence table are preprocessed, including data cleaning, denoising, standardization, and normalization. Data cleaning removes duplicate, erroneous, incomplete, or abnormal data to ensure accuracy and reliability, reduce noise interference, and improve the signal-to-noise ratio. For meteorological data, filtering techniques (such as low-pass filtering and median filtering) are used to remove high-frequency noise. For remote sensing image data, image processing algorithms (such as edge detection and morphological filtering) are used to remove image noise. Standardization converts the data to a form with zero mean and unit variance. Normalization scales the data to the [0,1] interval, ensuring the processing does not alter the data distribution shape. Feature extraction is then performed on the preprocessed data in the Kongdui aeolian sandstorm area data sequence table to obtain sand source change characteristics, namely, sand source distribution range, movement speed, and erosion intensity. The characteristics of sand source distribution range, including speed, erosion intensity, and sand source distribution, are analyzed using remote sensing image data. Image segmentation and classification algorithms are used to identify sand source areas, analyze their geographical distribution, extract boundaries using edge detection algorithms, and calculate area, perimeter, and shape index. For movement speed characteristics, remote sensing image data from multiple time points are selected, and image registration techniques are used to compare the locations of sand source areas at different time points. The movement distance and direction of the sand source areas are calculated, and the movement speed is obtained by dividing the movement distance by the time interval. For erosion intensity characteristics, wind and sand data collected from ground monitoring equipment are used to analyze the frequency, intensity, and duration of sandstorms. Soil erosion models (such as RUSLE and WEPP) are combined to estimate the erosion intensity of sand source areas based on rainfall intensity, vegetation cover, and soil type. The extracted features are integrated into a unified dataset, ensuring that each feature has a corresponding time and spatial label. The relevant feature data are encoded to obtain a feature dataset, presented in tabular form. Each row represents an observation record at a time point, and each column corresponds to a feature. The feature dataset includes the feature name, data type, and data source.

[0069] Step 3: Extract relevant data on sand source change characteristics from the feature dataset. Construct a dynamic assessment model for sand sources based on a time series analysis model. Calculate the dynamic assessment coefficients for sand sources through comprehensive analysis, analyze the trend of sand source change, and extract sand source distribution range characteristics, movement speed characteristics, and erosion intensity characteristics related to sand source change from the feature dataset. Traverse the feature dataset to obtain relevant data for each characteristic, arrange the extracted feature data in chronological order to form a time series, check the completeness of the time series to ensure no time points are missing, and plot the time series graph to observe the trend and periodicity of sand source change. Apply time series decomposition techniques to decompose the time series into trend, seasonal, and random components. Use the relevant data on sand source distribution range characteristics, movement speed characteristics, and erosion intensity characteristics related to sand source change from the feature dataset, and design a dynamic assessment model for sand sources. The evaluation indicators are the rate of change of sand source area, the rate of change of vegetation coverage, and the rate of change of soil moisture. By combining the autoregressive moving average model with the evaluation indicators, a dynamic evaluation model for sand sources is constructed to comprehensively evaluate the dynamic changes of sand sources. The dynamic evaluation indicators for sand sources can fully reflect the changes and trends of sand sources. Based on the output results of the dynamic evaluation model for sand sources and the preset evaluation indicators, the dynamic evaluation coefficient of sand sources is calculated to quantify the degree and trend direction of sand source changes. Based on historical sand source change data and the dynamic evaluation coefficient of sand sources, the change levels of the sand source change trend are preset to evaluate the change level, namely low change level, medium change level, and high change level, to evaluate the stability of sand sources, and to match the corresponding evaluation threshold for each change level. According to the change trend of the dynamic evaluation coefficient of sand sources and the evaluation results, the change level of the sand source change trend is determined to analyze the change trend and influencing factors of sand sources.

[0070] Furthermore, the formula for calculating the dynamic assessment coefficient of sand sources is as follows:

[0071] ;

[0072] ;

[0073] in, This is the dynamic assessment coefficient for sand sources. This is a comprehensive rate of change function. For the first Rate of change of sand source area over time period For the first The rate of change in vegetation cover over a period of time. For the first Soil moisture change rate over time period This represents the maximum rate of change in the area of ​​the sand source. This represents the maximum value of the rate of change in vegetation cover. This represents the maximum value of the rate of change in soil moisture. For wind speed, This represents the average wind speed. This represents the change in rainfall. This represents the average rainfall. This represents the number of time periods in the time series. The value ranges from 0 to 1. An increase indicates a decrease in the stability of the sand source, while A decrease indicates increased stability of the sand source;

[0074] Furthermore, multiple change levels correspond to multiple evaluation thresholds, where the evaluation thresholds include an upper threshold and a lower threshold;

[0075] Multiple change levels and multiple evaluation thresholds satisfy the following relationship:

[0076] Low change level This indicates that the sand source has changed little and is highly stable. Continued monitoring and existing management measures are recommended, although small-scale vegetation restoration may be necessary.

[0077] Medium change level This indicates that the sand source change is moderate, and further monitoring or preliminary intervention measures may be needed, such as increasing the monitoring frequency and implementing some control measures, such as planting vegetation and setting up sand barriers.

[0078] High change level This indicates significant changes in sand sources and low stability, requiring immediate and effective control measures, such as large-scale vegetation restoration, physical control of sand source areas, and environmental education.

[0079] in, This is the dynamic assessment coefficient for sand sources. These are the lower threshold corresponding to the medium change level and the upper threshold corresponding to the low change level. These are the lower threshold corresponding to high change levels and the upper threshold corresponding to medium change levels. , ;

[0080] Step 4: In conjunction with IoT technology, deploy a sensor network in the Kongdui sandstorm area to monitor wind speed, wind direction, and sand source change indicators in real time, and use drones and satellite remote sensing technology to regularly acquire ground image data and update the sand source distribution map.

[0081] Step 5: Combine the real-time monitoring data with the analysis results of the sand source dynamic assessment model to conduct a comprehensive analysis, identify the hot spots and trends of sand source changes, and evaluate the effectiveness of the current sand source control plan.

[0082] Step 6: Based on the assessment results, identify existing problems and deficiencies, optimize the sand source control plan, adjust the layout and density of control facilities, increase the vegetation coverage area and types, track the dynamic changes of sand sources, and adjust the sand source control effect.

[0083] Example 2, as Figure 3 As shown, based on Example 1, the present invention provides a technical solution: Preferably, in step 4, the process of updating the sand source distribution map is as follows:

[0084] Analyze monitoring needs, determine the parameters to be monitored, including wind speed, wind direction, and dust concentration, and define the scope and key areas of the monitoring area. Plan the layout of the sensor network to ensure comprehensive coverage of the monitoring area. Based on the determined monitoring parameters, select appropriate sensor equipment, namely wind speed sensors, wind direction sensors, and particulate matter concentration sensors. Install sensor nodes in the Kongdui sandstorm area, connect them to the data acquisition system, and configure data acquisition devices to collect sensor data. Ensure that the sensor equipment can withstand the impact of harsh environments such as sandstorms and maintain long-term stable operation. Build an Internet of Things (IoT) platform to realize remote monitoring and analysis of the data, and transmit the sensor-collected data through... The system is integrated into an IoT platform via a gateway for real-time data processing and storage. It plans drone flight routes to cover the entire Kongdui sandstorm area and acquire the latest ground image data. It selects satellite data sources based on satellite transit time and resolution, and uses high-resolution satellite remote sensing data as a supplement to expand the area of ​​ground image data. It preprocesses the image data acquired by drones and satellites, including operations such as correction, stitching, and cropping, extracts sand source distribution information, and identifies sand source types and distribution ranges through image classification and recognition technologies. It compares and analyzes the processed image data with existing sand source distribution maps and updates the sand source distribution maps to reflect the latest sand source changes.

[0085] Step 5 involves identifying hotspots and trends in sand source changes as follows:

[0086] Real-time monitoring data on wind speed, wind direction, and dust concentration are collected from an IoT sensor network. The latest ground imagery data is acquired from drones and satellite remote sensing platforms. Preprocessed real-time monitoring data and remote sensing imagery data are used as model inputs to run a dynamic assessment model for sand sources. This model comprehensively analyzes the impact of wind speed and direction on dust diffusion and the relationship between dust concentration and sand source distribution. Combining model analysis results with real-time monitoring data, spatial analysis is performed using a geographic information system (GIS) to identify areas of significant sand source change. Cluster analysis identifies hotspots of sand source change, and GIS technology is used to mark these hotspots on a map for intuitive display and analysis. The time-series data of sand source change is analyzed to identify long-term trends and short-term fluctuations. Based on preset assessment indicators (dust concentration reduction rate, vegetation coverage increase rate, and soil erosion rate reduction), combined with historical data and current monitoring results, real-time monitoring data is compared with data before and after the implementation of the control scheme to evaluate the control effect. The model analyzes whether the trend of sand source change has been effectively controlled or exacerbated, and assesses the actual effectiveness of the control scheme in reducing dust concentration, stabilizing sand sources, and improving the ecological environment.

[0087] Step 6 involves optimizing the sand source control scheme as follows:

[0088] The analysis and evaluation results identify problems in the current sand source control scheme, such as the lack of significant reduction in dust concentration in some areas and slow growth in vegetation cover. The actual monitoring data is compared with the expected targets to determine gaps and deficiencies. Issues include: layout and density: analyzing whether the layout of control facilities (such as windbreaks and sand barriers) can effectively block wind and sand erosion, and whether the density is sufficient to form an effective protective barrier; vegetation cover: assessing the existing vegetation cover, types, and growth status to determine whether vegetation restoration has achieved the expected results, and whether there are any vegetation species with poor ecological adaptability or that are easily damaged; and sand source dynamics: tracking the latest data on sand source changes, analyzing the trends in sand source distribution, movement speed, and diffusion range, identifying new hotspots or potential threats, and, based on the dynamic changes in sand sources and the effectiveness of existing control facilities, re-planning the layout of control facilities, adding or reducing them as needed. Reduce facility density to form an effective protective network in key areas. Increase vegetation planting area in areas with significant sand source changes and hotspots to expand the planting area and improve overall coverage. Select vegetation species that are adapted to local climate and soil conditions and can effectively fix sand to increase the ecological diversity and stability of vegetation. Use various methods such as sowing, cutting, and transplanting, combined with soil improvement and water management measures, to promote rapid vegetation recovery and growth. Develop a detailed implementation plan, clarifying the specific implementation steps, timetable, and responsible persons for each optimization measure to ensure that all work is carried out in an orderly manner. During the implementation of optimization measures, continuously monitor the dynamic changes of sand sources and the effectiveness of control, collect data in a timely manner and conduct evaluations. Based on the monitoring and evaluation results, dynamically adjust the optimization plan to ensure that the control effect achieves the expected goal. For measures with insignificant effects, analyze the reasons in a timely manner and take measures to improve them.

[0089] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A method for dynamic configuration of sand source control in the Kongdui wind-blown sand area, characterized in that: Includes the following steps: Step 1: Collect meteorological data, wind and sand data, and historical sand source change data of Kongdui wind and sand area, and use remote sensing technology and ground monitoring equipment to obtain geographical information and vegetation cover of sand source area, and monitor the dynamic changes of sand source. Step 2: Preprocess the collected data and perform feature analysis to extract the characteristics of sand source change, namely, sand source distribution range, movement speed, and erosion intensity, and construct a feature dataset; Step 3: Extract relevant data on sand source change characteristics from the feature dataset, construct a sand source dynamic assessment model based on a time series analysis model, comprehensively analyze and calculate the sand source dynamic assessment coefficient, and analyze the sand source change trend. The calculation formula for the sand source dynamic assessment coefficient is as follows: ； ； in, This is the dynamic assessment coefficient for sand sources. This is a comprehensive rate of change function. For the first Rate of change of sand source area over time period For the first The rate of change in vegetation cover over a period of time. For the first Soil moisture change rate over time period This represents the maximum rate of change in the area of ​​the sand source. This represents the maximum value of the rate of change in vegetation cover. This represents the maximum value of the rate of change in soil moisture. For wind speed, This represents the average wind speed. This represents the change in rainfall. This represents the average rainfall. This represents the number of time periods in the time series. Step 4: In conjunction with IoT technology, deploy a sensor network in the Kongdui sandstorm area to monitor wind speed, wind direction, and sand source change indicators in real time, and use drones and satellite remote sensing technology to regularly acquire ground image data and update the sand source distribution map. Step 5: Combine the real-time monitoring data with the analysis results of the sand source dynamic assessment model to conduct a comprehensive analysis, identify the hot spots and trends of sand source changes, and evaluate the effectiveness of the current sand source control plan. Step 6: Based on the evaluation results, optimize the sand source control plan, adjust the layout and density of control facilities, track the dynamic changes of sand sources, and adjust the sand source control effect.

2. The method for dynamic configuration of sand source control in the Kongdui wind-blown sand area according to claim 1, characterized in that: In step 1, the process of collecting meteorological data, wind and sand data, and historical sand source change data in the Kongdui wind and sand area is as follows: Step 101: Obtain real-time meteorological data, including key meteorological parameters such as wind speed, wind direction, temperature, humidity, and precipitation, through meteorological stations and automatic meteorological observation stations deployed in the Kongdui wind and sand area; and obtain historical meteorological data, including long-term meteorological monitoring data and statistical analysis results, from meteorological departments and research institutions. Step 102: Obtain real-time data on wind and sand through sandstorm monitoring stations and sandstorm early warning systems, including the time, intensity, and range of sandstorm occurrences; and obtain historical data on wind and sand from environmental protection departments, research institutions, and historical documents, including the frequency, duration, and impact range of sandstorms. Step 103: Obtain historical sand source change data through remote sensing image analysis, ground surveys and historical document records, including changes in topography, vegetation cover and soil erosion in the sand source area; Step 104: Use remote sensing technology and ground observation equipment to conduct real-time monitoring and obtain geographic information of the sand source area, including topography, vegetation cover and land use type; Step 105: Deploy ground monitoring equipment in the sand source area, set up sand collectors, collect and measure the sand content and particle size distribution information in the air, and use spectrometers and vegetation coverage monitoring equipment carried by UAVs to monitor the changes in vegetation coverage in the sand source area. Step 106: Establish a data warehouse, integrate the collected real-time and historical data, and associate data from different sources through key fields such as timestamps and geographic locations to form a data sequence table for the Kongdui wind and sand area.

3. The method for dynamic configuration of sand source control in the Kongdui wind-blown sand area according to claim 2, characterized in that: In step 2, the extraction process of sand source change characteristics is as follows: Step 201: Preprocess the relevant data in the Kongdui wind and sand area data sequence table, including data cleaning, noise reduction, standardization and normalization. Step 202: Extract features from relevant data in the preprocessed Kongdui wind-blown sand area data sequence table to obtain sand source change features, namely sand source distribution range, movement speed, and erosion intensity. Step 203: Sand source distribution range characteristics. Using remote sensing image data, sand source areas are identified through image segmentation and classification algorithms. The geographical distribution of sand source areas is analyzed. Edge detection algorithms are used to extract the boundaries of sand source areas. The area, perimeter, and shape index of sand source areas are calculated. Movement speed characteristics. By selecting remote sensing image data from multiple time points and combining image registration technology, the location of sand source areas at different time points is compared. The movement distance and direction of sand source areas are calculated. The movement distance is divided by the time interval to obtain the movement speed of sand sources. Erosion intensity characteristics. Using wind and sand data collected by ground monitoring equipment, the frequency, intensity, and duration of sandstorms are analyzed. Combined with soil erosion models, the erosion intensity of sand source areas is estimated based on rainfall intensity, vegetation coverage, and soil type factors. Step 204: Integrate the extracted features into a unified dataset and encode the relevant feature data to obtain a feature dataset in tabular form. Each row represents an observation record at a time point, and each column corresponds to a feature. The feature dataset includes the feature name, data type, and data source.

4. The method for dynamic configuration of sand source control in the Kongdui wind-blown sand area according to claim 3, characterized in that: In step 3, the calculation process for the dynamic evaluation coefficient of the sand source is as follows: Step 301: Extract the sand source distribution range features, movement speed features, and erosion intensity features related to sand source changes from the feature dataset, and traverse the feature dataset to obtain the relevant data for each feature; Step 302: Arrange the extracted feature data in chronological order to form a time series, and draw a time series graph to observe the trend and periodicity of sand source changes. Apply time series decomposition technology to decompose the time series into trend, seasonal and random components. Step 303: Use the relevant data of sand source distribution range characteristics, movement speed characteristics and erosion intensity characteristics related to sand source change in the feature dataset, and design sand source dynamic assessment indicators, namely sand source area change rate, vegetation coverage change rate and soil moisture change rate. Combine the autoregressive moving average model with the assessment indicators to construct a sand source dynamic assessment model to comprehensively assess the dynamic changes of sand sources. Step 304: Combine the output results of the sand source dynamic assessment model with the preset assessment indicators to calculate the sand source dynamic assessment coefficient and quantify the degree of change and trend direction of the sand source. Step 305: Based on historical sand source change data and sand source dynamic assessment coefficient, preset the change level of the sand source change trend assessment, namely low change level, medium change level and high change level, assess the stability of sand source, and match the corresponding assessment threshold for each change level. Step 306: Based on the dynamic assessment coefficient of the sand source and the changing trend of the assessment results, determine the change level of the sand source change trend in order to analyze the change trend and influencing factors of the sand source.

5. The method for dynamic configuration of sand source control in the Kongdui wind-blown sand area according to claim 4, characterized in that: The multiple change levels correspond to multiple evaluation thresholds, wherein the evaluation thresholds include an upper limit threshold and a lower limit threshold; The multiple change levels and the multiple evaluation thresholds satisfy the following relationship: Low change level ; Medium change level ; High change level ; in, This is the dynamic assessment coefficient for sand sources. These are the lower threshold corresponding to the medium change level and the upper threshold corresponding to the low change level. These are the lower threshold corresponding to high change levels and the upper threshold corresponding to medium change levels. , .

6. The method for dynamic configuration of sand source control in the Kongdui wind-blown sand area according to claim 5, characterized in that: In step 4, the process of updating the sand source distribution map is as follows: Step 401: Analyze monitoring needs, determine the parameters to be monitored, including wind speed, wind direction, and dust concentration, and determine the scope and key areas of the monitoring area, and plan the layout of the sensor network; Step 402: Based on the determined monitoring parameters, select the corresponding sensor devices, namely wind speed sensor, wind direction sensor, and particulate matter concentration sensor. Install sensor nodes in the Kongdui sandstorm area, connect them to the data acquisition system, and configure the data acquisition device to collect sensor data. Step 403: Build an IoT platform to realize remote monitoring and analysis of data. Integrate the data collected by the sensors into the IoT platform through the gateway for real-time data processing and storage. Step 404: Plan the UAV flight path to cover the entire Kongdui sandstorm area and obtain the latest ground image data. Select the satellite data source according to the satellite transit time and resolution, and use high-resolution satellite remote sensing data as a supplement to expand the area of ​​ground image data. Step 405: Preprocess the image data acquired by UAV and satellite, extract sand source distribution information, identify sand source type and distribution range through image classification and recognition technology, compare and analyze the processed image data with the existing sand source distribution map, and update the sand source distribution map to reflect the latest sand source changes.

7. The method for dynamic configuration of sand source control in the Kongdui wind-blown sand area according to claim 6, characterized in that: In step 5, the process of identifying hotspots and trends in sand source changes is as follows: Step 501: Collect real-time monitoring data on wind speed, wind direction, and dust concentration from the IoT sensor network, and obtain the latest ground image data from drones and satellite remote sensing platforms; Step 502: Use the preprocessed real-time monitoring data and remote sensing image data as model inputs, run the sand source dynamic assessment model, and comprehensively analyze the influence of wind speed and wind direction on sand and dust diffusion, as well as the relationship between sand and dust concentration and sand source distribution. Step 503: Combining the model analysis results and real-time monitoring data, spatial analysis is performed using a geographic information system to identify areas with significant changes in sand sources, and hotspot areas of sand source changes are identified through cluster analysis. These hotspot areas are then marked on a map using GIS technology. Step 504: Analyze the time series data of sand source changes to identify the long-term trend and short-term fluctuations of sand source changes; Step 505: Based on the preset evaluation indicators, combined with historical data and current monitoring results, compare the real-time monitoring data with the data before and after the implementation of the control plan, evaluate the control effect, analyze whether the trend of sand source change has been effectively controlled or aggravated, and evaluate the actual effect of the control plan in reducing sand and dust concentration, stabilizing sand sources, and improving the ecological environment.

8. The method for dynamic configuration of sand source control in the Kongdui wind-blown sand area according to claim 7, characterized in that: In step 6, the process of optimizing the sand source control scheme is as follows: Step 601: Analyze and evaluate the results, identify the problems in the current sand source control plan, compare the actual monitoring data with the expected targets, and determine the gaps and deficiencies. Step 602: Based on the dynamic changes of the sand source and the effectiveness of the existing control facilities, re-plan the layout of the control facilities, increase or decrease the density of the facilities, and form an effective protection network in key areas. Step 603: Develop a detailed implementation plan, clarify the specific implementation steps, timetable, and responsible persons for each optimization measure, and ensure that all work is carried out in an orderly manner. During the implementation of optimization measures, continuously monitor the dynamic changes of sand sources and the effectiveness of control, collect data in a timely manner and conduct evaluations, and dynamically adjust the optimization plan based on the monitoring and evaluation results to ensure that the control effect achieves the expected goals. For measures with insignificant effects, analyze the reasons in a timely manner and take measures to improve them.

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