Sand source blocking and controlling dynamic configuration method for hole-mixed windy and sandy area

Abstract

The invention discloses a sand source resistance and control dynamic configuration method for a hole-to-wind sand area, and relates to the technical field of wind and sand protection, and the method comprises the following steps: collecting meteorological data, wind and sand data and historical sand source change data of the hole-to-wind sand area, and obtaining geographic information and vegetation coverage conditions of the sand source area through a remote sensing technology and ground monitoring equipment; by monitoring key parameters such as wind speed, wind direction and dust concentration in real time and combining high-resolution ground image data acquired by an unmanned aerial vehicle and a satellite remote sensing technology, real-time sensing and accurate analysis of sand source changes are realized, the efficiency and accuracy of data collection are improved, and the detection accuracy is improved. In addition, the prevention and control measures can be dynamically and accurately arranged according to the specific sand source, and by dynamically adjusting the arrangement and density of prevention and control facilities and optimizing the type and area of vegetation coverage, sand storm invasion can be more effectively prevented, the occurrence frequency and intensity of sand storm are reduced, and the efficiency and accuracy of sand source prevention and control are remarkably improved.

Description

Technical Field

[0001] The invention relates to the technical field of wind and sand protection, and in particular to a sand source blocking and control dynamic configuration method in a Kongdui wind and sand area. Background Art

[0002] The control of sand sources in the Kongdui sandstorm zone is mainly due to the severe sandstorm control situation and ecological protection needs in the Kongdui area of ​​the Inner Mongolia section of the Yellow River. The Kongdui area, especially the "Top Ten Kongdu", is located in the upper reaches of the Yellow River with complex landforms, including loess hilly gully areas and downstream plain areas. These areas have frequent sandstorm activities and serious soil erosion, posing a threat to the water quality and ecological security of the mainstream of the Yellow River. Due to the special landform and climatic conditions in the Kongdui area, the problem of sandstorm erosion is prominent. A large amount of sediment enters the Yellow River through Kongdui, increasing the sand content of the mainstream of the Yellow River, causing serious impacts on water conservancy projects, agricultural production and the ecological environment in the downstream areas.

[0003] In the existing technology, the sand source control in the Kongdui sandstorm area focuses on static analysis, and lacks the ability to dynamically and real-time evaluate the sand source control effect, so that it is difficult for relevant personnel to adjust and optimize the configuration plan in time. In addition, the dynamic changes of sand sources in the Kongdui sandstorm area are affected by many factors, which makes the sand source control in the Kongdui sandstorm area more difficult. Therefore, how to enhance the dynamic evaluation capability of sand source control in the Kongdui sandstorm area to optimize the configuration plan is the problem we need to solve. To this end, a dynamic configuration method for sand source control in the Kongdui sandstorm area is proposed. Summary of the invention

[0004] The object of the present invention is to provide a method for dynamically configuring sand source control in the Kongdui sandstorm area to solve the problems raised in the above-mentioned background technology.

[0005] In order to solve the above technical problems, the technical solution adopted by the present invention is:

[0006] A method for dynamically configuring sand source control in a Kongdui sandstorm area comprises the following steps:

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

[0008] Step 2: preprocess the collected data, including cleaning, denoising, standardization and normalization, to prepare for subsequent analysis, and perform feature analysis to extract the characteristics of sand source changes, namely, sand source distribution range, movement speed, erosion intensity, and construct a feature data set;

[0009] Step 3: extract relevant data of sand source change characteristics from the feature data set, build 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: Combine the Internet of Things technology to 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 obtain 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 evaluation results, identify existing problems and deficiencies, optimize the sand source control plan, adjust the layout and density of control facilities, increase the area and types of vegetation coverage, track the dynamic changes of sand sources, and adjust the sand source control effect.

[0013] A further improvement of the technical solution of the present invention is that in step 1, the collection process of the meteorological data, wind and sand data and historical sand source change data of the Kongdui sandstorm area is as follows:

[0014] Step 101, obtaining 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 sandstorm area, and obtaining historical meteorological data from meteorological departments and scientific research institutions, including long-term meteorological monitoring data and statistical analysis results, covering the change trends and statistical characteristics of meteorological parameters such as wind speed, wind direction, and precipitation over the years;

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

[0016] Step 103, obtaining historical sand source change data through remote sensing image analysis, ground surveys and historical document records, including topographic changes in the sand source area, changes in vegetation coverage, and soil erosion levels;

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

[0018] Step 105, deploying ground monitoring equipment in the sand source area, arranging sand collectors, collecting and measuring the dust content and particle size distribution information in the air, and using spectrometers and vegetation coverage monitoring equipment carried by drones to monitor changes in vegetation coverage in the sand source area;

[0019] Step 106, establish a data warehouse, integrate the collected real-time data and historical data, and associate data from different sources through key fields such as timestamp and geographic location to form a data sequence table of the Kongdui sandstorm area.

[0020] A further improvement of the technical solution of the present invention is that in step 2, the process of extracting the sand source change characteristics is as follows:

[0021] Step 201, preprocessing the relevant data in the Kongdui sandstorm area data sequence table, including data cleaning, denoising, standardization and normalization processing, wherein data cleaning removes repeated, erroneous, incomplete or abnormal data to ensure the accuracy and reliability of the data, reduce noise interference in the data, and improve the signal-to-noise ratio of the data. For meteorological data, filtering technology (such as low-pass filtering, median filtering, etc.) is used to remove high-frequency noise. For remote sensing image data, image processing algorithms (such as edge detection, morphological filtering, etc.) are used to remove image noise. The data is converted into a form with zero mean and unit variance through standardization processing. The data is scaled to the interval [0,1] through normalization processing, and the processing process ensures that the distribution shape of the data is not changed;

[0022] Step 202, extracting features from relevant data in the pre-processed Kongdui sandstorm area data sequence table to obtain sand source change features, which are sand source distribution range, movement speed, and erosion intensity;

[0023] Step 203, the sand source distribution range feature uses remote sensing image data to identify the sand source area through image segmentation and classification algorithms, analyzes the geographical distribution of the sand source area, uses edge detection algorithms to extract the boundaries of the sand source area, and calculates the area, perimeter, and shape index of the sand source area. The moving speed feature selects remote sensing image data at multiple time points, combines image registration technology to compare the positions of the sand source areas at different time points, calculates the moving distance and direction of the sand source area, and divides the moving distance by the time interval to obtain the moving speed of the sand source. The erosion intensity feature uses the wind and sand data collected by ground monitoring equipment to analyze the frequency, intensity, and duration of sandstorms. Combined with soil erosion models (such as RUSLE, WEPP, etc.), the erosion intensity of the sand source area is estimated based on rainfall intensity, vegetation coverage, and soil type factors;

[0024] Step 204, integrate the extracted features into a unified data set, ensure that each feature has a corresponding time and space label, and encode the relevant feature data to obtain a feature data set in a table format, where each row represents an observation record at a time point and each column corresponds to a feature, wherein the feature data set includes the feature name, data type, and data source.

[0025] A further improvement of the technical solution of the present invention is that in step 3, the calculation process of the sand source dynamic assessment coefficient is:

[0026] Step 301, extracting the sand source distribution range feature, movement speed feature and erosion intensity feature related to the sand source change from the feature data set, and traversing the feature data set to obtain relevant data of each feature;

[0027] Step 302, arranging the extracted characteristic data in chronological order to form a time series, checking the integrity of the time series to ensure that there is no missing time point, and drawing a time series graph to observe the trend and periodicity of sand source changes, and applying time series decomposition technology to decompose the time series into trend, seasonal and random components;

[0028] Step 303, using the data related to the sand source change in the feature data set, including the sand source distribution range characteristics, movement speed characteristics, and erosion intensity characteristics, and designing sand source dynamic evaluation indicators, which are the sand source area change rate, vegetation coverage change rate, and soil moisture change rate. Combining the autoregressive moving average model with the evaluation indicators, a sand source dynamic evaluation model is constructed to comprehensively evaluate the dynamic changes of the sand source. The sand source dynamic evaluation indicators can fully reflect the change and trend of the sand source.

[0029] Step 304, combining the output results of the sand source dynamic assessment model and the preset assessment indicators, calculating the sand source dynamic assessment coefficient to quantify the degree of change and trend direction of the sand source;

[0030] Step 305, based on the historical sand source change data and the sand source dynamic assessment coefficient, preset the change levels for assessing the sand source change trend, namely low change level, medium change level and high change level, assess the sand source stability, and match the corresponding assessment threshold for each change level;

[0031] Step 306, determining the change level of the sand source change trend according to the change trend of the sand source dynamic evaluation coefficient and the evaluation result, so as to analyze the change trend and influencing factors of the sand source.

[0032] A further improvement of the technical solution of the present invention is that the calculation formula of the sand source dynamic assessment coefficient is:

[0033]

[0034] Among them, C is the dynamic assessment coefficient of sand source, f(A, V, H) is the comprehensive change rate function, ΔA t is the change rate of the sand source area in the tth period, ΔV t is the vegetation coverage change rate in the tth period, ΔH t is the soil moisture change rate in the tth period, ΔA max is the maximum value of the change rate of the sand source area, ΔV max is the maximum value of vegetation coverage change rate, ΔH max is the maximum value of soil moisture change rate, W is the wind speed, W avg is the average wind speed, ΔR is the change in rainfall, R avg is the average rainfall, n is the number of time periods in the time series, and the value of C ranges from 0 to 1. An increase in C indicates a decrease in the stability of the sand source, while a decrease in C indicates an increase in the stability of the sand source.

[0035] 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 threshold and a lower threshold;

[0036] The multiple change levels and the multiple evaluation thresholds satisfy the following relationship:

[0037] Low Change Level 0 <C≤C m ;

[0038] Medium change level C m <C≤C h ;

[0039] High Change Level C h <C<1;

[0040] Among them, C is the dynamic assessment coefficient of sand source, C m is the lower threshold corresponding to the medium change level and the upper threshold corresponding to the low change level, C h is the lower threshold corresponding to the high change level and the upper threshold corresponding to the medium change level, C m =0.3, C h =0.7.

[0041] A further improvement of the technical solution of the present invention is that in step 4, the process of updating the sand source distribution map is:

[0042] Step 401, analyzing monitoring needs, determining parameters to be monitored, including wind speed, wind direction, and dust concentration, determining the scope and key areas of the monitoring area, and planning the layout of the sensor network to ensure full coverage of the monitoring area;

[0043] Step 402, according to the determined monitoring parameters, select corresponding sensor equipment, namely wind speed sensor, wind direction sensor, and particle concentration sensor, install sensor nodes in the Kongdui sandstorm area, connect them to the data acquisition system, and configure a data collector to collect sensor data, so as to ensure that the sensor equipment can withstand the influence of harsh environments such as sandstorms and maintain long-term stable operation;

[0044] Step 403, building an Internet of Things platform to achieve remote monitoring and analysis of data, integrating the data collected by the sensor into the Internet of Things platform through a gateway, and performing real-time processing and storage of the data;

[0045] Step 404, planning the flight route of the drone to cover the entire Kongdui sandstorm area to obtain the latest ground image data, selecting the satellite data source according to the satellite transit time and resolution, using high-resolution satellite remote sensing data as a supplement, and expanding the area of ​​the ground image data;

[0046] Step 405, pre-processing the image data obtained by the drone and satellite, including operations such as correction, stitching and cropping, extracting sand source distribution information, and identifying the sand source type and distribution range through image classification and recognition technology, comparing and analyzing the processed image data with the existing sand source distribution map, and updating the sand source distribution map to reflect the latest sand source changes.

[0047] A further improvement of the technical solution of the present invention is that in step 5, the process of identifying the hotspot areas and trends of sand source changes is as follows:

[0048] Step 501, collecting monitoring data of wind speed, wind direction, and dust concentration from the sensor network of the Internet of Things in real time, and obtaining the latest ground image data from drones and satellite remote sensing platforms;

[0049] Step 502, using the pre-processed real-time monitoring data and remote sensing image data as model input, running the sand source dynamic assessment model, and comprehensively analyzing the impact of wind speed and wind direction on sand and dust diffusion and the relationship between sand and dust concentration and sand source distribution;

[0050] Step 503, combining the model analysis results and the real-time monitoring data, using the geographic information system to perform spatial analysis, identify areas with significant changes in sand source, and identify hot spots of sand source changes through cluster analysis, and use GIS technology to mark the hot spots on the map for intuitive display and analysis;

[0051] Step 504, analyzing the time series data of sand source changes to identify long-term trends and short-term fluctuations of sand source changes;

[0052] Step 505, based on the preset evaluation indicators (sand and dust concentration reduction rate, vegetation coverage increase rate, soil erosion rate reduction), 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 changes is 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.

[0053] A further improvement of the technical solution of the present invention is that in step 6, the process of optimizing the sand source control scheme is:

[0054] Step 601, analyzing the evaluation results, identifying the problems in the current sand source control plan, such as the dust concentration in some areas has not been significantly reduced, the vegetation coverage rate has increased slowly, etc., comparing the actual monitoring data with the expected goals, and determining the gaps and deficiencies;

[0055] Step 602, according to the dynamic changes of sand sources and the effects of existing control facilities, re-plan the layout of control facilities, increase or decrease the density of facilities, and form an effective protection network in key areas;

[0056] Step 603, formulate a detailed implementation plan, clarify the specific implementation steps, timetable and responsible persons of each optimization measure, ensure that all work is carried out in an orderly manner, and during the implementation of the optimization measures, continuously monitor the dynamic changes of sand sources and the control effects, collect data in a timely manner and conduct evaluations. According to the monitoring and evaluation results, dynamically adjust the optimization plan to ensure that the control effect achieves the expected goal. For measures that are not effective, analyze the reasons in a timely manner and take measures to improve them.

[0057] Due to the adoption of the above technical solution, the present invention has the following technical advances compared with the prior art:

[0058] 1. The present invention provides a method for dynamically configuring sand source control in the Kongdui sandstorm area. By real-time monitoring of key parameters such as wind speed, wind direction, and sand concentration, combined with high-resolution ground image data obtained by drone and satellite remote sensing technology, it can realize instant perception and accurate analysis of sand source changes, which not only improves the efficiency and accuracy of data collection, but also enables the precise layout of control measures according to the specific sand source dynamics. By dynamically adjusting the layout and density of control facilities and optimizing the types and areas of vegetation coverage, it can more effectively block wind and sand invasion, reduce the frequency and intensity of sandstorms, and significantly improve the efficiency and accuracy of sand source control.

[0059] 2. The present invention provides a method for dynamic configuration of sand source control in the Kongdui sandstorm area. By continuously monitoring and evaluating the changes in sand sources, the control strategy is adjusted and optimized in time, so that the system has stronger adaptability and flexibility. When the distribution, movement speed or diffusion range of sand sources changes, it can respond quickly. By adjusting the layout of control facilities, increasing the vegetation coverage area or replacing vegetation types with stronger adaptability, etc., new sand source threats can be effectively responded to, which not only improves the effect of sand source control, but also reduces the uncertainty and risk caused by sand source changes. BRIEF DESCRIPTION OF THE DRAWINGS

[0060] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in the present invention. For ordinary technicians in this field, other drawings can also be obtained based on these drawings.

[0061] Figure 1 is a flow chart of the method of the present invention;

[0062] Figure 2 The figure is a calculation flow chart of the dynamic evaluation coefficient of the sand source of the present invention;

[0063] Figure 3 The present invention is a flow chart for updating the sand source distribution map. DETAILED DESCRIPTION

[0064] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0065] Embodiment 1, as Figure 1 , Figure 2 As shown, the present invention provides a method for dynamically configuring sand source blocking control in Kongdui sandstorm area, comprising the following steps:

[0066] Step 1, collect meteorological data, wind and sand data and historical sand source change data in Kongdui sandstorm area, and use remote sensing technology and ground monitoring equipment to obtain geographical information and vegetation coverage of the sand source area, and monitor the dynamic changes of the sand source. Among them, the meteorological data and wind and sand data in Kongdui sandstorm area include real-time data and historical data. Real-time data of meteorological data are obtained through meteorological stations and automatic meteorological observation stations deployed in Kongdui sandstorm area, including key meteorological parameters of wind speed, wind direction, temperature, humidity and precipitation. Historical data of meteorological data are obtained from meteorological departments and scientific research institutions, including long-term meteorological monitoring data and statistical analysis results, covering the change trends and statistical characteristics of meteorological parameters of wind speed, wind direction and precipitation over the years. Real-time data of wind and sand data, including the time, intensity and range of sandstorms, are obtained through sandstorm monitoring stations and sandstorm early warning systems. The historical data of wind and sand data were obtained from the document, including the frequency, duration and impact range of sandstorms. The historical sand source change data were obtained through remote sensing image analysis, ground surveys and historical document records, including the topographic and geomorphic changes in the sand source area, changes in vegetation coverage, and soil erosion. Remote sensing technology and ground observation equipment were used for real-time monitoring to obtain the geographical information of the sand source area, including topography, vegetation coverage, and land use type. Ground monitoring equipment was deployed in the sand source area, and sand collectors were arranged to collect and measure the sand and dust content and particle size distribution information in the air. Spectrometers and vegetation coverage monitoring equipment carried by drones were used to monitor the changes in vegetation coverage in the sand source area. A data warehouse was established to integrate the collected real-time data and historical data, and the data from different sources were associated through the key fields of timestamp and geographic location to form a data sequence table of the Kongdui wind and sand area.

[0067] Step 2, preprocess the collected data, including cleaning, denoising, standardization and normalization, to prepare for subsequent analysis, and perform feature analysis to extract the characteristics of sand source changes, namely, sand source distribution range, moving speed, and erosion intensity, and construct a feature data set. Preprocess the relevant data in the Kongdui sandstorm area data sequence table, including data cleaning, denoising, standardization and normalization. Among them, data cleaning removes repeated, erroneous, incomplete or abnormal data to ensure the accuracy and reliability of the data, reduce noise interference in the data, and improve the signal-to-noise ratio of the data. For meteorological data, filtering techniques (such as low-pass filtering, median filtering, etc.) are used to remove high-frequency noise. For remote sensing image data, image processing algorithms (such as edge detection, morphological filtering, etc.) are used to remove image noise. The data is converted into a form with zero mean and unit variance through standardization. The data is scaled to the [0,1] interval through normalization, and the processing process ensures that the distribution shape of the data is not changed. Feature extraction is performed on the relevant data in the preprocessed Kongdui sandstorm area data sequence table to obtain sand source change characteristics, namely, sand source distribution range, moving speed, and erosion intensity. Speed, erosion intensity, and sand source distribution range characteristics use remote sensing image data to identify sand source areas through image segmentation and classification algorithms, analyze the geographical distribution of sand source areas, use edge detection algorithms to extract the boundaries of sand source areas, and calculate the area, perimeter, and shape index of sand source areas. The moving speed characteristics select remote sensing image data at multiple time points, combine image registration technology to compare the positions of sand source areas at different time points, calculate the moving distance and direction of the sand source areas, and divide the moving distance by the time interval to obtain the moving speed of the sand source. The erosion intensity characteristics use wind and sand data collected by ground monitoring equipment to analyze the frequency, intensity, and duration of sandstorms. Combined with soil erosion models (such as RUSLE, WEPP, etc.), the erosion intensity of the sand source area is estimated based on rainfall intensity, vegetation coverage, and soil type factors. The extracted features are integrated into a unified data set to ensure that each feature has a corresponding time and space label, and the relevant feature data is encoded to obtain a feature data set in a table format. Each row represents an observation record at a time point, and each column corresponds to a feature. The feature data set includes feature name, data type, and data source.

[0068] Step 3: Extract relevant data on sand source change characteristics from the feature data set, build a sand source dynamic assessment model based on the time series analysis model, comprehensively analyze and calculate the sand source dynamic assessment coefficient, analyze the sand source change trend, extract the sand source distribution range characteristics, movement speed characteristics and erosion intensity characteristics related to sand source changes from the feature data set, traverse the feature data set to obtain relevant data of each feature, arrange the extracted feature data in chronological order to form a time series, check the integrity of the time series to ensure that there is no missing time point, 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, use the relevant data on sand source distribution range characteristics, movement speed characteristics and erosion intensity characteristics related to sand source changes in the feature data set, and design a sand source dynamic assessment model. The evaluation indicators are the change rate of sand source area, the change rate of vegetation coverage and the change rate of soil moisture. The autoregressive moving average model is combined with the evaluation indicators to construct a sand source dynamic evaluation model to comprehensively evaluate the dynamic changes of sand sources. The sand source dynamic evaluation indicators can fully reflect the changes and trends of sand sources. The sand source dynamic evaluation coefficient is calculated by combining the output results of the sand source dynamic evaluation model and the preset evaluation indicators to quantify the degree of change and trend direction of sand sources. Based on the historical sand source change data and the sand source dynamic evaluation coefficient, the change levels of sand source change trends are preset, which are low change level, medium change level and high change level, to evaluate the stability of sand sources and match the corresponding evaluation thresholds for each change level. According to the change trend of the sand source dynamic evaluation coefficient 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.

[0069] Furthermore, the calculation formula of the sand source dynamic assessment coefficient is:

[0070]

[0071] Among them, C is the dynamic assessment coefficient of sand source, f(A, V, H) is the comprehensive change rate function, ΔA t is the change rate of the sand source area in the tth period, ΔV t is the vegetation coverage change rate in the tth period, ΔH t is the soil moisture change rate in the tth period, ΔA max is the maximum value of the change rate of the sand source area, ΔV max is the maximum value of vegetation coverage change rate, ΔH max is the maximum value of soil moisture change rate, W is the wind speed, W avg is the average wind speed, ΔR is the change in rainfall, R avg is the average rainfall, n is the number of periods in the time series, and C ranges from 0 to 1. An increase in C indicates a decrease in the stability of the sand source, while a decrease in C indicates an increase in the stability of the sand source;

[0072] Furthermore, multiple change levels correspond to multiple evaluation thresholds, where the evaluation thresholds include an upper threshold and a lower threshold;

[0073] The multiple change levels and the multiple evaluation thresholds satisfy the following relationship:

[0074] Low change level 0 < C ≤ C m ; It indicates that the sand source change is small, the sand source stability is high, continue to monitor, maintain the existing management measures, and small-scale vegetation restoration may be required;

[0075] Medium change level C m < C ≤ C h ; It indicates that the sand source change is moderate, further monitoring or preliminary intervention measures may be required, increase the monitoring frequency, and implement some control measures such as vegetation planting and sand barrier setting;

[0076] High change level C h < C < 1; It indicates that the sand source change is significant, the sand source stability is low, and effective control measures need to be taken immediately, and emergency measures such as large-scale vegetation restoration, physical control in the sand source area, and environmental education should be taken immediately;

[0077] Among them, C is the sand source dynamic evaluation coefficient, C m is the lower threshold corresponding to the medium change level and the upper threshold corresponding to the low change level, C h is the lower threshold corresponding to the high change level and the upper threshold corresponding to the medium change level, C m = 0.3, C h = 0.7;

[0078] Step 4: Combine the Internet of Things technology to deploy a sensor network in the Kongdui sandy area to monitor the wind speed, wind direction, and sand source change indicators in real time, and use unmanned aerial vehicle and satellite remote sensing technology to regularly obtain ground image data and update the sand source distribution map;

[0079] Step 5: Combine the real-time monitoring data with the analysis results of the sand source dynamic evaluation model for comprehensive analysis to identify the hot spots and trends of sand source change and evaluate the effectiveness of the current sand source control plan;

[0080] Step 6: According to the evaluation results, identify the existing problems and deficiencies, optimize the sand source control plan, adjust the layout and density of the control facilities, increase the vegetation coverage area and variety, track the dynamic changes of the sand source, and adjust the sand source control effect.

[0081] Example 2, as Figure 3 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:

[0082] Analyze monitoring needs, determine the parameters that need 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 full coverage of the monitoring area. According to the determined monitoring parameters, select the corresponding sensor equipment, namely wind speed sensor, wind direction sensor, and particle concentration sensor. Install sensor nodes in the Kongdui sandstorm area, connect them to the data acquisition system, and configure the data collector to collect sensor data. Ensure that the sensor equipment can withstand the impact of harsh environments such as wind and sand and maintain long-term stable operation. Build an Internet of Things platform to realize remote monitoring and analysis of data, and transmit the data collected by the sensors through Through the gateway, it is integrated into the IoT platform to process and store data in real time, plan the flight route of drones, cover the entire Kongdui sandstorm area to obtain the latest ground image data, select satellite data sources according to satellite transit time and resolution, use high-resolution satellite remote sensing data as a supplement, expand the area of ​​ground image data, pre-process the image data obtained by drones and satellites, including correction, splicing and cropping, extract sand source distribution information, and identify the type and distribution range of sand sources 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 changes in sand sources;

[0083] In step 5, the process of identifying hotspots and trends of sand source changes is as follows:

[0084] Real-time monitoring data of wind speed, wind direction, and dust concentration are collected from the sensor network of the Internet of Things, and the latest ground image data is obtained from drones and satellite remote sensing platforms. The pre-processed real-time monitoring data and remote sensing image data are used as model inputs to run the sand source dynamic assessment model, comprehensively analyze the impact of wind speed and wind direction on dust diffusion and the relationship between dust concentration and sand source distribution, combine the model analysis results and real-time monitoring data, use geographic information systems for spatial analysis, identify areas with significant changes in sand sources, and identify hot spots of sand source changes through cluster analysis. Use GIS technology to mark hot spots on the map for intuitive display and analysis, analyze the time series data of sand source changes, identify long-term trends and short-term fluctuations in sand source changes, and compare the real-time monitoring data with the data before and after the implementation of the control plan according to the preset evaluation indicators (sand and dust concentration reduction rate, vegetation coverage increase rate, soil erosion rate reduction), combined with historical data and current monitoring results, evaluate the control effect, analyze whether the trend of sand source changes 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;

[0085] In step 6, the process of optimizing the sand source control scheme is as follows:

[0086] Analyze the evaluation results, identify the problems in the current sand source control plan, such as the failure to significantly reduce the dust concentration in some areas and the slow growth of vegetation coverage, compare the actual monitoring data with the expected goals, determine the gaps and deficiencies, layout and density issues: analyze whether the layout of control facilities (such as windbreaks, sand barriers, etc.) can effectively block the invasion of wind and sand, and whether the density is sufficient to form an effective protective barrier; vegetation coverage issues: evaluate the coverage, types and growth conditions of existing vegetation, determine whether vegetation restoration has achieved the expected results, and whether there are vegetation types with poor ecological adaptability and easy damage; dynamic changes in sand sources: track the latest data on sand source changes, analyze the changing trends in sand source distribution, movement speed and diffusion range, identify new hot spots or potential threats, and re-plan the layout of control facilities according to the dynamic changes in sand sources and the effectiveness of existing control facilities, and increase or reduce Reduce the density of facilities, form an effective protection network in key areas, increase the vegetation planting area in areas and hot spots where sand sources change significantly, expand the planting area to improve the overall coverage rate, select vegetation types that adapt to local climate and soil conditions and can effectively fix sand, increase the ecological diversity and stability of vegetation, adopt a variety of methods such as sowing, cuttings, and transplanting, combined with soil improvement, water management and other measures to promote rapid recovery and growth of vegetation, formulate a detailed implementation plan, clarify the specific implementation steps, timetable and responsible persons of each optimization measure, ensure that all work is carried out in an orderly manner, and continuously monitor the dynamic changes of sand sources and the control effects during the implementation of the optimization measures, collect data and evaluate them in a timely manner, and dynamically adjust the optimization plan according to the monitoring and evaluation results to ensure that the control effect achieves the expected goal. For measures with unobvious effects, analyze the reasons in a timely manner and take measures to improve them.

[0087] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

Claims

1. A method for dynamically configuring sand source control in Kongdui sandstorm area, characterized by: The following steps are involved: Step 1: Collect meteorological data, wind and sand data, and historical sand source change data in the Kongdui sandstorm area, and use remote sensing technology and ground monitoring equipment to obtain geographic information and vegetation coverage in the sand source area to monitor the dynamic changes of the sand source; Step 2: preprocess the collected data and perform feature analysis to extract the characteristics of sand source changes, including sand source distribution range, movement speed, and erosion intensity, and construct a feature data set; Step 3: extract relevant data of sand source change characteristics from the feature data set, build 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; Step 4: Combine the Internet of Things technology to 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 obtain 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. A method for dynamic configuration of sand source control in Kongdui sandstorm area according to claim 1, characterized in that: In step 1, the collection process of meteorological data, wind and sand data and historical sand source change data in Kongdui sandstorm area is as follows: Step 101, obtaining 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 sandstorm area, and obtaining historical meteorological data, including long-term meteorological monitoring data and statistical analysis results, from meteorological departments and scientific research institutions; Step 102, obtaining real-time data of wind and sand data, including the time, intensity, and range of sandstorms, through sandstorm monitoring stations and sandstorm warning systems, and obtaining historical data of wind and sand data, including the frequency, duration, and impact range of sandstorms, from environmental protection departments, scientific research institutions, and historical documents; Step 103, obtaining historical sand source change data through remote sensing image analysis, ground surveys and historical document records, including topographic changes in the sand source area, changes in vegetation coverage, and soil erosion levels; Step 104, using remote sensing technology and ground observation equipment to conduct real-time monitoring to obtain geographical information of the sand source area, including topography, vegetation coverage, and land use type; Step 105, deploying ground monitoring equipment in the sand source area, arranging sand collectors, collecting and measuring the dust content and particle size distribution information in the air, and using spectrometers and vegetation coverage monitoring equipment carried by drones to monitor changes in vegetation coverage in the sand source area; Step 106, establish a data warehouse, integrate the collected real-time data and historical data, and associate data from different sources through key fields such as timestamp and geographic location to form a data sequence table of the Kongdui sandstorm area.

3. The method for dynamic configuration of sand source control in Kongdui sandstorm area according to claim 2, characterized in that: In step 2, the process of extracting the sand source change characteristics is as follows: Step 201, preprocessing the relevant data in the Kongdui sandstorm area data sequence table, including data cleaning, denoising, standardization and normalization processing; Step 202, extracting features from relevant data in the pre-processed Kongdui sandstorm area data sequence table to obtain sand source change features, which are sand source distribution range, movement speed, and erosion intensity; Step 203, the sand source distribution range feature uses remote sensing image data to identify the sand source area through image segmentation and classification algorithms, analyzes the geographical distribution of the sand source area, uses edge detection algorithms to extract the boundaries of the sand source area, and calculates the area, perimeter, and shape index of the sand source area. The moving speed feature selects remote sensing image data at multiple time points, combines image registration technology to compare the positions of the sand source areas at different time points, calculates the moving distance and direction of the sand source area, and divides the moving distance by the time interval to obtain the moving speed of the sand source. The erosion intensity feature uses the wind and sand data collected by ground monitoring equipment to analyze the frequency, intensity, and duration of sandstorms. Combined with the soil erosion model, the erosion intensity of the sand source area is estimated based on rainfall intensity, vegetation coverage, and soil type factors. Step 204, integrate the extracted features into a unified data set, and encode the relevant feature data to obtain a feature data set in a table format, where each row represents an observation record at a time point and each column corresponds to a feature, wherein the feature data set includes a feature name, a data type, and a data source.

4. The method for dynamic configuration of sand source control in Kongdui sandstorm area according to claim 3, characterized in that: In step 3, the calculation process of the sand source dynamic assessment coefficient is: Step 301, extracting the sand source distribution range feature, movement speed feature and erosion intensity feature related to the sand source change from the feature data set, and traversing the feature data set to obtain relevant data of each feature; Step 302, arranging the extracted characteristic data in chronological order to form a time series, and drawing a time series graph to observe the trend and periodicity of sand source changes, and applying time series decomposition technology to decompose the time series into trend, seasonal and random components; Step 303, using the data related to the sand source change in the feature data set, including the sand source distribution range characteristics, movement speed characteristics, and erosion intensity characteristics, and designing sand source dynamic evaluation indicators, namely, sand source area change rate, vegetation coverage change rate, and soil moisture change rate, combining the autoregressive moving average model with the evaluation indicators, constructing a sand source dynamic evaluation model, and comprehensively evaluating the dynamic changes of sand sources; Step 304, combining the output results of the sand source dynamic assessment model and the preset assessment indicators, calculating the sand source dynamic assessment coefficient to quantify the degree of change and trend direction of the sand source; Step 305, based on the historical sand source change data and the sand source dynamic assessment coefficient, preset the change levels for assessing the sand source change trend, namely low change level, medium change level and high change level, assess the sand source stability, and match the corresponding assessment threshold for each change level; Step 306, determining the change level of the sand source change trend according to the change trend of the sand source dynamic evaluation coefficient and the evaluation result, so as to analyze the change trend and influencing factors of the sand source.

5. A method for dynamic configuration of sand source control in Kongdui sandstorm area according to claim 4, characterized in that: The calculation formula of the sand source dynamic assessment coefficient is: Among them, C is the dynamic assessment coefficient of sand source, f(A, V, H) is the comprehensive change rate function, ΔA t is the change rate of the sand source area in the tth period, ΔV t is the vegetation coverage change rate in the tth period, ΔH t is the soil moisture change rate in the tth period, ΔA max is the maximum value of the change rate of the sand source area, ΔV max is the maximum value of vegetation coverage change rate, ΔH max is the maximum value of soil moisture change rate, W is the wind speed, W avg is the average wind speed, ΔR is the change in rainfall, R avg is the average rainfall amount, and n is the number of periods in the time series.

6. A method for dynamic configuration of sand source control in Kongdui sandstorm area according to claim 5, characterized in that: A plurality of the change levels correspond to a plurality of the evaluation thresholds, wherein the evaluation thresholds include an upper threshold and a lower threshold; The multiple change levels and the multiple evaluation thresholds satisfy the following relationship: Low Change Level 0 <C≤C m ; Medium change level C m <C≤C h ; High Change Level C h <C<1; Among them, C is the dynamic assessment coefficient of sand source, C m is the lower threshold corresponding to the medium change level and the upper threshold corresponding to the low change level, C h is the lower threshold corresponding to the high change level and the upper threshold corresponding to the medium change level, C m =0.3, C h =0.

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7. A method for dynamically configuring sand source control in Kongdui sandstorm area according to claim 6, characterized in that: In step 4, the process of updating the sand source distribution map is as follows: Step 401, analyzing monitoring needs, determining parameters to be monitored, including wind speed, wind direction, dust concentration, determining the scope and key areas of the monitoring area, and planning the layout of the sensor network; Step 402, according to the determined monitoring parameters, select corresponding sensor equipment, namely, a wind speed sensor, a wind direction sensor, and a particle concentration sensor, install sensor nodes in the Kongdui sandstorm area, connect them to the data acquisition system, and configure a data collector to collect sensor data; Step 403, building an Internet of Things platform to achieve remote monitoring and analysis of data, integrating the data collected by the sensor into the Internet of Things platform through a gateway, and performing real-time processing and storage of the data; Step 404, planning the flight route of the drone to cover the entire Kongdui sandstorm area to obtain the latest ground image data, selecting the satellite data source according to the satellite transit time and resolution, using high-resolution satellite remote sensing data as a supplement, and expanding the area of ​​the ground image data; Step 405, pre-process the image data obtained by the drone and satellite, extract the sand source distribution information, and identify the 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.

8. A method for dynamic configuration of sand source control in Kongdui sandstorm area according to claim 7, characterized in that: In step 5, the process of identifying the hotspot areas and trends of sand source changes is as follows: Step 501, collecting monitoring data of wind speed, wind direction, and dust concentration from the sensor network of the Internet of Things in real time, and obtaining the latest ground image data from drones and satellite remote sensing platforms; Step 502, using the pre-processed real-time monitoring data and remote sensing image data as model input, running the sand source dynamic assessment model, and comprehensively analyzing the impact of wind speed and wind direction on sand and dust diffusion and the relationship between sand and dust concentration and sand source distribution; Step 503, combining the model analysis results and the real-time monitoring data, using the geographic information system to perform spatial analysis, identify areas with significant changes in sand sources, and identify hot spots of sand source changes through cluster analysis, and use GIS technology to mark the hot spots on the map; Step 504, analyzing the time series data of sand source changes to identify long-term trends and short-term fluctuations of sand source changes; Step 505, based on 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 changes is effectively controlled or aggravated, and evaluate the actual effect of the control plan in reducing sand concentration, stabilizing sand sources, and improving the ecological environment.

9. A method for dynamic configuration of sand source control in Kongdui sandstorm area according to claim 8, characterized in that: In step 6, the process of optimizing the sand source control scheme is as follows: Step 601, analyzing the evaluation results, identifying the problems in the current sand source control plan, comparing the actual monitoring data with the expected goals, and determining the gaps and deficiencies; Step 602, according to the dynamic changes of sand sources and the effects of existing control facilities, re-plan the layout of control facilities, increase or decrease the density of facilities, and form an effective protection network in key areas; Step 603, formulate a detailed implementation plan, clarify the specific implementation steps, timetable and responsible persons of each optimization measure, ensure that all work is carried out in an orderly manner, and during the implementation of the optimization measures, continuously monitor the dynamic changes of sand sources and the control effects, collect data in a timely manner and conduct evaluations. According to the monitoring and evaluation results, dynamically adjust the optimization plan to ensure that the control effect achieves the expected goal. For measures that are not effective, analyze the reasons in a timely manner and take measures to improve them.

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