Mine ecological problem identification method based on artificial intelligence

Abstract

The invention discloses a mine ecological problem identification method based on artificial intelligence, belongs to the technical field of ecological problem identification, and realizes partition identification of widely distributed mines with complex terrain through fusion analysis of remote sensing images, sensor data and meteorological data by fusing a remote sensing technology and an Internet of Things sensor, obtaining multi-source data, and obtaining the ecological problem of the mine. Mining area ecological information is obtained for a mining area, feature ecological data related to the corresponding feature ecological problem model are extracted according to the mining area ecological information, the feature ecological data are substituted into the corresponding feature ecological problem model, feature ecological risk data are obtained and used for evaluating feature risks, and therefore the type of a target mine ecological problem is recognized; and for the land degradation area, the ecological restoration information is obtained, so that the ecological restoration hidden danger problem of the land degradation area is determined, a scientific basis is provided for mine ecological protection and restoration, and the problems of low efficiency and incomplete coverage of traditional manual monitoring are effectively solved.

Description

Technical Field

[0001] The present invention relates to the technical field of ecological problem identification, and more specifically, to a method for identifying mine ecological problems based on artificial intelligence. Background Art

[0002] Due to problems such as land subsidence, vegetation damage, soil erosion, and biodiversity decline caused by mine exploitation, it is necessary to investigate the ecological situation of mines and the restoration situation of mines after exploitation.

[0003] Traditional manual investigation has low efficiency and limited coverage. Patrols require a large amount of time and labor costs. Moreover, due to the wide distribution and complex terrain of mines, it is not easy for equipment and personnel to reach some places. The time and labor costs of investigation exceed the costs of general projects, and the data on mine ecological problems obtained are not complete and accurate enough, and the accuracy of identifying the types of mine ecological problems is not high.

[0004] Therefore, in view of the above technical problems, the following technical solution is now proposed. Summary of the Invention

[0005] The purpose of the present invention is to solve the existing problems and provide a method for identifying mine ecological problems based on artificial intelligence compared with the existing technology.

[0006] The purpose of the present invention can be achieved by the following technical solutions: A method for identifying mine ecological problems based on artificial intelligence, including the following steps:

[0007] Step 1: Divide the target mine into regions and collect remote sensing images of the mine in each region. Analyze the color features of the mine remote sensing images to identify the bare soil exposure areas.

[0008] Step 2: Collect images of the bare soil exposure areas to obtain bare soil images. Analyze the bare soil features of the bare soil images, and divide the bare soil exposure areas into bare rock areas, land degradation areas, and mining areas according to the analysis results.

[0009] Step 3: Extract features from the existing mine ecological problem data to obtain the characteristics of the actual mine ecological problem data. Establish a corresponding characteristic ecological problem model based on the characteristics of the actual mine ecological problem data. Collect the ecological information of the mining areas, extract the characteristic ecological data related to the corresponding characteristic ecological problem model according to the ecological information of the mining areas, substitute the characteristic ecological data into the corresponding characteristic ecological problem model to obtain the characteristic ecological risk data, which is used to evaluate the characteristic risks, and determine the types of target mine ecological problems according to the evaluation results.

[0010] Step 4: Set a monitoring period, collect the ecological restoration information of the land degradation areas within the monitoring period, and determine the ecological restoration hidden problems of the land degradation areas according to the ecological restoration information.

[0011] As a preferred embodiment of the present invention, the process of analyzing the bare soil characteristics of a bare soil image includes:

[0012] Enlarge the bare soil image into a pixel grid image and perform grayscale transformation to obtain the grayscale value of each pixel grid. Calculate the difference between the grayscale values of adjacent pixel grids and take the absolute value to obtain the grayscale floating value. Sum multiple grayscale floating values to obtain the grayscale jump value; compare the grayscale jump value with a preset grayscale jump threshold, and determine the bare soil exposure area corresponding to the bare soil image as a bare rock area or a bare soil area.

[0013] As a preferred embodiment of the present invention, continuously collect images of the bare soil area at a preset monitoring interval to obtain multiple groups of bare soil images, and perform a peripheral range comparison and analysis on the multiple groups of front and back bare soil images;

[0014] When the perimeters of multiple groups of front and back bare soil images coincide or the peripheral range of the bare soil image gradually shrinks with the update of the acquisition time, generate an image static shrinkage signal. When the perimeters of multiple groups of front and back bare soil images do not coincide and the peripheral range of the bare soil image gradually expands with the update of the acquisition time, generate an image dynamic expansion signal. Mark the bare soil area corresponding to the image static shrinkage signal as a land degradation area, and mark the bare soil area corresponding to the image dynamic expansion signal as a mining area.

[0015] As a preferred embodiment of the present invention, the characteristic ecological problem model includes a soil erosion model, a goaf collapse model, a dust pollution diffusion model, and a heavy metal diffusion model.

[0016] As a preferred embodiment of the present invention, the ecological information of the mining area includes terrain data, meteorological data, and pollution data. Among them, the terrain data includes vegetation coverage rate, fault density, slope angle, and thickness of the roof of the underground goaf. The meteorological data includes rainfall, wind speed, and humidity. The pollution data includes the open-air stacking amount of slag, dust amount, soil heavy metal concentration, underground water flow rate, and soil permeability coefficient.

[0017] As a preferred embodiment of the present invention, the process of determining the type of ecological problem of the target mine includes:

[0018] Substitute the vegetation coverage rate, slope angle, and rainfall as characteristic ecological data into the soil erosion model. The obtained characteristic ecological risk data is the soil loss amount. Compare the soil loss amount with a preset soil loss amount threshold, evaluate the characteristic risk, and determine whether to determine the ecological problem of the target mine as a soil erosion problem;

[0019] Substitute the fault density, the thickness of the roof of the underground goaf, and the rainfall as characteristic ecological data into the goaf collapse model to obtain the collapse probability, and compare the collapse probability with the preset collapse probability threshold to determine whether to identify the target mine ecological problem as a goaf collapse problem;

[0020] Substitute the open-piled amount of slag, the amount of dust, the wind speed, and the humidity as characteristic ecological data into the dust pollution diffusion model to obtain the dust pollution coefficient for comparison, and determine whether to identify the target mine ecological problem as a dust pollution problem;

[0021] Substitute the soil heavy metal concentration, the underground water flow velocity, and the soil permeability coefficient as characteristic ecological data into the heavy metal diffusion model to obtain the heavy metal pollution coefficient for comparison, and determine whether to identify the target mine ecological problem as a heavy metal pollution problem.

[0022] As a preferred embodiment of the present invention, the process of determining the ecological restoration hidden danger problem in the land degradation area is as follows: Collect the ecological restoration information in the land degradation area during the monitoring period. The ecological restoration information includes the vegetation coverage growth rate, the landslide frequency decrease rate, and the soil heavy metal content decrease rate. The vegetation coverage growth rate, the landslide frequency decrease rate, and the soil heavy metal content decrease rate are respectively formed into set A, set B, and set C, and the change curves of the vegetation coverage growth rate, the landslide frequency decrease rate, and the soil heavy metal content decrease rate are respectively drawn;

[0023] Obtain the number of points below the preset vegetation coverage growth rate threshold in the vegetation coverage growth rate change curve. If it exceeds the preset upper limit of the number of times, it is determined that there is an abnormal vegetation restoration problem in the land degradation area;

[0024] Obtain the number of points below the preset landslide frequency decrease rate threshold in the landslide frequency decrease rate change curve. If it exceeds the preset upper limit of the number of times, it is determined that there is a landslide hidden danger problem in the land degradation area;

[0025] Obtain the number of points below the preset soil heavy metal content decrease rate threshold in the soil heavy metal content decrease rate change curve. If it exceeds the preset upper limit of the number of times, it is determined that there is a soil pollution persistence problem in the land degradation area.

[0026] Compared with the prior art, the advantages of the present invention are:

[0027] 1. This solution obtains multi-source data by integrating remote sensing technology and Internet of Things sensors. Through the integrated analysis of remote sensing images, sensor data, and meteorological data, it realizes the zonal identification of mines with a wide distribution and complex terrain. Then, for the mining area, it obtains the ecological information of the mining area, extracts the characteristic ecological data related to the corresponding characteristic ecological problem model from the ecological information of the mining area, substitutes the characteristic ecological data into the corresponding characteristic ecological problem model to obtain the characteristic ecological risk data for evaluating the characteristic risks, thereby identifying the types of ecological problems in the target mine, and improving the accuracy of identifying mine ecological problems through the collaborative judgment of multiple models, solving the problems of low efficiency and incomplete coverage of traditional manual monitoring.

[0028] 2. This solution also collects ecological restoration information for the land degradation area, determines the potential ecological restoration problems in the land degradation area based on the ecological restoration information, provides a scientific basis for the ecological protection and restoration of mines, effectively promotes the full-chain intelligence of "monitoring - early warning - restoration", and helps the development of green mining. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] Figure 1 is the flowchart of the method of the present invention;

[0030] Figure 2 is the flowchart of the method for determining the types of ecological problems in the target mine according to the present invention;

[0031] Figure 3 is the flowchart of the method for determining the potential ecological restoration problems in the land degradation area according to the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0032] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0033] Embodiment 1: The present invention discloses a method for identifying mine ecological problems based on artificial intelligence. Please refer to Figure 1 , including the following steps:

[0034] Step 1: Divide the target mine into regions and collect mine remote sensing images by remote sensing technology for zonal analysis of the color features of the mine remote sensing images to identify the bare soil exposure areas;

[0035] Step 2: Collect images of the bare soil exposure areas to obtain bare soil images, analyze the bare soil features of the bare soil images, and divide the bare soil exposure areas into bare rock areas, land degradation areas, and mining areas according to the analysis results;

[0036] Step 3: Extract features from the existing mine ecological problem data to obtain the characteristics of the actual mine ecological problem data. Establish a corresponding characteristic ecological problem model based on the characteristics of the actual mine ecological problem data. Collect the ecological information of the mining area, extract the characteristic ecological data related to the corresponding characteristic ecological problem model from the ecological information of the mining area, substitute the characteristic ecological data into the corresponding characteristic ecological problem model to obtain the characteristic ecological risk data, which is used to evaluate the characteristic risk, and determine the target mine ecological problem type according to the evaluation result;

[0037] Step 4: Set a monitoring period, collect the ecological restoration information of the land degradation area during the monitoring period, and determine the ecological restoration hidden problems of the land degradation area according to the ecological restoration information.

[0038] In Step 2, the process of analyzing the bare soil features of the bare soil image includes:

[0039] Enlarge the bare soil image into a pixel grid image and perform gray-scale transformation to obtain the gray-scale value of each pixel grid. Calculate the difference between the gray-scale values of adjacent pixel grids and take the absolute value to obtain the gray-scale floating value. Sum multiple gray-scale floating values to obtain the gray-scale jump value;

[0040] Compare the gray-scale jump value with the preset gray-scale jump threshold. When the gray-scale jump value is greater than or equal to the preset gray-scale jump threshold, determine the bare soil exposure area corresponding to the bare soil image as the bare rock area. The rock surface is rough, angular, with a sharp change in local gray scale and a high texture contrast. Otherwise, determine the bare soil exposure area corresponding to the bare soil image as the bare soil area. The degraded soil surface is relatively smooth. Especially after mechanical turning, the gray-scale transition is uniform and the texture is periodically obvious. Through image gray-scale texture analysis, select and exclude the bare rock area from the bare soil exposure area. The bare rock area is not regarded as a mine ecological problem caused by human activities.

[0041] Continuously collect images of the bare soil area at a preset monitoring interval to obtain multiple groups of bare soil images, and perform peripheral range comparison and analysis on multiple groups of front and back bare soil images;

[0042] When the peripheries of multiple groups of front and back bare soil images coincide or the peripheral range of the bare soil image gradually shrinks with the update of the acquisition time, generate an image static shrinkage signal. When the peripheries of multiple groups of front and back bare soil images do not coincide and the peripheral range of the bare soil image gradually expands with the update of the acquisition time, generate an image dynamic expansion signal. Mark the bare soil area corresponding to the image static shrinkage signal as the land degradation area, and mark the bare soil area corresponding to the image dynamic expansion signal as the mining area, indicating that with the passage of the monitoring acquisition time, due to artificial mining activities, the range of the bare soil area is continuously increasing.

[0043] In Step 3, the characteristic ecological problem model includes a soil erosion model, a goaf collapse model, a dust pollution diffusion model, and a heavy metal diffusion model;

[0044] Collect the ecological information of the mining area through Internet of Things sensors. The ecological information of the mining area includes topographic data, meteorological data, and pollution data;

[0045] Among them, the topographic data includes vegetation coverage rate, fault density, slope angle, and the thickness of the roof of the underground goaf. The meteorological data includes rainfall, wind speed, and humidity. The pollution data includes the open-air stacking volume of slag, dust volume, soil heavy metal concentration, underground water flow velocity, and soil permeability coefficient;

[0046] The process of determining the type of ecological problems in the target mine includes:

[0047] Please refer to Figure 2 , substitute the vegetation coverage rate, slope angle, and rainfall as characteristic ecological data into the soil erosion model. The obtained characteristic ecological risk data is the soil loss amount. Compare the soil loss amount with the preset soil loss amount threshold. When the soil loss amount is greater than the maximum value of the preset soil loss amount threshold range, generate a high-risk signal of soil loss, and determine the ecological problem of the target mine as a soil erosion problem. When the soil loss amount is within the preset soil loss amount threshold range, generate a low-risk signal of soil loss, limit operations, and increase monitoring density. Otherwise, generate a normal signal and strengthen inspections;

[0048] Substitute the fault density, the thickness of the roof of the underground goaf, and rainfall as characteristic ecological data into the goaf collapse model. The obtained characteristic ecological risk data is the collapse probability. Compare the collapse probability with the preset collapse probability threshold. When the collapse probability is greater than the maximum value of the preset collapse probability threshold range, generate a high-risk signal of collapse, and determine the ecological problem of the target mine as a goaf collapse problem. When the collapse probability is within the preset collapse probability threshold range, generate a high-risk signal of collapse, limit operations, and increase monitoring density. Otherwise, generate a normal signal and strengthen inspections;

[0049] Substitute the open-air stacking volume of slag, dust volume, wind speed, and humidity as characteristic ecological data into the dust pollution diffusion model. The obtained characteristic ecological risk data is the dust pollution coefficient. Compare the dust pollution coefficient with the preset dust pollution coefficient threshold. Similarly, judge the risk level. When the dust pollution coefficient is greater than the maximum value of the preset dust pollution coefficient threshold range, determine the ecological problem of the target mine as a dust pollution problem;

[0050] The soil heavy metal concentration, groundwater flow velocity, and soil permeability coefficient are used as characteristic ecological data and substituted into the heavy metal diffusion model. The obtained characteristic ecological risk data is the heavy metal pollution coefficient. The heavy metal pollution coefficient is compared with the preset heavy metal pollution coefficient threshold. Similarly, the risk level is judged. When the heavy metal pollution coefficient is greater than the maximum value of the preset heavy metal pollution coefficient threshold range, the ecological problem of the target mine is determined as a heavy metal pollution problem.

[0051] For the mining area, obtain the ecological information of the mining area. According to the ecological information of the mining area, extract the characteristic ecological data related to the corresponding characteristic ecological problem model, substitute the characteristic ecological data into the corresponding characteristic ecological problem model, obtain the characteristic ecological risk data, and use it to evaluate the characteristic risk, so as to identify the type of ecological problem of the target mine, and improve the accuracy of identifying the ecological problem of the mine according to the multi-model collaborative determination.

[0052] Embodiment 2: In step four, the process of determining the ecological restoration hidden danger problem in the land degradation area is as follows: Please refer to Figure 3 , collect the ecological restoration information of the land degradation area during the monitoring period. The ecological restoration information includes the vegetation coverage growth rate, the landslide frequency decreasing rate, and the soil heavy metal content decreasing rate. The vegetation coverage growth rate, the landslide frequency decreasing rate, and the soil heavy metal content decreasing rate are respectively formed into set A, set B, and set C, and the vegetation coverage growth rate change curve of set A, the landslide frequency decreasing rate change curve of set B, and the soil heavy metal content decreasing rate change curve of set C are respectively drawn;

[0053] If the vegetation coverage growth rates are all higher than the preset vegetation coverage growth rate threshold or the number of those lower than the preset vegetation coverage growth rate threshold is extremely small, it indicates that a virtuous cycle has been entered. Obtain the number of those lower than the preset vegetation coverage growth rate threshold in the vegetation coverage growth rate change curve. If it exceeds the preset upper limit of the number of times, a slow vegetation coverage growth signal is generated, and it is determined that there is an abnormal vegetation restoration problem in the land degradation area;

[0054] If the landslide frequency decreasing rates are all higher than the preset landslide frequency decreasing rate threshold or the number of those lower than the preset landslide frequency decreasing rate threshold is extremely small, it indicates that the landslide frequency continues to decrease and the soil erosion resistance ability is enhanced. Obtain the number of those lower than the preset landslide frequency decreasing rate threshold in the landslide frequency decreasing rate change curve. If it exceeds the preset upper limit of the number of times, a potential landslide risk signal is generated, and it is determined that there is a landslide hidden danger problem in the land degradation area;

[0055] If the number of soil heavy metal content decreasing rates that exceed the preset soil heavy metal content decreasing rate threshold or are extremely few below the preset soil heavy metal content decreasing rate threshold indicates that the soil heavy metal content is continuously decreasing and the soil pollution is developing towards a controllable trend. Obtain the number of soil heavy metal content decreasing rates below the preset soil heavy metal content decreasing rate threshold in the change curve of the soil heavy metal content decreasing rate. If it exceeds the preset upper limit of the number of times, a soil pollution persistence signal is generated to determine that there is a soil pollution persistence problem in the land degradation area.

[0056] In summary, by integrating remote sensing technology and Internet of Things sensors, multi-source data is obtained, and through the fusion analysis of remote sensing images, sensor data, and meteorological data, it is possible to achieve the zonal identification of mines with a wide distribution and complex terrain. Then, for the mining area, ecological information of the mining area is obtained, and characteristic ecological data related to the corresponding characteristic ecological problem model is extracted from the ecological information of the mining area. The characteristic ecological data is substituted into the corresponding characteristic ecological problem model to obtain characteristic ecological risk data for evaluating characteristic risks, thereby identifying the types of ecological problems in the target mine, and improving the accuracy of identifying ecological problems in mines according to the collaborative determination of multiple models.

[0057] And collect the ecological restoration information of the land degradation area, and determine the ecological restoration hidden danger problems of the land degradation area according to the ecological restoration information, providing a scientific basis for the ecological protection and restoration of mines.

[0058] This solution involves multiple parameter thresholds. It should be noted that the thresholds, such as preset values and preset ranges, are set for result comparison and analysis to determine good or bad. Regarding their magnitude values, they are set and stored based on the combined analysis of the large model of sample data and manual experience, and can also be appropriately adjusted according to seasonal or regular influencing conditions.

[0059] The above; is only the preferred specific implementation manner of the present invention; but the protection scope of the present invention is not limited thereto; any person skilled in the art within the technical scope disclosed by the present invention; according to the technical solution and its improvement concept of the present invention, equivalent replacement or change should be covered within the protection scope of the present invention.

Claims

1. An artificial intelligence-based method for identifying mine ecological problems, characterized in that: The steps are as follows: Step 1: Divide the target mine into regions and collect mine remote sensing images for each region. Analyze the color features of the mine remote sensing images to identify the bare soil exposure areas; Step 2: Collect images of the bare soil exposure areas to obtain bare soil images. Analyze the bare soil features of the bare soil images. According to the analysis results, divide the bare soil exposure areas into bare rock areas, land degradation areas, and mining areas; Step 3: Extract the features of the existing mine ecological problem data to obtain the actual mine ecological problem data features. Establish corresponding characteristic ecological problem models based on the actual mine ecological problem data features. Collect the mine area ecological information of the mining areas. Extract the characteristic ecological data related to the corresponding characteristic ecological problem models according to the mine area ecological information. Substitute the characteristic ecological data into the corresponding characteristic ecological problem models to obtain the characteristic ecological risk data for evaluating the characteristic risks. Determine the types of target mine ecological problems according to the evaluation results; Step 4: Set a monitoring period. Collect the ecological restoration information of the land degradation areas during the monitoring period and determine the ecological restoration hidden problems of the land degradation areas according to the ecological restoration information.

2. The method for identifying mine ecological problems based on artificial intelligence according to claim 1, wherein: The process of analyzing the bare soil features of the bare soil images includes: Enlarge the bare soil images into pixel grid images and perform grayscale transformation to obtain the grayscale values of each pixel grid. Calculate the difference between the grayscale values of adjacent pixel grids and take the absolute value to obtain the grayscale floating value. Sum multiple grayscale floating values to obtain the grayscale jump value. Compare the grayscale jump value with the preset grayscale jump threshold, and determine whether the bare soil exposure area corresponding to the bare soil image is a bare rock area or a bare soil area. Collect and analyze the images of the bare soil areas.

3. The method for identifying mine ecological problems based on artificial intelligence according to claim 2, wherein: The process of collecting and analyzing the images of the bare soil areas includes: Continuously collect images of the bare soil areas at a preset monitoring interval to obtain multiple groups of bare soil images, and compare and analyze the peripheral ranges of the front and back multiple groups of bare soil images; When the peripheral ranges of the front and back multiple groups of bare soil images coincide or the peripheral range of the bare soil image gradually shrinks with the update of the collection time, generate an image static shrinkage signal. When the front and back multiple groups of bare soil images do not coincide and the peripheral range of the bare soil image gradually expands with the update of the collection time, generate an image dynamic expansion signal. Mark the bare soil areas corresponding to the image static shrinkage signal as land degradation areas, and mark the bare soil areas corresponding to the image dynamic expansion signal as mining areas.

4. The method for identifying mine ecological problems based on artificial intelligence according to claim 1, wherein: The characteristic ecological problem models include soil erosion models, goaf collapse models, dust pollution diffusion models, and heavy metal diffusion models.

5. The method for identifying mine ecological problems based on artificial intelligence according to claim 4, characterized in that: The mine area ecological information includes terrain data, meteorological data, and pollution data. Among them, the terrain data includes vegetation coverage rate, fault density, slope angle, and the thickness of the roof of the underground goaf. The meteorological data includes rainfall, wind speed, and humidity. The pollution data includes the open-air stacking volume of slag, dust volume, soil heavy metal concentration, underground water flow velocity, and soil permeability coefficient.

6. The method for identifying mine ecological problems based on artificial intelligence according to claim 5, characterized in that: The process of determining the types of target mine ecological problems includes: Substitute the vegetation coverage rate, slope angle, and rainfall as characteristic ecological data into the soil erosion model. The obtained characteristic ecological risk data is the soil loss amount. Compare the soil loss amount with the preset soil loss amount threshold to evaluate the characteristic risk and determine whether to identify the target mine ecological problem as a soil erosion problem; Substitute the fault density, roof thickness of the underground mined - out area, and rainfall as characteristic ecological data into the mined - out area collapse model. Obtain the collapse probability. Compare the collapse probability with the preset collapse probability threshold to determine whether to identify the target mine ecological problem as a mined - out area collapse problem; Substitute the open - air stacking amount of slag, dust amount, wind speed, and humidity as characteristic ecological data into the dust pollution diffusion model. Obtain the dust pollution coefficient for comparison to determine whether to identify the target mine ecological problem as a dust pollution problem; Substitute the soil heavy metal concentration, underground water flow velocity, and soil permeability coefficient as characteristic ecological data into the heavy metal diffusion model. Obtain the heavy metal pollution coefficient for comparison to determine whether to identify the target mine ecological problem as a heavy metal pollution problem.

7. The method for identifying mine ecological problems based on artificial intelligence according to claim 6, characterized in that: The process of determining the ecological restoration hidden danger problem in the land degradation area is as follows: Collect the ecological restoration information of the land degradation area during the monitoring period. The ecological restoration information includes the vegetation coverage growth rate, the decreasing rate of landslide frequency, and the decreasing rate of soil heavy metal content. Respectively form set A, set B, and set C with the vegetation coverage growth rate, the decreasing rate of landslide frequency, and the decreasing rate of soil heavy metal content. Respectively plot the change curves of the vegetation coverage growth rate, the decreasing rate of landslide frequency, and the decreasing rate of soil heavy metal content; Obtain the number of points on the vegetation coverage growth rate change curve that are lower than the preset vegetation coverage growth rate threshold. If it exceeds the preset upper limit of the number of times, determine that there is an abnormal vegetation restoration problem in the land degradation area; Obtain the number of points on the decreasing rate of landslide frequency change curve that are lower than the preset decreasing rate of landslide frequency threshold. If it exceeds the preset upper limit of the number of times, determine that there is a landslide hidden danger problem in the land degradation area; Obtain the number of points on the decreasing rate of soil heavy metal content change curve that are lower than the preset decreasing rate of soil heavy metal content threshold. If it exceeds the preset upper limit of the number of times, determine that there is a problem of persistent soil pollution in the land degradation area.

Images