Soil pollution monitoring method and device

Abstract

The invention provides a soil pollution monitoring method and device, and belongs to the technical field of environmental protection. The method comprises the following steps: determining coordinates of a plurality of first sampling points based on pollution source distribution, pollution source types and geological features of a to-be-monitored area so as to obtain a plurality of first sampling data; determining coordinates of a plurality of second sampling points based on the first sampling data so as to obtain a plurality of second sampling data; and processing the first sampling data and the second sampling data to obtain a soil pollution monitoring result of the to-be-monitored area. According to the invention, a pollution source and an area possibly influenced by the pollution source can be covered in a targeted manner for sampling and soil pollution monitoring, so that the number of unnecessary sampling points is reduced, the sampling efficiency and representativeness are improved, and the accuracy of a soil pollution monitoring result is ensured.

Description

Technical Field

[0001] The present invention relates to the field of environmental protection technology, and in particular to a soil pollution monitoring method and device. Background Art

[0002] The problem of soil pollution is becoming increasingly serious, posing a major threat to the ecological environment, agricultural production and human health. In order to effectively assess and control soil pollution, it is essential to accurately monitor the spatial distribution and concentration changes of pollutants. Traditional soil pollution monitoring methods select sampling points based on experience or simple rules (such as the grid method) to cover the study area. Soil samples are collected at the selected sampling points, and the samples are chemically or physically analyzed to obtain pollutant concentration data. The pollution status is evaluated based on the monitoring results and control measures are formulated.

[0003] However, although traditional soil pollution monitoring methods have met basic needs to a certain extent, the uniform grid method or random sampling method fails to fully consider the uncertainty of pollutant distribution, resulting in insufficient representativeness of sampling points, insufficient sampling in high-concentration areas, and excessive sampling in low-concentration areas, resulting in waste of resources and poor monitoring results. Summary of the invention

[0004] The embodiments of the present invention provide a soil pollution monitoring method and device to solve the problem of improving the accuracy of soil pollution monitoring.

[0005] In a first aspect, an embodiment of the present invention provides a soil pollution monitoring method, comprising: Based on the distribution of pollution sources, the type of pollution sources and the geological characteristics of the area to be monitored, the coordinates of the plurality of first sampling points are determined to obtain a plurality of first sampling data; Based on each of the first sampling data, determine the coordinates of a plurality of second sampling points to obtain a plurality of second sampling data; The first sampling data and the second sampling data are processed to obtain the soil pollution monitoring result of the area to be monitored.

[0006] In a possible implementation, the pollution source distribution includes coordinates and concentration ranges of one or more pollution sources; based on the pollution source distribution, pollution source type and geological characteristics of the area to be monitored, the coordinates of the plurality of first sampling points are determined, including: Determine the pollutant diffusion model for the area to be monitored based on the pollution source type and geological characteristics of the area to be monitored; Substituting the coordinates and lower limits of the concentration range of each pollution source into the pollutant diffusion model to determine the range of the first diffusion area and the distribution of pollutant concentration; Bring the coordinates and upper limits of the concentration range of each pollution source into the pollutant diffusion model to determine the range of the second diffusion area and the distribution of pollutant concentration; wherein the range of the second diffusion area is larger than the first diffusion area; Based on the pollutant concentration distribution in the first diffusion area and the second diffusion area, a first sampling density is determined, and first sampling points are set in the first diffusion area and the second diffusion area based on the first sampling density to obtain coordinates of multiple first sampling points.

[0007] In a possible implementation, the pollutant concentration distribution includes an average concentration and a concentration gradient; and determining the first sampling density based on the pollutant concentration distributions in the first diffusion area and the second diffusion area includes: Based on the average concentration and concentration gradient of pollutants in the overlapping area, a basic sampling density is determined, and the basic sampling density is used as the sampling density of the overlapping area; wherein the overlapping area is an area in both the first diffusion area and the second diffusion area; Based on the occurrence probability of the lower limit of the concentration range, the basic sampling density is adjusted to obtain the sampling density of the first difference area; wherein the first difference area is an area within the first diffusion area and not within the second diffusion area; Based on the occurrence probability of the upper limit of the concentration range, the basic sampling density is adjusted to obtain the sampling density of the second difference area; wherein the second difference area is an area within the second diffusion area and not within the first diffusion area.

[0008] In one possible implementation, the formula for determining the basic sampling density is:

[0009] in, is the basic sampling density, , is the preset coefficient, is the average concentration of pollutants in the overlapping area, is the concentration gradient of the pollutant in the overlapping area.

[0010] In one possible implementation, the formula for adjusting the basic sampling density based on the probability of occurrence of the lower limit of the concentration range is:

[0011] in, is the sampling density of the first difference area, is the basic sampling density, is the probability of occurrence of the lower limit of the concentration range;

[0012] in, is the sampling density of the second difference area, is the basic sampling density, is the probability of occurrence of the upper limit of the concentration range.

[0013] In a possible implementation, before adjusting the basic sampling density based on the probability of occurrence of the lower limit of the concentration range, it also includes: Obtain multiple historical concentrations of each pollution source; Calculate the occurrence ratio of the historical concentration within the first concentration range as the probability of occurrence of the upper limit of the concentration range; wherein the first concentration range is (lower limit of the concentration range, lower limit of the concentration range + first difference area / first diffusion area * (upper limit of the concentration range - lower limit of the concentration range)); Calculate the occurrence ratio of historical concentrations within the second concentration range as the probability of occurrence of the upper limit of the concentration range; wherein the second concentration range is (upper limit of concentration range - second difference area / second diffusion area*(upper limit of concentration range - lower limit of concentration range), upper limit of concentration range).

[0014] In a possible implementation, based on each of the first sampling data, determining coordinates of a plurality of second sampling points to obtain the plurality of second sampling data includes: Clustering each first sampling data to obtain a plurality of clusters; For each cluster, based on the average concentration and concentration gradient of the pollutants in the cluster, determine the sampling density of the cluster; Based on the sampling density of each cluster, a second sampling point is set in the area corresponding to each cluster to obtain the coordinates of the plurality of second sampling points.

[0015] In a second aspect, an embodiment of the present invention provides a soil pollution monitoring device, comprising: A first sampling module, used to determine the coordinates of a plurality of first sampling points based on the distribution of pollution sources, the type of pollution sources and the geological characteristics of the area to be monitored, so as to obtain a plurality of first sampling data; A second sampling module, used for determining the coordinates of a plurality of second sampling points based on each of the first sampling data, so as to obtain a plurality of second sampling data; The data processing module is used to process each of the first sampling data and the second sampling data to obtain the soil pollution monitoring result of the area to be monitored.

[0016] In a third aspect, an embodiment of the present invention provides a terminal, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, the steps of the method described in the first aspect or any possible implementation manner of the first aspect are implemented.

[0017] In a fourth aspect, an embodiment of the present invention provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the method described in the first aspect or any possible implementation method of the first aspect are implemented.

[0018] The embodiment of the present invention provides a soil pollution monitoring method and device. According to the location, type and regional geological characteristics of the pollution source, the possible diffusion range of the pollutant is preliminarily delineated, and on this basis, a representative first sampling point is selected to ensure that the main pollution source and its potential impact area are covered, and then the actual distribution characteristics of the pollutant are analyzed using the data of the first sampling point. Combined with the model prediction and the actual monitoring results, the sampling point layout is adjusted, and the position of the second sampling point is determined to further refine the monitoring of the high concentration area and the transition zone. The pollution source and the area that may be affected can be covered in a targeted manner, the number of unnecessary sampling points is reduced, the efficiency and representativeness of sampling are improved, and the accuracy of the soil pollution monitoring results is ensured. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative labor.

[0020] Figure 1 This is a flow chart of a soil pollution monitoring method according to an embodiment of the present invention; Figure 2 It is a structural schematic diagram of a soil pollution monitoring device provided by one embodiment of the present invention; Figure 3 is a schematic diagram of a terminal provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0021] In the following description, specific details such as specific system structures, technologies, etc. are provided for the purpose of illustration rather than limitation, so as to provide a thorough understanding of the embodiments of the present invention. However, it should be clear to those skilled in the art that the present invention may be implemented in other embodiments without these specific details. In other cases, detailed descriptions of well-known systems, devices, circuits, and methods are omitted to prevent unnecessary details from obstructing the description of the present invention.

[0022] In order to make the purpose, technical solutions and advantages of the present invention more clear, specific embodiments will be described below in conjunction with the accompanying drawings.

[0023] See also Figure 1, which shows a flow chart of a soil pollution monitoring method provided by an embodiment of the present invention, which is described in detail as follows: Step 101, based on the pollution source distribution, pollution source type and geological characteristics of the area to be monitored, determine the coordinates of multiple first sampling points to obtain multiple first sampling data.

[0024] In this embodiment, the area to be monitored refers to a specific geographical area where soil pollution monitoring is required, which is usually defined by research objectives or management needs. Pollution source distribution refers to the spatial distribution of pollutant emission sources, including point sources (such as factories), surface sources (such as agricultural activities) and line sources (such as transportation). The type of pollution source includes the nature of the pollution source and its emission characteristics, such as industrial emissions, agricultural activities, domestic sewage, etc. Geological characteristics refer to the natural environmental conditions in the study area, including soil type, hydrological conditions, topography, etc. These factors will affect the migration and distribution of pollutants.

[0025] The specific implementation method may include the following steps: Collect pollution source information: obtain the location (coordinates), type and emission intensity of pollution sources through on-site surveys, historical records or remote sensing images.

[0026] Analyze geological characteristics: Collect data on soil type, porosity, permeability, groundwater flow direction, etc. in the study area.

[0027] Construct diffusion model: Based on the distribution of pollution sources, pollution source types and geological characteristics, mathematical models (such as convection-dispersion equations or random walk models) are used to predict the possible diffusion range of pollutants.

[0028] Determine the first sampling point: Based on the results of the diffusion model, evenly distribute the first sampling point in the predicted high concentration area, medium concentration area, and low concentration area to ensure the representativeness of the first sampling point.

[0029] Collect the first sampling data: Collect soil samples at the determined first sampling point and obtain pollutant concentration data through laboratory analysis.

[0030] Step 102: determine the coordinates of a plurality of second sampling points based on each of the first sampling data to obtain a plurality of second sampling data.

[0031] In this embodiment, the pollutant concentration data of the first sampling point can be statistically analyzed to identify areas with large concentration gradients and areas with large prediction deviations. The density of sampling points is increased in high-concentration areas and areas with large concentration gradients, and the number of sampling points is reduced in low-concentration areas or areas with gentle concentration changes. The new sampling points are used as the second sampling points to optimize the sampling layout.

[0032] Step 103: Process each of the first sampling data and the second sampling data to obtain a soil pollution monitoring result of the area to be monitored.

[0033] In this embodiment, the first sampling data and the second sampling data can be combined to form a complete pollutant concentration data set, and then a statistical method (such as interpolation and regression analysis) can be used to generate a spatial distribution map of pollutants. Machine learning algorithms (such as cluster analysis and random forests) can be used to mine the deep-level rules in the data, and the analysis results can be displayed in the form of a map, marking the concentration distribution, diffusion range and key pollution areas of the pollutants.

[0034] In addition, monitoring reports can be generated to summarize the spatial distribution characteristics of pollutants, concentration trends and potential risks, providing a basis for subsequent governance.

[0035] This embodiment improves monitoring efficiency and enhances the reliability of results, providing strong technical support for environmental protection.

[0036] The embodiment of the present invention preliminarily delineates the possible diffusion range of pollutants according to the location, type and regional geological characteristics of the pollution source, and on this basis, selects a representative first sampling point to ensure that the main pollution source and its potential impact area are covered, and then uses the data of the first sampling point to analyze the actual distribution characteristics of the pollutants, combines the model prediction and the actual monitoring results, adjusts the sampling point layout, and determines the location of the second sampling point to further refine the monitoring of high-concentration areas and transition areas. It can cover the pollution source and the area that may be affected in a targeted manner, reduce the number of unnecessary sampling points, improve the efficiency and representativeness of sampling, and ensure the accuracy of soil pollution monitoring results.

[0037] In a possible implementation, the pollution source distribution includes coordinates and concentration ranges of one or more pollution sources; based on the pollution source distribution, pollution source type and geological characteristics of the area to be monitored, the coordinates of the plurality of first sampling points are determined, including: Determine the pollutant diffusion model for the area to be monitored based on the pollution source type and geological characteristics of the area to be monitored; Substituting the coordinates and lower limits of the concentration range of each pollution source into the pollutant diffusion model to determine the range of the first diffusion area and the distribution of pollutant concentration; Bring the coordinates and upper limits of the concentration range of each pollution source into the pollutant diffusion model to determine the range of the second diffusion area and the distribution of pollutant concentration; wherein the range of the second diffusion area is larger than the first diffusion area; Based on the pollutant concentration distribution in the first diffusion area and the second diffusion area, a first sampling density is determined, and first sampling points are set in the first diffusion area and the second diffusion area based on the first sampling density to obtain coordinates of multiple first sampling points.

[0038] In this embodiment, the diffusion model can predict the distribution pattern of pollutants in space, and combined with the type of pollution source and geological characteristics, it can more accurately reflect the actual migration characteristics of pollutants.

[0039] Substituting the upper and lower limits of the pollution source concentration range into the model, the first diffusion area and the second diffusion area are obtained respectively, thereby quantifying the uncertainty of pollutant diffusion.

[0040] By analyzing the pollutant concentration distribution in the first diffusion area and the second diffusion area, the density rule of the sampling points is determined. The density of sampling points is increased in high-concentration areas and areas with large concentration gradients, and the density of sampling points is appropriately reduced in low-concentration areas to ensure the representativeness of the sampling.

[0041] Assume that there is a heavy metal pollution source (cadmium) in an industrial area, with coordinates (30°N, 120°E) and emission concentration ranging from 50 to 150 mg / kg. The geological characteristics of the study area are as follows: Soil type: Sandy soil, highly permeable.

[0042] Groundwater flow direction: northeast, flow rate is 0.1m / d.

[0043] The specific steps for setting the first sampling point are as follows: Step 1: Determine the pollutant dispersion model Select the appropriate diffusion model based on the type of pollution source (industrial emissions) and geological characteristics (sandy soil, groundwater flow direction): The advection-dispersion model is used to describe the horizontal transport of pollutants in soil.

[0044] Groundwater seepage models (such as MODFLOW+MT3DMS) are used to describe the vertical migration of pollutants with groundwater.

[0045] The model parameters are set as follows: Diffusion coefficient D = 0.01 m2 / d Groundwater velocity v = 0.1 m / d Pollutant adsorption coefficient Kd=0.5 L / kg Step 2: Determine the first diffusion area Substitute the pollution source coordinates (30°N, 120°E) and the lower limit of the concentration range (50 mg / kg) into the diffusion model to calculate the first diffusion area.

[0046] Model input: Pollution source coordinates: (30°N, 120°E) Concentration range lower limit: 50mg / kg Model parameters: diffusion coefficient, groundwater flow rate, adsorption coefficient Model output: The first diffusion area range: a circular area with a radius of 30 meters.

[0047] Pollutant concentration distribution: The central concentration is 50 mg / kg, which gradually decreases with increasing distance.

[0048] Step 3: Determine the Second Diffusion Area Substitute the pollution source coordinates (30°N, 120°E) and the upper limit of the concentration range (150 mg / kg) into the diffusion model to calculate the second diffusion area.

[0049] Model input: Pollution source coordinates: (30°N, 120°E) Upper limit of concentration range: 150mg / kg Model parameters: diffusion coefficient, groundwater flow rate, adsorption coefficient Model output: The second diffusion area: a circular area with a radius of 50 meters.

[0050] Pollutant concentration distribution: The central concentration is 150 mg / kg, which gradually decreases with increasing distance.

[0051] Step 4: Determine the first sampling density The overlapping area, expansion area and contraction area of ​​the first diffusion area and the second diffusion area are analyzed, and the sampling density is adjusted according to the concentration gradient.

[0052] Overlapping area: Range: Circular area with a radius of 30 meters.

[0053] Features: The distribution of pollutant concentration is relatively clear.

[0054] Sampling density: Basic sampling density Dbase = 6 m (a sampling point is set at an interval of 6 meters).

[0055] Extension area: Range: Circular area with a radius of 30~50 meters.

[0056] Features: There is great uncertainty in the diffusion of pollutants.

[0057] Sampling density: Encrypted sampling density Dexpanded = 4 m (a sampling point is set at an interval of 4 meters).

[0058] Contraction area: There is no shrinkage region in this example.

[0059] Step 5: Set the first sampling point Implementation: Sampling points are arranged in the first diffusion region and the second diffusion region according to the determined first sampling density.

[0060] Overlap area (30m radius): Sampling point interval: 6 meters.

[0061] Point distribution method: Sampling points are evenly distributed with the pollution source as the center.

[0062] Number of sampling points: about 16 (assuming a regular hexagonal grid).

[0063] Extended area (radius 30~50 meters): Sampling point interval: 4 meters.

[0064] Point distribution method: Sampling points are evenly distributed along the circular area.

[0065] Number of sampling points: about 24.

[0066] Total number of sampling points: Overlapping areas: 16.

[0067] Expansion areas: 24.

[0068] Total: 40 sampling points.

[0069] In this way, soil pollution monitoring work can be effectively guided and a scientific basis can be provided for subsequent governance.

[0070] In a possible implementation, the pollutant concentration distribution includes an average concentration and a concentration gradient; and determining the first sampling density based on the pollutant concentration distributions in the first diffusion area and the second diffusion area includes: Based on the average concentration and concentration gradient of pollutants in the overlapping area, a basic sampling density is determined, and the basic sampling density is used as the sampling density of the overlapping area; wherein the overlapping area is an area in both the first diffusion area and the second diffusion area; Based on the occurrence probability of the lower limit of the concentration range, the basic sampling density is adjusted to obtain the sampling density of the first difference area; wherein the first difference area is an area within the first diffusion area and not within the second diffusion area; Based on the occurrence probability of the upper limit of the concentration range, the basic sampling density is adjusted to obtain the sampling density of the second difference area; wherein the second difference area is an area within the second diffusion area and not within the first diffusion area.

[0071] In this embodiment, the average concentration represents the average level of pollutant concentration in the region, and the concentration gradient is the change in pollutant concentration within a unit distance, reflecting the severity of the concentration change.

[0072] The first diffusion area is the pollutant diffusion range determined based on the lower limit of the pollution source concentration range, which represents a conservative estimate of the pollutant diffusion. The second diffusion area is the pollutant diffusion range determined based on the upper limit of the pollution source concentration range, which represents the maximum possible range of pollutant diffusion.

[0073] The overlapping area is the part that belongs to both the first diffusion area and the second diffusion area, indicating the area where the pollutant concentration distribution is relatively clear. Therefore, the basic sampling density can be directly determined based on the average concentration and concentration gradient.

[0074] The first difference area is the part that only belongs to the first diffusion area and not the second diffusion area, indicating an area with low possibility of pollutant diffusion. The second difference area is the part that only belongs to the second diffusion area and not the first diffusion area, indicating an area with greater uncertainty in pollutant diffusion.

[0075] The probability of occurrence of the lower limit of the concentration range is the possibility when the pollutant concentration is at the lower limit of its range, which can reflect the possibility of the existence of the first difference area. The probability of occurrence of the upper limit of the concentration range is the possibility when the pollutant concentration is at the upper limit of its range, which can reflect the possibility of the existence of the second difference area and can be used to adjust the sampling density of the second difference area.

[0076] Based on the previous embodiment, the specific steps of determining the first sampling density may be as follows: Step 1: Determine the first diffusion area and the second diffusion area First diffusion area: Based on the lower limit of the concentration range (50 mg / kg), the first diffusion area is a circular area with a radius of 30 meters.

[0077] Second diffusion area: Based on the upper limit of the concentration range (150 mg / kg), the second diffusion region is a circular area with a radius of 50 meters.

[0078] Step 2: Determine the base sampling density for the overlapping area Overlapping area: Range: Circular area with a radius of 30 meters.

[0079] Average concentration: Assumed to be 100 mg / kg.

[0080] Concentration gradient: Assumed to be 2 mg / kg / m.

[0081] The basic sampling density is calculated according to the formula of basic sampling density: Dbase=0.1⋅100+0.5⋅2=11m That is, the sampling point interval is 11 meters.

[0082] Step 3: Determine the sampling density of the first difference area First difference area: Range: None (there is no first difference area in this case).

[0083] The probability of occurrence of the lower limit of the concentration range (assumed to be 0.2).

[0084] If there is a first difference region, then: Dfirst = 11⋅(1−0.2) = 8.8 m Step 4: Determine the sampling density of the second difference area Second difference area: Range: Circular area with a radius of 30~50 meters.

[0085] Average concentration: Assumed to be 75 mg / kg.

[0086] Concentration gradient: Assumed to be 3 mg / kg / m.

[0087] The probability of occurrence of the upper limit of the concentration range (assumed to be 0.3).

[0088] Calculated: Dsecond = 11⋅(1+0.3) = 14.3 m In one possible implementation, the formula for determining the basic sampling density is:

[0089] in, is the basic sampling density, , is the preset coefficient, is the average concentration of pollutants in the overlapping area, is the concentration gradient of the pollutant in the overlapping area.

[0090] In this embodiment, the average concentration of pollutants in the overlapping area reflects the overall pollution level. A higher average concentration usually means that a higher sampling density is required to capture the details of the pollutant distribution. The concentration gradient of pollutants in the overlapping area represents the amount of change in pollutant concentration per unit distance. A larger concentration gradient means that the pollutant distribution changes dramatically, so more dense sampling points are required to accurately reflect this change. , It is used to balance the effects of average concentration and concentration gradient on sampling density. The specific value needs to be adjusted according to actual monitoring needs and regional characteristics.

[0091] This formula comprehensively considers the overall pollution level (average concentration) and local variation characteristics (concentration gradient) of pollutants, and can scientifically determine the basic sampling density.

[0092] In one possible implementation, the formula for adjusting the basic sampling density based on the probability of occurrence of the lower limit of the concentration range is:

[0093] in, is the sampling density of the first difference area, is the basic sampling density, is the probability of occurrence of the lower limit of the concentration range;

[0094] in, is the sampling density of the second difference area, is the basic sampling density, is the probability of occurrence of the upper limit of the concentration range.

[0095] In this embodiment, the probability of occurrence of the lower limit of the concentration range indicates the probability that the pollutant concentration is at the lower limit of its range. A lower probability means that the possibility of pollutant diffusion in the area is smaller, so the sampling density can be appropriately reduced. , achieving a reduction in the sampling density of the first difference area.

[0096] The probability of occurrence of the upper limit of the concentration range indicates the likelihood of the pollutant concentration being at the upper limit of its range. A higher probability means that the pollutant is more likely to be dispersed in that area, so the sampling density needs to be increased to capture potential high concentration areas. , to improve the sampling density of the second difference area.

[0097] In a possible implementation, before adjusting the basic sampling density based on the probability of occurrence of the lower limit of the concentration range, it also includes: Obtain multiple historical concentrations of each pollution source; Calculate the occurrence ratio of the historical concentration within the first concentration range as the probability of occurrence of the upper limit of the concentration range; wherein the first concentration range is (lower limit of the concentration range, lower limit of the concentration range + first difference area / first diffusion area * (upper limit of the concentration range - lower limit of the concentration range)); Calculate the occurrence ratio of historical concentrations within the second concentration range as the probability of occurrence of the upper limit of the concentration range; wherein the second concentration range is (upper limit of concentration range - second difference area / second diffusion area*(upper limit of concentration range - lower limit of concentration range), upper limit of concentration range).

[0098] In this embodiment, the historical concentration may be the historical value of the sum of the concentrations of each pollution source. The historical concentration is an important basis for evaluating the distribution law of pollutant concentration. By counting the occurrence proportions of historical concentrations in different ranges, the probability of occurrence of the lower and upper limits of the concentration range can be more accurately estimated.

[0099] The first concentration range and the second concentration range correspond to possible areas where the pollutant concentration is close to the lower limit and the upper limit, respectively. By introducing the area ratio of the first difference area and the second difference area into the calculation of the concentration range, the uncertainty of the concentration distribution is quantified.

[0100] Assume that there is a heavy metal pollution source (cadmium) in an industrial area, and its concentration ranges from 50 to 150 mg / kg. The following parameters are known: Area of ​​the first diffusion zone: A1=2827 m2 A 1=2827m2 (radius 30 meters).

[0101] Area of ​​the second diffusion zone: A2=7854 m2 A 2=7854m2 (radius 50 meters).

[0102] The area of ​​the first difference area: Adiff1=1000 m2 A diff1=1000m2.

[0103] Second difference area: Adiff2=5000 m2 A diff2=5000m2.

[0104] Historical concentration data: 50, 60, 70, 80, 90, 100, 110, 120, 130, 140, 150 mg / kg.

[0105] The specific steps for determining the probability of occurrence of the lower limit of the concentration range and the probability of occurrence of the upper limit of the concentration range may include: Step 1: Calculate the first concentration range 1. The first concentration range formula Δ C low= A diff1 / A1⋅(upper limit of concentration range − lower limit of concentration range) Calculation: ΔClow = 10002827⋅(150−50)≈35.4 mg / kg Therefore: The first concentration range = [50,85.4) 2. The percentage of historical concentrations within the first concentration range: The historical concentration values ​​in [50,85.4) are: 50,60,70,80.

[0106] Occurrence ratio: Plow=411≈0.36 Step 2: Calculate the second concentration range 1. The second concentration range formula: ΔChigh=Adiff2 / A2⋅(upper limit of concentration range−lower limit of concentration range) Calculation: ΔChigh = 50007854⋅(150−50)≈76.4 mg / kg Therefore: The second concentration range = (73.6,150] 2. The percentage of historical concentrations within the second concentration range: The historical concentration values ​​in (73.6,150] are: 80, 90, 100, 110, 120, 130, 140, 150.

[0107] Occurrence ratio: Phigh=811≈0.73In a possible In an implementation, based on each of the first sampling data, coordinates of a plurality of second sampling points are determined to obtain a plurality of second sampling data, including: Clustering each first sampling data to obtain a plurality of clusters; For each cluster, based on the average concentration and concentration gradient of the pollutants in the cluster, determine the sampling density of the cluster; Based on the sampling density of each cluster, a second sampling point is set in the area corresponding to each cluster to obtain the coordinates of the plurality of second sampling points.

[0108] In this embodiment, the first sampling data is grouped by a clustering algorithm to form a plurality of data sets with similar characteristics (such as pollutant concentration, spatial position, etc.), and each cluster represents an area with specific pollution characteristics. The average level of pollutant concentration at all sampling points in each cluster is used to reflect the overall pollution level of the area. The change in pollutant concentration within a unit distance is used to reflect the severity of the concentration change inside or at the edge of the cluster. Based on the average concentration and concentration gradient of each cluster, the sampling density of the area can be scientifically determined.

[0109] A specific implementation method of setting the second sampling points in the area corresponding to each cluster based on the sampling density of each cluster may be to divide the spatial area corresponding to each cluster into a grid or a regular shape, and according to the sampling density D, evenly arrange the second sampling points in the area corresponding to each cluster, and output the coordinates of each second sampling point.

[0110] The layout of the second sampling point is determined by combining the average concentration and concentration gradient. This method not only improves the scientific nature of sampling and the accuracy of monitoring results, but also significantly reduces resource waste, providing an efficient technical means for soil pollution monitoring.

[0111] It should be understood that the order of execution of the steps in the above embodiment does not necessarily mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiment of the present invention.

[0112] The following is an embodiment of the device of the present invention. For details not described in detail therein, reference may be made to the corresponding method embodiment described above.

[0113] Figure 2 A schematic diagram of the structure of a soil pollution monitoring device provided by an embodiment of the present invention is shown. For ease of explanation, only the parts related to the embodiment of the present invention are shown, which are described in detail as follows: like Figure 2 As shown, a soil pollution monitoring device 2 includes: A first sampling module 21 is used to determine the coordinates of a plurality of first sampling points based on the pollution source distribution, pollution source type and geological characteristics of the area to be monitored to obtain a plurality of first sampling data; A second sampling module 22, configured to determine the coordinates of a plurality of second sampling points based on each of the first sampling data, so as to obtain a plurality of second sampling data; The data processing module 23 is used to process each of the first sampling data and the second sampling data to obtain the soil pollution monitoring result of the area to be monitored.

[0114] In a possible implementation, the pollution source distribution includes the coordinates and concentration ranges of one or more pollution sources; the first sampling module 21 is specifically used for: Determine the pollutant diffusion model for the area to be monitored based on the pollution source type and geological characteristics of the area to be monitored; Substituting the coordinates and lower limits of the concentration range of each pollution source into the pollutant diffusion model to determine the range of the first diffusion area and the distribution of pollutant concentration; Bring the coordinates and upper limits of the concentration range of each pollution source into the pollutant diffusion model to determine the range of the second diffusion area and the distribution of pollutant concentration; wherein the range of the second diffusion area is larger than the first diffusion area; Based on the pollutant concentration distribution in the first diffusion area and the second diffusion area, a first sampling density is determined, and first sampling points are set in the first diffusion area and the second diffusion area based on the first sampling density to obtain coordinates of multiple first sampling points.

[0115] In a possible implementation, the pollutant concentration distribution includes an average concentration and a concentration gradient; the first sampling module 21 is specifically used for: Based on the average concentration and concentration gradient of pollutants in the overlapping area, a basic sampling density is determined, and the basic sampling density is used as the sampling density of the overlapping area; wherein the overlapping area is an area in both the first diffusion area and the second diffusion area; Based on the occurrence probability of the lower limit of the concentration range, the basic sampling density is adjusted to obtain the sampling density of the first difference area; wherein the first difference area is an area within the first diffusion area and not within the second diffusion area; Based on the occurrence probability of the upper limit of the concentration range, the basic sampling density is adjusted to obtain the sampling density of the second difference area; wherein the second difference area is an area within the second diffusion area and not within the first diffusion area.

[0116] In one possible implementation, the formula for determining the basic sampling density is:

[0117] in, is the basic sampling density, , is the preset coefficient, is the average concentration of pollutants in the overlapping area, is the concentration gradient of the pollutant in the overlapping area.

[0118] In one possible implementation, the formula for adjusting the basic sampling density based on the probability of occurrence of the lower limit of the concentration range is:

[0119] in, is the sampling density of the first difference area, is the basic sampling density, is the probability of occurrence of the lower limit of the concentration range;

[0120] in, is the sampling density of the second difference area, is the basic sampling density, is the probability of occurrence of the upper limit of the concentration range.

[0121] In a possible implementation, the first sampling module 21 is further configured to: Obtain multiple historical concentrations for each pollution source before adjusting the basic sampling density based on the probability of occurrence of the lower limit of the concentration range; Calculate the occurrence ratio of the historical concentration within the first concentration range as the probability of occurrence of the upper limit of the concentration range; wherein the first concentration range is (lower limit of the concentration range, lower limit of the concentration range + first difference area / first diffusion area * (upper limit of the concentration range - lower limit of the concentration range)); Calculate the occurrence ratio of historical concentrations within the second concentration range as the probability of occurrence of the upper limit of the concentration range; wherein the second concentration range is (upper limit of concentration range - second difference area / second diffusion area*(upper limit of concentration range - lower limit of concentration range), upper limit of concentration range).

[0122] In a possible implementation, the second sampling module is specifically used for: Clustering each first sampling data to obtain a plurality of clusters; For each cluster, based on the average concentration and concentration gradient of the pollutants in the cluster, determine the sampling density of the cluster; Based on the sampling density of each cluster, a second sampling point is set in the area corresponding to each cluster to obtain the coordinates of the plurality of second sampling points.

[0123] The embodiment of the present invention preliminarily delineates the possible diffusion range of pollutants according to the location, type and regional geological characteristics of the pollution source, and on this basis, selects a representative first sampling point to ensure that the main pollution source and its potential impact area are covered, and then uses the data of the first sampling point to analyze the actual distribution characteristics of the pollutants, combines the model prediction and the actual monitoring results, adjusts the sampling point layout, and determines the location of the second sampling point to further refine the monitoring of high-concentration areas and transition areas. It can cover the pollution source and the area that may be affected in a targeted manner, reduce the number of unnecessary sampling points, improve the efficiency and representativeness of sampling, and ensure the accuracy of soil pollution monitoring results.

[0124] Figure 3 is a schematic diagram of a terminal provided by an embodiment of the present invention. Figure 3 As shown, the terminal 3 of this embodiment includes: a processor 30, a memory 31, and a computer program 32 stored in the memory 31 and executable on the processor 30. When the processor 30 executes the computer program 32, the steps in each of the above-mentioned soil pollution monitoring method embodiments are implemented. Alternatively, when the processor 30 executes the computer program 32, the functions of each module / unit in the above-mentioned device embodiments are implemented.

[0125] Exemplarily, the computer program 32 may be divided into one or more modules / units, which are stored in the memory 31 and executed by the processor 30 to implement the present invention. The one or more modules / units may be a series of computer program instruction segments capable of implementing specific functions, which are used to describe the execution process of the computer program 32 in the terminal 3.

[0126] The terminal 3 may be a computing device such as a desktop computer, a notebook, a PDA, or a cloud server. The terminal 3 may include, but is not limited to, a processor 30 and a memory 31. Those skilled in the art will appreciate that Figure 3 It is only an example of terminal 3 and does not constitute a limitation on terminal 3. It may include more or fewer components than shown in the figure, or a combination of certain components, or different components. For example, the terminal may also include input and output devices, network access devices, buses, etc.

[0127] The processor 30 may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor, etc.

[0128] The memory 31 may be an internal storage unit of the terminal 3, such as a hard disk or memory of the terminal 3. The memory 31 may also be an external storage device of the terminal 3, such as a plug-in hard disk, a smart media card (SMC), a secure digital (SD) card, a flash card, etc. equipped on the terminal 3. Further, the memory 31 may also include both an internal storage unit and an external storage device of the terminal 3. The memory 31 is used to store the computer program and other programs and data required by the terminal. The memory 31 may also be used to temporarily store data that has been output or is to be output.

[0129] The technicians in the relevant field can clearly understand that for the convenience and simplicity of description, only the division of the above-mentioned functional units and modules is used as an example for illustration. In practical applications, the above-mentioned function allocation can be completed by different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiment can be integrated in a processing unit, or each unit can exist physically separately, or two or more units can be integrated in one unit. The above-mentioned integrated unit can be implemented in the form of hardware or in the form of software functional units. In addition, the specific names of the functional units and modules are only for the convenience of distinguishing each other, and are not used to limit the scope of protection of this application. The specific working process of the units and modules in the above-mentioned system can refer to the corresponding process in the aforementioned method embodiment, which will not be repeated here.

[0130] In the above embodiments, the description of each embodiment has its own emphasis. For parts that are not described or recorded in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0131] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of the present invention.

[0132] In the embodiments provided by the present invention, it should be understood that the disclosed devices / terminals and methods can be implemented in other ways. For example, the device / terminal embodiments described above are only schematic. For example, the division of the modules or units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0133] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0134] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0135] If the integrated module / unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the present invention implements all or part of the processes in the above-mentioned embodiment method, and can also be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium. When the computer program is executed by the processor, it can implement the steps of each of the above-mentioned soil pollution monitoring method embodiments. Among them, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form. The computer-readable medium may include: any entity or device that can carry the computer program code, recording medium, U disk, mobile hard disk, disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), electric carrier signal, telecommunication signal and software distribution medium. It should be noted that the content contained in the computer-readable medium can be appropriately increased or decreased according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, computer-readable media do not include electric carrier signals and telecommunication signals.

[0136] The embodiments described above are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features may be replaced by equivalents. Such modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention, and should all be included in the protection scope of the present invention.

Claims

1. A soil pollution monitoring method, characterized in that: include: Based on the distribution of pollution sources, the type of pollution sources and the geological characteristics of the area to be monitored, the coordinates of a plurality of first sampling points are determined to obtain a plurality of first sampling data; Based on each of the first sampling data, determine the coordinates of a plurality of second sampling points to obtain a plurality of second sampling data; The first sampling data and the second sampling data are processed to obtain the soil pollution monitoring result of the area to be monitored.

2. A soil pollution monitoring method according to claim 1, characterized in that: The pollution source distribution includes the coordinates and concentration range of one or more pollution sources; the coordinates of multiple first sampling points are determined based on the pollution source distribution, pollution source type and geological characteristics of the area to be monitored, including: Determine the pollutant diffusion model of the area to be monitored according to the pollution source type and geological characteristics of the area to be monitored; Substituting the coordinates and lower limits of the concentration range of each pollution source into the pollutant diffusion model to determine the range and pollutant concentration distribution of the first diffusion area; Bringing the coordinates and the upper limit of the concentration range of each pollution source into the pollutant diffusion model to determine the range and pollutant concentration distribution of the second diffusion area; wherein the range of the second diffusion area is larger than the first diffusion area; Based on the pollutant concentration distribution in the first diffusion area and the second diffusion area, a first sampling density is determined, and first sampling points are set in the first diffusion area and the second diffusion area based on the first sampling density to obtain coordinates of multiple first sampling points.

3. A soil pollution monitoring method according to claim 2, characterized in that: The pollutant concentration distribution includes an average concentration and a concentration gradient; the first sampling density is determined based on the pollutant concentration distribution of the first diffusion area and the second diffusion area, including: Based on the average concentration and concentration gradient of pollutants in the overlapping area, a basic sampling density is determined, and the basic sampling density is used as the sampling density of the overlapping area; wherein the overlapping area is an area in both the first diffusion area and the second diffusion area; Based on the probability of occurrence of the lower limit of the concentration range, the basic sampling density is adjusted to obtain the sampling density of the first difference area; wherein the first difference area is an area within the first diffusion area and not within the second diffusion area; Based on the occurrence probability of the upper limit of the concentration range, the basic sampling density is adjusted to obtain the sampling density of the second difference area; wherein the second difference area is an area within the second diffusion area and not within the first diffusion area.

4. A soil pollution monitoring method according to claim 3, characterized in that: The formula for determining the basic sampling density is: in, is the basic sampling density, , is the preset coefficient, is the average concentration of pollutants in the overlapping area, is the concentration gradient of the pollutant in the overlapping area.

5. A soil pollution monitoring method according to claim 3, characterized in that: Based on the probability of occurrence of the lower limit of the concentration range, the formula for adjusting the basic sampling density is: in, is the sampling density of the first difference area, is the basic sampling density, is the probability of occurrence of the lower limit of the concentration range; in, is the sampling density of the second difference region, is the basic sampling density, is the probability of occurrence of the upper limit of the concentration range.

6. A soil pollution monitoring method according to claim 3, characterized in that: Before adjusting the basic sampling density based on the probability of occurrence of the lower limit of the concentration range, the method further includes: Obtain multiple historical concentrations of each pollution source; Calculate the occurrence ratio of the historical concentration within the first concentration range as the occurrence probability of the upper limit of the concentration range; wherein the first concentration range is ((lower limit of the concentration range, lower limit of the concentration range + first difference area / first diffusion area * (upper limit of the concentration range - lower limit of the concentration range)); Calculate the occurrence ratio of historical concentrations within the second concentration range as the probability of occurrence of the upper limit of the concentration range; wherein the second concentration range is (upper limit of concentration range - second difference area / second diffusion area*(upper limit of concentration range - lower limit of concentration range), upper limit of concentration range).

7. A soil pollution monitoring method according to claim 6, characterized in that: The step of determining the coordinates of a plurality of second sampling points based on each of the first sampling data to obtain the plurality of second sampling data comprises: Clustering each first sampling data to obtain a plurality of clusters; For each cluster, based on the average concentration and concentration gradient of the pollutants in the cluster, determine the sampling density of the cluster; Based on the sampling density of each cluster, a second sampling point is set in the area corresponding to each cluster to obtain the coordinates of the plurality of second sampling points.

8. A soil pollution monitoring device, characterized in that: include: A first sampling module, used to determine the coordinates of a plurality of first sampling points based on the distribution of pollution sources, the type of pollution sources and the geological characteristics of the area to be monitored, so as to obtain a plurality of first sampling data; A second sampling module, used for determining the coordinates of a plurality of second sampling points based on each of the first sampling data, so as to obtain a plurality of second sampling data; The data processing module is used to process each of the first sampling data and the second sampling data to obtain the soil pollution monitoring result of the area to be monitored.

9. A terminal comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the steps of the method as claimed in any one of claims 1 to 7 are implemented.

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