3D Interpolation Method, Device, Electronic Device, and Computer-Readable Storage Medium

Abstract

The present application provides a three-dimensional interpolation method, apparatus, electronic device, and computer-readable storage medium; the method includes: determining a first sampling point of the surface soil layer in the spatial partition of the regional polluted surface; sampling a first soil layer in the spatial partition according to the first sampling point to obtain a second sampling point; performing three-dimensional interpolation on a target position of the first soil layer based on the soil pollution content value corresponding to the second sampling point and the spatial distribution characteristics of the soil pollution content corresponding to the second sampling point. The three-dimensional interpolation method provided by the present application can perform accurate three-dimensional interpolation on the soil layer based on the soil pollution content of small samples.

Description

Technical Field

[0001] This application relates to the technical field of three-dimensional interpolation of soil pollution, and particularly to a three-dimensional interpolation method, device, electronic device and computer-readable storage medium. Background Art

[0002] Three-dimensional interpolation of soil pollution plays an important role in ecological environment assessment and social and economic activities. In actual scenarios, it is often very difficult and costly to obtain a sufficient number and high density of soil sampling data. Therefore, it is necessary to perform three-dimensional interpolation prediction of soil pollution based on sparse samples. Thus, there is an urgent need to develop a new three-dimensional interpolation method for soil pollution content based on sparse samples. Summary of the Invention

[0003] Embodiments of this application provide a three-dimensional interpolation method, device, electronic device and computer-readable storage medium, which can accurately perform three-dimensional interpolation on soil layers based on the spatial distribution characteristics of soil pollution content in sparse samples.

[0004] The technical solution of the embodiments of this application is implemented as follows:

[0005] In a first aspect, embodiments of this application provide a three-dimensional interpolation method, including:

[0006] Determine the first sampling points of the surface soil layer in the spatial partition of the polluted surface of the area;

[0007] Sample the first soil layer of the underground soil layer in the spatial partition according to the first sampling points to obtain second sampling points;

[0008] Perform three-dimensional interpolation on the target position of the first soil layer based on the soil pollution content values corresponding to the second sampling points and the spatial distribution characteristics of the soil pollution content corresponding to the second sampling points.

[0009] In the above solution, the step of sampling the first soil layer of the underground soil layer in the spatial partition according to the first sampling points to obtain second sampling points includes:

[0010] Stratify the underground soil layer according to soil types to obtain the first soil layer;

[0011] Select a first number of sampling points from the first sampling points, and sample the first soil layer along the direction perpendicular to the sampling points downward to obtain the second sampling points;

[0012] Determine the soil pollution content values corresponding to each sampling point in the second sampling points.

[0013] In the above solution, the three-dimensional interpolation of the target position of the first soil layer based on the soil pollution content value corresponding to the second sampling point and the spatial distribution characteristics of the soil pollution content corresponding to the second sampling point includes:

[0014] Determine a second number of third sampling points within a first distance range from the second sampling points to the target position;

[0015] According to the spatial distribution characteristics of the third sampling points among the second sampling points, determine the weight corresponding to each sampling point among the third sampling points;

[0016] Perform a weighted average operation on the soil pollution content values corresponding to the third sampling points according to the weights to obtain a predicted value of the soil pollution content corresponding to the target position;

[0017] Perform three-dimensional interpolation on the target position according to the predicted value.

[0018] In the above solution, the determining the weight corresponding to each sampling point among the third sampling points according to the spatial distribution characteristics of the third sampling points among the second sampling points includes:

[0019] Determine the constraint condition between the predicted value of the soil pollution content corresponding to the target position and the true value of the soil pollution content corresponding to the target position;

[0020] According to the constraint condition, determine the bias coefficient between the soil pollution content corresponding to each sampling point among the third sampling points and the soil pollution content corresponding to the target position;

[0021] According to the constraint condition, the bias coefficient, and the spatial distribution characteristics, determine the weight corresponding to each sampling point among the third sampling points.

[0022] In the above solution, the determining the constraint condition between the predicted value of the soil pollution content corresponding to the target position and the true value of the soil pollution content corresponding to the target position includes:

[0023] Determine the first constraint condition according to the expectations corresponding to the predicted value of the soil pollution content corresponding to the target position and the true value of the soil pollution content corresponding to the target position respectively;

[0024] Determine the second constraint condition according to the expectation corresponding to the square of the difference between the predicted value of the soil pollution content corresponding to the target position and the true value of the soil pollution content corresponding to the target position.

[0025] In the above solution, the determining the weight corresponding to each sampling point among the third sampling points according to the constraint condition, the bias coefficient, and the spatial distribution characteristics includes:

[0026] Determine the weight according to the covariance of the soil pollution content values of every two sampling points in the third sampling points, the bias coefficient between the soil pollution content corresponding to each sampling point in the third sampling points and the soil pollution content corresponding to the target location, and the covariance between the soil pollution content value corresponding to each sampling point in the third sampling points and the predicted value of the soil pollution content corresponding to the target location.

[0027] In the above solution, determining the covariance between the soil pollution content value corresponding to the sampling point and the predicted value of the soil pollution content corresponding to the target location as the third value includes:

[0028] Determine the covariance according to the expectation of the soil pollution content value corresponding to the sampling point and the expectation of the predicted value of the soil pollution content corresponding to the target location.

[0029] In a second aspect, an embodiment of the present application provides a three-dimensional interpolation device, and the three-dimensional interpolation device includes:

[0030] A first module, configured to determine a first sampling point of the surface soil layer in the spatial partition of the regional polluted surface;

[0031] A second module, configured to sample a first soil layer of the underground soil layer in the spatial partition according to the first sampling point to obtain a second sampling point;

[0032] An interpolation module, configured to perform three-dimensional interpolation on a target location of the first soil layer based on the soil pollution content value corresponding to the second sampling point and the spatial distribution characteristics of the soil pollution content corresponding to the second sampling point.

[0033] In a third aspect, an embodiment of the present application provides an electronic device, and the electronic device includes: at least one processor; and a memory communicatively connected to the at least one processor; wherein, the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute the three-dimensional interpolation method provided by the embodiment of the present application.

[0034] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium, and the storage medium includes a set of computer-executable instructions, which are used to execute the three-dimensional interpolation method provided by the embodiment of the present application when the instructions are executed.

[0035] The three-dimensional interpolation method provided by the embodiments of the present application determines the first sampling points of the surface soil layer in the spatial partition of the polluted surface area of the region; samples the first soil layer in the underground soil layer in the spatial partition according to the first sampling points to obtain second sampling points; and performs three-dimensional interpolation on the target positions of the first soil layer based on the soil pollution content values corresponding to the second sampling points and the spatial distribution characteristics of the soil pollution content corresponding to the second sampling points. The three-dimensional interpolation method provided by the present application determines the second sampling points in the underground soil layer based on the first sampling points of the surface soil layer in the spatial partition, ensuring the geographical correspondence of the sampled sample points and facilitating the determination of the coordinate positions of the second sampling points at different depths at the same location. At the same time, in the present application, the soil pollution content at the target position is predicted based on the spatial distribution characteristics of the soil pollution content of the second sampling points, and more accurate prediction values can be obtained, facilitating accurate three-dimensional interpolation of the soil layer. Description of the Drawings

[0036] The drawings are used to better understand the solution and do not limit the present application. Among them:

[0037] Figure 1 is an optional processing flow diagram of the three-dimensional interpolation method provided by the embodiments of the present application;

[0038] Figure 2 is a schematic diagram of the three-dimensional interpolation model of the soil pollution content provided by the embodiments of the present application;

[0039] Figure 3 is an optional structural diagram of the three-dimensional interpolation device provided by the embodiments of the present application;

[0040] Figure 4 is a schematic block diagram of an optional electronic device provided by the embodiments of the present application. Detailed Embodiments

[0041] In order to make the objectives, technical solutions, and advantages of the present application clearer, the present application will be further described in detail below in conjunction with the drawings. The described embodiments should not be regarded as limitations on the present application. All other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the scope of protection of the present application.

[0042] In the following descriptions, reference is made to "some embodiments", which describe a subset of all possible embodiments. However, it can be understood that "some embodiments" can be the same subset or different subsets of all possible embodiments and can be combined with each other without conflict.

[0043] In the following description, the terms "first / second" only distinguish similar objects and do not represent a specific order for the objects. Understandably, "first / second" can be interchanged with a specific order or sequence when permitted, so that the embodiments of the present application described herein can be implemented in an order other than that illustrated or described herein.

[0044] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those of ordinary skill in the technical field to which this application belongs. The terms used herein are only for the purpose of describing the embodiments of this application and are not intended to limit this application.

[0045] A three-dimensional interpolation method provided by an embodiment of the present application will be introduced below. Refer to Figure 1 , Figure 1 which is a schematic diagram of an optional processing flow of the three-dimensional interpolation method provided by an embodiment of the present application. The following will be described in conjunction with Figure 1 the steps S101 - S103 shown.

[0046] Step S101, determine the first sampling points of the surface soil layer in the spatial partition of the regional polluted surface.

[0047] In some embodiments, the regional polluted surface can be divided into different spatial partitions based on the boundaries corresponding to the functional areas. Among them, each spatial partition represents a specific functional area corresponding to the pollutant treatment process, such as a pollution source generation area, a transmission area such as a polluted road and pipeline, a pollutant storage area such as a warehouse, a pollutant diffusion area, etc. By dividing the regional polluted surface into different functional areas, researchers can study the soil pollution characteristics of each area more precisely, improve the pertinence and accuracy of sampling, improve the accuracy of three-dimensional interpolation, and at the same time can more accurately evaluate the soil pollution level of each area.

[0048] In some embodiments, pollution content sampling can be performed on the surface soil layer in each spatial partition. During the process of sampling the pollution content of the surface soil, the sample size of the first sampling point can be determined based on factors such as cost budget, soil content of the surface soil layer, and statistical variance of the pollution level of the surface soil layer. For example, the total sample size of all regions can be determined according to the total cost budget and the cost of each drilling. The sample size of the first sampling point can be determined according to the soil content of the surface soil corresponding to each region, or the sample size of the first sampling point can also be determined according to the statistical variance of the pollution level corresponding to each region. Generally, the sample size of the first sampling point is proportional to the statistical variance of the pollution level. The statistical variance of the pollution level can be obtained based on experience or through on-site measurement, which is not limited here. The method of collecting the first sampling point can be based on the stratified sampling method, dividing the surface soil layer into different layers, collecting sample points for each layer, and the small and sparse sample points collected from all layers in the surface soil layer can be determined as the first sample points. In the case of limited budget, by reasonably allocating the sample quantity, the efficiency of information acquisition can be maximized.

[0049] In some embodiments, three-dimensional interpolation can be performed on the surface soil layer according to the pollution content values corresponding to each sampling point in the first sampling point. Before performing three-dimensional interpolation, the semivariogram can be calculated based on the pollution content data corresponding to the first sample size of the surface soil, as shown in formula (1). Through the semivariogram, it can be determined how the soil pollution content values corresponding to different sampling points change with distance, which can be used to describe the distribution law of the spatial correlation between the soil pollution content values corresponding to different sampling points at different positions in space, and can also reflect the spatial distribution characteristics of the soil pollution content corresponding to different sampling points.

[0050] Mg=E[(Z(S1)-Z(S2)) 2 (1)

[0051] In formula (1), M is a preset parameter value, and the preset parameter value can be an integer value, which can be 2 here. g represents the function value of the semivariogram, E represents the mathematical expectation, Z(S1) and Z(S2) respectively represent the soil pollution content values of two sampling points located at positions S1 and S2 in the surface soil with a distance of a, and the positions of S1 and S2 can be represented by three-dimensional coordinates such as longitude value, latitude value, and distance from the surface depth value.

[0052] It can be determined from formula (1) that the value of the semivariogram between the sampling points at position S1 and the sampling points at position S2 is: the ratio between the expected value of the square of the difference between the expected value of the soil pollution content value corresponding to the sampling points at position S1 and the expected value of the soil pollution content corresponding to the sampling points at position S2, and the preset parameter value M set in advance. The semivariogram can be used to describe the spatial correlation between the soil pollution contents of different sampling points, and can improve the accuracy of three-dimensional interpolation based on the spatial distribution characteristics between the soil pollution contents of different sampling points.

[0053] In some embodiments, a certain number of grid points can be preset in advance. Each grid point can represent the position to be interpolated, and the spatial weights can be determined based on the Kriging method for weighted averaging the observed values of the known first sample points to the position to be interpolated. After the interpolation is completed for all grid points, the predicted values or estimated values of the soil pollution contents corresponding to these grid points can be combined into an interpolation map, which can be used to display the spatial distribution of the soil surface pollution level, facilitating further environmental assessment and decision-making.

[0054] Step S102: Sample the first soil layer in the underground soil layer of the spatial partition according to the first sampling points to obtain second sampling points.

[0055] In some embodiments, the underground soil layer can be divided into soil layers corresponding to different soil types according to the differences in soil types in the vertical direction of the underground soil layer, and the three-dimensional coordinate ranges of each soil layer are clarified. Among them, the three-dimensional coordinates include longitude values, latitude values, and the depth values from the ground surface, a total of three dimensions of values.

[0056] In some embodiments, each soil layer in the underground soil layer can be represented as the first soil layer. According to the cost budget and the topographic features of the first soil layer, a preset ratio corresponding to the first soil layer is determined, and the product value of the number of the first sampling points and the preset ratio is determined as the first number of the second sampling points corresponding to the first soil layer. By sampling the soil layers at different depths of the same geographical location respectively, the soil pollution degrees at different depths of this location can be captured. At the same time, it is convenient to obtain the vertical distribution characteristics of soil pollution, which can help to obtain more comprehensive and accurate soil pollution data. It is beneficial to more accurate three-dimensional interpolation and subsequent environmental risk assessment.

[0057] When sampling the first soil layer, the first number of first sampling points can be randomly selected, and drilling is performed vertically downward at the location of each first sampling point to obtain the second sampling points in the first soil layer. It can be determined that the longitude values and latitude values of the second sampling points and the randomly selected first number of first sampling points are the same in the three-dimensional coordinates, but the depth values from the ground surface are different.

[0058] Step S103: performing three-dimensional interpolation on the target position of the first soil layer based on the soil pollution content value corresponding to the second sampling point and the spatial distribution characteristics of the soil pollution content corresponding to the second sampling point.

[0059] In some embodiments, the target position in the first soil layer can represent any specified position of the sample point to be interpolated. A second number of third sampling points can be obtained from the adjacent points within the first distance range from the target position, and the weight of each sampling point in the third sampling points can be determined according to the spatial distribution characteristics of the third sampling points. The soil pollution content value corresponding to the third sampling point is weighted averaged according to the weight of each sampling point to obtain the predicted value of the soil pollution content corresponding to the target position. Among them, the first distance and the second number can be determined according to actual conditions, and this application does not limit it. The predicted value of the soil pollution content corresponding to the target position can be shown as formula (2).

[0060]

[0061] In formula (2), w i Represents the weight of the i-th sampling point in the third sampling point. Represents the predicted or estimated value of soil pollution content at the target location, y i Indicates the soil pollution content value corresponding to the i-th sampling point in the third sampling point.

[0062] In some embodiments, according to the spatial distribution characteristics of the third sampling points in the second sampling points, the process of determining the weight corresponding to each sampling point in the third sampling points can be determined by linear unbiased estimation under the constraint condition and the unbiased optimal constraint target.

[0063] The soil pollution content corresponding to the target location can be obtained based on the two constraints of the predicted value of the soil pollution content corresponding to the target location and the true value of the soil pollution content corresponding to the target location. The first constraint is shown in formula (3), and the second constraint is shown in formula (4).

[0064]

[0065] In formula (3) and formula (4), represents the predicted value of soil pollution content corresponding to the target location, that is, the estimated value, y0 represents the true value of soil pollution content at the target location, and the constraint condition of formula (3) indicates that the true soil pollution content value corresponding to the target location is equal to the statistical expectation of the predicted value of soil content at the target location.

[0066] The constraint condition of formula (4) represents the variance of the predicted value of the soil pollution content at the target location, that is, the expected value of the square of the difference between the predicted value and the true value of the soil pollution content at the target location, min w represents a set of weights w to be determined to minimize the estimation the expected value of the square of the error between and y0, that is can be determined by setting the expected value of the square of the error between and y0 to be less than a preset minimum threshold, or other methods of minimizing the estimation. Through the constraints of formula (3) and formula (4), the predicted value of the soil pollution content at the target location can be made closer to the true value.

[0067] According to the unbiased constraints of the above formula (3) and formula (4), E(y0) in formula (2) can be further determined as shown in formula (5).

[0068]

[0069] According to the above formula (5), formula (6) can be obtained as follows.

[0070]

[0071] In formula (6), E(y i ) / E(y0) can represent the spatial heterogeneity between the soil pollution content value corresponding to the i-th sampling point in the first soil layer and the predicted value of the soil pollution content at the target location, that is, the bias coefficient. Since in reality, the spatial distribution of the spatial heterogeneity of soil pollution E(y i )≠E(y0), therefore, it can be defined Therefore, formula (6) can be written as formula (7) as follows.

[0072]

[0073] In formula (7), b i represents the distribution of the spatial heterogeneity between the soil pollution content value of the i-th sampling point and the predicted value of the soil pollution content at the target location, and can also be used to represent the bias coefficient between the soil pollution content value of the i-th sampling point and the predicted value of the soil pollution content at the target location.

[0074] Taking formula (4) and formula (7) and further with respect to w iTaking the partial derivative with respect to μ, formula (8) can be obtained. As shown below, based on the covariance of the soil pollution content values between every two sampling points in the third sampling point, the bias coefficient between the soil pollution content corresponding to each sampling point in the third sampling point and the soil pollution content corresponding to the target location, and the covariance between the soil pollution content value corresponding to each sampling point in the third sampling point and the predicted value of the soil pollution content corresponding to the target location, the weight corresponding to each sampling point in the third sampling point can be determined.

[0075] w = C -1 ·D(8)

[0076] Wherein,

[0077] In formula (8), in order to express the statistical relationship between two sampling points, i and j are respectively used to represent the numbers of any two sampling points in the third sampling point (i = 1, 2... n; j = 1, 2... n), (y i y j ) represents a combination of any two sampling points; C(y i y j ) represents the covariance between the soil pollution content value y i corresponding to the i-th sampling point and the predicted value y j of the soil pollution content corresponding to the j-th sampling point; C(y i y0) represents the covariance between the soil pollution content value y i corresponding to the i-th sampling point and the predicted value y0 of the soil pollution content corresponding to the target location, C(y i y j ) and C(y i y0) can be determined by the spatial distribution characteristics of the third sampling point (formula (1)); w i represents the weight of the i-th sampling point in the third sampling point; μ is the Lagrange multiplier; b i has the same meaning as b i in formula (7). According to formula (8), the Lagrange multiplier and the weight corresponding to each sampling point can be calculated.

[0078] In the method of the present application, the spatial correlation of the soil pollution content can be determined based on the designed semivariogram, and the spatial heterogeneity of the soil pollution content can be determined based on the unbiased estimate, so as to be able to correct the sparse samples, and further obtain a more accurate predicted value of the soil pollution content.

[0079] In some embodiments, a mapping relationship can be established between the predicted value of the soil pollution content corresponding to the target location and the target location in the three-dimensional space of the first soil layer, so as to interpolate the sparse second sampling points in the first soil layer to obtain sample points corresponding to the soil pollution content with a sufficient quantity and high density, and a more accurate spatial pollution distribution in the first soil layer can be obtained. At the same time, a more accurate spatial pollution distribution of the soil pollution content in the entire underground soil layer can be further determined.

[0080] The three-dimensional interpolation model for soil pollution content provided by the embodiments of the present application is introduced below.

[0081] As Figure 2 shown. Figure 2 The region formed by the cube in can represent the three-dimensional space corresponding to a spatial partition of the regional polluted surface. The upper plane marked with "surface" in the cube can represent the surface soil layer, and the lower plane marked with "soil layer m" in the cube can represent each soil layer of the underground soil layer, that is, the first soil layer.

[0082] For the surface soil layer, three-dimensional interpolation can be performed on the surface soil layer based on the first sampling points. When performing three-dimensional interpolation, a certain number of grid points can be preset in advance. According to the covariance and bias coefficient between the sample points to be interpolated and the sample points in the first sampling points, and according to the Kriging method, the soil pollution content values corresponding to all grid points in the surface soil layer are predicted. In Figure 2 , it is assumed that there is a certain correlation between all the sample points corresponding to the grid points in the surface soil layer. y represents the sample point to be interpolated at the target location in the surface soil layer, C represents the covariance between the soil pollution content corresponding to the sample point to be interpolated and the soil pollution content corresponding to the first sampling point, and b represents the bias coefficient between the soil pollution content corresponding to the sample point to be interpolated and the soil pollution content corresponding to the first sampling point.

[0083] For the underground soil layer, according to the different soil types in the vertical direction of the underground soil layer, the underground soil layer can be divided into soil layers corresponding to different soil types, and the three-dimensional coordinate ranges corresponding to each soil layer are clarified. Among them, the three-dimensional coordinates can include longitude values, latitude values, and distance from the surface depth values in three dimensions. Each soil layer in the underground soil layer can be represented as the first soil layer. It can be determined by the dashed line in the vertical downward direction in Figure 2 that the sampling points in the first soil layer can be obtained by sampling a part of the sampling points randomly selected from the first sampling points in the surface soil layer in the vertical downward direction. Therefore, the longitude values and latitude values in the coordinates of the sampling points in the first soil layer are the same as the longitude values and latitude values of the corresponding sampling points in the first sampling points, and the depth values of the two are different. As an example, in Figure 2In the first soil layer, the sampling point y0 represents the sampling point corresponding to the sampling point y in the surface soil layer. The longitude and latitude values of the coordinates of the two are the same, only the depth values are different. In Figure 2 it is assumed that there is a certain correlation between all sampling points in the first soil layer. y0 represents the sampling point to be interpolated at the target position in the first soil layer, C n0 represents the covariance between the soil pollution content corresponding to y n in the second sampling point and the soil pollution content corresponding to the sampling point y0 to be interpolated, C 1n represents the covariance between the soil pollution content corresponding to y1 in the second sampling point and the soil pollution content corresponding to the sampling point y n in the second sampling point, C 12 represents the covariance between the soil pollution content corresponding to y1 in the second sampling point and the soil pollution content corresponding to the sampling point y2 in the second sampling point, C 23 represents the covariance between the soil pollution content corresponding to y2 in the second sampling point and the soil pollution content corresponding to the sampling point y3 in the second sampling point, C 30 represents the covariance between the soil pollution content corresponding to y3 in the second sampling point and the soil pollution content corresponding to the sampling point y0 to be interpolated, C 20 represents the covariance between the soil pollution content corresponding to y2 in the second sampling point and the soil pollution content corresponding to the sampling point y0 to be interpolated, C 10 represents the covariance between the soil pollution content corresponding to y1 in the second sampling point and the soil pollution content corresponding to the sampling point y0 to be interpolated, b n represents the second sampling point of y n corresponding to the bias coefficient between the soil pollution content and the soil pollution content corresponding to the sampling point y0 to be interpolated. b1 represents the bias coefficient between the soil pollution content corresponding to y1 in the second sampling point and the soil pollution content corresponding to the sampling point y0 to be interpolated. b2 represents the bias coefficient between the soil pollution content corresponding to y2 in the second sampling point and the soil pollution content corresponding to the sampling point y0 to be interpolated. b3 represents the bias coefficient between the soil pollution content corresponding to y3 in the second sampling point and the soil pollution content corresponding to the sampling point y0 to be interpolated.

[0084] Among them, the spatial correlation of soil pollution content between different sampling points can be determined by the semivariogram, and then the covariance between different sampling points can be determined. As an example, the covariance between the soil pollution contents corresponding to the sampling points located at positions S1 and S2 with a distance of a can be determined as: the ratio between the expected value of the square of the difference between the expected value of the soil pollution content value corresponding to the sampling point at position S1 and the expected value of the soil pollution content corresponding to the sampling point at position S2, and a preset parameter value M. The preset parameter value M can be determined according to the actual situation, such as it can be 2.

[0085] The ratio of the expectations of the soil pollution contents corresponding to two different sampling points can be determined as the bias coefficient between the sampling points. The third sampling points with a second quantity within a first distance range from the target position can be determined from the second sampling points. According to the sum of the products of the weight value of each sampling point in the third sampling points and the bias coefficient between the soil pollution content value corresponding to each sampling point and the soil pollution content value corresponding to the target position being a specific value, the constraint condition can be determined. For example, the specific value is 1.

[0086] The first constraint condition can be determined as: the expected value of the true value of the soil pollution content corresponding to the target position is equal to the expected value of the predicted value of the soil pollution content corresponding to the target position; the second constraint condition can be determined as: the value corresponding to the expectation of the square of the difference between the predicted value of the soil pollution content corresponding to the target position and the true value of the soil pollution content corresponding to the target position is as small as possible. The second constraint condition can be determined by setting this value to be less than a preset minimum threshold or other methods of minimizing the estimation.

[0087] According to the above constraint conditions, the spatial distribution characteristics and bias coefficients of the sampling points can be fully utilized to determine the weight corresponding to each sampling point in the third sampling points. Among them, the method for determining the weight can be, for each sampling point in the third sampling points, determining the covariance of the soil pollution content values of every two sampling points in the third sampling points according to the spatial distribution characteristics, the bias coefficient between the soil pollution content corresponding to each sampling point in the third sampling points and the soil pollution content corresponding to the target position, and the covariance between the soil pollution content value corresponding to each sampling point in the third sampling points and the predicted value of the soil pollution content corresponding to the target position, determining the corresponding equation, as shown in formula (8), and the weight corresponding to each sampling point can be determined by solving this equation.

[0088] Perform a weighted average operation on the soil pollution content corresponding to each sampling point in the third sampling points and the corresponding weight to accurately obtain the predicted value of the soil pollution content corresponding to the target position, and perform three-dimensional interpolation of the soil pollution content for the target position based on the predicted value.

[0089] The three-dimensional interpolation model for soil pollution content provided by this application improves the prediction of soil pollution content with high precision and accuracy based on the spatial distribution characteristics of sampling points with sparse small samples, which is crucial for environmental assessment and governance decision-making. At the same time, it can save sampling costs, provide an effective tool for the study of the spatial characteristics of soil pollution, and is very important for large-scale soil pollution surveys.

[0090] It should be noted that the three-dimensional interpolation model in the embodiments of this application is similar to the description of the three-dimensional interpolation method embodiments above and has similar beneficial effects to the method embodiments, so it will not be elaborated here. For the technical details not covered in the three-dimensional interpolation model provided in the embodiments of this application, they can be understood according to Figure 1 the description of the accompanying drawings in Figure 3 FIG. 7 is a schematic structural diagram of an optional device for the three-dimensional interpolation device provided in the embodiments of this application. The three-dimensional interpolation device 300 includes a first module 301, a second module 302, and an interpolation module 303. Among them,

[0091] The first module 301 is used to determine the first sampling points of the surface soil layer in the spatial partition of the regional polluted surface;

[0092] The second module 302 is used to sample the first soil layer of the underground soil layer in the spatial partition according to the first sampling points to obtain second sampling points;

[0093] The interpolation module 303 is used to perform three-dimensional interpolation on the target position of the first soil layer based on the soil pollution content values corresponding to the second sampling points and the spatial distribution characteristics of the soil pollution content corresponding to the second sampling points.

[0094] In some embodiments, the second module 302 is further used to: layer the underground soil layer according to soil types to obtain the first soil layer; randomly select a first number of sampling points from the first sampling points, and sample the first soil layer along the direction perpendicular to the sampling points downward to obtain the second sampling points.

[0095] In some embodiments, the interpolation module 303 is further used to: determine a second number of third sampling points within a first distance range from the target position from the second sampling points; determine the weight corresponding to each sampling point among the third sampling points according to the spatial distribution characteristics of the third sampling points among the second sampling points; perform a weighted average operation on the soil pollution content values corresponding to the third sampling points according to the weights to obtain a predicted value of the soil pollution content corresponding to the target position; perform three-dimensional interpolation on the target position according to the predicted value.

[0096] In some embodiments, the interpolation module 303 is further configured to: determine a constraint condition between a predicted value of the soil pollution content corresponding to the target position and a true value of the soil pollution content corresponding to the target position; according to the constraint condition, determine a bias coefficient between the soil pollution content corresponding to each sampling point in the third sampling points and the soil pollution content corresponding to the target position; and according to the constraint condition, the bias coefficient, and the spatial distribution characteristics, determine the weight corresponding to each sampling point in the third sampling points.

[0097] In some embodiments, the interpolation module 303 is further configured to: determine a first constraint condition according to the expectations corresponding to the predicted value of the soil pollution content corresponding to the target position and the true value of the soil pollution content corresponding to the target position respectively; and determine a second constraint condition according to the expectation corresponding to the square of the difference between the predicted value of the soil pollution content corresponding to the target position and the true value of the soil pollution content corresponding to the target position.

[0098] In some embodiments, the interpolation module 303 is further configured to: determine the weight according to the covariance of the soil pollution content values of every two sampling points in the third sampling points, the bias coefficient between the soil pollution content corresponding to each sampling point in the third sampling points and the soil pollution content corresponding to the target position, and the covariance between the soil pollution content value corresponding to each sampling point in the third sampling points and the predicted value of the soil pollution content corresponding to the target position.

[0099] In some embodiments, the interpolation module 303 is further configured to: determine the covariance according to the expectation of the soil pollution content value corresponding to the sampling point and the expectation of the predicted value of the soil pollution content corresponding to the target position.

[0100] It should be noted that the three-dimensional interpolation device in the embodiments of the present application is similar to the description of the three-dimensional interpolation method embodiments above and has beneficial effects similar to those of the method embodiments, so details will not be repeated. For the technical details not described in the three-dimensional interpolation device provided in the embodiments of the present application, they can be understood according to Figure 1 the description of the accompanying drawings.

[0101] Figure 4FIG. 0 shows a schematic block diagram of an exemplary electronic device 400 that can be used to implement embodiments of the present disclosure. The electronic device 400 is used to implement the three-dimensional interpolation method of the embodiments of the present disclosure. In some alternative embodiments, the electronic device 400 can implement the three-dimensional interpolation method provided in the embodiments of the present application by running a computer program. For example, the computer program can be a software module in an operating system; it can be a native APP (Application), that is, a program that needs to be installed in the operating system to run; it can also be a small program, that is, a program that only needs to be downloaded to the browser environment to run; it can also be a small program that can be embedded into any APP. In short, the above computer program can be any form of application program, module or plug-in.

[0102] In practical applications, the electronic device 400 can be an independent physical server, or a server cluster or distributed system composed of multiple physical servers. It can also be a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, CDN, and big data and artificial intelligence platforms. Among them, cloud technology (Cloud Technology) refers to a hosting technology that unifies a series of resources such as hardware, software, and networks within a wide area network or a local area network to achieve data calculation, storage, processing, and sharing. The electronic device 400 can be a smart phone, a tablet computer, a notebook computer, a desktop computer, a smart speaker, a smart TV, a smart watch, etc., but is not limited thereto.

[0103] The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processors, cellular phones, smart phones, wearable devices, in-vehicle terminals, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present application described and / or claimed herein.

[0104] As Figure 4As shown, the electronic device 400 includes a computing unit 401, which can perform various appropriate actions and processes according to the computer program stored in the read-only memory (ROM) 402 or the computer program loaded from the storage unit 408 into the random access memory (RAM) 403. In the RAM 403, various programs and data required for the operation of the electronic device 400 can also be stored. The computing unit 401, the ROM 402, and the RAM 403 are connected to each other via a bus 404. The input / output (I / O) interface 405 is also connected to the bus 404.

[0105] Multiple components in the electronic device 400 are connected to the I / O interface 405, including: an input unit 406, such as a keyboard, a mouse, etc.; an output unit 407, such as various types of displays, speakers, etc.; a storage unit 408, such as a magnetic disk, an optical disc, etc.; and a communication unit 409, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 409 allows the electronic device 400 to exchange information / data with other devices via a computer network such as the Internet and / or various telecommunication networks.

[0106] The computing unit 401 can be various general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of the computing unit 401 include but are not limited to a central processing unit (CPU), a graphics processing unit (GPU), various dedicated artificial intelligence (AI) computing chips, various computing units running machine learning model algorithms, a digital signal processor (DSP), and any appropriate processor, controller, microcontroller, etc. The computing unit 401 executes the various methods and processes described above, such as the three-dimensional interpolation method. For example, in some alternative embodiments, the three-dimensional interpolation method can be implemented as a computer software program, which is tangibly included in a machine-readable medium, such as the storage unit 408. In some alternative embodiments, part or all of the computer program can be loaded and / or installed onto the electronic device 400 via the ROM 402 and / or the communication unit 409. When the computer program is loaded into the RAM 403 and executed by the computing unit 401, one or more steps of the three-dimensional interpolation method described above can be executed. Alternatively, in other embodiments, the computing unit 401 can be configured as the three-dimensional interpolation method in any other appropriate way (e.g., by means of firmware).

[0107] An embodiment of the present application provides a computer-readable storage medium storing executable instructions, where the executable instructions, when executed by a processor, will cause the processor to execute the three-dimensional interpolation method provided by the embodiment of the present application.

[0108] In some embodiments, the computer-readable storage medium may be a memory such as FRAM, ROM, PROM, EPROM, EEPROM, flash memory, magnetic surface memory, optical disc, or CD-ROM; or it may be various devices including one or any combination of the above memories.

[0109] In some embodiments, the executable instructions may be in the form of a program, software, software module, script, or code, written in any form of programming language (including compiled or interpreted languages, or declarative or procedural languages), and may be deployed in any form, including being deployed as a stand-alone program or being deployed as a module, component, subroutine, or other unit suitable for use in a computing environment.

[0110] As an example, the executable instructions may be deployed to execute on one computing device, or on multiple computing devices located at one location, or alternatively, on multiple computing devices distributed at multiple locations and interconnected by a communication network.

[0111] This application is described with reference to the flowcharts and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the present application. It should be understood that each flow and / or block in the flowchart and / or block diagram, and the combination of flows and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to the processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing device to generate a machine, such that the instructions executed by the processor of the computer or other programmable data processing device generate means for implementing the functions specified in Figure 1 one or more of the flows Figure 1 or blocks or multiple blocks.

[0112] These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to work in a specific manner, such that the instructions stored in the computer-readable memory generate a manufacture including instruction means that implement the functions specified in Figure 1 one or more of the flows Figure 1 or blocks or multiple blocks.

[0113] It should be understood that in various embodiments of the present application, the magnitudes of the sequence numbers of the various implementation processes do not imply the order of execution. The order of execution of each process should be determined by its function and internal logic, and should not constitute any limitation to the implementation process of the embodiments of the present application.

[0114] The above are only embodiments of the present application and are not intended to limit the protection scope of the present application. Any modifications, equivalent replacements, improvements, etc. made within the spirit and scope of the present application are all included in the protection scope of the present application.

Claims

1. A three-dimensional interpolation method, characterized in that, The method includes: Determining a first sampling point of the surface soil layer in the spatial partition of the polluted surface area; Sampling a first soil layer in the underground soil layer in the spatial partition according to the first sampling point to obtain a second sampling point; Performing three-dimensional interpolation on the target position of the first soil layer based on the soil pollution content value corresponding to the second sampling point and the spatial distribution characteristics of the soil pollution content corresponding to the second sampling point; The performing three-dimensional interpolation on the target position of the first soil layer based on the soil pollution content value corresponding to the second sampling point and the spatial distribution characteristics of the soil pollution content corresponding to the second sampling point includes: Determining a second number of third sampling points within a first distance range from the target position among the second sampling points; Determining the weight corresponding to each sampling point among the third sampling points according to the spatial distribution characteristics of the third sampling points in the second sampling points; Performing a weighted average operation on the soil pollution content values corresponding to the third sampling points according to the weights to obtain a predicted value of the soil pollution content corresponding to the target position; Performing three-dimensional interpolation on the target position according to the predicted value; The determining the weight corresponding to each sampling point among the third sampling points according to the spatial distribution characteristics of the third sampling points in the second sampling points includes: Determining a constraint condition between the predicted value of the soil pollution content corresponding to the target position and the true value of the soil pollution content corresponding to the target position; Determining a bias coefficient between the soil pollution content corresponding to each sampling point among the third sampling points and the soil pollution content corresponding to the target position according to the constraint condition; Determining the weight corresponding to each sampling point among the third sampling points according to the constraint condition, the bias coefficient, and the spatial distribution characteristics; The determining a constraint condition between the predicted value of the soil pollution content corresponding to the target position and the true value of the soil pollution content corresponding to the target position includes: Determining a first constraint condition according to the expectations corresponding to the predicted value of the soil pollution content corresponding to the target position and the true value of the soil pollution content corresponding to the target position respectively; Determining a second constraint condition according to the expectation corresponding to the square of the difference between the predicted value of the soil pollution content corresponding to the target position and the true value of the soil pollution content corresponding to the target position.

2. The method according to claim 1, wherein The sampling a first soil layer in the underground soil layer in the spatial partition according to the first sampling point to obtain a second sampling point includes: Layering the underground soil layer according to soil types to obtain a first soil layer; Selecting a first number of sampling points from the first sampling points and sampling the first soil layer along the direction vertically downward from the sampling points to obtain the second sampling point; Determining the soil pollution content value corresponding to each sampling point in the second sampling points.

3. The method according to claim 1, characterized in that, The determining the weight corresponding to each sampling point among the third sampling points according to the constraint condition, the bias coefficient, and the spatial distribution characteristics includes: Determine the weight according to the covariance of the soil pollution content values of every two sampling points in the third sampling points, the bias coefficient between the soil pollution content corresponding to each sampling point in the third sampling points and the soil pollution content corresponding to the target position, and the covariance between the soil pollution content value corresponding to each sampling point in the third sampling points and the predicted value of the soil pollution content corresponding to the target position.

4. A three-dimensional interpolation device, characterized in that, The device includes: A first module, configured to determine a first sampling point of the surface soil layer in the spatial partition of the regional polluted surface; A second module, configured to sample a first soil layer of the subsurface soil layer in the spatial partition according to the first sampling point to obtain a second sampling point; An interpolation module, configured to perform three-dimensional interpolation on a target position of the first soil layer based on the soil pollution content value corresponding to the second sampling point and the spatial distribution characteristics of the soil pollution content corresponding to the second sampling point; The interpolation module is further configured to perform three-dimensional interpolation on a target position of the first soil layer based on the soil pollution content value corresponding to the second sampling point and the spatial distribution characteristics of the soil pollution content corresponding to the second sampling point, including: Determine a second number of third sampling points within a first distance range from the second sampling points to the target position; Determine the weight corresponding to each sampling point in the third sampling points according to the spatial distribution characteristics of the third sampling points in the second sampling points; Perform a weighted average operation on the soil pollution content value corresponding to the third sampling point according to the weight to obtain a predicted value of the soil pollution content corresponding to the target position; Perform three-dimensional interpolation on the target position according to the predicted value; The determining the weight corresponding to each sampling point in the third sampling points according to the spatial distribution characteristics of the third sampling points in the second sampling points includes: Determine the constraint condition between the predicted value of the soil pollution content corresponding to the target position and the true value of the soil pollution content corresponding to the target position; Determine the bias coefficient between the soil pollution content corresponding to each sampling point in the third sampling points and the soil pollution content corresponding to the target position according to the constraint condition; Determine the weight corresponding to each sampling point in the third sampling points according to the constraint condition, the bias coefficient and the spatial distribution characteristics; The determining the constraint condition between the predicted value of the soil pollution content corresponding to the target position and the true value of the soil pollution content corresponding to the target position includes: Determine a first constraint condition according to the expectations corresponding to the predicted value of the soil pollution content corresponding to the target position and the true value of the soil pollution content corresponding to the target position respectively; Determine a second constraint condition according to the expectation corresponding to the square of the difference between the predicted value of the soil pollution content corresponding to the target position and the true value of the soil pollution content corresponding to the target position.

5. An electronic device, characterized in that, The electronic device includes: At least one processor; and a memory communicatively connected to the at least one processor; Wherein, the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to execute the method according to any one of claims 1-3.

6. A computer-readable storage medium, characterized in that, The storage medium includes a set of computer-executable instructions that, when executed, are used to execute the three-dimensional interpolation method according to any one of claims 1-3.

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