Electromagnetic Radiation Inverse Distance Weighted Extended Space Interpolation Method and Device

Abstract

The present invention discloses an electromagnetic radiation inverse distance weighted extended space interpolation method and device, and the method includes: obtaining uneven electromagnetic radiation sampling data of an electromagnetic radiation monitoring area; generating regularly distributed preset points within the sampling data range according to the expected nearest neighbor short distance; dividing the preset points into multiple levels from the outside to the inside according to the Ripley's K method; using the IDW extended space interpolation method for the preset points of the multiple levels to generate the electromagnetic radiation value of each preset point; and the electromagnetic radiation values of each preset point are used to construct an electromagnetic radiation space field. The present invention can improve the IDW interpolation accuracy, and further improve the accuracy of constructing an electromagnetic radiation space field with non-uniform sampling point distribution.

Description

Technical Field

[0001] The present invention relates to the technical field of electromagnetic radiation spatial interpolation, and particularly to an inverse distance weighted extended spatial interpolation method and device for electromagnetic radiation with non-uniform sampling point distribution. Background Art

[0002] This section aims to provide background or context for the embodiments of the present invention stated in the claims. The description herein is not admitted to be prior art merely because it is included in this section.

[0003] With the rapid development of high-frequency microwave technology in aspects such as substations, high-voltage power transmission and transformation lines, and mobile communication base stations, the level of electromagnetic radiation in the environment has gradually increased. Electromagnetic pollution has now become the fourth largest source of pollution after water pollution, air pollution, and noise pollution. Electromagnetic radiation may have certain effects on the physiological activities of cells and genetic materials, etc., and can also act on multiple systems such as the human cardiovascular, nervous, and reproductive systems, potentially posing impacts and threats to public health. Obtaining the electromagnetic radiation intensity and constructing an electromagnetic radiation spatial field contribute to the monitoring and prevention of urban electromagnetic pollution.

[0004] The inverse distance weighted interpolation method (IDW) performs weighted averaging with the distance between the interpolation point and the sample points as the weight. During the interpolation process, only the distance between the interpolation point and the neighboring points needs to be considered, and it can be used to construct electromagnetic radiation. However, affected by buildings, electromagnetic radiation data is usually collected along streets or roads, the data distribution is uneven, the sampling area is locally linearly distributed, and there are large vacancies in the central area, resulting in low IDW interpolation accuracy. Therefore, there are certain problems in directly using IDW to construct an electromagnetic radiation spatial field, and the construction accuracy of the electromagnetic radiation spatial field with non-uniform sampling point distribution is also low. Summary of the Invention

[0005] Embodiments of the present invention provide an inverse distance weighted extended spatial interpolation method for electromagnetic radiation to improve the IDW interpolation accuracy, and further improve the construction accuracy of the electromagnetic radiation spatial field with non-uniform sampling point distribution. The method includes:

[0006] Obtain non-uniform electromagnetic radiation sampling data in the electromagnetic radiation monitoring area;

[0007] Generate regularly distributed preset points within the sampling data range according to the expected nearest neighbor short distance;

[0008] Divide the preset points into multiple levels from the outside to the inside according to the Ripley’s K method;

[0009] The IDW extended space interpolation method is used to generate the electromagnetic radiation value of each preset point for the preset points at the multiple levels; the electromagnetic radiation values of each preset point are used to construct an electromagnetic radiation space field.

[0010] An embodiment of the present invention further provides an electromagnetic radiation inverse distance weight extended space interpolation device, which is used to improve the IDW interpolation accuracy, and further improve the accuracy of constructing an electromagnetic radiation space field with non-uniform sampling point distribution. The device includes:

[0011] An acquisition unit, configured to acquire non-uniform electromagnetic radiation sampling data of an electromagnetic radiation monitoring area;

[0012] A generation unit, configured to generate regularly distributed preset points within the sampling data range according to the expected nearest neighbor short distance;

[0013] A layering unit, configured to divide the preset points into multiple levels from the outside to the inside according to the Ripley’s K method;

[0014] An interpolation unit, configured to use the IDW extended space interpolation method for the preset points at the multiple levels to generate the electromagnetic radiation value of each preset point; the electromagnetic radiation values of each preset point are used to construct an electromagnetic radiation space field.

[0015] An embodiment of the present invention further provides a computer device, including a memory, a processor, and a computer program stored on the memory and executable on the processor. When the processor executes the computer program, the above-mentioned electromagnetic radiation inverse distance weight extended space interpolation method is implemented.

[0016] An embodiment of the present invention further provides a computer-readable storage medium, where the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the above-mentioned electromagnetic radiation inverse distance weight extended space interpolation method is implemented.

[0017] An embodiment of the present invention further provides a computer program product, where the computer program product includes a computer program, and when the computer program is executed by a processor, the above-mentioned electromagnetic radiation inverse distance weight extended space interpolation method is implemented.

[0018] In an embodiment of the present invention, for the electromagnetic radiation inverse distance weight extended space interpolation solution, by: acquiring non-uniform electromagnetic radiation sampling data of an electromagnetic radiation monitoring area; generating regularly distributed preset points within the sampling data range according to the expected nearest neighbor short distance; dividing the preset points into multiple levels from the outside to the inside according to the Ripley’s K method; using the IDW extended space interpolation method for the preset points at the multiple levels to generate the electromagnetic radiation value of each preset point; the electromagnetic radiation values of each preset point are used to construct an electromagnetic radiation space field, this solution improves the IDW interpolation accuracy, and further improves the accuracy of constructing an electromagnetic radiation space field with non-uniform sampling point distribution. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for the description of the embodiments or the prior art. Obviously, the following drawings are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings. In the drawings:

[0020] Figure 1 It is the overall flowchart of the electromagnetic radiation inverse distance weighted extended space interpolation method with non-uniform sampling distribution provided by the present invention;

[0021] Figure 2 It is a schematic diagram of non-uniform electromagnetic radiation sampling data provided by an embodiment of the present invention;

[0022] Figure 3 It is a schematic diagram of preset points generated by an embodiment of the present invention;

[0023] Figure 4 It is a schematic diagram of the Ripley's K function curve and the maximum aggregation distance obtained by an embodiment of the present invention;

[0024] Figure 5 It is a multi-level schematic diagram of preset points in an embodiment of the present invention;

[0025] Figure 6 It is a schematic diagram of the electromagnetic radiation space field provided by an embodiment of the present invention;

[0026] Figure 7 It is a schematic flowchart of the electromagnetic radiation inverse distance weighted extended space interpolation method in an embodiment of the present invention;

[0027] Figure 8 It is a schematic diagram of the structure of the electromagnetic radiation inverse distance weighted extended space interpolation device in an embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0028] In order to make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the following will further describe the embodiments of the present invention in detail with reference to the drawings. Herein, the illustrative embodiments of the present invention and their descriptions are used to explain the present invention, but do not limit the present invention.

[0029] An embodiment of the present invention provides an electromagnetic radiation inverse distance weighted extended space interpolation scheme, which is a non-uniform sampling distribution electromagnetic radiation inverse distance weighted extended space interpolation scheme. The scheme includes the following steps: obtaining non-uniform electromagnetic radiation sampling data sampled along roads in an electromagnetic radiation monitoring area; generating regularly distributed preset points within the sampling data range according to the expected nearest neighbor short distance; dividing the preset points into multiple levels from the outside to the inside according to Ripley's K method; using the IDW extended space interpolation method for the points in the multiple levels to generate the electromagnetic radiation value of each preset point; constructing an electromagnetic radiation space field based on the electromagnetic radiation values of the generated preset points and the sampling data. The non-uniform sampling distribution electromagnetic radiation inverse distance weighted extended space interpolation method provided by the embodiment of the present invention can solve the problem of low accuracy in constructing the electromagnetic radiation space field caused by uneven distribution of electromagnetic radiation sampling data.

[0030] Figure 7 It is a schematic flowchart of the electromagnetic radiation inverse distance weighted extended space interpolation method in an embodiment of the present invention. As Figure 7 shown, the method includes the following steps:

[0031] Step 101: Obtain non-uniform electromagnetic radiation sampling data in the electromagnetic radiation monitoring area;

[0032] Step 102: Generate regularly distributed preset points within the sampling data range according to the expected nearest neighbor short distance;

[0033] Step 103: Divide the preset points into multiple levels from the outside to the inside according to Ripley's K method;

[0034] Step 104: Use the IDW extended space interpolation method for the preset points in the multiple levels to generate the electromagnetic radiation value of each preset point; the electromagnetic radiation value of each preset point is used to construct an electromagnetic radiation space field.

[0035] When the electromagnetic radiation inverse distance weighted extended space interpolation method provided by the embodiment of the present invention works: obtain non-uniform electromagnetic radiation sampling data in the electromagnetic radiation monitoring area; generate regularly distributed preset points within the sampling data range according to the expected nearest neighbor short distance; divide the preset points into multiple levels from the outside to the inside according to Ripley's K method; use the IDW extended space interpolation method for the preset points in the multiple levels to generate the electromagnetic radiation value of each preset point; the electromagnetic radiation value of each preset point is used to construct an electromagnetic radiation space field. This method improves the IDW interpolation accuracy, and further improves the accuracy of constructing the electromagnetic radiation space field with non-uniform sampling point distribution. The following is a detailed introduction to the electromagnetic radiation inverse distance weighted extended space interpolation method.

[0036] In the prior art, directly performing spatial interpolation on electromagnetic radiation data with a non-uniform sampling distribution to generate an electromagnetic radiation field will result in low interpolation accuracy due to uneven sampling point distribution and a void in the center of the sampling area. Based on this, an embodiment of the present invention provides an inverse distance extension spatial interpolation method for non-uniform sampling distribution of electromagnetic radiation.

[0037] In view of the defects and deficiencies in the prior art, an embodiment of the present invention provides an inverse distance weighted extension spatial interpolation method to solve the problem of low accuracy in constructing the electromagnetic radiation spatial field with non-uniform sampling point distribution. The specific content is as follows:

[0038] Step S1: Obtain non-uniform electromagnetic radiation sampling data of the electromagnetic radiation monitoring area, which corresponds to step 101 above;

[0039] Step S2: Generate regularly distributed preset points within the sampling data range according to the expected nearest neighbor short distance, which corresponds to step 102 above;

[0040] Further, the above step S2 of generating regularly distributed preset points within the sampling data range according to the expected nearest neighbor short distance specifically includes:

[0041] Step S2.1: Calculate the maximum bounding rectangle of the sampling data;

[0042] Step S2.2: Calculate the expected nearest neighbor short distance D of the sampling data, and generate regularly distributed points within the maximum bounding rectangle with the interval between each point being D;

[0043] Step S2.3: Eliminate the points located outside the sampling data range to obtain the points G located within the sampling data range.

[0044] As can be seen from the above, in one embodiment, generating regularly distributed preset points within the sampling data range according to the expected nearest neighbor short distance may include:

[0045] Calculate the maximum bounding rectangle of the sampling data;

[0046] Calculate the expected nearest neighbor short distance of the sampling data, and generate regularly distributed points within the maximum bounding rectangle with the interval between each preset point being the expected nearest neighbor short distance;

[0047] Eliminate the points located outside the sampling data range from the regularly distributed points to obtain the preset points located within the sampling data range as the final regularly distributed preset points.

[0048] In specific implementation, the above implementation manner of generating regularly distributed preset points within the sampling data range according to the expected nearest neighbor short distance further improves the IDW interpolation accuracy, and further improves the accuracy of constructing the electromagnetic radiation spatial field with non-uniform sampling point distribution.

[0049] Step S3: Divide the above-mentioned preset points into multiple layers from the outside to the inside according to Ripley's K method, which corresponds to the above-mentioned step 103;

[0050] Further, in the above step S3, dividing the above-mentioned preset points into multiple layers from the outside to the inside according to Ripley's K method specifically includes:

[0051] Step S3.1: Use Ripley's K function to calculate the observed K-value curve and the predicted K-value curve;

[0052] Step S3.2: Calculate the maximum aggregation distance of the electromagnetic radiation sampling data, that is, the distance H corresponding to the maximum positive difference between the observed K-value curve and the predicted K-value curve;

[0053] Step S3.3: Taking H as the standard, divide the above-mentioned regularly distributed points G into L layers from the outside to the inside. The points in the first layer surround the points in the second layer, the points in the second layer surround the points in the third layer, and so on.

[0054] In one embodiment, dividing the above-mentioned preset points into multiple layers from the outside to the inside according to Ripley's K method may include:

[0055] Use Ripley's K function to calculate the observed K-value curve and the predicted K-value curve;

[0056] Calculate the maximum aggregation distance of the electromagnetic radiation sampling data; the maximum aggregation distance is the distance corresponding to the maximum positive difference between the observed K-value curve and the predicted K-value curve;

[0057] Taking the maximum aggregation distance as the standard, divide the above-mentioned regularly distributed preset points into multiple layers from the outside to the inside. Among them, the preset points in the first layer surround the preset points in the second layer, the preset points in the second layer surround the preset points in the third layer, and so on, that is, the preset points in each layer surround the preset points in the adjacent inner layer.

[0058] Specifically in implementation, the above implementation manner of dividing the above-mentioned preset points into multiple layers from the outside to the inside according to Ripley's K method further improves the IDW interpolation accuracy, and further improves the accuracy of constructing the electromagnetic radiation spatial field of the non-uniform sampling point distribution.

[0059] Step S4: Use the IDW extended space interpolation method for the above-mentioned multiple layers of points to generate the electromagnetic radiation value of each preset point, which corresponds to the above-mentioned step 104;

[0060] Further, in the above step S4, using the IDW extended space interpolation method for the above-mentioned multiple layers of points to generate the electromagnetic radiation value of each point specifically includes:

[0061] Step S4.1: Generate the electromagnetic radiation values of the first-layer preset points according to the IDW method using the electromagnetic radiation sampling data;

[0062] Step S4.2: Add the generated first-layer preset points to the sampling data to generate new sampling data;

[0063] Step S4.3: Generate the electromagnetic radiation values of the second-layer preset points according to the IDW method using the new sampling data;

[0064] Step S4.4: Add the electromagnetic radiation values of each generated layer of preset points to the sampling data for generating the electromagnetic radiation values of the next layer of preset points, and repeat continuously until the electromagnetic radiation values of each layer of preset points are calculated.

[0065] As can be seen from the above, in one embodiment, to generate the electromagnetic radiation values of each preset point by using the IDW extended space interpolation method for the multiple layers of preset points, it may include:

[0066] Generate the electromagnetic radiation values of the first-layer preset points according to the IDW method using the electromagnetic radiation sampling data;

[0067] Add the generated first-layer preset points to the sampling data to generate new sampling data;

[0068] Generate the electromagnetic radiation values of the second-layer preset points according to the IDW method using the new sampling data;

[0069] Add the electromagnetic radiation values of each generated layer of preset points to the sampling data for generating the electromagnetic radiation values of the next layer of preset points, and repeat continuously until the electromagnetic radiation values of each layer of preset points are calculated.

[0070] In specific implementation, the above implementation manner of generating the electromagnetic radiation values of each preset point by using the IDW extended space interpolation method for the multiple layers of preset points further improves the IDW interpolation accuracy, and further improves the accuracy of constructing the electromagnetic radiation space field with non-uniform sampling point distribution.

[0071] In addition, to further improve the IDW interpolation accuracy, in one embodiment, generating the electromagnetic radiation values of the first-layer preset points according to the IDW method using the electromagnetic radiation sampling data may include generating the electromagnetic radiation values of the first-layer preset points according to the following formula:

[0072]

[0073] where g represents the preset point, s represents the sampling data point, gele(g) is the electromagnetic radiation value of the preset point, q represents the number of the closest ones to the preset point g, and ele i is the q sampling data s closest to the preset point g iThe electromagnetic radiation value of the point, d i It represents the distance between the i-th point among the q data closest to the preset point g and the preset point.

[0074] Step S5: According to the electromagnetic radiation values of the preset points generated above and the sampling data, construct an electromagnetic radiation spatial field using IDW, that is, the step of constructing the electromagnetic radiation spatial field after the above step 104.

[0075] The beneficial effects of the embodiments of the present invention are as follows: The electromagnetic radiation inverse distance weight extended spatial interpolation method with non-uniform sampling point distribution of the present invention first presets points with regular distribution within the research area according to the expected nearest neighbor short distance, which are used to fill the central blank area caused by uneven sampling data distribution; then uses the Ripley's K method to calculate the maximum aggregation distance H of the sampling data, and divides the preset points into multiple layers from the outside to the inside using the maximum aggregation distance H. The distance between adjacent layers is the maximum aggregation distance. The preset points in the first layer surround the points in the second layer, and the preset points in the second layer surround the preset points in the third layer, and so on. The points in each layer are independent of each other; use the IDW method to generate the electromagnetic radiation values of the preset points starting from the first layer, and then add the generated preset points to the sampling points to generate the electromagnetic radiation values of the second layer of preset points, and so on to generate the electromagnetic radiation values of all preset points. Finally, use the preset points and sampling points to construct the electromagnetic radiation spatial field. The present invention uses the IDW method to generate the electromagnetic radiation intensity of regularly distributed points layer by layer, gradually expanding the information of the sampling data, and better solving the problem of low construction accuracy of the electromagnetic radiation spatial field caused by uneven sampling data distribution, and can be applied to urban electromagnetic radiation geographical environment mapping.

[0076] To facilitate understanding of how the present invention is implemented, the present invention will be further described in detail below with reference to the accompanying drawings and specific implementation methods.

[0077] Figure 1 This is the overall flowchart of the electromagnetic radiation inverse distance weight extended spatial interpolation method with non-uniform sampling distribution provided by the embodiments of the present invention. The specific method of the present invention includes the following steps:

[0078] Step S1: Obtain the uneven electromagnetic radiation sampling data of the electromagnetic radiation monitoring area, specifically as follows:

[0079] Denote the electromagnetic radiation sampling data set as S = {s1, s2,... s i ,..., s n , i = 1, 2, 3,..., n}, s i = {sx i , sy i , sele i}, where sx i and sy i are the sx and sy coordinates of the point s iThe component values on the x and y coordinates, sele i is s i of the electromagnetic radiation value, where n is the number of sampling data;

[0080] Step S2: Generate regularly distributed preset points within the sampling data range according to the expected nearest neighbor proximity, specifically as follows:

[0081] Step S2.1: Calculate the minimum bounding rectangle of the sampling data.

[0082] Specifically, calculate the maximum and minimum values of the x and y components of the sampling data coordinates, xmax, xmin, ymax, ymin. The minimum bounding rectangle is O = {(xmax, ymin), (xmax, ymax), (xmin, ymax), (xmin, ymin)}, and there are a total of 4 points in the minimum bounding rectangle;

[0083] Step S2.2: Calculate the expected nearest neighbor proximity D of the sampling data, and generate regularly distributed points within the minimum bounding rectangle with an interval of D between each point.

[0084] Specifically, the expected nearest neighbor proximity D of the sampling data is calculated by Equation (1):

[0085]

[0086] where A represents the area of the region enclosed by the electromagnetic radiation sampling data, and N represents the number of electromagnetic radiation sampling data.

[0087] Within the minimum bounding rectangle range, generate regularly distributed preset points with an interval distance of D between adjacent points, and denote the preset points as G = {g1, g2,..., g i ,..., g m , i = 1, 2,..., m}, where m is the number of generated preset points, and g i is expressed as (gx i , gy i , gele i ), where gx i and gy i are the component values of point g i on the x and y coordinates respectively, and gele i is the value of point g i , and this value needs to be generated by the IDW extension interpolation method.

[0088] Step S2.3: Remove the points located outside the sampling data range to obtain the points G located within the sampling data range.

[0089] Step S3: Divide the above preset points into multiple layers from the outside to the inside according to Ripley's K method.

[0090] Step S3.1: Calculate the observed K-value curve and the predicted K-value curve using Ripley's K function.

[0091] Specifically, the observed curve K is calculated through the following steps. In one embodiment, the observed K-value curve is calculated using Ripley's K function, including:

[0092] 1. Set the starting distance d, the step size Δd, H = d, and the number of loops M.

[0093] 2. Taking the first point s1 of the sampling data as the center, according to Equation (3), count the number I1 (the first quantity) of other sampling points (excluding the sampling point of this first point s1) whose distance to s1 is less than H.

[0094] 3. Taking the second point s2 as the center, according to Equation (3), count the number I2 (the second quantity) of other sampling points (excluding the sampling point of the second point s2) whose distance to s2 is less than H.

[0095] 4. Repeat steps 2 - 3 until the I i value is counted for each sampling point, and then add all the values to obtain ∑I(H).

[0096] 5. Substitute the obtained ∑I(H) value into Equation (2) to calculate the K(H) value when the distance is H, and substitute the K(H) value into Equation (4) to calculate the transformation of the K function.

[0097] 6. H = H + Δd, repeat steps 2 - 5 to obtain the K values at different distances H until after M loops, and obtain the observed curve K, with H as the horizontal axis and L(H) as the vertical axis for this curve.

[0098]

[0099] In the formula, A represents the area of the region enclosed by the electromagnetic radiation sampling data, and r ij represents the Euclidean distance between sampling point i and sampling point j.

[0100] The predicted curve K value is obtained by randomly distributing points with the same number as the sampling data points within the sampling area range, and obtaining the K value curve of the randomly distributed points in the same way as the calculation steps of the above observed curve K value, that is, the predicted curve K.

[0101] Step S3.2: Calculate the maximum aggregation distance of the electromagnetic radiation sampling data, that is, the distance H corresponding to the maximum positive difference between the observed K-value curve and the predicted K-value curve. Through the observed K values and predicted K values calculated in step S3.1, starting from the starting distance H = d, with a step size of Δd, calculate the difference between the observed K value and the predicted K value at different distances to obtain the maximum difference K maxThe corresponding distance H.

[0102] Step S3.3: Taking H as the standard, divide the regularly distributed points G generated above into L layers from the outside to the inside. The points in the first layer (the outermost layer) surround the points in the second layer, the points in the second layer surround the points in the third layer, and so on. Specifically, according to the vertex coordinates {t1, t2, …, t i , …, t o , i = 1, 2, …, o} of the convex hull polygon T generated from the sampling data point set S, where o is the number of vertices of the convex hull polygon T, the convex hull polygon T shrinks inward at an interval distance of H to form a new convex hull polygon T1 = {t’1, t’2, …, t’ i , …, t’ o , i = 1, 2, …, o}, t’ represents the vertex coordinates of the newly generated polygon T1. The preset points G located between the polygon T and T1 are divided into the first layer L1. Then the polygon T1 shrinks inward again at an interval distance of H to form a new convex hull polygon T2, and the preset points G located between the polygon T1 and T2 are divided into the second layer L2. And so on, every time a new convex hull polygon T i shrinks inward, the preset points G are divided into a new layer L i . Finally, the preset points G are divided into L layers, L = {L1, L2, …, L i , … L k , i = 1, 2, …, k}, where k is the number of layers, L1 represents the first layer, and L k represents the kth layer. GL k = {g1, g2, …, g i , … g j , i = 1, 2, …, j}, GL k represents the preset points in the kth layer L k , g i = {gx i , gy i , gele i} represents the ith point in this layer, gx i and gy i represent the values on the x-axis and y-axis components, representing the coordinates of this point, and gele i represents the electromagnetic radiation value to be solved for the preset point.

[0103] Step S4: Use the IDW extended space interpolation method for the points at the above multiple levels to generate the electromagnetic radiation value of each preset point.

[0104] Step S4.1: Use the sampling data to generate the electromagnetic radiation value of the preset points in the first layer according to the IDW method. Specifically, calculate the electromagnetic radiation value gele(g) of the preset points GL1 in the first layer according to Equation (5).

[0105]

[0106] Among them, g represents the preset point, s represents the sampling data point, gele(g) is the electromagnetic radiation value of the preset point, q represents the number closest to the preset point g, and ele i is the electromagnetic radiation value of the q sampling data s closest to the preset point g i points, and d i represents the distance between the i-th point and the preset point among the q data closest to the preset point g.

[0107] Step S4.2: Add the generated first-layer preset points to the sampling data to generate new sampling data. Specifically, after calculating the electromagnetic radiation value of the first-layer preset points in the above steps, add the GL1 data to the sampling data S to form new sampling data, that is, S = {S, GL1}.

[0108] Step S4.3: Use the new sampling data to generate the electromagnetic radiation value of the second-layer preset points according to the IDW method. Specifically, calculate the electromagnetic radiation value of the second-layer preset point GL2 according to Equation (5) using the new sampling data S = {S, GL1} obtained in the above steps.

[0109] Step S4.4: The electromagnetic radiation value of each generated layer of preset points should be added to the sampling data to be used for generating the electromagnetic radiation value of the next layer of preset points, and repeat continuously until the electromagnetic radiation value of each layer of preset points is calculated.

[0110] Specifically, add the data of the second-layer preset point GL2 obtained above to the sampling data set, that is, S = {S, GL2}, and then use the sampling data set S to generate the electromagnetic radiation value of the third-layer preset point GL3, and then add the data of the third-layer preset point to the sampling data set, that is, S = {S, GL3}. The electromagnetic radiation value of each obtained layer of preset points should be added to the sampling data, and then use the sampling data to generate the electromagnetic radiation value of the next layer of preset points, and finally form the final sampling data set S = {S0, GL1, GL2,..., GL k}, S0 represents the original sampling data, GL1 represents the preset point data in the first layer, and GL k represents the preset point data in the k-th layer.

[0111] The specific process is as follows:

[0112] 1. Let j = 1;

[0113] 2. Use the sampling data set S to generate the value GL of the j-th layer of preset points j ;

[0114] 3. j = j + 1, and add the preset point GLj Add it to the sampling data set S, i.e., S = {S0, GL j};

[0115] 4. Continuously loop step 3 until j = k, that is, the value of the last preset point is calculated;

[0116] 5. Obtain the final interpolation data set S = {S0, GL1, GL2, …, GL k};

[0117] Step S5: According to the electromagnetic radiation values of the preset points and the sampling data generated above, construct an electromagnetic radiation spatial field using IDW.

[0118] As can be seen from the above description, the electromagnetic radiation inverse distance weight extended space interpolation method with non-uniform sampling point distribution provided by the embodiments of the present invention first presets some regularly distributed points within the research area according to the expected nearest neighbor short distance to fill the central blank area caused by the uneven distribution of sampling data; then calculates the maximum aggregation distance H of the sampling data using Ripley’s K, and divides the preset points into multiple layers from the outside to the inside using the maximum aggregation distance H, with the interval distance between adjacent layers being the maximum aggregation distance. The points in the first layer surround the points in the second layer, the points in the second layer surround the points in the third layer, and so on; use the IDW method to generate the electromagnetic radiation values of the preset points starting from the first layer, and then add the generated preset points to the sampling points to generate the electromagnetic radiation values of the second layer of preset points until the electromagnetic radiation values of all preset points are generated. Finally, construct an electromagnetic radiation spatial field using the preset points and the sampling points. The present invention uses the IDW method to generate the electromagnetic radiation intensity of regularly distributed points layer by layer, gradually expanding the information of the sampling data, and better solving the problem of low construction accuracy of the electromagnetic radiation spatial field caused by the uneven distribution of sampling data, and can be applied to urban electromagnetic radiation geographical environment mapping.

[0119] To further illustrate the present solution, the embodiments of the present invention provide a specific application example of the electromagnetic radiation inverse distance weight extended space interpolation method with non-uniform sampling distribution, which specifically includes the following steps:

[0120] Step S1: Obtain uneven electromagnetic radiation sampling data of the electromagnetic radiation monitoring area.

[0121] Figure 2 The electromagnetic radiation sampling data points with non-uniform sampling distribution in a certain area provided by the present invention, a total of 204 points, sampling data points:

[0122] S = {(2751087.57, 585428.30, 3.30), (2751080.24, 585430.72, 5.60), …, (2751143.19, 585567.30, 15.90)}.

[0123] Step S2: Generate regularly distributed preset points within the sampling data range according to the expected nearest neighbor distance.

[0124] Step S2.1: Calculate the minimum bounding rectangle of the sampling data.

[0125] Specifically, calculate the maximum and minimum values of the x and y components of the coordinates of the sampling data. xmax = 2751277.38, xmin = 2750973.23, ymax = 585622.66, ymin = 585403.03. The minimum bounding rectangle is O = {(2751277.38, 585403.03), (2751277.38, 585622.66), (2750973.23, 585622.66), (2750973.23, 585403.03))}.

[0126] Step S2.2: Calculate the expected nearest neighbor distance D of the sampling data. With an interval of D between each point, generate regularly distributed points within the minimum bounding rectangle. The area A enclosed by the sampling data set S = 43255.72, the number of sampling points = 204. According to Equation (1), D = 7 is calculated. Generate preset points G within the minimum bounding rectangle, a total of 1408 points

[0127] Step S2.3: Eliminate the points outside the sampling data range to obtain the points G within the sampling data range = {(2750980.23, 585494.03), (2750980.23, 585501.03), …, (2751274.23, 585529.03)}. After elimination, there are 890 preset points in total. Figure 3 Schematic diagram of the preset points generated in the embodiment of the present invention.

[0128] Step S3: Divide the above preset points into multiple layers from the outside to the inside according to the Ripley's K method.

[0129] Step S3.1: Use the Ripley's K function to calculate the observed K value curve and the predicted K value curve.

[0130] Specifically, calculate the observed curve K through the following steps:

[0131] 1. Set the starting distance d = 5, the step size Δd = 2.5, H = d = 5, and the number of loops M = 30.

[0132] 2. Taking the first point s1 of the sampling data as the center, according to Equation (3), count the number I1 = 5 of other points whose distance to s1 is less than H = 10.

[0133] 3. Centered at the second point s2, count the number of other points whose distance to s2 is less than H = 10 according to Equation (3), and the quantity I2 = 4.

[0134] 4. Repeat steps 2 - 3 until the I i values of all points are counted, and then sum up all the values to obtain ∑I(H).

[0135] 5. Substitute the obtained ∑I(H) value into Equation (2) to calculate the K(H) value when the distance is H, and then substitute the K(H) value into Equation (4) to calculate the L(H) transform of the K function.

[0136] 6. H = H + Δd = 7.5, repeat steps 2 - 5 to obtain the K values at different distances H until after M loops, and obtain the observed curve K, with H as the horizontal axis and L(H) as the vertical axis for this curve.

[0137] For the predicted curve K value, randomly distribute points within the sampling area with the same number as the sampling data points. The K value curve of the randomly distributed points is obtained in the same way as the calculation steps of the above observed curve K value, which is the predicted curve K.

[0138] Step S3.2: Calculate the maximum aggregation distance of the electromagnetic radiation sampling data, that is, the distance H corresponding to the largest positive difference between the observed K value curve and the predicted K value curve. Based on the observed K values and predicted K values obtained through step S3.1, starting from the initial distance H = 5 with a step size of Δd = 2.5, calculate ΔK = observed K value - predicted K value, and obtain the corresponding distance H = 25 for ΔK. max The corresponding distance H = 25.

[0139] Figure 4 This is the schematic diagram of the Ripley's K function curve and the maximum aggregation distance obtained in this embodiment.

[0140] Step S3.3: Taking H = 25 as the standard, divide the above - generated regularly distributed points G into L layers from the outside to the inside. The points in the first layer surround the points in the second layer, the points in the second layer surround the points in the third layer, and so on. The points in each layer are independent of each other. Specifically, according to the vertex coordinates of the convex hull polygon T generated by the sampling data point set S:

[0141] {(2750997.29, 585622.66), (2750973.59, 585535.40), (2750997.26, 585467.82), (2751159.49, 585404.99), (2751238.38, 585433.35), (2751277.38, 585532.14)}, a total of 6 points. The convex hull polygon T shrinks inward at an interval distance of H = 25 to form a new convex hull polygon T1 = {(2750997.29, 585622.66), (2750973.59, 585535.40), (2750997.26, 585467.82), (2751159.49, 585404.99), (2751238.38, 585433.35), (2751277.38, 585532.14)}. The preset point G between the polygon T and T1 is divided into the first layer L1. Then the polygon T1 shrinks inward at an interval distance of H = 25 again to form a new convex hull polygon T2. The preset point G between the polygon T1 and T2 is divided into the second layer L2. And so on. Finally, the preset point G is divided into 3 layers. There are 427 points in the preset point GL1 in the first layer, 277 points in the preset point GL2 in the second layer, and 186 points in the preset point GL3 in the third layer.

[0142] Figure 5 It is a schematic diagram of multiple levels of preset points in an embodiment of the present invention.

[0143] Step S4: Use the IDW extended space interpolation method for the points of the above multiple levels to generate the electromagnetic radiation value of each preset point.

[0144] Step S4.1: Use the sampling data to generate the electromagnetic radiation value of the preset points in the first layer according to the IDW method.

[0145] Calculate the electromagnetic radiation value gele1 of the preset point GL1 in the first layer according to Equation (5), where the number of nearest neighbors q = 5, gele1 = {1.51, 2.07, …, 6.66}, a total of 427 values.

[0146] Step S4.2: Add the generated preset points in the first layer to the sampling data to generate new sampling data.

[0147] Add the GL1 data to the sampling data S to form new sampling data, that is, S = {(2751087.57, 585428.30, 3.30), (2751080.24, 585430.72, 5.60), …, (2751143.19, 585567.30, 15.90)}, a total of 631 data.

[0148] Step S4.3: Generate the electromagnetic radiation values of the second-layer preset points according to the IDW method using the new sampling data. Using the new sampling data S obtained in the above steps, calculate the electromagnetic radiation value gele2 of the second-layer preset point GL2 according to Equation (5), and obtain gele2 = {3.10, 2.31, 2.65, …, 5.74}, a total of 277 data points.

[0149] Step S4.4: The electromagnetic radiation values of each generated layer of preset points should be added to the sampling data for generating the electromagnetic radiation values of the next layer of preset points, and this is repeated continuously until the electromagnetic radiation values of each layer of preset points are calculated.

[0150] Add the obtained second-layer preset points to the sampling data set, that is, S = {(2751087.57, 585428.30, 3.30), (2751080.24, 585430.72, 5.60), …, (2751143.19, 585567.30, 15.90), …, (2751001.23, 585536.03, 3.10), (2751008.23, 585515.03, 2.31), …, (2751239.23, 585515.03, 5.74)}, a total of 908 data points. Then use the sampling data set S to generate the electromagnetic radiation values of the third-layer preset points, and add the 186 electromagnetic radiation data of the generated third-layer preset points to the sampling data to obtain the final sampling data set:

[0151] S = {(2751087.57, 585428.30, 3.30), (2751080.24, 585430.72, 5.60), …, (2751143.19, 585567.30, 15.90), …, (2751001.23, 585536.03, 3.10), (2751008.23, 585515.03, 2.31), …, (2751239.23, 585515.03, 5.74), (2751029.23, 585529.03, 2.64330286474293), …, (2751204.23, 585501.03, 5.65)}}, a total of 1094 data points.

[0152] Step S5: Construct an electromagnetic radiation spatial field using IDW based on the electromagnetic radiation values of the preset points and the sampling data generated above.

[0153] Figure 6This is a schematic diagram of the electromagnetic radiation spatial field provided by the embodiments of the present invention. The construction accuracy of the electromagnetic radiation spatial field directly constructed using IDW is 3.22, and the construction accuracy of the electromagnetic radiation spatial field constructed using the electromagnetic radiation IDW extended space interpolation method with non-uniform sampling distribution of the present invention is 2.28. It can be shown from the aspect of error that the electromagnetic radiation spatial field constructed according to the method of this specific application example has a smaller error.

[0154] The advantages of the electromagnetic radiation inverse distance weight extended space interpolation method provided by the embodiments of the present invention are: using the IDW method to generate the electromagnetic radiation intensity of regularly distributed points layer by layer, gradually expanding the information of the sampling data, and better solving the problem of low construction accuracy of the electromagnetic radiation spatial field caused by uneven distribution of sampling data.

[0155] The embodiments of the present invention also provide an electromagnetic radiation inverse distance weight extended space interpolation device as described in the following embodiments. Since the principle of this device to solve problems is similar to that of the electromagnetic radiation inverse distance weight extended space interpolation method, the implementation of this device can refer to the implementation of the electromagnetic radiation inverse distance weight extended space interpolation method, and the repeated parts will not be elaborated.

[0156] Figure 8 This is a schematic diagram of the structure of the electromagnetic radiation inverse distance weight extended space interpolation device in the embodiments of the present invention, as Figure 8 shown, the device includes:

[0157] An acquisition unit 01, configured to acquire non-uniform electromagnetic radiation sampling data of an electromagnetic radiation monitoring area;

[0158] A generation unit 02, configured to generate regularly distributed preset points within the range of the sampling data according to the expected nearest neighbor short distance;

[0159] A layering unit 03, configured to divide the preset points into multiple layers from the outside to the inside according to the Ripley's K method;

[0160] An interpolation unit 04, configured to generate the electromagnetic radiation value of each preset point for the preset points of the multiple layers by using the IDW extended space interpolation method; the electromagnetic radiation value of each preset point is used to construct the electromagnetic radiation spatial field together with the sampling data using IDW.

[0161] In one embodiment, the generation unit may specifically be configured to:

[0162] Calculate the maximum bounding rectangle of the sampling data;

[0163] Calculate the expected nearest neighbor short distance of the sampling data, and generate regularly distributed points within the maximum bounding rectangle with the interval between each preset point being the expected nearest neighbor short distance;

[0164] Eliminate the points located outside the sampling data range from the regularly distributed points, and obtain the preset points within the sampling data range as the final regularly distributed preset points.

[0165] In one embodiment, the layering unit may specifically be configured to:

[0166] Calculate the observed K-value curve and the predicted K-value curve using Ripley’s K function;

[0167] Calculate the maximum aggregation distance of the electromagnetic radiation sampling data; the maximum aggregation distance is the distance corresponding to the maximum positive difference between the observed K-value curve and the predicted K-value curve;

[0168] Taking the maximum aggregation distance as the standard, divide the preset points of the regular distribution into multiple layers from the outside to the inside. Among them, the preset points of the first layer surround the preset points of the second layer, the preset points of the second layer surround the preset points of the third layer, and so on.

[0169] In one embodiment, the interpolation unit may specifically be configured to:

[0170] Generate the electromagnetic radiation value of the preset points of the first layer according to the IDW method using the electromagnetic radiation sampling data;

[0171] Add the generated preset points of the first layer to the sampling data to generate new sampling data;

[0172] Generate the electromagnetic radiation value of the preset points of the second layer according to the IDW method using the new sampling data;

[0173] For each layer of generated electromagnetic radiation values of the preset points, add them to the sampling data to generate the electromagnetic radiation values of the next layer of preset points, and repeat continuously until the electromagnetic radiation values of each layer of preset points are calculated.

[0174] In one embodiment, generating the electromagnetic radiation value of the preset points of the first layer according to the IDW method using the electromagnetic radiation sampling data may include generating the electromagnetic radiation value of the preset points of the first layer according to the following formula:

[0175]

[0176] Among them, g represents the preset point, s represents the sampling data point, gele(g) is the electromagnetic radiation value of the preset point, q represents the number of the closest points to the preset point g, and ele i is the electromagnetic radiation value of the q sampling data points s i closest to the preset point g i The subscript i represents the distance between the i-th point and the preset point among the q closest points to the preset point g.

[0177] An embodiment of the present invention further provides a computer device, including a memory, a processor, and a computer program stored on the memory and executable on the processor. When the processor executes the computer program, the above electromagnetic radiation inverse distance weighted extended spatial interpolation method is implemented.

[0178] An embodiment of the present invention further provides a computer-readable storage medium. The computer-readable storage medium stores a computer program. When the computer program is executed by a processor, the above electromagnetic radiation inverse distance weighted extended spatial interpolation method is implemented.

[0179] An embodiment of the present invention further provides a computer program product. The computer program product includes a computer program. When the computer program is executed by a processor, the above electromagnetic radiation inverse distance weighted extended spatial interpolation method is implemented.

[0180] In an embodiment of the present invention, for the electromagnetic radiation inverse distance weighted extended spatial interpolation scheme, by: obtaining uneven electromagnetic radiation sampling data of an electromagnetic radiation monitoring area; generating regularly distributed preset points within the sampling data range according to the expected nearest neighbor short distance; dividing the preset points into multiple layers from the outside to the inside according to the Ripley’s K method; using the IDW extended spatial interpolation method for the preset points of the multiple layers to generate the electromagnetic radiation value of each preset point; the electromagnetic radiation values of each preset point are used to construct an electromagnetic radiation spatial field. This scheme improves the IDW interpolation accuracy, and further improves the accuracy of constructing the electromagnetic radiation spatial field with non-uniform sampling point distribution.

[0181] Those skilled in the art should understand that the embodiments of the present invention can be provided as a method, a system, or a computer program product. Therefore, the present invention can take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present invention can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0182] The present invention is described with reference to the flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each flow and / or block in the flowcharts and / or block diagrams, and the combination of flows and / or blocks in the flowcharts and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to the processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing devices to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing devices generate means for implementing the functions specified in Figure 1 one or more flows and / or blocks Figure 1 one or more blocks.

[0183] These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to work in a particular manner, such that the instructions stored in the computer-readable memory produce a manufacture including an instruction device that implements the functions specified in one or more processes and / or blocks Figure 1 in the process Figure 1 or blocks or multiple blocks or multiple blocks

[0184] These computer program instructions can also be loaded onto a computer or other programmable data processing apparatus, such that a series of operational steps are performed on the computer or other programmable apparatus to produce a computer-implemented process, whereby the instructions executed on the computer or other programmable apparatus provide steps for implementing the functions specified in one or more processes and / or blocks Figure 1 in the process Figure 1 or blocks or multiple blocks or multiple blocks

[0185] The specific embodiments described above further elaborate on the objectives, technical solutions, and beneficial effects of the present invention. It should be understood that the above are only specific embodiments of the present invention and are not intended to limit the protection scope of the present invention. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principles of the present invention shall be included within the protection scope of the present invention.

Claims

1. An electromagnetic radiation inverse distance weighted extended space interpolation method, characterized in that, Including: Obtaining non-uniform electromagnetic radiation sampling data of an electromagnetic radiation monitoring area; Generating regularly distributed preset points within the range of the sampling data according to the expected nearest neighbor close distance; Dividing the preset points into multiple layers from the outside to the inside according to Ripley’s K method, which includes: calculating the observed K value curve and the predicted K value curve using the Ripley’s K function; calculating the maximum aggregation distance of the electromagnetic radiation sampling data; the maximum aggregation distance is the distance corresponding to the largest positive difference between the observed K value curve and the predicted K value curve; using the maximum aggregation distance as a standard, dividing the regularly distributed preset points into multiple layers from the outside to the inside, where the preset points of the first layer surround the preset points of the second layer, the preset points of the second layer surround the preset points of the third layer, and so on; Generating the electromagnetic radiation value of each preset point by using the IDW extended space interpolation method for the preset points of the multiple layers, which includes: generating the electromagnetic radiation value of the preset points of the first layer according to the IDW method using the electromagnetic radiation sampling data; adding the generated preset points of the first layer to the sampling data to generate new sampling data; generating the electromagnetic radiation value of the preset points of the second layer according to the IDW method using the new sampling data; each time the electromagnetic radiation value of a layer of preset points is generated, it is added to the sampling data for generating the electromagnetic radiation value of the next layer of preset points, and this is repeated continuously until the electromagnetic radiation value of each layer of preset points is calculated; the electromagnetic radiation value of each preset point is used to construct an electromagnetic radiation space field.

2. The electromagnetic radiation inverse distance weighted extended spatial interpolation method according to claim 1, characterized in that Generating regularly distributed preset points within the range of the sampling data according to the expected nearest neighbor close distance, including: Calculating the maximum circumscribed rectangle of the sampling data; Calculating the expected nearest neighbor close distance of the sampling data, using the interval between each preset point as the expected nearest neighbor close distance, and generating regularly distributed points within the maximum circumscribed rectangle; Eliminating the points located outside the range of the sampling data from the regularly distributed points to obtain the preset points located within the range of the sampling data as the final regularly distributed preset points.

3. The electromagnetic radiation inverse distance weighted extended spatial interpolation method according to claim 1, wherein Generating the electromagnetic radiation value of the preset points of the first layer according to the IDW method using the electromagnetic radiation sampling data, including generating the electromagnetic radiation value of the preset points of the first layer according to the following formula: Among them, g represents a preset point, s represents a sampling data point, gele(g) is the electromagnetic radiation value of the preset point, q represents the quantity closest to the preset point g, and ele i is the electromagnetic radiation value of the q sampling data points s closest to the preset point g i point, and d i represents the distance between the i-th point and the preset point among the q data points closest to the preset point g.

4. An electromagnetic radiation inverse distance weighted extended space interpolation device, characterized in that, Including: An acquisition unit for acquiring non-uniform electromagnetic radiation sampling data of an electromagnetic radiation monitoring area; A generation unit for generating regularly distributed preset points within the range of the sampling data according to the expected nearest neighbor close distance; A layering unit for dividing the preset points into multiple layers from the outside to the inside according to Ripley’s K method, which includes: calculating the observed K value curve and the predicted K value curve using the Ripley’s K function; calculating the maximum aggregation distance of the electromagnetic radiation sampling data; the maximum aggregation distance is the distance corresponding to the largest positive difference between the observed K value curve and the predicted K value curve; using the maximum aggregation distance as a standard, dividing the regularly distributed preset points into multiple layers from the outside to the inside, where the preset points of the first layer surround the preset points of the second layer, the preset points of the second layer surround the preset points of the third layer, and so on; An interpolation unit, which is used to generate the electromagnetic radiation values of each preset point by using the IDW extended space interpolation method for the preset points at multiple levels, includes: generating the electromagnetic radiation values of the preset points in the first layer according to the IDW method by using the electromagnetic radiation sampling data; adding the generated preset points in the first layer to the sampling data to generate new sampling data; generating the electromagnetic radiation values of the preset points in the second layer according to the IDW method by using the new sampling data; each time the electromagnetic radiation values of a layer of preset points are generated, they are added to the sampling data for generating the electromagnetic radiation values of the next layer of preset points, and the process is repeated continuously until the electromagnetic radiation values of each layer of preset points are calculated; the electromagnetic radiation values of each preset point are used to construct an electromagnetic radiation space field.

5. The electromagnetic radiation inverse distance weighted extended spatial interpolation device according to claim 4, characterized in that, The generating unit is specifically used for: Calculating the maximum bounding rectangle of the sampling data; Calculating the expected nearest neighbor distance, taking the interval between each preset point as the expected nearest neighbor distance, and generating regularly distributed points within the maximum bounding rectangle; Removing the points located outside the sampling data range from the regularly distributed points to obtain the preset points located within the sampling data range as the finally regularly distributed preset points.

6. A computer device, comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the method according to any one of claims 1 to 3.

7. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program, and when the computer program is executed by the processor, it implements the method according to any one of claims 1 to 3.

8. A computer program product, characterized in that, The computer program product includes a computer program, and when the computer program is executed by the processor, it implements the method according to any one of claims 1 to 3.

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