An electromagnetic radiation geographical environment mapping method, device, computer-readable storage medium and computer equipment for sampling along urban roads

Abstract

The present invention discloses a method for mapping the electromagnetic radiation geographical environment along urban roads. The method for mapping the electromagnetic radiation geographical environment along urban roads includes generating grid points within the sampling range and dividing the grid points into layers; using an iterative method to continuously generate the values of each layer of grid points from the outer layer of sampling points inward according to the radial basis spatial interpolation method; calculating the interpolation accuracy using the obtained grid point values and sampling points and generating an electromagnetic radiation geographical environment map; having the advantages of improving the interpolation accuracy of the radial basis spatial interpolation method for sampling data along urban roads and making the visualization of the urban electromagnetic radiation map more reasonable, etc.

Description

Technical Field

[0001] The present invention relates to the technical field of electromagnetic radiation spatial interpolation mapping methods, and in particular, to an electromagnetic radiation geographical environment mapping method, device, computer-readable storage medium, and computer device for sampling along urban roads. Background Art

[0002] Electromagnetic radiation geographical environment mapping usually first collects discrete electromagnetic radiation information, and then visualizes it based on a certain spatial interpolation method. The radial basis spatial interpolation method can be used for electromagnetic radiation geographical environment mapping. However, urban electromagnetic radiation data is mainly collected along urban roads. There is a problem of lack of sampling points in the central area of the sampled data due to the existence of buildings, which leads to low radial basis interpolation accuracy and unreasonable visualization results.

[0003] In summary, in the prior art, how to solve the problem of low interpolation accuracy caused by the lack of sampling data in the central area of the sampling data and improve the accuracy of electromagnetic radiation geographical environment mapping is a technical problem that needs to be solved urgently.

[0004] Therefore, providing an electromagnetic radiation geographical environment mapping method, device, computer-readable storage medium, and computer device for sampling along urban roads can solve the above problems. Summary of the Invention

[0005] The technical problem to be solved by the present invention is how to solve the problem of low interpolation accuracy caused by the lack of sampling data in the central area of the sampling data and improve the accuracy of electromagnetic radiation geographical environment mapping. Therefore, an electromagnetic radiation geographical environment mapping method, device, computer-readable storage medium, and computer device for sampling along urban roads are provided. The electromagnetic radiation geographical environment mapping method for sampling along urban roads includes:

[0006] Generate grid points within the sampling range and layer the grid points;

[0007] Using an iterative method, continuously generate the values of each layer of grid points from the outer-layer sampling points to the inner layer according to the radial basis spatial interpolation method;

[0008] Calculate the interpolation accuracy using the obtained grid point values and sampling points and claim an electromagnetic radiation geographical environment map.

[0009] Further, the generating grid points within the sampling range and layering the grid points includes:

[0010] Calculate the average nearest neighbor distance between each point in the electromagnetic radiation data;

[0011] Set the density of the grid points according to the average nearest neighbor distance;

[0012] Set the layering distance according to twice the average nearest neighbor distance;

[0013] Layer the grid points from the outside to the inside according to the layer distance.

[0014] Further, using the iterative method, generate the values of each layer of grid points layer by layer from the outer sampling points inward according to the radial basis space interpolation method, including:

[0015] Perform iterative operations, and obtain the electromagnetic radiation attribute values of the outermost layer of grid points according to the radial basis space interpolation method and the discrete electromagnetic radiation data;

[0016] Obtain the electromagnetic radiation attribute values of the next layer of grid points according to the electromagnetic radiation measurement of the outermost layer of grid points and the discrete electromagnetic radiation data;

[0017] Update the interpolation data point set until the attribute values of the last layer of grid points are generated.

[0018] Further, calculate the interpolation accuracy using the obtained grid point values and sampling points and generate an electromagnetic radiation geographical environment map, including:

[0019] Set the form of the basis function of the radial basis space interpolation method;

[0020] Set the shape parameters of the radial basis space interpolation method;

[0021] The accuracy of the electromagnetic radiation geographical environment map.

[0022] Further, the accuracy of the electromagnetic radiation geographical environment map includes:

[0023] Take out a point from the interpolation data set and calculate the estimated value of this point using the remaining data points;

[0024] Calculate the error value of this point according to the estimated value;

[0025] Keep looping until the error values of all sampling points are calculated, and finally calculate the interpolation accuracy.

[0026] On the other hand, the present invention also provides an electromagnetic radiation geographical environment mapping device for sampling along urban roads, and the device includes:

[0027] A grid point generation and layering unit for generating grid points and layering the sampling area range;

[0028] A layering interpolation unit for generating the electromagnetic radiation attribute values of grid points layer by layer;

[0029] An accuracy evaluation unit for calculating the interpolation error accuracy;

[0030] An electromagnetic radiation map construction unit for visualizing the electromagnetic radiation geographical environment map.

[0031] Further, the grid point generation and layering unit includes:

[0032] A grid point generation module, configured to generate grid points with a certain density within the research area;

[0033] A grid point layering module, configured to layer the grid points from the outside to the inside, and divide the grid points into different layers through the convex hull of the sampling point set and the nearest neighbor distance;

[0034] The layering interpolation unit includes:

[0035] An interpolation data storage module, configured to store the original sampling points and the grid points generated by interpolation, and the data used for interpolation.

[0036] A layering interpolation module, configured to continuously generate grid points by layering interpolation of the sampling data.

[0037] Further, it further includes: presetting the morphological parameters in the radial basis space interpolation method.

[0038] On the other hand, the present invention also provides a computer-readable storage medium, storing a computer program, and the readable storage medium stores the method as described above.

[0039] On the other hand, the present invention provides a computer device, the computer device includes a memory, a processor, and a computer program stored on the memory and executable on the processor, and the processor executes the computer program to implement the steps of the method as described above.

[0040] Implementing the present invention has the following beneficial effects:

[0041] 1. The electromagnetic radiation geographical environment mapping method and device for sampling along urban roads provided by the present invention are based on a discrete sampling data point set with coordinate information and electromagnetic radiation information. Then, grid points are generated and the grid points are layered. The electromagnetic radiation values of the grid points are interpolated from the outside to the inside in an iterative manner. Finally, an urban electromagnetic radiation map is generated using the sampled discrete electromagnetic radiation points and the grid points generated by interpolation. The present invention solves the problem of low accuracy of the radial basis space interpolation method caused by the lack of central sampling data due to sampling along urban roads. It is a radial basis space interpolation method for generating electromagnetic radiation maps for sampling along urban roads and has high accuracy, which can improve the interpolation accuracy of the radial basis space interpolation method for sampling data along urban roads and make the visualization of the urban electromagnetic radiation map more reasonable. Description of the Drawings

[0042] Figure 1 It is a schematic flowchart of the electromagnetic radiation mapping method for sampling along urban streets in the embodiment of the present invention;

[0043] Figure 2Schematic flowchart of step 01 of the electromagnetic radiation mapping method in an embodiment of the present invention;

[0044] Figure 3 Schematic flowchart of step 02 of the electromagnetic radiation mapping method in an embodiment of the present invention;

[0045] Figure 4 Schematic flowchart of the electromagnetic radiation mapping method for sampling along urban streets in a specific application example of the present invention;

[0046] Figure 5 Mind map of the electromagnetic radiation mapping method for sampling along urban streets in a specific application example of the present invention;

[0047] Figure 6 Schematic diagram of the spatial distribution of discrete electromagnetic radiation sampling points in a specific application example of the present invention;

[0048] Figure 7 Schematic diagram of the grid hierarchical structure in a specific application example of the present invention;

[0049] Figure 8 Schematic diagram of the electromagnetic radiation map in a specific application example of the present invention;

[0050] Figure 9 Schematic diagram of the structure of the electromagnetic radiation geographical environment mapping method device for sampling along urban streets in an embodiment of the present invention;

[0051] Figure 10 Schematic diagram of the grid hierarchical unit in an embodiment of the present invention;

[0052] Figure 11 Schematic diagram of the hierarchical interpolation unit in an embodiment of the present invention;

[0053] Figure 12 Schematic diagram of the structure of the electronic device in an embodiment of the present invention. Detailed implementation manners

[0054] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the following clearly and completely describes the technical solutions in the embodiments of the present invention with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are some but not all of the embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts fall within the scope of protection of the present invention.

[0055] In the prior art, when performing spatial interpolation on electromagnetic radiation data sampled along a road, the interpolation accuracy is low due to the lack of a certain amount of sampling points in the center of the sampling area. Based on this, an embodiment of the present invention provides a specific implementation manner of an electromagnetic radiation geographical environment mapping method for sampling along an urban road. Refer to Figure 1 , and the method specifically includes the following contents:

[0056] Step 01: Set grid points within the sampling area and layer the grid points.

[0057] Step 02: Interpolate to generate grid point attribute values by using the layer-by-layer radial basis interpolation method.

[0058] Step 03: Generate an urban electromagnetic radiation map by using the radial basis spatial interpolation method, the generated grid points, and the original sampling points.

[0059] As can be seen from the above description, the electromagnetic radiation production and generation method for sampling along an urban road provided by the embodiment of the present invention is based on a discrete set of sampling data points with coordinate information and electromagnetic radiation attributes. Then, a certain range of grid points are arranged within the research area, and the grid points are layered at a certain interval distance. The sampling point data is used to interpolate layer by layer from the outermost grid points inward. The grid points generated by each layer of interpolation are added to the sampling point data until the electromagnetic radiation attribute values of the last layer of grid points are interpolated and generated. Finally, an electromagnetic radiation map is generated by using the radial basis interpolation method, the grid points, and the sampling points. The present invention solves the problem of low interpolation accuracy caused by the vacancy of the sampling center, and is a radial basis spatial interpolation method with high interpolation accuracy for the urban electromagnetic radiation environment.

[0060] In summary, the electromagnetic radiation geographical environment mapping method for sampling along an urban road provided by the embodiment of the present invention can solve the problem that the interpolation accuracy is affected due to the lack of data points inside the sampling area, thereby making the urban electromagnetic radiation mapping accuracy higher.

[0061] In one embodiment, refer to Figure 2 , and Step 01 includes:

[0062] First, make a symbol description: Denote P = { p 1, p 2,… p i ,…, p n , i = 1, 2, 3,…, n} as the discrete point data set of electromagnetic radiation sampled along the street, where p i represents the data of a certain electromagnetic radiation sampling point, n is the number of sampling electromagnetic radiation point data, p 1 represents as (px 1, py 1, pele 1), px 1、 py 1 are respectively the component values of point p 1 on x and y coordinates, and pele 1 is p the electric field / magnetic field intensity value sampled at point 1; similarly p 2 is expressed as ([[]] px 2, py 2, pele 2), px 2、 py 2 are respectively the component values of point p 2 on x and y coordinates, and pele 1 is p the electric field / magnetic field intensity value sampled at point 2; p i is expressed as ([[]] px i , py i , pele i ), px i and py i are respectively the component values of point p i on x and y coordinates, and pele i is p i the electric field / magnetic field intensity value sampled at point

[0063] Let G = { g 1, g 2,…, g i ,…, g m , i = 1, 2, …, m} be the generated grid point data set, where g i represents a certain grid point, m is the number of generated grid points, and g 1 is expressed as ([[]] gx 1, gy 1, ele 1), gx 1、 gy 1 are respectively the component values of point g 1 on x and yComponent values on the coordinate ele 1 is g The electric / magnetic field intensity value of 1 grid point, which is generated by the spatial interpolation method; similarly g i Expressed as ( gx i , gy i , ele i ), [[ID=4l]]gx i 、 gy i Are respectively the point g i On x 、 y Component values on the coordinate ele i Is g i The electric / magnetic field intensity value of the grid point, which is generated by the spatial interpolation method.

[0064] Step 0101: Generate the study area range.

[0065] Specifically, according to the sampling data set P, obtain the convex hull polygon D = { d 1, d 2,…, d i ,…, d o , i = 1,2,…, o}, D is the polygon area enclosed by the sampling data set P d i Is the vertex coordinate of the convex hull polygon D o Is the number of vertices of the convex hull polygon D d 1 is expressed as ( dx 1, dy 1), dx 1 and dy 1 are respectively expressed as d 1 on x 、 y Component values.

[0066] Step 0102: Calculate the average nearest neighbor distance of the sampling data set.

[0067] Specifically, use the x 、 y Coordinates of each point in the sampling data set P to calculate the nearest neighbor distance of each point, and then calculate the average nearest neighbor distance H of the sampling data set P. Set the value of H as in Equation (1).

[0068] (1)

[0069] Set H to the nearest neighbor distance of the data set P, which represents the distance from each data point to its nearest point, and int means taking the integer.

[0070] Step 0103: Layout of regular grid points.

[0071] Specifically, set the interval of the regular grid points q , and set the value of q through Equation (2).

[0072] (2)

[0073] H is the nearest neighbor distance of the data set P. Calculate the size of the coordinate range of the sampled data set P, including: pxmin , pymin , pxmax , pymax . Generate a grid point data set G with an interval of q according to the size range of the coordinate range, and at the same time exclude the grid points not within the convex hull polygon D. Finally, obtain the grid point data set G, and the range of the grid data set G is within the convex hull polygon D. g , while excluding the grid points not within the convex hull polygon D, and finally obtaining the grid point data set G, and the range of the grid data set G is within the convex hull polygon D.

[0074] Step 0104: Laying of regular grid points in layers.

[0075] Specifically, according to the convex hull polygon D generated by the sampled data point set P and the vertex coordinates of the polygon D { d 1, d 2,…, d i ,…, d o , i =1,2,…, o}, the convex hull polygon D shrinks inward at an interval distance of 4*H to form a new convex hull polygon D1 = { d’ 1, d’ 2,…, d’ i ,…, d’ o , i =1,2,…, o}, d’Denote the vertex coordinates of the newly generated polygon D1. The grid points between polygon D and D1 are divided into the first layer L1. Then, polygon D1 shrinks inward at an interval distance of 4*H to form a new convex hull polygon D2. The grid points between polygon D1 and D2 are divided into the second layer L2. And so on, the newly generated convex hull polygon D i Each time it shrinks inward, the grid points are divided into a new layer L i until the number of remaining grid points is less than H, then the layering stops. Finally, the grid points are divided into L layers, L = {L1, L2, …, L i , … L k , k = 1, 2, …, k}, k where L is the number of layers, L1 represents the first layer, and L k represents the k layer.

[0076] The specific process is as follows:

[0077] 1. Generate the convex hull D of the study area range;

[0078] 2. Calculate the nearest neighbor distance H of the sampling point dataset P;

[0079] 3. Generate regular grid points at an interval of q and remove the grid points outside the range of the convex hull D to form a new grid point dataset G;

[0080] 4. The convex hull D continuously shrinks inward at an interval of 4*H, and the grid points are divided into L layers.

[0081] Finally, the grid points are divided into L layers, and each layer contains a different number of grid points.

[0082] In one embodiment, referring to FIG. 3, step 02 specifically includes:

[0083] First, make a symbol description. Denote S as the interpolation dataset. In the initial state, S contains the original sampling dataset P. G L1 ={ g 1, g 2, …, g i, … g j , i = 1, 2, …, j}, G L1 represents the grid points in the first layer L1, j represents the number of grid points in the first layer, g i ={ x i ,y i , ele i} represents the i th point at this layer, x i and y i represent the values on the x axis and y axis components, representing the coordinates of this point. ele i represents the attribute value of the grid point, which is calculated by the interpolation method; G Lk ={ g 1, g 2,…, g i ,… g j , i =1,2,…, j}, G Lk represents the grid points within the k th layer L k . g i ={ x i , y i , ele i} represents the i th point at this layer, x i and y i represent the values on the x axis and y axis components, representing the coordinates of this point. ele i represents the attribute value of the grid point, which is calculated by the interpolation method.

[0084] Step 0201: Interpolate to generate the electromagnetic radiation attribute values of the grid points in the first layer.

[0085] Specifically, first initialize the interpolation data set S, S = {P}. P represents the sampling data set, and the interpolation data set S contains the sampling data set P during initialization. Using the radial basis interpolation method, interpolate the interpolation data set S to generate the attribute values of the grid points G L1 of ele L1 , and calculate the value of ele L1 through equations (3)-(5).

[0086] (3)

[0087] (4)

[0088] (5)

[0089] represents the basis function in the radial basis function interpolation method, where a is the shape parameter of the basis function, used to adjust the shape of the basis function. It refers to the Euclidean distance, representing the point x k and x i the Euclidean distance between them. c i is the coefficient of the basis function with the center point at x i The coefficient is obtained by solving the linear equation of formula (3). Substitute the coefficient c i into equation (5), and the electromagnetic radiation attribute value of the grid points in the first layer L1 can be obtained. c i

[0090] Step 0202: Interpolate the electromagnetic radiation attribute values of the grid points in the second layer.

[0091] Specifically, add the dataset G of the grid points in the first layer interpolated in step 0201 L1 to the interpolation dataset S. That is, use the radial basis interpolation method to interpolate the attribute value ele L2 of the grid points G L2 in the second layer through equations (3)-(5), and calculate the value of ele L2 .

[0092] Step 0203: Continue to interpolate the values of the grid points in the next layer until the values of the grid points in the last layer are calculated.

[0093] Specifically, add the dataset GL2 of the grid points in the second layer interpolated in step 0202 to the interpolation dataset S. That is, use the interpolation dataset S to interpolate the attribute value ele L3 of the grid points G L3 in the third layer through equations (3)-(5), and calculate the value of ele L3 .

[0094] Step 0204: Generate the final interpolation dataset S.

[0095] Specifically, step 0203 is a loop mode. Starting from the grid points in one layer, after interpolating the grid points in the first layer by the interpolation dataset S, add the grid points in this layer to the interpolation dataset S, and then use the interpolation dataset to interpolate the values of the grid points in the next layer. Keep looping like this. When the grid points G in the last layer​Lk After the value is calculated, it is added to the interpolation data set S, and the final interpolation data set S is as follows.

[0096] The specific process is as follows:

[0097] 1. Initialize the interpolation data set S, S = {P};

[0098] 2. Let j = 1;

[0099] 3. Generate the values G of the grid points at the j-th layer using the interpolation data set S Lj ;

[0100] 4. j = j + 1, and add the grid point G Lj to the interpolation data S, that is;

[0101] 5. Continuously loop step 4 until j = k , that is, the value of the last grid point is calculated;

[0102] 6. Obtain the final interpolation data set S = {P, G L1 , G L2 , …, G Lk}.

[0103] In one embodiment, the electromagnetic radiation geographical environment mapping method further includes:

[0104] Step 0205: Calculate the interpolation accuracy

[0105] Specifically, calculate the accuracy E of the sampling data set P in the interpolation data set S = {P, G}, and use e to represent the interpolation accuracy. Take a point p i = { x i , y i , pele i} from the sampling data set P, then the interpolation data set S* = {P - p i , G}, P - p i represents the remaining point set after taking the i -th point from the data set P, p i The pele i of the point p i is the electromagnetic radiation attribute value of this point. Use the interpolation data set S* to calculate the pele interpolation result of the point pelei *, and then use the interpolation result pele i * to subtract the original electromagnetic radiation value pele i to obtain the interpolation error at this point e i . If there are n points in the sampling data set P, then n values of e i will be calculated. Then, the accuracy E of the sampling data set P is calculated through formula (6).

[0106] (6)

[0107] The process is as follows:

[0108] 1. Set i = 1;

[0109] 2. Take p i from the interpolation data set S. Then, the interpolation data set S becomes S* = {P - p i , G};

[0110] 3. Calculate the interpolation value p i of pele i *, and calculate the error p i at point e i ;

[0111] 4. i = i + 1;

[0112] 5. Repeat steps 2 - 4 until the interpolation errors of all points in the sampling data set P are calculated;

[0113] 6. Calculate the sampling point error accuracy E according to formula (6).

[0114] As can be seen from the above description, the electromagnetic radiation geographical environment mapping method provided by the embodiments of the present invention is based on a discrete set of sampling data points with coordinate information and electromagnetic radiation information. Then, the convex hull polygon of the sampling data point set is obtained, and regular grid points are generated within the convex hull polygon, and the grid points are stratified. Then, interpolation is started from the first layer of grid points using the sampling data point set. After the interpolation of each layer of grid points is completed, the grid points are added to the sampling data set to continue interpolating the next layer of grid points until all the grid points are interpolated. Then, an electromagnetic radiation geographical environment map is generated using the sampling data set and the grid point data set. The electromagnetic radiation data sampled along the urban road has low accuracy and unreasonable interpolation results in the central area when constructing the electromagnetic radiation map using the radial basis interpolation method because there are few sampling points in the center. The present invention uses a hierarchical inward interpolation method to generate the electromagnetic radiation map, avoiding the problem of low interpolation accuracy caused by few sampling points, especially the lack of central sampling points. It is a hierarchical radial basis spatial interpolation method with high accuracy for sampling along urban roads.

[0115] In summary, the electromagnetic radiation geographical environment mapping method provided by the embodiments of the present invention uses a hierarchical radial basis spatial interpolation method, making the generated electromagnetic radiation map have higher accuracy.

[0116] To further illustrate the solution, the present invention provides a specific application example of the electromagnetic radiation map method for sampling along urban roads. The specific application example specifically includes the following content. See Figure 4 and Figure 5 .

[0117] This specific application example selects 150 discrete electromagnetic radiation sampling point sets P = {(1301864.6187, 3486398.8516, 0.131), (1303012.6322, 3485520.7513, 0.259), …, (1301881.212, 3486432.768, 0.164)} sampled along the road within a certain area.

[0118] Step1: Load 150 discrete electromagnetic radiation sampling points.

[0119] The loading result (spatial distribution characteristics) is as Figure 6 shown.

[0120] Step2: Grid point layout and stratification

[0121] When performing hierarchical radial basis interpolation, let the research area range be D, the regular grid points be G, the stratification interval be H, and the stratification set be L.

[0122] 2.1. Obtain the research area range D. Based on the sampling data set P, obtain the vertex coordinates of the convex hull polygon d = {(1303539.555, 3485670.373), (1303277.706, 3486638.2313), (1303238.3911, 3486748.3088), (1303181.325, 3486852.4599), (1303115.442, 3486941.669), (1303045.612, 3487012.9,), (1302711.715, 3487331.09), (1301704.036, 3486939.125), (1302239.381, 3485241.941)}. There are 9 points in total. The range enclosed by these 9 vertices is the research area range D.

[0123] 2.2. Obtain the average nearest neighbor distance H of the sampling point set. Calculate the nearest neighbor distance H through the following formula (7);

[0124] (7)

[0125] represents the distance from each point data to its nearest point. int represents taking an integer. The calculated H is 42.

[0126] 2.3. Obtain the sampling data set according to the sampling data set x , y the maximum and minimum values of the coordinates ymin = 3485241.9413, ymax = 3487331.08970000, xmin = 1301665.3464, xmax = 1303539.5553, calculate y The interval of the maximum and minimum values is 2089.1483, x The maximum and minimum interval is 1874.2088. Set the interval of the regular grid points to the average nearest neighbor distance 42. According to the regular grid point interval, y the maximum and minimum value intervals and xThe maximum-minimum interval generates grid points \(G =\{(1301665.3464, 3485241.9413),(1301665.3464, 3485286.9413),…,(1303510.3464,3487311.9413)\}\), a total of 2250 grid points. Grid points not within the research scope \(D\) are excluded, and finally the grid points \(G=\{(1301710.3464, 3486816.9413),(1301710.3464, 3486861.9413),…,(1303510.3464,3485736.9413)\}\) are generated. The final grid data set \(G\) has a total of 1407 grid points.

[0127] 2.4. Set the hierarchical interval to \(4\times H = 168\) according to the average nearest neighbor distance \(H = 42\). Shrink inward from the research area \(D\) at an interval of 168 to form a new area \(D1=\{(1303147.85927795,3486473.14362536),(1303082.94071555,3486682.395946),(1303039.44349524,3486761.78254053),(1302987.266,3486832.433),(1302927.645,3486893.251),(1302673.364,3487135.569),(1301876.307,3486822.8),(1302344.586,3485453.494),(1303335.744,3485780.099)\}\). The area between \(D\) and \(D1\) is used as the first layer \(L1\), and the grid points in this layer are divided into the first-layer grid points \(G\) L1={(1301707.346, 3486837.941), (1301707.346, 3486879.941), …, (1303513.346, 3485745.941)}, a total of 519 points. The research area D1 shrinks inward at an interval of 168 to form a new research area D2={(1302927.49, 3486616.483), (1302897.562, 3486671.105), (1302859.091, 3486723.197), (1302809.677, 3486773.602), (1302635.014, 3486940.049), (1302087.268, 3486725.111), (1302449.791, 3485665.047), (1303131.933, 3485889.826), (1302986.505, 3486426.259)}. The area between the ranges D1 and D2 is used as the second layer L2, and the grid points in this layer are divided into the second layer grid points G L2 ={(1301917.346, 3486711.941), (1301917.346, 3486753.941), …, (1303303.346, 3485871.941)}, a total of 409 points. This process is continuously looped, and finally the research area is divided into 5 layers, as Figure 7 shown, the fifth layer grid points G L5 ={(1302547.346, 3486459.941), (1302547.346, 3486501.941), …, (1302715.346, 3486123.941)}, a total of 20 points.

[0128] Step3: Layered radial basis interpolation.

[0129] Denote the interpolation data set as S, the sampling point set as P, and the grid point set of each layer as G.

[0130] 3.1. Initialize the interpolation data set S. The data S includes the sampling point set P, that is, S = {P} = {(1301864.6187, 3486398.8516, 0.131), (1303012.6322, 3485520.7513, 0.259), …, (1301881.212, 3486432.768, 0.164)}.

[0131] 3.2. Let k = 1;

[0132] 3.3. Interpolate the values of the grid points in the first layer: Interpolate the grid points G in the first layer using the interpolation data set L1The values, calculated by Equations (8)-(10), are = {0.1599, 0.1495, …, 0.5604}, a total of 519 values.

[0133] (8)

[0134] (9)

[0135] (10)

[0136] Then the grid points G of the first layer L1 = {(1301707.346, 3486837.941, 0.1599), (1301707.346, 3486879.941, 0.1495), …, (1303513.346, 3485745.941, 0.5604)}.

[0137] 3.4. k = k + 1, and add the interpolated grid points G of the first layer L1 to the data set S, and update the interpolated data set S, then S = {P, G L1}.

[0138] 3.5. Using the updated interpolated data set S, interpolate to generate the values of the grid points of the second layer, calculated by Equations (8)-(10) to be = {0.0362, 0.014, …, 0.423}, a total of 409 values. Then the grid points GL2 of the second layer = {(1301917.346, 3486711.941, 0.036), (1301917.346, 3486753.941, 0.014), …, (1303303.346, 3485871.941, 0.423)}.

[0139] 3.6. k = k + 1. Add the interpolated grid points G of the first layer L2 to the data set S, and update the interpolated data set S, then S = {P, G L1 , G L2}.

[0140] 3.7. Continuously repeat steps 3.4 - 3.5. The values of the grid points interpolated each time will be added to the interpolated data set S to update the data set S until the values of the grid points of the last layer are calculated and added to the interpolated data set.

[0141] 3.8. The final interpolated data set S = {P, GL1, GL2, …, GL5} = {(1301864.6187, 3486398.8516, 0.131), (1303012.6322, 3485520.7513, 0.259), …, (1301881.212, 3486432.768, 0.164), (1301707.346, 3486837.941, 0.1599), (1301707.346, 3486879.941, 0.1495), …, (1303513.346, 3485745.941, 0.5604), …, (1302547.346, 3486459.941, 0.3114), (1302547.346, 3486501.941, 0.3384), …, (1302715.346, 3486123.941, 0.2112)}, a total of 1535 data points.

[0142] Step4: Calculate the interpolation error.

[0143] 4.1. Set i i = 1;

[0144] 4.2. Take out the i i-th p point

[0145] Pi = (1301864.6187, 3486398.8516, 0.131) from the interpolated data set S, then there are 1534 data points remaining in the interpolated data set S; p 1's interpolated value p Pi* = 0.179, and calculate p the error of point e 1 ei = 0.131 - 0.179 = -0.048;

[0146] (11)

[0147] (12)

[0148] (13)

[0149] 4.4. i i = i i + 1;

[0150] 4.5. Repeat steps 2 - 4 until the interpolation errors of all points in the sampling data set P are calculated;

[0151] 4.6. Calculate the sampling point error accuracy E = 1.5801 according to formula (14).

[0152] (14)

[0153] Step 5: Construct an electromagnetic geographical radiation map by using the interpolation data set S and the radial basis interpolation method.

[0154] The electromagnetic radiation map constructed by using the hierarchical radial basis interpolation method is as Figure 8 shown. Its interpolation accuracy is 1.5801, and the accuracy E of only using the original sampling data set for interpolation is 1.746. From the aspect of error, it can be reflected that the accuracy of the hierarchical radial basis interpolation method is higher than that of the original radial basis interpolation method, indicating that the electromagnetic radiation map constructed by the method according to this specific application example has smaller errors.

[0155] As can be seen from the above description, the electromagnetic radiation geographical environment mapping method provided by the embodiments of the present invention is based on a discrete set of sampling data points with coordinate information and electromagnetic radiation information. Then, the convex hull polygon of the sampling data point set is obtained, and regular grid points are generated within the convex hull polygon and stratified. Then, interpolation is started from the first layer of grid points by using the sampling data point set. After the interpolation of each layer of grid points is completed, the grid points are added to the sampling data set to continue interpolating the next layer of grid points until all the grid points are interpolated. Then, an electromagnetic radiation map is generated by using the sampling data set and the grid point data set. The electromagnetic radiation data sampled along the urban road has low accuracy and unreasonable interpolation results in the central area when constructing the electromagnetic radiation map by using the radial basis interpolation method because there are few sampling points in the center. The present invention adopts a method of hierarchical inward interpolation to generate an electromagnetic radiation map, avoiding the problem of low interpolation accuracy caused by few sampling points, especially the lack of central sampling points. It is a hierarchical radial basis space interpolation method with high accuracy for sampling along urban roads.

[0156] In summary, the electromagnetic radiation geographical environment mapping method for detecting along urban streets provided by the embodiments of the present invention can automatically calculate the density of regular grid points and the distance of stratification, and perform grid point interpolation from the outside to the inside in a hierarchical manner, making the generated electromagnetic radiation map have higher accuracy.

[0157] Based on the same inventive concept, the embodiments of the present application further provide an electromagnetic radiation geographical environment mapping device for sampling along urban roads, which can be used to implement the methods described in the above embodiments, such as the following embodiments. Since the electromagnetic radiation geographical environment mapping device for sampling along urban roads is similar to the electromagnetic radiation geographical environment mapping method for sampling along urban roads, the implementation of this device can refer to the implementation of the electromagnetic radiation geographical environment mapping method, and the repeated parts will not be described again. As used hereinafter, the term "unit" or "module" can be a combination of software or / and hardware that can achieve a predetermined function. Although the systems described in the embodiments are implemented with better software, the implementation of hardware or a combination of software and hardware is also possible and contemplated.

[0158] An embodiment of the present invention provides a specific implementation method for an electromagnetic radiation mapping device capable of sampling along urban roads. Refer to Figure 9 , the electromagnetic radiation mapping device for sampling along urban roads specifically includes the following:

[0159] A grid point generation and layering unit 100, which is used to generate grid points and layer the sampling area range;

[0160] A layered interpolation unit 200, which is used to generate electromagnetic radiation attribute values of grid points layer by layer;

[0161] An accuracy evaluation unit 300, which is used to calculate the interpolation error accuracy;

[0162] An electromagnetic radiation map construction unit 400, which is used for visualizing the electromagnetic radiation geographical environment map.

[0163] Preferably, refer to Figure 10 , the grid point generation and layering unit includes:

[0164] A study area range generation module 101, which is used to generate a convex hull polygon of the study area range;

[0165] A grid point generation module 102, which is used to generate a certain density of grid points within the convex hull polygon of the study area;

[0166] A grid point layering module 103, which is used to layer the grid points from the outside to the inside, and divide the grid points into different layers through the convex hull of the sampling point set and the nearest neighbor distance.

[0167] Preferably, refer to Figure 11 , the layered interpolation unit includes:

[0168] An interpolation data storage module 201, which is used to store the original sampling points and the grid points generated by interpolation, and is used for interpolation data.

[0169] A layered interpolation module 202, which is used to continuously perform layered interpolation on the interpolation data set to generate grid points.

[0170] Preferably, the electromagnetic radiation geographical environment mapping device for sampling along urban roads includes:

[0171] Preset the morphological parameters in the radial basis space method.

[0172] As can be seen from the above description, the electromagnetic radiation geographical environment mapping device provided by the embodiments of the present invention is based on a discrete set of sampling data points with coordinate information and electromagnetic radiation information. Then, the convex hull polygon of the sampling data point set is obtained, and regular grid points are generated within the convex hull polygon and stratified. Then, interpolation starts from the first layer of grid points using the sampling data point set. After the interpolation of each layer of grid points is completed, the grid points are added to the sampling data set to continue interpolating the next layer of grid points until all grid points are interpolated. Then, an electromagnetic radiation map is generated using the sampling data set and the grid point data set. Since there are few sampling points in the center of the electromagnetic radiation data sampled along urban roads, when using the radial basis interpolation method to construct an electromagnetic radiation map, the accuracy is low and the interpolation results in the central area are unreasonable. The present invention uses a method of hierarchical inward interpolation to generate an electromagnetic radiation map, avoiding the problem of low interpolation accuracy caused by few sampling points, especially the lack of central sampling points. It is a high-precision hierarchical radial basis space interpolation method for sampling along urban roads.

[0173] In summary, the electromagnetic radiation geographical environment mapping device for detecting along urban streets provided by the embodiments of the present invention can automatically calculate the density of regular grid points and the distance of stratification, and continuously interpolate the values of grid points from the outside to the inside layer by layer, making the generated electromagnetic radiation map more accurate.

[0174] The embodiments of the present application also provide a specific implementation manner of an electronic device that can implement all steps in the electromagnetic radiation mapping method in the above embodiments. See Figure 12 , and the electronic device specifically includes the following contents:

[0175] A processor 1201, a memory 1202, a communication interface 1203, and a bus 1204;

[0176] Among them, the processor 1201, the memory 1202, and the communication interface 1203 communicate with each other through the bus 1204; the communication interface 1203 is used to implement information transmission between related devices such as server-side devices, acquisition devices, and user-side devices.

[0177] The processor 1201 is used to call the computer program in the memory 1202. When the processor executes the computer program, all steps in the electromagnetic radiation mapping method in the above embodiments are implemented. For example, when the processor executes the computer program, the following steps are implemented:

[0178] Step 01: Set grid points within the sampling area and layer the grid points.

[0179] Step 02: Use the radial basis space interpolation method to layer and interpolate to generate grid point attribute values.

[0180] Step 03: Use the radial basis space interpolation method, the generated grid points, and the original sampling points to generate an urban electromagnetic radiation map.

[0181] As can be seen from the above description, the electronic device in the embodiment of the present invention is based on a discrete set of sampling data points with coordinate information and electromagnetic radiation information. Regular grid points are generated within the convex hull polygon of the sampling points, and the grid points are layered. Then, starting from the first layer of grid points, interpolation is performed using the sampling data point set. After the interpolation of each layer of grid points is completed, the grid points are added to the sampling data set to continue interpolating the next layer of grid points until all grid points are interpolated. Then, an electromagnetic radiation map is generated using the sampling data set and the grid point data set. The electromagnetic radiation data sampled along urban roads has a lack of sampling points in the center, resulting in low accuracy and unreasonable interpolation results in the central area when using the radial basis interpolation method to construct the electromagnetic radiation map. The present invention uses a method of layer-by-layer inward interpolation to generate an electromagnetic radiation map, avoiding the problem of low interpolation accuracy caused by few sampling points, especially the lack of central sampling points. It is a high-precision layer-by-layer radial basis space interpolation method for sampling along urban roads.

[0182] In summary, the electronic device of the present invention can automatically calculate the density of regular grid points and the distance of layering, and continuously layer and interpolate the values of grid points inward from the outside, making the generated electromagnetic radiation map more accurate.

[0183] An embodiment of the present application also provides a computer-readable storage medium capable of implementing all steps in the electromagnetic radiation map construction method in the above embodiment. A computer program is stored on the computer-readable storage medium. When the computer program is executed by a processor, all steps in the electromagnetic radiation map construction method in the above embodiment are implemented. For example, when the processor executes the computer program, the following steps are implemented:

[0184] Step 01: Set grid points within the sampling area and layer the grid points.

[0185] Step 02: Use the radial basis space interpolation method to layer and interpolate to generate grid point attribute values.

[0186] Step 03: Use the radial basis space interpolation method, the generated grid points, and the original sampling points to generate an urban electromagnetic radiation map.

[0187] As can be seen from the above description, the computer-readable storage medium in the embodiments of the present application is based on a discrete set of sampled data points with coordinate information and electromagnetic radiation information. Regular grid points are generated within the convex hull polygon of the sampled points and the grid points are stratified. Then, interpolation starts from the grid points of the first layer using the set of sampled data points. After the interpolation of the grid points of each layer is completed, the grid points are added to the sampled data set to continue interpolating the grid points of the next layer. Until all the grid points are interpolated, an electromagnetic radiation map is generated using the sampled data set and the grid point data set. The electromagnetic radiation data sampled along urban roads has low accuracy and unreasonable interpolation results in the central area when constructing an electromagnetic radiation map using the radial basis interpolation method because there are few sampled points in the center. The present invention uses a method of hierarchical inward interpolation to generate an electromagnetic radiation map, avoiding the problem of low interpolation accuracy caused by few sampled points, especially the lack of central sampled points. It is a hierarchical radial basis spatial interpolation method with high accuracy for sampling along urban roads.

[0188] Each embodiment in this specification is described in a progressive manner. The same or similar parts among the embodiments can be referred to each other, and each embodiment focuses on the differences from other embodiments. In particular, for the hardware + program type embodiments, since they are basically similar to the method embodiments, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiments.

[0189] The specific embodiments of this specification are described above. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recited in the claims can be executed in a different order than in the embodiments and still achieve the desired results. Additionally, the processes depicted in the drawings do not necessarily require the specific order or sequential order shown to achieve the desired results. In certain embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0190] 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.

[0191] The present invention is described with reference to the flowcharts and / or block diagrams of methods, apparatuses (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, as well as 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, such that the instructions executed by the processor of the computer or other programmable data processing devices generate means for implementing the functions specified in one or more flows and / or blocks Figure 1 in one or more flows and / or blocks Figure 1 or in one or more blocks.

[0192] 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 manufactured article including instruction means that implement the functions specified in one or more flows and / or blocks Figure 1 in one or more flows and / or blocks Figure 1 or in one or more blocks.

[0193] These computer program instructions can also be loaded onto a computer or other programmable data processing device, such that a series of operation steps are executed on the computer or other programmable device to generate a computer-implemented process, and thus the instructions executed on the computer or other programmable device provide steps for implementing the functions specified in one or more flows and / or blocks Figure 1 in one or more flows and / or blocks Figure 1 or in one or more blocks.

[0194] Specific embodiments are applied in the present invention to elaborate on the principles and implementation manners of the present invention. The description of the above embodiments is only used to help understand the method and its core idea of the present invention; at the same time, for those of ordinary skill in the art, based on the idea of the present invention, there will be changes in the specific implementation manners and application scopes. In summary, the content of this specification should not be construed as a limitation to the present invention.

Claims

1. An electromagnetic radiation geographical environment mapping method for sampling along urban roads, characterized in that Including: Generating grid points within the sampling range and dividing the grid points into layers; Specifically including: calculating the average nearest neighbor distance between each point in the electromagnetic radiation data; Setting the density of the grid points according to the average nearest neighbor distance; Setting the layer distance according to twice the average nearest neighbor distance; Continuously dividing the grid points from the outside to the inside according to the layer distance; Using an iterative method to continuously generate the values of each layer of grid points from the outer sampling points layer by layer inward according to the radial basis space interpolation method; Specifically including: performing iterative operations to obtain the electromagnetic radiation attribute values of the outermost layer of grid points according to the radial basis space interpolation method and the discrete electromagnetic radiation data; Obtaining the electromagnetic radiation attribute values of the next layer of grid points according to the electro-measured radiation of the outermost layer of grid points and the discrete electromagnetic radiation data; Updating the interpolation data point set until the attribute values of the last layer of grid points are generated; Calculating the interpolation accuracy using the obtained grid point values and sampling points and generating an electromagnetic radiation geographical environment map.

2. The electromagnetic radiation geographical environment mapping method for sampling along urban roads according to claim 1, characterized in that The calculating the interpolation accuracy using the obtained grid point values and sampling points and generating an electromagnetic radiation geographical environment map includes: Setting the form of the basis function of the radial basis space interpolation method; Setting the shape parameters of the radial basis space interpolation method; Calculating the accuracy of the electromagnetic radiation geographical environment map.

3. The method for mapping the electromagnetic radiation geographical environment by sampling along urban roads according to claim 2, wherein The calculating the accuracy of the electromagnetic radiation geographical environment map includes: Taking out a point from the interpolation data set and calculating the estimated value of this point using the remaining data points; Calculating the error value of this point according to the estimated value; Continuously looping until the error values of all sampling points are calculated, and finally calculating the interpolation accuracy.

4. An electromagnetic radiation geographical environment mapping device for sampling along urban roads, which is used for the electromagnetic radiation geographical environment mapping method for sampling along urban roads according to any one of claims 1-3, and is characterized in that, Including: A grid point generation and layering unit for calculating the average nearest neighbor distance between each point in the electromagnetic radiation data; Setting the density of the grid points according to the average nearest neighbor distance; setting the layer distance according to twice the average nearest neighbor distance; continuously dividing the grid points from the outside to the inside according to the layer distance; using an iterative method to continuously generate the values of each layer of grid points from the outer sampling points layer by layer inward according to the radial basis space interpolation method; A layer-by-layer interpolation unit for performing iterative operations to obtain the electromagnetic radiation attribute values of the outermost layer of grid points according to the radial basis space interpolation method and the discrete electromagnetic radiation data; obtaining the electromagnetic radiation attribute values of the next layer of grid points according to the electro-measured radiation of the outermost layer of grid points and the discrete electromagnetic radiation data; updating the interpolation data point set until the attribute values of the last layer of grid points are generated; An accuracy evaluation unit for calculating the interpolation error accuracy; An electromagnetic radiation map construction unit for visualizing the electromagnetic radiation geographical environment map.

5. The electromagnetic radiation geographical environment mapping device for sampling along urban roads according to claim 4, characterized in that, The grid point generation and layering unit includes: A grid point generation module for generating grid points with a certain density within the research area; A grid point layering module for dividing the grid points from the outside to the inside, and dividing the grid points into different layers through the convex hull of the sampling point set and the nearest neighbor distance; The layer-by-layer interpolation unit includes: An interpolation data storage module for storing the original sampling points and the grid points generated by interpolation and the data used for interpolation; A layer-by-layer interpolation module for continuously generating grid points by layer-by-layer interpolation of the sampling data.

6. The electromagnetic radiation geographical environment mapping device for sampling along urban roads according to claim 5, characterized in that Also including: Presetting the shape parameters in the radial basis space interpolation method.

7. A computer-readable storage medium stores a computer program, characterized in that, The readable storage medium stores the method described in any one of claims 1-3.

8. A computer device, the computer device comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, characterized in that, The processor executes a computer program to implement the steps of the method described in any one of claims 1-3.

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