A data processing system for obtaining AOI boundaries

Abstract

The application provides a data processing system for obtaining an AOI boundary, characterized in that the data processing system comprises a database, a processor and a memory storing a computer program, and realizes the following steps: traversing pixel points of a final map image to obtain a first boundary point; obtaining pixel values of all pixel points of the final map image; when the pixel value is 255, marking the access identifier of the point as 'identified', and traversing the next point; until the first boundary point is traversed; traversing the four neighborhoods of the first boundary point to determine whether the four neighborhoods of the first boundary point are boundary points until the four neighborhoods of the boundary point are boundary points, and performing second-layer traversal until the access identifiers of all pixel points are 'identified'; connecting all boundary points according to corresponding latitude and longitude to obtain a target AOI boundary, and more accurately obtaining the area of the associated AOI.

Description

Technical Field

[0001] This invention relates to the field of data processing, and more specifically to a data processing system for obtaining AOI boundaries. Background Technology

[0002] Point of Interest (POI) data is a powerful tool for identifying urban areas. Previously, point location information was commonly used to assess the concentration of various urban functions, but this neglected aspects such as the area, volume, and nature of functional zones, making it difficult to accurately reflect the reality. Area of ​​Interest (AOI) data is a valuable supplement to POI data. Third-party servers and other electronic maps have invested significant effort in defining the boundaries of various urban functions (using color coding). For example, when searching for a hospital, map software might circle a red area to represent the hospital's boundaries (along with corresponding building forms). However, many map software programs only display points of interest. Most open-source data is in POI format, but POI data lacks sufficient detail, failing to provide the area of ​​points of interest, and it's difficult to combine POI and AOI data. Current technologies often use web scraping to obtain the relationship between POI and AOI, but the results are unsatisfactory. Summary of the Invention

[0003] To address the aforementioned technical problems, the present invention adopts the following technical solution: a data processing system for acquiring AOI boundaries, wherein the data processing for acquiring AOI boundaries includes a database, a processor, and a memory storing a computer program, and when the computer program is executed by the processor, the following steps are implemented:

[0004] S301, Traverse the final map image A″ ij , obtain the first boundary point from the pixels;

[0005] S301 obtains the first boundary point by including the following steps:

[0006] S3011, Obtain the final map image A″ ij All pixels U ij ={U ij1 , ..., U ijd , ..., U ije}, U ijd It refers to A″ ij The d-th pixel, where d ranges from 1 to e, and e refers to A″. ij The total number of pixels;

[0007] S3013, Obtain pixel U ijd Pixel values;

[0008] S3015, when pixel value G(U) ijdWhen ) = 255, mark the access identifier of that point as "identified" and proceed to the next point;

[0009] S3017, when pixel value G(U) ijd When ) = 0, mark the access identifier of that point as "identified" and obtain U. ijd The pixel values ​​of the eight neighboring regions;

[0010] S3019, when U ijd When the pixel value of any eight neighboring area is equal to 255, determine U. ijd This is the first boundary point;

[0011] S303, Traverse the four neighboring regions of the first boundary point and determine whether the four neighboring regions of the first boundary point are boundary points;

[0012] S305, continue to traverse the four-neighborhood of the e-th boundary point until the four-neighborhood of the boundary point is the boundary point;

[0013] S307, perform the second traversal until the access flag of all pixels is "recognized";

[0014] S309 connects all boundary points according to their corresponding latitude and longitude to obtain the target AOI boundary.

[0015] The present invention has at least the following technical effects: by finding the first boundary point and then finding the boundary point based on the boundary point, the complete closed target AOI boundary is obtained, and the target POI and the target AOI boundary are associated, so that the boundary of the target AOI can be associated with the target POI when using the target POI, and the area of ​​the associated AOI can be obtained more accurately. Attached Figure Description

[0016] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0017] Figure 1 This is a flowchart illustrating the execution of a computer program in a data processing system for acquiring a target geographic image, as provided in an embodiment of the present invention.

[0018] Figure 2 This is a flowchart illustrating the execution of a computer program in a data processing system based on map image erosion and dilation, as provided in an embodiment of the present invention.

[0019] Figure 3 This is a flowchart of a computer program executed by a data processing system for obtaining AOI boundaries, provided as an embodiment of the present invention. Detailed Implementation

[0020] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0021] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of the invention described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or server that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or devices.

[0022] Example 1

[0023] This invention provides a data processing system for acquiring target geographic images. The system includes a database, a processor, and a memory storing a computer program. When the computer program is executed by the processor, it performs the following steps: Figure 1 As shown:

[0024] S101, Obtain the set of geographic images P = {P1, ..., P1} corresponding to the target POI. i , ..., P m}, P i ={P i1 , ..., P ij , ..., P in}, P ij =(W ij L ij ), W ij L is the width of the j-th geographic image in the set of geographic images of type i corresponding to the target POI. ij The length of the j-th geographic image in the set of geographic images of type i corresponding to the target POI;

[0025] Specifically, preceding S101 also includes:

[0026] S1, obtain parameter information of the region to be processed.

[0027] Specifically, the parameter information includes the size and shape of the region to be processed, the total number of images after division, the length X of the region to be processed, and the width Y of the region to be processed.

[0028] Preferably, the area to be processed is rectangular; more preferably, the area to be processed is square.

[0029] S2, Based on the parameter information of the area to be processed, divide the area to be processed according to a preset method, and obtain the map type list P = {P1, ..., P...} corresponding to the target POI. i , ..., P m}

[0030] In one embodiment, the image type list P = {P1, ..., P2} is obtained through the following preset method. i , ..., P m}: Divide the region to be processed into images of each type, that is, each type of image is one image, and adjacent images share the same boundary.

[0031] Among them, W ij Meets the following conditions:

[0032] W ij =W (i-1)j +W α Among them, W α This is the width growth parameter factor;

[0033] Among them, L ij Meets the following conditions:

[0034] L ij =L (i-1)j +L α , where L α This is a length growth parameter factor;

[0035] Furthermore, W α Meets the following conditions:

[0036] W ɑ =K1*W 1j K1 is the first size adjustment factor.

[0037] L α The following conditions must be met: L ɑ =K1*L 1j .

[0038] It can be understood as P i The length and width are adjusted according to the first size adjustment factor of P1, gradually increasing the length and width of the map image.

[0039] In another embodiment of the invention, W α Meets the following conditions:

[0040] W ɑ =K2*W (i-1)j K2 is the second size adjustment factor.

[0041] L α The following conditions must be met: L ɑ =K2*L (i-1)j .

[0042] Technical effect: can be understood as P i The length and width are based on P i-1 The length and width are adjusted using the second size adjustment factor, gradually increasing the length and width of the map image. This is different from directly adjusting based on P1. i-1 The adjustments are more flexible.

[0043] Among them, K2 satisfies the following condition: K2≤1.

[0044] The present invention further includes the following steps after S101:

[0045] S1011, retrieve the j-th geographic image P from the m-th type map image set. mj =(W mj L mj );

[0046] S1013, when W mj ≥X and L mj When X is greater than or equal to Y, execute S103, where X is the width of the target map image corresponding to the target POI and Y is the length of the target map image corresponding to the target POI.

[0047] Based on this, it can be understood that when W mj ≥X and L mj When ≥Y, the m-th type map image includes the entire map image corresponding to the target POI, ensuring that the largest type image includes the entire target geographic image.

[0048] S103, input P into the preset geographic image model to obtain the target geographic image corresponding to the target POI, wherein the target geographic image is a geographic image including the target POI and the boundary area corresponding to the target POI.

[0049] Specifically, in the map image model, the set of geographic images corresponding to the target POI is P = {P1, ..., P2}. i , ..., P m} and scaling index, where the scaling index is a fixed value that can be determined according to the actual situation.

[0050] Optionally, when the map image model outputs the target geographical image corresponding to the target POI, different colors can be selected for different regions according to actual needs.

[0051] Based on S101 - S103, through the iteration of the step size, the present invention enables the acquisition of overlapping map images, so that the obtained AOI boundaries can be completely presented on one map image, avoiding the situation where the AOI boundaries are on multiple map images. By obtaining the lengths and widths of different types of images according to the preset step size, there are overlapping parts between each type of image, avoiding the AOI being split into several images and avoiding obtaining incomplete AOI boundaries in the images. Therefore, by using the step size to obtain overlapping images, the complete AOI boundary can be obtained in one image, avoiding the need to splice the partial AOI boundaries obtained from multiple map images to obtain the complete AOI boundary.

[0052] Embodiment 2

[0053] The present invention further includes a data processing system based on erosion and dilation of map images. On the basis of Embodiment 1, when the computer program is executed by a processor, the following steps are implemented, as Figure 2 shown:

[0054] S201, preprocess the target geographical image to obtain a first intermediate geographical image.

[0055] Specifically, preprocessing the target geographical image includes the following steps:

[0056] S2011, perform image binarization on the target geographical image to obtain a preliminary geographical image.

[0057] Specifically, performing image binarization on the target geographical image means setting the pixel values of the pixel points of the target geographical image to 0 or 255.

[0058] In an embodiment of the present invention, image binarization is achieved through the following steps:

[0059] Obtain the pixel value list S of all pixel points of the target geographical image ij ={S ij1 , ……, S ijx1 , ……, S ijx}, S ijx1 refers to the pixel value of the x1th pixel point of the target geographical image, and the value range of x1 is from 1 to x, where x refers to the total number of pixel points in the target geographical image; when S ijx1 ≥T, S′ ijx1 =255; when S ijx1 <T, S′ ijx1 =0; where T refers to the preset gray threshold, and T can be input according to actual situations, and S′ijx1 refers to the pixel value of the x1-th pixel of the first intermediate geographic image.

[0060] Based on this, binarizing the target geographical ethical image can reduce the data volume and the computational load, making the data processing system occupy less memory and run more smoothly during operation.

[0061] In another embodiment of the invention, image binarization is achieved through the following steps:

[0062] Obtain the pixel value list S of all pixels of the target geographical image ij ={S ij1 , ……, S ijx1 , ……, S ijx}, where S ijx1 refers to the pixel value of the x1-th pixel of the target geographical image, and the value range of x1 is from 1 to x, where x refers to P ij ; for S ijx1 , take the circular area with a radius of r around S ijx1 , calculate the area average value of the circular area and denote it as T, and T satisfies the following conditions:

[0063] T = [∑ r s＝-r ∑ r t＝-r f(x + s, y + t)] / (2r + 1) 2 ;

[0064] When S ijx1 ≥ T, S' ijx1 = 255; when S ijx1 < T, S' ijx1 = 0; S' ijx1 refers to the pixel value of the x1-th pixel of the first intermediate geographic image.

[0065] Based on this, using an adaptive threshold can adopt different thresholds according to the local features of the map image, avoiding the influence of shadows or high brightness in some areas of the whole image on the preset gray threshold of the whole image, thus avoiding misjudgment in image binarization and improving the accuracy of image binarization.

[0066] S2013, delete the boundary blocks in the preliminary geographic image to obtain the first intermediate geographic image, where the boundary blocks refer to the areas in the preliminary geographic image that do not include the boundary regions corresponding to the target POI.

[0067] Specifically, the boundary blocks are obtained through the following steps:

[0068] S10, obtain the RGB value B1 of the boundary point B.

[0069] S30, obtain the RGB values ​​{B2, B3, B4} of the three neighborhoods of boundary point B.

[0070] S50, when B1 = B t At that time, B t Add to the boundary block, t=2, t=3, or t=4.

[0071] S70, Traverse B t The three neighborhoods of the corresponding boundary point.

[0072] S90: When all RGB values ​​are different, the traversal ends.

[0073] Based on S10-S90, boundary blocks are obtained from boundary points and their neighborhoods. Deleting these boundary blocks can be understood as obtaining the AOI boundary on a map image that contains the complete AOI boundary. Since the boundary blocks in the map image do not contain the complete AOI boundary, they are deleted to reduce the computational load.

[0074] S203, Dilate the first intermediate geographic image using the first convolution kernel to obtain the second intermediate image;

[0075] Specifically, those skilled in the art will know that the first convolution kernel is composed of the numbers "0" and "1", and the first convolution kernel presents various structures with a center point. The first convolution kernel of the present invention can be input according to the actual situation.

[0076] Preferably, the first convolution kernel satisfies the following condition: S 11 It is positively correlated with X*Y, S 11 X*Y represents the number of "1"s in the first convolution kernel, and X*Y represents the area of ​​the target map image.

[0077] In one embodiment of the present invention, the first convolution kernel meets the following conditions:

[0078] S 11 =R1*X*Y, where R1 is the first area correlation coefficient.

[0079] Specifically, the center point of the first convolution kernel traverses every pixel of the first intermediate map image. Among the pixels of the traversed first intermediate map image, the position of the first convolution kernel with "1" corresponds to "255". Then the position corresponding to the center point of the first convolution kernel becomes 255, thereby generating the second intermediate map image.

[0080] S205, use the second convolution kernel to perform erosion processing on the second intermediate map image to obtain the final map image;

[0081] The second convolution kernel satisfies the following condition: S 21 It is positively correlated with X*Y and S 11>S 21 S21 represents the number of "1"s in the second convolution kernel.

[0082] Specifically, those skilled in the art will know that the first and second convolution kernels have the same number of bits.

[0083] In one embodiment of the present invention, the second convolution kernel meets the following conditions:

[0084] S 21 =R²*X*Y, where R² is the second area correlation coefficient.

[0085] Optionally, R1 ≥ R2; preferably, R1 > R2.

[0086] Specifically, the center point of the second convolution kernel traverses every pixel of the second intermediate map image. In the traversed pixels of the second intermediate map image, the position of the second convolution kernel with "1" corresponds to "0", then the position corresponding to the center point of the second convolution kernel becomes "0", thereby generating the final map image.

[0087] Specifically, S205 also includes:

[0088] S2051, Obtain the number of dilated pixels A1 in the second intermediate map image;

[0089] S2053, obtain the number of eroded pixels A2 in the final map image;

[0090] S2055, A1 and A2 satisfy the following conditions:

[0091] A1-A2 = A, where A is the preset expansion width value.

[0092] Based on this, the first intermediate map image is subjected to dilation and erosion using a first convolution kernel and a second convolution kernel. The number of "1"s in the first convolution kernel is greater than the number of "1"s in the second convolution kernel. This can be understood as the range of dilation of the first intermediate map image being greater than the range of erosion of the second intermediate map image. That is, after the dilation and erosion operation, the original white area will increase to a certain extent, which can expand the white area between adjacent areas to be processed in the first intermediate map image. Or, for some special cases, such as when there is a white and black interval in the middle of the areas to be processed in the first intermediate map image, the white area can be connected after erosion and dilation, so that the AOI boundary can be identified more accurately when performing AOI boundary recognition.

[0093] Example 3

[0094] Based on Embodiment 2, when the computer program is executed by the processor, the following steps are performed on the final map image list A″: Figure 3 As shown:

[0095] S301, Traverse the final map image A′ ij , obtain the first boundary point from the pixels;

[0096] Among them, the final map image A″ ij Each pixel has an access identifier, which is used to determine A″ ij Whether the corresponding pixel is recognized, and further, whether the access identifier is "1" or "0"; this can be understood as, those skilled in the art knowing, when A′ ij Once the corresponding pixel is identified, the access flag is set to "1" or "0"; otherwise, when A″ ij When the corresponding pixel is not recognized, the access identifier is "0" or "1".

[0097] Preferably, when A″ ij Once the corresponding pixel is identified, the access flag is set to "1"; otherwise, when A′′ ij When the corresponding pixel is not recognized, the access flag is "0".

[0098] Specifically, obtaining the first boundary point includes the following steps:

[0099] S3011, Obtain the final map image A″ ij All pixels U ij ={U ij1 , ..., U ijd , ..., U ije}, U ijd It refers to A″ ij The d-th pixel, where d ranges from 1 to e, and e refers to A″. ij The total number of pixels;

[0100] S3013, Obtain pixel U ijd Pixel values;

[0101] S3015, when pixel value G(U) ijd When ) = 255, mark the access flag of that point as "1" and traverse to the next point;

[0102] S3017, when pixel value G(U) ijd When ) = 0, mark the access identifier of that point as "1" and obtain U. ijd The pixel values ​​of the eight neighboring regions;

[0103] S3019, when U ijd When the pixel value of any eight neighboring area is equal to 255, determine U. ijd This is the first boundary point;

[0104] Based on S3011-S3019, the pixels of the final map image are obtained, and it is determined whether the pixel value is the first boundary point until the first boundary point is found. At the same time, the access flag of the pixels that have been determined to be the first boundary point is marked as "1". When accessing again, the access flag can be used to skip directly without accessing, saving time and improving efficiency.

[0105] S303, Traverse the four neighboring regions of the first boundary point and determine whether the four neighboring regions of the first boundary point are boundary points;

[0106] Specifically, determine whether it is a boundary point based on S805-S807;

[0107] S305, continue to traverse the four-neighborhood of the e-th boundary point until the four-neighborhood of the boundary point is the boundary point;

[0108] S307, perform the second level of traversal until the access flag of all pixels is "1";

[0109] Specifically, the second level of traversal refers to continuing the traversal according to the first level of traversal after the first boundary point, while the first level of traversal refers to the traversal in S301-S305 to find the first boundary point and obtain the complete boundary.

[0110] Based on S301-S307, the final map image is traversed to obtain the first boundary point. The next boundary point is then found through the four neighbors of the first boundary point until a complete closed boundary is found. This method of finding boundaries through boundaries is faster and more convenient than directly traversing all pixels. At the same time, the access flag of the visited pixels is marked as "1", which facilitates faster traversal in the second layer. In addition, the second traversal can discover the case where the AOI boundary contains another AOI boundary, thus avoiding the situation where the case of AOI boundary containing another AOI boundary is not discovered. This results in a more comprehensive and complete acquisition of the boundaries of all AOIs in the final map image.

[0111] In another embodiment of the present invention, boundary points can also be obtained through the following steps:

[0112] S3031, Obtain the final map image A″ ij The width of the final map image is c*h; where c refers to the width of the final map image being c pixels and h refers to the length of the final map image being h pixels.

[0113] S3033, obtain R j i Q j i R i j Q j i ,in,

[0114] R j i =F j i+1 -F j i Q j i =F j i -F j i+1 ;R i j =K i j+1 -K j i Q j i =K j i -K j i+1 Among them, F j i+1 F refers to the pixel value of the j-th pixel in the (i+1)-th row of the final map image. j i K refers to the value of the j-th pixel in the i-th row of the final map image. j i+1 K refers to the pixel value of the j-th pixel in the (i+1)-th column of the final map image. j i It refers to the pixel value of the j-th pixel in the i-th column of the final map image;

[0115] S3035, when R j i When R is not equal to 0, j i Store in the target set; when Q j i When Q is not equal to 0, Q will be... j i Store in the target set; when R i j When R is not equal to 0, i j Store the target set; when Q j i When Q is not equal to 0, Q will be... j i Store in the target set; the target set is used to store boundary points.

[0116] S309: Connect all boundary points according to their corresponding latitude and longitude to obtain the target AOI boundary;

[0117] Specifically, the number of samples is determined based on the size of the target AOI boundary, the boundary points are sampled to obtain the target boundary points, and then connected according to the corresponding latitude and longitude to obtain the target AOI boundary.

[0118] As those skilled in the art will know, any method for obtaining a closed boundary by connecting the latitude and longitude of known points falls within the protection scope of this invention, and will not be elaborated further here.

[0119] S311, associate the target POI with the target AOI boundary;

[0120] Specifically, those skilled in the art will know that any method in the prior art that associates a target POI with a target AOI boundary is within the scope of protection of this invention, such as using an association table to associate a target POI with a target AOI boundary.

[0121] Based on this invention, firstly, map images and POIs of the area to be processed are obtained through a third-party server. During the acquisition process, an iterative method with a step size is used to obtain map images, resulting in overlapping parts of the map images. This allows the complete boundary of the target AOI to be obtained from a single map image. Dilation and erosion operations are performed on the first intermediate map image to make previously indistinct or discontinuous boundaries more apparent, facilitating the acquisition of the target AOI boundary in subsequent processes. By finding the first boundary point and then using a method of finding other boundary points based on the boundary points, the complete closed boundary of the target AOI is obtained. The target POI and the target AOI boundary are then associated, so that the boundary of the target AOI can be associated with the target POI, thus obtaining the area of ​​the associated AOI more accurately.

[0122] While specific embodiments of the invention have been described in detail by way of example, those skilled in the art should understand that the above examples are for illustrative purposes only and are not intended to limit the scope of the invention. Those skilled in the art should also understand that various modifications can be made to the embodiments without departing from the scope and spirit of the invention. The scope of the invention is defined by the appended claims.

Claims

1. A data processing system for acquiring AOI boundaries, characterized in that, The data processing for obtaining the AOI boundary includes a database, a processor, and a memory storing a computer program. When the computer program is executed by the processor, the following steps are implemented: S301, Traverse the final map image AE ij , obtain the first boundary point from the pixels; S301 obtains the first boundary point by including the following steps: S3011, Obtain the final map image (AE) ij All pixels U ij ={U ij1 , ..., U ijd , ..., U ije }, U ijd It refers to AE ij The d-th pixel, where d ranges from 1 to e, and e refers to AE. ij The total number of pixels; S3013, Obtain pixel U ijd Pixel values; S3015, when pixel value G(U) ijd When ) = 255, mark the access identifier of that point as "identified" and proceed to the next point; S3017, when pixel value G(U) ijd When ) = 0, mark the access identifier of that point as "identified" and obtain U. ijd The pixel values ​​of the eight neighboring regions; S3019, when U ijd When the pixel value of any eight neighboring area is equal to 255, determine U. ijd This is the first boundary point; S303, Traverse the four neighboring regions of the first boundary point and determine whether the four neighboring regions of the first boundary point are boundary points; S305, continue to traverse the four-neighborhood of the e-th boundary point until the four-neighborhood of the boundary point is the boundary point; S307, perform the second traversal until the access flag of all pixels is "recognized"; S309 connects all boundary points according to their corresponding latitude and longitude to obtain the target AOI boundary.

2. The data processing system for obtaining AOI boundaries according to claim 1, characterized in that, Boundary points can also be obtained through the following steps: S3031, Obtain the final map image (AE) ij The width of the final map image is c × h; where c means the width of the final map image is c pixels and h means the length of the final map image is h pixels. S3033, obtain R j i Q j i R i j Q i j ,in, R j i =F j i+1 -F j i Q j i =F j i -F j i+1 ;R i j =K i j+1 -K i j Q i j =K i j -K i j+1 Among them, F j i+1 F refers to the pixel value of the j-th pixel in the (i+1)-th row of the final map image. j i K refers to the value of the j-th pixel in the i-th row of the final map image. j i+1 K refers to the pixel value of the j-th pixel in the (i+1)-th column of the final map image. j i It refers to the pixel value of the j-th pixel in the i-th column of the final map image; S3035, when R j i When R is not equal to 0, j i Store in the target set; when Q j i When Q is not equal to 0, Q will be... j i Store in the target set; when R i j When R is not equal to 0, i j Store the target set; when Q i j When Q is not equal to 0, Q will be... i j Store in the target set; the target set is used to store boundary points.

3. The data processing system for obtaining AOI boundaries according to claim 1, characterized in that, In S301, the final map image is obtained as follows: S101, Obtain the set of geographic images P={P1, ..., P1} corresponding to the target POI. i , ..., P m }, P i ={P i1 , ..., P ij , ..., P in }, P ij =(W ij L ij ), W ij L is the width of the j-th geographic image in the set of geographic images of type i corresponding to the target POI. ij The length of the j-th geographic image in the set of geographic images of type i corresponding to the target POI; Among them, W ij Meets the following conditions: W ij =W （i-1）j +W α Among them, W α This is the width growth parameter factor; Among them, L ij Meets the following conditions: L ij =L （i-1）j +L α , where L α This is a length growth parameter factor; S103, input P into the preset geographic image model to obtain the target geographic image corresponding to the target POI, wherein the target geographic image is a geographic image including the target POI and the boundary area corresponding to the target POI.

4. The data processing system for obtaining AOI boundaries according to claim 1, characterized in that, The sampling quantity is determined based on the size of the target AOI boundary.

5. The data processing system for obtaining AOI boundaries according to claim 1, characterized in that, Following S309 is S311, which associates the target POI with the target AOI boundary.

6. The data processing system for obtaining AOI boundaries according to claim 3, characterized in that, The following steps are also included after S103: S201, Preprocess the target geographic image to obtain the first intermediate geographic image; S203, Dilate the first intermediate geographic image using the first convolution kernel to obtain the second intermediate map image; The first convolution kernel satisfies the following condition: S11 is positively correlated with X×Y, where S11 is the number of "1"s in the first convolution kernel and X×Y is the area of ​​the target map image. S205, use the second convolution kernel to perform erosion processing on the second intermediate map image to obtain the final map image; The second convolution kernel satisfies the following conditions: S21 is positively correlated with X×Y and S11>S21, where S21 is the number of "1"s in the second convolution kernel.

7. The data processing system for obtaining AOI boundaries according to claim 3, characterized in that, The following steps are also included after S101: S1011, retrieve the j-th geographic image P from the m-th type map image set. mj =(W mj L mj ); S1013, when W mj ≥X and L mj When X is greater than or equal to Y, execute S103, where X is the width of the target map image corresponding to the target POI and Y is the length of the target map image corresponding to the target POI.

8. The data processing system for obtaining AOI boundaries according to claim 3, characterized in that, W α Meets the following conditions: W ɑ =K1×W 1j K1 is the first size adjustment factor.

9. The data processing system for obtaining AOI boundaries according to claim 3, characterized in that, L α Meets the following conditions: L ɑ =K1×L 1j 。 10. The data processing system for obtaining AOI boundaries according to claim 6, characterized in that, S205 also includes: S2051, obtain the number of dilated pixels A1 in the second intermediate map image; S2053, obtain the number of eroded pixels A2 in the final map image; S2055, A1 and A2 satisfy the following conditions: A1-A2=A, where A is the preset expansion width value.

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