Virtual driving test method, device, electronic device and storage medium

Abstract

Embodiments of the application disclose a virtual road testing method and device, electronic equipment and storage medium. The method comprises: obtaining a map picture of a target area where a to-be-tested road is located from a map software, and obtaining a latitude and longitude range corresponding to the map picture. In the map picture, the to-be-tested road is of a target color and different from the background color. The original point set corresponding to the to-be-tested road is obtained from the map picture according to the target color and the latitude and longitude range. The original point set comprises pixel points of the to-be-tested road and latitude and longitude information of each pixel point. A plurality of multi-point objects are obtained from the original point set, and the road boundary of each multi-point object corresponding to a road section is determined. For each road section, MR data in the road section is obtained according to the road boundary, and the wireless signal coverage strength of the road section is determined according to the MR data. The display color of the road section is determined according to the wireless signal coverage strength, and the road section is displayed in the map with the display color. The embodiments of the application can improve the efficiency of virtual road testing.

Description

Technical Field

[0001] The embodiments of the present application relate to the field of communication technology, and in particular to a virtual drive test method, device, electronic device, and storage medium. Background Art

[0002] High-speed rail and railway road coverage is a key focus during wireless network planning and optimization. Operators have transitioned from traditional manual road testing to virtual road testing to understand wireless network coverage. A crucial step in virtualized road testing is to extract MR (Measurement Report) data from the entire network, covering the roads to be tested. This requires the precise definition of road boundary areas.

[0003] Current virtual drive testing systems require obtaining road data from third parties or manually drawing road boundaries on a map. Map software can provide road data for highways, subways, and other areas through an API (Application Programming Interface). For example, typing a road name into the map software's search box will display the road's location on the map.

[0004] However, mapping software doesn't directly provide high-speed rail and railway road data. When you enter the names of high-speed rail and railway lines in the mapping software's search box using the same method, the roads won't appear on the map, making virtual road testing impossible. Furthermore, city center roads often have numerous names, some even lacking them. Calling the mapping software API to retrieve data for all of these roads is labor-intensive, making virtual road testing inefficient. Summary of the Invention

[0005] The embodiments of the present application provide a virtual drive test method, device, electronic device, and storage medium, which help to implement virtual drive tests on high-speed railways and railways and can improve the efficiency of virtual drive tests.

[0006] To solve the above problems, in a first aspect, an embodiment of the present application provides a virtual drive test method, including:

[0007] Obtain a map image of the target area where the road to be tested is located from map software, and obtain the latitude and longitude range corresponding to the map image, wherein the road to be tested is in the target color and is different from the background color in the map image;

[0008] According to the target color and the longitude and latitude range, an original point set corresponding to the road to be measured is obtained from the map image, wherein the original point set includes pixel points corresponding to the road to be measured and longitude and latitude information corresponding to each pixel point;

[0009] Acquire a plurality of multi-point objects from the original point set, and determine a road boundary of a road segment corresponding to each of the multi-point objects;

[0010] For each of the road sections, obtaining measurement report MR data within the road section according to the road boundary of the road section, and determining the wireless signal coverage strength of the road section according to the MR data;

[0011] The display color of the road section is determined according to the wireless signal coverage strength, and the road section is displayed in the map using the display color.

[0012] In a second aspect, an embodiment of the present application provides a virtual drive test device, including:

[0013] A map image acquisition module is used to obtain a map image of the target area where the road to be tested is located from the map software, and obtain the latitude and longitude range corresponding to the map image, wherein the road to be tested is in the target color and is different from the background color in the map image;

[0014] An original point set acquisition module is used to acquire an original point set corresponding to the road to be measured from the map image according to the target color and the latitude and longitude range, wherein the original point set includes pixel points corresponding to the road to be measured and latitude and longitude information corresponding to each pixel point;

[0015] a road boundary determination module, configured to obtain a plurality of multi-point objects from the original point set and determine a road boundary of a road segment corresponding to each of the multi-point objects;

[0016] a signal strength determination module, configured to obtain, for each of the road sections, measurement report MR data within the road section according to the road boundary of the road section, and determine the wireless signal coverage strength of the road section according to the MR data;

[0017] The drive test result display module is used to determine the display color of the road section according to the wireless signal coverage strength, and display the road section in the map with the display color.

[0018] In a third aspect, an embodiment of the present application further provides an electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the virtual drive test method described in the embodiment of the present application when executing the computer program.

[0019] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the virtual drive test method disclosed in the embodiment of the present application.

[0020] The virtual drive test method, apparatus, electronic device, and storage medium provided in the embodiments of the present application obtain a map image of a target area where a road to be tested is located from map software, and obtain the latitude and longitude range corresponding to the map image. In the map image, the road to be tested is a target color that is different from the background color. Based on the target color and the latitude and longitude range, an original point set corresponding to the road to be tested is obtained from the map image. Multiple multi-point objects are obtained from the original point set, and the road boundary of the road segment corresponding to each multi-point object is determined. For each road segment, MR data within the road segment is obtained based on the road boundary, and the wireless signal coverage strength of the road segment is determined based on the MR data. The display color of the road segment is determined based on the wireless signal coverage strength, and each road segment is displayed on the map using the display color. By processing the map image, the road boundary of each section of the road to be tested can be accurately determined. Therefore, the road boundary of railways, high-speed railways, urban roads, etc. can be obtained by processing the map image, thereby realizing virtual drive testing of high-speed railways and railways. There is no need to manually mark road boundaries or obtain the boundaries of each road based on road names, which can improve the efficiency of virtual drive testing. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the following briefly introduces the drawings required for use in the embodiments or descriptions of the prior art. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0022] Figure 1 This is a flow chart of a virtual drive test method provided by an embodiment of the present application;

[0023] Figure 2 This is a schematic diagram of displaying the extracted pixel points of the road to be tested on a map in an embodiment of the present application;

[0024] Figure 3 This is a flow chart of obtaining multiple multi-point objects from an original point set in an embodiment of the present application;

[0025] Figure 4a is a schematic diagram of a dot set obtained in an embodiment of the present application;

[0026] Figure 4b is a schematic diagram of another set of points obtained with the closest point as the center of the circle in an embodiment of the present application;

[0027] Figure 4c This is an example diagram of a road boundary obtained through Buffer processing in an embodiment of the present application;

[0028] Figure 5 This is a flowchart of determining a road boundary based on an original point set in an embodiment of the present application;

[0029] Figure 6 This is a flow chart of a virtual drive test method provided by an embodiment of the present application;

[0030] Figure 7 This is an example diagram of an embodiment of the present application in which the initial center point is not inside the road segment;

[0031] Figure 8 This is a flow chart of a virtual drive test method provided by an embodiment of the present application;

[0032] Figure 9 Schematic diagram of a virtual drive test device provided in an embodiment of the present application;

[0033] Figure 10 This is a schematic diagram of the structure of an electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0034] The following will be combined with the drawings in the embodiments of this application to clearly and completely describe the technical solutions in the embodiments of this application. Obviously, the embodiments described are part of the embodiments of this application, not all of them. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of this application.

[0035] Figure 1 This is a flow chart of a virtual drive test method provided by an embodiment of the present application. Figure 1 As shown, the method includes: steps 110 to 150.

[0036] Step 110 , obtaining a map image of the target area where the road to be tested is located from map software, and obtaining the latitude and longitude range corresponding to the map image, wherein the road to be tested is in the target color and is different from the background color in the map image.

[0037] The map software is an application that provides map services. The road to be tested is a specific type of road that requires virtual road testing, such as a railway, high-speed railway, or urban road.

[0038] Based on the personalized map setting function provided by the map software, the display color of the road to be tested can be set in the map software, that is, the road to be tested can be set to the target color, and other map elements can be used as the background, and the background color can be significantly different from the target color of the road to be tested, for example, the target color is white and the background color is black.

[0039] Based on the above settings, the map software displays the target area map page according to the above settings, converts part of the map displayed in the map page into a picture format, obtains the map picture of the target road in the target area, and obtains the latitude and longitude range of the part of the map displayed in the map page based on the map page, and obtains the latitude and longitude range corresponding to the map picture.

[0040] In an embodiment of the present application, the map picture of the target area where the target road is located is obtained from the map software, and the latitude and longitude range corresponding to the map picture is obtained, including: obtaining a style file set in the map software, the style file including: only displaying the target road, displaying the target road in the target color, and displaying the background in the color of the background; applying the style file to the HTML page code corresponding to the target area in the map software to display the target map of the target area in the style in the style file; converting the target map into the map picture, and obtaining the latitude and longitude range corresponding to the target map by a boundary obtaining method.

[0041] Each map software currently supports the "personalized map" setting function, based on which, only the target road can be displayed in the map software, and the target color of the target road and the color of the background can be set. Taking Baidu Map API as an example, in Baidu Map API-Console-Characteristic Service Platform-Personalization Settings, the map background can be set to black, and only the target road (such as high-speed rail line) is displayed, and other roads and elements are not displayed, so that the color of the target road is white, and the color of the background is black, and the colors of the two have obvious difference. After setting, a JSON format style file is obtained, and the style file can be downloaded and exported. In the JSON format style file, the style setting of only displaying the target road is specified.

[0042] The style file is applied to the HTML page code, and a target map with only the target road (high-speed rail line) is displayed on the HTML page through a Map instance. In the HTML page code, the map software displays the target map of the target area in the style in the style file, the map can be converted into a map picture (such as a jpg format picture) by using html2canvas, and the left upper corner latitude and longitude coordinates and the right lower corner latitude and longitude coordinates of the current target map are obtained by using the boundary obtaining method (getBounds() method) of the map software API, that is, the latitude and longitude range corresponding to the target map is obtained, and the left upper corner latitude and longitude coordinates and the right lower corner latitude and longitude coordinates are saved in the file name of the picture file of the map picture, so as to facilitate subsequent obtaining and processing of the latitude and longitude range.

[0043] Among them, getBounds() is a method in the Leaflet map framework that is used to obtain the latitude and longitude boundaries of the map view. This method returns a boundary object containing all the latitude and longitude coordinates within the current view range of the map. This boundary object is usually used to determine the display range of the map. For example, when adding markers to the map or performing other map operations, ensure that these elements or operations are performed within the correct range. html2canvas is a JavaScript plug-in that can convert HTML elements on a web page into Canvas objects, so that web page screenshots can be output as images or PDF files. The principle is to traverse the DOM tree, convert each HTML element into a Canvas object, and stack them together to form a complete image or PDF file.

[0044] By obtaining the set style file in the map software, applying the style file to the HTML page code corresponding to the target area in the map software, displaying the target map of the target area with the style in the style file, converting the target map into a map image, and obtaining the latitude and longitude range corresponding to the target map through the boundary acquisition method, the road to be tested and the background can be distinguished in the map image, which is convenient for subsequent processing of each pixel and extraction of the boundary of the road to be tested.

[0045] Step 120 : obtaining an original point set corresponding to the road to be measured from the map image according to the target color and the latitude and longitude range, wherein the original point set includes pixel points corresponding to the road to be measured and latitude and longitude information corresponding to each pixel point.

[0046] Pixels with the target color are identified from the map image, and the longitude and latitude information corresponding to the pixel can be determined based on the position of the pixel in the map image and the longitude and latitude range corresponding to the map image. The pixels with the target color and the longitude and latitude information constitute the original point set of the road to be tested.

[0047] In one embodiment of the present application, obtaining the original point set corresponding to the road to be tested from the map image based on the target color and the longitude and latitude range includes: opening the image file of the map image through the Python image processing library, and determining the longitude and latitude information of each pixel in the map image based on the longitude and latitude range; filtering out pixel points with the target color from the map image through the Python image processing library, and using the filtered pixel points and the longitude and latitude information corresponding to each pixel point as the original point set.

[0048] Among them, the Python Image Library (PIL) is one of the standard libraries for image processing in the Python programming language. It provides rich functions and tools for opening, saving, processing, and editing images; it can open and save image files in multiple formats, such as JPEG, PNG, BMP, GIF, etc.; it can perform various common image processing operations, such as resizing, cropping, rotating, flipping, filter effects, etc.; it can directly access and manipulate the pixel data of the image for pixel-level processing and modification.

[0049] When the latitude and longitude range of the map image (the latitude and longitude coordinates of the upper left corner and the latitude and longitude coordinates of the lower right corner) is recorded through the file name of the map image, the latitude and longitude range of the map image can be extracted from the file name of the map image, and the image file of the map image is opened using the Python image processing library. According to the latitude and longitude coordinates of the upper left corner and the latitude and longitude coordinates of the lower right corner of the map image, the image width and image height (in pixels) of the map image, the latitude and longitude coordinates of each pixel point (i.e., the latitude and longitude information) can be calculated. The color of each pixel point in the map image is identified by the Python image processing library, and the pixel points with the target color are screened out from the map image. The screened pixel points and the corresponding latitude and longitude information constitute the original point set of the road to be measured. For example, in a map image, the target color is white and the background color is black. The color of each pixel is identified through the Python image processing library, and the map image is converted into HSV format (in the HSV color space, the hue (H) of white is 0 degrees, the saturation (S) is 0, and the brightness (V) is 100%; the value range of black is generally H: 0~360, S: 0~100, V: 0~20). The H value can be 0, the S value can be 0, and the V value can be greater than the brightness threshold (such as 20%) for filtering. The pixel points of the road to be tested (such as high-speed rail or railway lines) in the map image and the latitude and longitude coordinates of these pixel points can be filtered out.

[0050] HSV is a color description method that breaks down colors into three components: hue, saturation, and value. Hue indicates the type or type of color and is its position on the circle. Its value range is 0° to 360°, with 0° and 360° representing red, 120° representing green, 240° representing blue, and so on. Hue can describe whether a color is red, green, blue, or another hue. Saturation indicates the purity or vividness of a color and is its intensity or concentration. When saturation is 0, the color is gray and lacks distinct color; when saturation is 1, the color is at its most vivid and pure. Value indicates the lightness or darkness of a color and is its brightness. When brightness is 0, the color is black; when brightness is 1, the color is at its brightest. The advantage of the HSV color model is that by adjusting hue, saturation, and value, color changes and appearance can be more intuitively controlled, better matching human color perception. The HSV color model is more consistent with the way humans perceive color than the RGB color model, so it is often used in image processing and computer vision.

[0051] The Python image processing library can be used to conveniently operate on each pixel in the map image, thereby extracting the pixel with the target color and the latitude and longitude information of each pixel, providing a data basis for determining the road boundary.

[0052] In one embodiment of the present application, filtering out pixels whose color is the target color from the map image through the Python image processing library includes: sampling pixels to be identified from all pixels in the map image according to a preset sampling rate; and filtering out pixels whose color is the target color from the pixels to be identified through the Python image processing library.

[0053] In a map image, the road to be tested may appear very thin. However, if zoomed in to the pixel level, the width of the road to be tested is actually composed of many pixels. Therefore, the pixels in the map image can be sampled at a preset sampling rate to obtain the pixels to be identified. The Python image processing library is then used to identify and extract the colors of the pixels to be identified, thereby extracting the pixels with the target color. The preset sampling rate can be set based on the resolution of the map image and the width of the road to be tested. For example, the preset sampling rate can be set to sample every 2 or 3 pixels.

[0054] After extracting the pixel points of the road to be tested, the extracted pixel points can be displayed on the map, such as Figure 2 As shown, there are many pixels, which can be extracted after sampling.

[0055] By sampling the pixels of the map image according to a preset sampling rate and then identifying and extracting the pixels of the road to be tested, the amount of calculation can be reduced and the efficiency of virtual road testing can be improved.

[0056] Step 130: Acquire multiple multi-point objects from the original point set, and determine the road boundary of the road segment corresponding to each multi-point object.

[0057] Among them, the MultiPoint object is an ordered collection of points.

[0058] Based on the latitude and longitude information of each pixel in the original point set, the pixels within a certain range can be treated as a multi-point object. Boundary acquisition processing can be performed on each multi-point object to obtain the road boundary of the road section corresponding to each multi-point object. Road boundaries can be represented in the form of polygonal boundaries.

[0059] Figure 3 This is a flow chart of obtaining multiple multi-point objects from an original point set in an embodiment of the present application, such as Figure 3 As shown, the step of obtaining multiple multi-point objects from the original point set includes steps 131 to 135:

[0060] Step 131 : Select any pixel point from the original point set, obtain pixel points within a circle with the selected pixel point as the center and a preset distance as the radius from the original point set, and obtain a circle point set.

[0061] During initialization, an empty road point set can be defined. A circle is drawn with a preset distance (e.g., 50 meters) as the radius by selecting any point in the original point set as the center of the circle. This circle contains the points in the original point set whose latitude and longitude information are within the circle, resulting in a point set. Figure 4a is a schematic diagram of the dot set obtained in the embodiment of the present application, such as Figure 4a As shown, this point set (i.e. all points within the circle) is composed of multiple points.

[0062] Step 132: Delete the circle point set from the original point set, and add the circle point set as a multi-point object to the empty road point set.

[0063] Delete the points in this circle point set from the original point set, and form a multipoint object with these points in the circle point set, and add it to the road point set. At this point, there is one multipoint object in the road point set.

[0064] Step 133: determine the pixel point with the shortest distance from each pixel point in the road point set from the original point set, and obtain the pixel points within a circle with the pixel point as the center and the preset distance as the radius from the original point set to obtain a new point set.

[0065] The pixel point closest to the current road point set in the original point set is calculated, and a new circle point set is obtained by taking the closest point as a new circle center and drawing a circle with a preset distance as a radius. Figure 4b is a schematic diagram of another circle point set obtained by taking the closest point as a circle center in the embodiment of the application, as Figure 4b indicated, a new circle point set is obtained by taking the pixel point closest to the road point set as a circle center and drawing a circle with a preset distance as a radius.

[0066] In step 134, the new circle point set is deleted from the original point set, and the new circle point set is added to the road point set as a multi-point object.

[0067] The new circle point set is deleted from the original point set, and the pixel points in the new circle point set form a multi-point object, which is added to the road point set. After processing the two circle point sets, there are two MultiPoint objects in the road point set.

[0068] In step 135, it is determined whether the original point set is empty.

[0069] If the original point set is not empty, steps 133 to 135 are executed in a loop, and the operations of obtaining a new circle point set from the original point set, deleting the new circle point set from the original point set, and adding the new circle point set to the road point set as a multi-point object are executed in a loop until the original point set is empty. If the original point set is empty, the processing is ended, and a plurality of multi-point objects in the road point set are obtained.

[0070] It should be noted that the embodiment of the application only takes the circle drawing mode as an example to illustrate the process of obtaining a multi-point object, and is not limited thereto. Those skilled in the art can understand that the multi-point object can also be obtained in other ways, for example, by a square.

[0071] It should be noted that at the edge of the map picture, there may be a case that the number of pixel points in the circle point set is too small (for example, less than 50). In this case, the circle point set can be directly deleted from the original point set without being added to the road point set. When the data in the road point set is empty (i.e., during initial processing), a point in the original point set can be selected as a circle center.

[0072] By taking a determined pixel point as a circle center and a preset distance as a radius to obtain a circle point set and then obtaining a multi-point object, all points on the road to be measured can be accurately obtained, and the accuracy of road boundary determination can be improved.

[0073] In one embodiment of the present application, determining the road boundary of the road section corresponding to each of the multi-point objects includes: for each of the multi-point objects, performing buffer processing on the multi-point objects separately to obtain one or more polygons, and determining the boundaries of the one or more polygons as the road boundary of the road section corresponding to the multi-point object.

[0074] By performing buffer processing on each multi-point object in the road point set, one or more polygons can be obtained. The boundaries of the one or more polygons are the road boundaries of the road section corresponding to the multi-point object, such as Figure 4c Buffer is commonly used in spatial analysis and spatial queries in Geographic Information Systems (GIS). It is used to expand or contract the boundaries of geometric objects within a given distance. It performs buffer processing on geometric objects. Based on the original geometric object, a new geometric object is generated. This new geometric object is the union of all points in the original geometric object whose distance to the original geometric object does not exceed the specified distance. For example, for a MultiPoint object, performing a Buffer operation will generate a Polygon object. This polygon object is the union of all points in the original multipoint object to the buffer at the specified distance.

[0075] When performing a Buffer operation to form a polygon, there is a parameter called the buffer radius, also known as the buffer distance. By adjusting this parameter, the width of the road boundary can be controlled, enabling flexible setting of the road boundary to be measured.

[0076] Figure 5 This is a flowchart of determining road boundaries based on the original point set in an embodiment of the present application. Figure 5As shown, when determining the road boundary, an empty road point set is defined, and a pixel point is selected from the original point set as the center of the circle; when the number of pixel points in the original point set is greater than 0, a circle is drawn with a preset distance as the radius to obtain a circle point set; the circle point set is deleted from the original point set; it is determined whether the number of pixel points in the circle point set is greater than a number threshold (for example, it can be 50); when the number of pixel points in the circle point set is greater than the number threshold, the circle point set is added to the road point set, and then it is determined whether the original point set is empty; when the number of pixel points in the circle point set is not greater than the number threshold, it is determined whether the road point set is empty; if the road If the point set is empty, select any point from the original point set as the new center of the circle, and then perform the operation of determining whether the number of pixels in the original point set is greater than 0; if the road point set is not empty, perform the operation of determining whether the original point set is empty; if the original point set is not empty, calculate the closest point in the original point set to the road point set, use the closest point as the new center of the circle, and then perform the operation of determining whether the number of pixels in the original point set is greater than 0; if the original point set is empty or the number of pixels in the original point set is not greater than 0, perform buffer processing on each multi-point object in the road point set to obtain the road boundary of the road section.

[0077] Step 140 : For each of the road sections, obtain MR data within the road section according to the road boundary of the road section, and determine the wireless signal coverage strength of the road section according to the MR data.

[0078] After determining the road boundaries of each road section, the longitude and latitude information is converted to the WGS84 format. MR (Measurement Report) coverage data is collected within each road boundary (polygon boundary). The RSRP (Reference Signal Received Power) values ​​in the MR data are averaged to obtain the wireless signal coverage strength for each road section (polygon area).

[0079] Step 150: Determine a display color for the road section according to the wireless signal coverage strength, and display the road section in the map using the display color.

[0080] The correspondence between wireless signal coverage strength and display color can be pre-set. After determining the wireless signal coverage strength of each road section, the display color of each road section can be determined based on the wireless signal coverage strength of each road section and the pre-set correspondence. Each road section can be displayed in its display color on the map, realizing a virtual road test of the road to be tested.

[0081] Virtual drive testing utilizes wireless network MR and XDR big data for data analysis, obtaining multi-dimensional road conditions (coverage, interference, overlap, etc.). This replaces traditional manual road testing and in-depth building testing, significantly reducing the labor and time costs of network optimization data collection. Furthermore, because MR collects mobile phone signal data from active users, it reduces the potential for human intervention and sampling errors in manual testing.

[0082] The virtual drive test method provided in an embodiment of the present application obtains a map image of a target area where a road to be tested is located from map software, and obtains the latitude and longitude range corresponding to the map image. In the map image, the road to be tested is a target color that is different from the background color. Based on the target color and the latitude and longitude range, an original point set corresponding to the road to be tested is obtained from the map image. Multiple multi-point objects are obtained from the original point set, and the road boundary of the road segment corresponding to each multi-point object is determined. For each road segment, MR data within the road segment is obtained based on the road boundary, and the wireless signal coverage strength of the road segment is determined based on the MR data. The display color of the road segment is determined based on the wireless signal coverage strength, and each road segment is displayed on the map using the display color. By processing the map image, the road boundary of each section of the road to be tested can be accurately determined. Therefore, the road boundary of railways, high-speed railways, urban roads, etc. can be obtained by processing the map image, thereby realizing virtual drive testing of high-speed railways and railways. There is no need to manually mark road boundaries or obtain the boundary of each road based on road name, which can improve the efficiency of virtual drive testing.

[0083] Figure 6 This is a flow chart of a virtual drive test method provided by an embodiment of the present application. Figure 6 As shown, the method includes: steps 610 to 660.

[0084] Step 610: Obtain a map image of the target area where the road to be tested is located from map software, and obtain the latitude and longitude range corresponding to the map image. In the map image, the road to be tested is in the target color and is different from the background color.

[0085] Step 620: Acquire an original point set corresponding to the road to be measured from the map image according to the target color and the latitude and longitude range, wherein the original point set includes pixel points corresponding to the road to be measured and latitude and longitude information corresponding to each pixel point.

[0086] Step 630: Acquire multiple multi-point objects from the original point set, and determine the road boundary of the road segment corresponding to each multi-point object.

[0087] Step 640 : Determine the target center point of each road section based on the longitude and latitude information corresponding to each pixel point on the road boundary and the longitude and latitude information corresponding to each pixel point in the multi-point object.

[0088] During a virtual drive test, when displaying the coverage of each road section (the area within the polygonal boundary) on a map, it is not desirable to fill the entire polygon. Instead, it is desirable to display the coverage effect by dotting the road, as with traditional drive test software. To this end, after obtaining the road boundary (polygonal boundary) of each road section using a dot set, the longitude and latitude information of each pixel point on the road boundary can be averaged to obtain a center point. The target center point of the road section can then be determined based on this center point and the longitude and latitude information corresponding to each pixel point in the multi-point object. For example, the center point obtained by averaging the longitude and latitude information of each pixel point on the road boundary can be directly used as the target center point.

[0089] In one embodiment of the present application, the target center point of each road section is determined based on the longitude and latitude information corresponding to each pixel point on the road boundary and the longitude and latitude information corresponding to each pixel point in the multi-point object, including: determining the initial center point of the road section based on the longitude and latitude information corresponding to each pixel point on the road boundary; and determining the pixel point with the smallest distance from the initial center point as the target center point based on the longitude and latitude information corresponding to each pixel point in the multi-point object.

[0090] The latitude and longitude information (latitude and longitude coordinates) of each pixel point on the road boundary of each road section are averaged to obtain the coordinates of a center point. This center point is the initial center point of the road section. In the curved part of the road to be tested, the initial center point may not necessarily be on the road (that is, the initial center point of a road section is not within the polygon corresponding to the road section). For example Figure 7 To avoid this situation, we can calculate the distance between each pixel and the initial center point based on the latitude and longitude information corresponding to each pixel in the multi-point object. We then determine the pixel with the smallest distance from the initial center point and use that pixel as the target center point. Since the pixels in the multi-point object (dot set) are always on the road to be measured, this ensures that each target center point displayed on the map is also on the road to be measured.

[0091] Step 650 : For each of the road sections, obtain measurement report MR data within the road section according to the road boundary of the road section, and determine the wireless signal coverage strength of the road section according to the MR data.

[0092] Step 660: Determine the display color of the road section according to the wireless signal coverage strength, and display the target center point corresponding to the road section in the map using the display color.

[0093] After determining the display color for each road section, you can display it on the map using a dotted display. That is, the target center point corresponding to each road section is displayed on the map in the corresponding display color. By drawing these target center points of different display colors on the map and displaying each center point along the road to be tested, you can achieve a display effect that is almost identical to that of an actual road test.

[0094] For example, the display color of the target center point of each road section (polygon) is set according to the wireless signal coverage strength, and the setting rules are as follows: dark green (RSRP>-75), green (-85 <RSRP<=-75),蓝色(-95<RSRP<=-85),黄色(-105<RSRP<=-95),粉红色(-110<RSRP<=-105),红色(RSRP<=-110),RSRP表示无线信号覆盖强度。

[0095] The virtual drive test method provided in the embodiment of the present application determines the target center point of the road section based on the longitude and latitude information corresponding to each pixel point on the road boundary and the longitude and latitude information corresponding to each pixel point in the multi-point object. Therefore, when displaying the results of the virtual drive test, it is only necessary to display the target center point of the road section in the display color corresponding to the road section, thereby further improving the efficiency of the virtual drive test.

[0096] Figure 8 This is a flow chart of a virtual drive test method provided by an embodiment of the present application. Figure 8 As shown, the method includes: steps 810 to 860.

[0097] Step 810 , dividing the area where the road to be tested is located into multiple map blocks, setting the map center point of the HTML page of each map block according to the center point of each map block, and obtaining multiple HTML files, wherein each map block is used as the target area.

[0098] The route of the road to be tested may be very long, such as a high-speed rail or railway route. An HTML page can only display a portion of the route map, and it is not certain which longitudes and latitudes the high-speed rail route passes through. In this case, the area where the road to be tested is located (such as the entire province or the entire city) can be divided into multiple small map blocks. Based on personalized settings, each map block only displays the road to be tested, and all other elements serve as background. Based on a map with a certain zoom ratio, a different map center point is set for each HTML page, so that each HTML page can display a different map block. Each HTML page corresponds to an HTML file. Each map block is treated as a target area for subsequent processing.

[0099] The only difference between the HTML pages is the center point of the map. These HTML files can be generated in batches using simple programming.

[0100] Step 820: Run the multiple HTML files in sequence in the map software, obtain the map images of the HTML pages corresponding to the running HTML files, obtain the map images of the road to be tested in the target area corresponding to each map block, and obtain the latitude and longitude range corresponding to each map image.

[0101] When the route of the road to be tested is long, many HTML pages will be generated (generally, the number of HTML pages in a province will exceed 1,000). An automatic save as image function can be added to the HTML code so that the map software can automatically save the map blocks corresponding to each HTML file as map images when displaying them in the set style file.

[0102] For example, a batch execution file can be written to automatically run these HTML files. Based on the directory where the HTML files are saved, the program can automatically read the HTML files in the directory (such as the d:\html directory), read two HTML files each time using a browser, and automatically close the browser after waiting for 30 seconds each time. In this way, the image files of all map images can be generated in batches.

[0103] Step 830: Acquire an original point set corresponding to the road to be measured from the map image according to the target color and the latitude and longitude range, wherein the original point set includes pixel points corresponding to the road to be measured and latitude and longitude information corresponding to each pixel point.

[0104] By performing the processing from step 830 to step 860 on each map image obtained in batches, the result of the virtual road test of the entire road to be tested can be obtained.

[0105] Step 840: Acquire multiple multi-point objects from the original point set, and determine the road boundary of the road segment corresponding to each multi-point object.

[0106] Step 850 : For each of the road sections, obtain measurement report MR data within the road section according to the road boundary of the road section, and determine the wireless signal coverage strength of the road section according to the MR data.

[0107] Step 860: Determine the display color of the road section according to the wireless signal coverage strength, and display the road section in the map using the display color.

[0108] The virtual road testing method provided in the embodiments of the present application can divide the area where the road to be tested is located into a plurality of map blocks when the route of the road to be tested is long, set the map center point of the HTML page of each map block according to the center point of each map block, obtain a plurality of HTML files, sequentially run the plurality of HTML files in the map software, obtain the map picture of the HTML page corresponding to the running HTML file, and obtain the map picture of the road to be tested in each target area corresponding to each map block, so that the map pictures of the road to be tested in different target areas can be obtained in batches during virtual road testing, and the efficiency of virtual road testing can be further improved.

[0109] In the embodiments of the present application, the boundaries of the high-speed rail, railway and other roads to be tested in the whole province or city can be automatically generated without manual line drawing or boundary drawing; the distance between the target center points is approximately equal to the radius of a circle, and the distance between the target center points can be adjusted by changing the radius of the circle when drawing the circle; the width of the generated road boundary can also be freely adjusted by setting the distance of the buffer zone during the buffering; the road testing is no longer limited by the road name, and the boundaries of the high-speed rail or railway road to be tested can be obtained without knowing the name of the high-speed rail or railway route in a province, so that virtual road testing can be realized; in the roads in the city center, the road names are very numerous, and some even have no road names, so it is only necessary to set the personalized map to display only the urban roads, and then identify and generate a polygon of the urban roads.

[0110] Figure 9 is a structural schematic diagram of a virtual road testing device provided in the embodiments of the present application, as shown in Figure 9 The device comprises:

[0111] The map picture acquisition module 910 is configured to acquire the map picture of the target area where the road to be tested is located from the map software, and acquire the latitude and longitude range corresponding to the map picture, wherein the road to be tested is of a target color and different from the background color in the map picture.

[0112] The original point set acquisition module 920 is configured to acquire the original point set corresponding to the road to be tested from the map picture according to the target color and the latitude and longitude range, wherein the original point set comprises pixel points corresponding to the road to be tested and latitude and longitude information corresponding to each pixel point.

[0113] The road boundary determination module 930 is configured to acquire a plurality of multi-point objects from the original point set, and determine the road boundary of each road segment corresponding to each multi-point object.

[0114] a signal strength determination module 940 configured to obtain, for each of the road sections, measurement report MR data within the road section according to the road boundary of the road section, and determine the wireless signal coverage strength of the road section based on the MR data;

[0115] The drive test result display module 950 is configured to determine a display color for the road section according to the wireless signal coverage strength, and display the road section in the map using the display color.

[0116] Optionally, the device further includes:

[0117] a road section center point determination module, configured to determine a target center point of each road section based on the longitude and latitude information corresponding to each pixel point on the road boundary and the longitude and latitude information corresponding to each pixel point in the multi-point object before displaying the road section in the display color on the map;

[0118] The drive test result display module includes:

[0119] The drive test result display unit is used to display the target center point corresponding to the road section in the display color on the map.

[0120] Optionally, the road section center point determination module is specifically used to:

[0121] Determining the initial center point of the road section based on the latitude and longitude information corresponding to each pixel point on the road boundary;

[0122] According to the longitude and latitude information corresponding to each pixel point in the multi-point object, the pixel point with the smallest distance from the initial center point is determined as the target center point.

[0123] Optionally, the map image acquisition module includes:

[0124] A style file acquisition unit is used to acquire a style file set in the map software, wherein the style text includes: displaying only the road to be measured, displaying the road to be measured in the target color, and displaying the background in the background color;

[0125] A target map display unit, configured to apply the style file to the HTML page code corresponding to the target area in the map software, and display the target map of the target area in the style of the style file;

[0126] The map image acquisition unit is used to convert the target map into the map image and obtain the longitude and latitude range corresponding to the target map through a boundary acquisition method.

[0127] Optionally, the original point set acquisition module includes:

[0128] A pixel longitude and latitude determination unit, configured to open the image file of the map image through a Python image processing library and determine the longitude and latitude information of each pixel in the map image according to the longitude and latitude range;

[0129] The original point set acquisition unit is used to filter out pixel points with the target color from the map image through the Python image processing library, and use the filtered pixel points and the longitude and latitude information corresponding to each pixel point as the original point set.

[0130] Optionally, the original point set acquisition unit is specifically configured to:

[0131] Sampling pixels to be identified from all pixels in the map image according to a preset sampling rate;

[0132] The Python image processing library is used to filter out pixel points with the target color from the pixel points to be identified.

[0133] Optionally, the road boundary determination module includes a multi-point object acquisition unit, and the multi-point object acquisition unit is configured to:

[0134] Selecting a pixel point from the original point set, obtaining pixel points within a circle with the selected pixel point as the center and a preset distance as the radius from the original point set, to obtain a circle point set;

[0135] Deleting the point set from the original point set, and adding the point set as a multi-point object to the empty road point set;

[0136] Determining a pixel point with the smallest distance from each pixel point in the road point set from the original point set, and obtaining pixel points within a circle with the pixel point as the center and a preset distance as the radius from the original point set to obtain a new circle point set;

[0137] Deleting the new point set from the original point set, and adding the new point set as a multi-point object to the road point set;

[0138] The above operations of obtaining a new point set from the original point set, deleting the new point set from the original point set, and adding the new point set as a multi-point object to the road point set are performed cyclically until the original point set is empty, thereby obtaining a plurality of the multi-point objects in the road point set.

[0139] Optionally, the road boundary determination module includes:

[0140] The road boundary determination unit is used to perform buffer processing on each of the multi-point objects to obtain one or more polygons, and determine the boundaries of the one or more polygons as the road boundaries of the road section corresponding to the multi-point object.

[0141] Optionally, the device further includes:

[0142] The map division module is used to divide the area where the road to be tested is located into multiple map blocks before obtaining the map image of the target area where the road to be tested is located from the map software, and set the map center point of the HTML page of each map block according to the center point of each map block to obtain multiple HTML files, wherein each map block is used as the target area.

[0143] Optionally, the map image acquisition module includes:

[0144] The map image acquisition unit is used to run the multiple HTML files in sequence in the map software, and obtain the map images of the HTML pages corresponding to the running HTML files, so as to obtain the map images of the road to be tested in the target area corresponding to each map block.

[0145] The virtual drive test device provided in the embodiment of the present application is used to implement each step of the virtual drive test method described in the embodiment of the present application. The specific implementation of each module of the device can be found in the corresponding step and will not be repeated here.

[0146] The virtual drive test device provided in an embodiment of the present application obtains a map image of a target area where a road to be tested is located from map software, and obtains the latitude and longitude range corresponding to the map image. In the map image, the road to be tested is a target color that is different from the background color. Based on the target color and the latitude and longitude range, an original point set corresponding to the road to be tested is obtained from the map image. Multiple multi-point objects are obtained from the original point set, and the road boundary of the road segment corresponding to each multi-point object is determined. For each road segment, MR data within the road segment is obtained based on the road boundary, and the wireless signal coverage strength of the road segment is determined based on the MR data. The display color of the road segment is determined based on the wireless signal coverage strength, and each road segment is displayed on the map using the display color. By processing the map image, the road boundary of each section of the road to be tested can be accurately determined. Therefore, the road boundary of railways, high-speed railways, urban roads, etc. can be obtained by processing the map image, thereby realizing virtual drive testing of high-speed railways and railways. There is no need to manually mark road boundaries or obtain the boundary of each road based on road name, which can improve the efficiency of virtual drive testing.

[0147] Figure 10 is a structural diagram of an electronic device provided in an embodiment of the present application, such as Figure 10As shown, the electronic device 1000 can include one or more processors 1010 and one or more memories 1020 connected to the processors 1010. The electronic device 1000 can further include an input interface 1030 and an output interface 1040 for communicating with another apparatus or system. Program codes executed by the processors 1010 can be stored in the memories 1020.

[0148] The processor 1010 in the electronic device 1000 invokes program codes stored in the memory 1020 to perform the virtual road testing method in the above-described embodiments.

[0149] The embodiments of the present application further provide a computer readable storage medium, which stores a computer program. The program is executed by a processor to implement the steps of the virtual road testing method according to the embodiments of the present application.

[0150] The embodiments of the present application further provide a computer program product. The computer program product is executed by a processor to implement the steps of the virtual road testing method according to the embodiments of the present application.

[0151] Each of the embodiments in the present specification is described in a progressive manner, and each embodiment focuses on the difference from other embodiments. The same or similar parts of each embodiment can be referred to each other. For the device embodiments, since they are basically similar to the method embodiments, the description is relatively simple, and the relevant parts can be referred to the part of the method embodiments.

[0152] The virtual road testing method, device, electronic device and storage medium provided by the embodiments of the present application are described in detail above, and the principles and implementation manners of the present application are described by applying specific examples. The above embodiment description is only used to help understand the method and its core idea of the present application; meanwhile, for those skilled in the art, according to the idea of the present application, the specific implementation manner and application range can be changed, and the above description should not be understood as a limitation of the present application.

[0153] Through the description of the above embodiments, those skilled in the art can clearly understand that each embodiment can be realized by means of software and necessary general hardware platform, and of course, it can also be realized by hardware. Based on such understanding, the above technical solutions can be embodied in the form of software product, which can be stored in a computer readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes a plurality of instructions to make a computer device (which can be a personal computer, server, or network device, etc.) execute the method described in each embodiment or some parts of the embodiment.

Claims

1. A virtual drive test method, characterized in that: include: Obtain a map image of the target area where the road to be tested is located from map software, and obtain the latitude and longitude range corresponding to the map image, wherein the road to be tested is in the target color and is different from the background color in the map image; According to the target color and the longitude and latitude range, an original point set corresponding to the road to be measured is obtained from the map image, wherein the original point set includes pixel points corresponding to the road to be measured and longitude and latitude information corresponding to each pixel point; Acquire a plurality of multi-point objects from the original point set, and determine a road boundary of a road segment corresponding to each of the multi-point objects; For each of the road sections, obtaining measurement report MR data within the road section according to the road boundary of the road section, and determining the wireless signal coverage strength of the road section according to the MR data; The display color of the road section is determined according to the wireless signal coverage strength, and the road section is displayed in the map using the display color.

2. The method according to claim 1, characterized in that Before displaying the road segment in the display color on the map, the method further includes: Determining the target center point of each road section according to the latitude and longitude information corresponding to each pixel point on the road boundary and the latitude and longitude information corresponding to each pixel point in the multi-point object; The displaying of the road section in the map using the display color includes: The target center point corresponding to the road segment is displayed in the map using the display color.

3. The method according to claim 2, characterized in that Determining the target center point of each road section according to the latitude and longitude information corresponding to each pixel point on the road boundary and the latitude and longitude information corresponding to each pixel point in the multi-point object includes: Determining the initial center point of the road section based on the latitude and longitude information corresponding to each pixel point on the road boundary; According to the longitude and latitude information corresponding to each pixel point in the multi-point object, the pixel point with the smallest distance from the initial center point is determined as the target center point.

4. The method according to any one of claims 1 to 3, characterized in that The step of obtaining a map image of the target area where the road to be tested is located from the map software and obtaining the latitude and longitude range corresponding to the map image includes: Obtaining a set style file in the map software, the style file including: displaying only the road to be measured, displaying the road to be measured in the target color, and displaying the background in the background color; Applying the style file to the HTML page code corresponding to the target area in the map software to display the target map of the target area in the style of the style file; The target map is converted into the map image, and the latitude and longitude range corresponding to the target map is obtained through a boundary acquisition method.

5. The method according to any one of claims 1 to 3, characterized in that The step of obtaining an original point set corresponding to the road to be measured from the map image according to the target color and the latitude and longitude range includes: Open the image file of the map image using the Python image processing library, and determine the longitude and latitude information of each pixel in the map image based on the longitude and latitude range; Pixels having the target color are filtered out from the map image using the Python image processing library, and the filtered pixels and the longitude and latitude information corresponding to the pixels are used as the original point set.

6. The method according to claim 5, characterized in that The step of filtering out pixels having the target color from the map image using the Python image processing library includes: Sampling pixels to be identified from all pixels in the map image according to a preset sampling rate; The Python image processing library is used to filter out pixel points with the target color from the pixel points to be identified.

7. The method according to any one of claims 1 to 3, characterized in that The acquiring of a plurality of multi-point objects from the original point set comprises: Selecting a pixel point from the original point set, obtaining pixel points within a circle with the selected pixel point as the center and a preset distance as the radius from the original point set, to obtain a circle point set; Deleting the point set from the original point set, and adding the point set as a multi-point object to the empty road point set; Determining a pixel point with the smallest distance from each pixel point in the road point set from the original point set, and obtaining pixel points within a circle with the pixel point as the center and a preset distance as the radius from the original point set to obtain a new circle point set; Deleting the new point set from the original point set, and adding the new point set as a multi-point object to the road point set; The above operations of obtaining a new point set from the original point set, deleting the new point set from the original point set, and adding the new point set as a multi-point object to the road point set are performed cyclically until the original point set is empty, thereby obtaining a plurality of the multi-point objects in the road point set.

8. The method according to any one of claims 1 to 3, characterized in that Determining the road boundary of the road segment corresponding to each of the multi-point objects includes: For each of the multi-point objects, buffer processing is performed on the multi-point objects to obtain one or more polygons, and the boundaries of the one or more polygons are determined as the road boundaries of the road section corresponding to the multi-point object.

9. The method according to any one of claims 1 to 3, characterized in that Before obtaining a map image of the target area where the road to be tested is located from the map software, the method further includes: The area where the road to be tested is located is divided into multiple map blocks, and the map center point of the HTML page of each map block is set according to the center point of each map block to obtain multiple HTML files, wherein each map block is used as the target area.

10. The method according to claim 9, characterized in that The step of obtaining a map image of a target area where the road to be tested is located from map software includes: The plurality of HTML files are sequentially run in the map software, and map images of HTML pages corresponding to the running HTML files are obtained to obtain map images of the road to be tested in the target area corresponding to each map block.

11. A virtual drive test device, characterized in that: include: A map image acquisition module is used to obtain a map image of the target area where the road to be tested is located from the map software, and obtain the latitude and longitude range corresponding to the map image, wherein the road to be tested is in the target color and is different from the background color in the map image; An original point set acquisition module is used to acquire an original point set corresponding to the road to be measured from the map image according to the target color and the latitude and longitude range, wherein the original point set includes pixel points corresponding to the road to be measured and latitude and longitude information corresponding to each pixel point; a road boundary determination module, configured to obtain a plurality of multi-point objects from the original point set and determine a road boundary of a road segment corresponding to each of the multi-point objects; a signal strength determination module, configured to obtain, for each of the road sections, measurement report MR data within the road section according to the road boundary of the road section, and determine the wireless signal coverage strength of the road section according to the MR data; The drive test result display module is used to determine the display color of the road section according to the wireless signal coverage strength, and display the road section in the map with the display color.

12. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the computer program, the virtual drive testing method according to any one of claims 1 to 10 is implemented.

13. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the steps of the virtual drive test method according to any one of claims 1 to 10 are implemented.

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