Hub exploration method and device, computer equipment and readable storage medium

Abstract

The application provides a hub exploration method and device, computer equipment and a readable storage medium. The method comprises the following steps: obtaining a target tile map corresponding to a target area; performing target detection on the target tile map to obtain a first hub set of suspected hubs and a first position information set corresponding to each suspected hub; obtaining a second position information set corresponding to a hub that already exists in the target area; and performing screening on the first hub set based on the first position information set and the second position information set to obtain a second hub set after screening. The application improves the efficiency and accuracy of hub exploration.

Description

Technical Field

[0001] This application relates to the field of logistics information technology, specifically to a method, apparatus, computer equipment, and readable storage medium for exploring distribution centers. Background Technology

[0002] Distribution centers are nodes in the logistics network system of a systematic logistics organization, and are the places where basic logistics functions are fully demonstrated. The basic logistics operations of general cargo distribution require corresponding logistics infrastructure and equipment. However, existing distribution centers suffer from high exploration costs and low timeliness.

[0003] Therefore, improving the timeliness and reducing the cost of exploring distribution centers is a technical problem that urgently needs to be solved in the field of logistics information technology. Summary of the Invention

[0004] This application provides a distribution center exploration method, apparatus, computer equipment, and readable storage medium, aiming to solve the technical problem of how to improve the timeliness and reduce the cost of distribution center exploration.

[0005] On the one hand, this application provides a method for exploring distribution centers, the method comprising:

[0006] Obtain the target tile map corresponding to the target area;

[0007] Target detection is performed on the target tile map to obtain a first set of suspected distribution centers and a first set of location information corresponding to each suspected distribution center;

[0008] Obtain the second location information set corresponding to the currently existing distribution centers within the target area;

[0009] Based on the first set of location information and the second set of location information, the first set of distribution centers is filtered to obtain the filtered second set of distribution centers.

[0010] In one possible implementation of this application, the step of filtering the first set of distribution centers based on the first set of location information and the second set of location information to obtain a filtered second set of distribution centers includes:

[0011] The intersection of the first location information set and the second location information set is used to obtain the third location information set;

[0012] Obtain the original distribution center set corresponding to all location information in the third location information set;

[0013] All distribution centers in the original distribution center set are removed from the first distribution center set to obtain the filtered second distribution center set.

[0014] In one possible implementation of this application, after filtering the first set of distribution centers based on the first set of location information and the second set of location information to obtain a filtered second set of distribution centers, the method further includes:

[0015] Obtain the fourth location information set of all distribution centers in the second set of distribution centers;

[0016] Determine the geographic attributes corresponding to each location information in the fourth location information set;

[0017] Remove the target distribution centers in the second distribution center set whose geographical attributes are of a preset type to obtain the third distribution center set.

[0018] In one possible implementation of this application, obtaining the target tile map corresponding to the target region includes:

[0019] For the target area, acquire trajectory data of the target object group generated within a preset time period. The trajectory data includes the positioning information, velocity parameters, and azimuth parameters of multiple trajectory points.

[0020] Based on the positioning information of the multiple trajectory points, determine the magnitude parameters of the trajectory points of each pixel position in the initial tile map corresponding to the positioning information.

[0021] Based on the velocity parameters and azimuth parameters of each trajectory point, as well as the magnitude parameters of the trajectory points, the color parameters of each current pixel position are determined to obtain the target tile map corresponding to the target region. The color parameters are displayed according to a first color system, which is established with reference to the RGB color mode. The color parameters include R values, G values, and B values, and the R, G, and B values ​​in the color parameters correspond to the velocity parameters, the azimuth parameters, and the magnitude parameters of the trajectory points, respectively.

[0022] In one possible implementation of this application, determining the trajectory point magnitude parameters of each pixel position in the initial tile image corresponding to the current positioning information based on the positioning information of the plurality of trajectory points includes:

[0023] Based on the positioning information of the multiple trajectory points, the coordinate information of each trajectory point in the initial tile map is determined, and the coordinate information includes tile coordinates and pixel coordinates;

[0024] Based on the coordinate information, the number of trajectory points at each pixel position in the initial tile image is counted to obtain the magnitude parameter of the trajectory points at each pixel position.

[0025] In one possible implementation of this application, the R, G, and B values ​​in the color parameters correspond to the velocity parameter, the azimuth parameter, and the trajectory point magnitude parameter, respectively. The method further includes:

[0026] For each pixel position, based on the velocity parameters of each trajectory point, the number of trajectory points falling into each velocity range interval is counted, and the R value corresponding to the velocity range interval with the largest number of trajectory points is selected as the R value of the corresponding pixel position.

[0027] For each pixel position, based on the azimuth parameters of each trajectory point, the number of trajectory points falling into each azimuth range interval is counted, and the G value corresponding to the azimuth range interval with the largest number of trajectory points is selected as the G value of the corresponding pixel position.

[0028] For each pixel position, based on the trajectory point magnitude parameter, the corresponding trajectory point magnitude range is determined, and the trajectory point magnitude range is used as the B value for the corresponding pixel position.

[0029] In one possible implementation of this application, the creation of the first color system includes:

[0030] Take a preset speed range interval, classify the speed parameters of all trajectory points in the trajectory data according to the speed range interval, and obtain a set of trajectory points for the preset speed range interval, with each speed range interval corresponding to a different R value;

[0031] Take a preset range of azimuth angles, classify the azimuth angle parameters of all trajectory points in the trajectory data according to the range of azimuth angles, and obtain a set of trajectory points for the preset range of azimuth angles, with each range of azimuth angles corresponding to a different G value;

[0032] Take a preset range of trajectory point magnitudes, classify the trajectory point magnitude parameters of all trajectory points in the trajectory data, and obtain a set of trajectory points within the preset range of trajectory point magnitudes, with each of the trajectory point magnitude ranges corresponding to a different B value;

[0033] Based on the preset sets of trajectory points within a certain speed range, the preset sets of trajectory points within a certain azimuth range, and the preset sets of trajectory point magnitude ranges, the color parameters of the pixel positions corresponding to all trajectory points in the trajectory data in the initial tile map are determined to obtain the current target tile map within the target area. Different combinations of the speed range, azimuth range, and trajectory point magnitude range correspond to different preset display colors.

[0034] In one possible implementation of this application, the step of determining the color parameters of the pixel positions corresponding to all trajectory points in the trajectory data in the initial tile map based on the preset velocity range range of the trajectory point set, the preset azimuth range range of the trajectory point set, and the preset trajectory point magnitude range range of the trajectory point set, to obtain the current target tile map within the target area, includes:

[0035] The RGB color mode is used to render the trajectory point set within the preset speed range, the trajectory point set within the preset speed range, and the trajectory point set within the preset trajectory point magnitude range, to obtain the RGB values ​​of the pixel positions of all trajectory points in the trajectory data in the initial tile map, so as to determine the target tile map corresponding to the target region.

[0036] On the other hand, this application provides a distribution center exploration device, the device comprising:

[0037] The first acquisition unit is used to acquire the target tile map corresponding to the target area;

[0038] The first target detection unit is used to perform target detection on the target tile map to obtain a first set of suspected distribution centers and a first set of location information corresponding to each suspected distribution center.

[0039] The second acquisition unit is used to acquire a second set of location information corresponding to the currently existing distribution centers within the target area;

[0040] The first filtering unit is used to filter the first distribution center set based on the first location information set and the second location information set to obtain the filtered second distribution center set.

[0041] In one possible implementation of this application, the first filtering unit is specifically used for:

[0042] The intersection of the first location information set and the second location information set is used to obtain the third location information set;

[0043] Obtain the original distribution center set corresponding to all location information in the third location information set;

[0044] All distribution centers in the original distribution center set are removed from the first distribution center set to obtain the filtered second distribution center set.

[0045] In one possible implementation of this application, after filtering the first set of distribution centers based on the first set of location information and the second set of location information to obtain a filtered second set of distribution centers, the device is used to:

[0046] Obtain the fourth location information set of all distribution centers in the second set of distribution centers;

[0047] Determine the geographic attributes corresponding to each location information in the fourth location information set;

[0048] Remove the target distribution centers in the second distribution center set whose geographical attributes are of a preset type to obtain the third distribution center set.

[0049] In one possible implementation of this application, the first acquisition unit specifically includes:

[0050] The third acquisition unit is used to acquire trajectory data of a group of target objects generated within a preset time period for the target area. The trajectory data includes positioning information, speed parameters, and azimuth parameters of multiple trajectory points.

[0051] The first determining unit is used to determine the magnitude parameters of the trajectory points of each pixel position in the initial tile map corresponding to the positioning information based on the positioning information of the plurality of trajectory points.

[0052] The second determining unit is used to determine the color parameters of the current pixel position based on the velocity parameters and azimuth parameters of each trajectory point, as well as the magnitude parameters of the trajectory points, to obtain the target tile map corresponding to the target region. The color parameters are displayed according to a first color system, which is established with reference to the RGB color mode. The color parameters include R values, G values, and B values, and the R values, G values, and B values ​​in the color parameters correspond to the velocity parameters, the azimuth parameters, and the magnitude parameters of the trajectory points, respectively.

[0053] In one possible implementation of this application, the first determining unit is specifically used for:

[0054] Based on the positioning information of the multiple trajectory points, the coordinate information of each trajectory point in the initial tile map is determined, and the coordinate information includes tile coordinates and pixel coordinates;

[0055] Based on the coordinate information, the number of trajectory points at each pixel position in the initial tile image is counted to obtain the magnitude parameter of the trajectory points at each pixel position.

[0056] In one possible implementation of this application, the R, G, and B values ​​in the color parameters correspond to the velocity parameter, the azimuth parameter, and the trajectory point magnitude parameter, respectively. The device is further used for:

[0057] For each pixel position, based on the velocity parameters of each trajectory point, the number of trajectory points falling into each velocity range interval is counted, and the R value corresponding to the velocity range interval with the largest number of trajectory points is selected as the R value of the corresponding pixel position.

[0058] For each pixel position, based on the azimuth parameters of each trajectory point, the number of trajectory points falling into each azimuth range interval is counted, and the G value corresponding to the azimuth range interval with the largest number of trajectory points is selected as the G value of the corresponding pixel position.

[0059] For each pixel position, based on the trajectory point magnitude parameter, the corresponding trajectory point magnitude range is determined, and the trajectory point magnitude range is used as the B value for the corresponding pixel position.

[0060] In one possible implementation of this application, the second determining unit specifically includes:

[0061] The first classification unit is used to take a preset number of speed range intervals and classify the speed parameters of all trajectory points in the trajectory data according to the speed range intervals to obtain a set of trajectory points for the preset number of speed range intervals, with each speed range interval corresponding to a different R value;

[0062] The second classification unit is used to take a preset number of azimuth range intervals and classify the azimuth parameters of all trajectory points in the trajectory data according to the azimuth range intervals to obtain a set of trajectory points for the preset number of azimuth range intervals, with each azimuth range interval corresponding to a different G value.

[0063] The third classification unit is used to take a preset range of trajectory point magnitudes and classify the trajectory point magnitude parameters of all trajectory points in the trajectory data to obtain a set of trajectory points within a preset range of trajectory point magnitudes, with each of the trajectory point magnitude ranges corresponding to a different B value.

[0064] The third determining unit is used to determine the color parameters of the pixel positions of all trajectory points in the trajectory data in the initial tile map based on the trajectory point set of the preset velocity range interval, the trajectory point set of the preset azimuth angle range interval, and the trajectory point magnitude range interval, so as to obtain the current target tile map in the target area. Different combinations of the velocity range interval, azimuth angle range interval, and trajectory point magnitude range interval correspond to different preset color rendering colors.

[0065] In one possible implementation of this application, the third determining unit is specifically used for:

[0066] The RGB color mode is used to render the trajectory point set within the preset speed range, the trajectory point set within the preset speed range, and the trajectory point set within the preset trajectory point magnitude range, to obtain the RGB values ​​of the pixel positions of all trajectory points in the trajectory data in the initial tile map, so as to determine the target tile map corresponding to the target region.

[0067] On the other hand, this application also provides a computer device, the computer device comprising:

[0068] One or more processors;

[0069] Memory; and

[0070] One or more applications, wherein the one or more applications are stored in the memory and configured to be executed by the processor to implement the hub exploration method.

[0071] On the other hand, this application also provides a computer-readable storage medium having a computer program stored thereon, the computer program being loaded by a processor to perform the steps in the distribution center exploration method.

[0072] This application embodiment obtains a target tile map corresponding to a target area; performs target detection on the target tile map to obtain a first set of suspected distribution centers and a first set of location information corresponding to each suspected distribution center; obtains a second set of location information corresponding to existing distribution centers within the target area; and filters the first set of distribution centers based on the first and second location information sets to obtain a filtered second set of distribution centers. Compared with traditional methods, this approach creatively obtains a preliminary set of suspected distribution centers and a first set of location information for all suspected distribution centers by performing target detection on the target tile map. This allows for efficient acquisition of the suspected distribution center set, which is then filtered to exclude all existing distribution centers within the target area, improving the efficiency and accuracy of distribution center exploration. Attached Figure Description

[0073] To more clearly illustrate the technical solutions in the embodiments of this application, 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 this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0074] Figure 1 This is a schematic diagram of a distribution center exploration system provided in an embodiment of this application;

[0075] Figure 2This is a schematic flowchart of an embodiment of the distribution center exploration method provided in this application.

[0076] Figure 3 This is a schematic flowchart of an embodiment of filtering the second distribution center set provided in this application;

[0077] Figure 4 This is a schematic flowchart of an embodiment of obtaining the target tile map corresponding to the target area provided in this application;

[0078] Figure 5 This is a schematic diagram of an embodiment of the distribution center exploration device provided in this application.

[0079] Figure 6 This is a schematic diagram of an embodiment of the computer device provided in this application.

[0080] Figure 7 This is a schematic diagram of a model training sample provided in the embodiments of this application. Detailed Implementation

[0081] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0082] In the description of this application, it should be understood that the terms "center," "longitudinal," "lateral," "length," "width," "thickness," "upper," "lower," "front," "rear," "left," "right," "vertical," "horizontal," "top," "bottom," "inner," and "outer," etc., indicating orientation or positional relationships based on the orientation or positional relationships shown in the accompanying drawings, are used only for the convenience of describing this application and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation, and therefore should not be construed as a limitation of this application. Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of indicated technical features. Thus, features defined with "first" and "second" may explicitly or implicitly include one or more of the stated features. In the description of this application, "a plurality of" means two or more, unless otherwise explicitly specified.

[0083] In this application, the term "exemplary" is used to mean "used as an example, illustration, or description." Any embodiment described as "exemplary" in this application is not necessarily to be construed as being more preferred or advantageous than other embodiments. The following description is provided to enable any person skilled in the art to make and use this application. Details are set forth in the following description for purposes of explanation. It should be understood that those skilled in the art will recognize that this application can be made without using these specific details. In other instances, well-known structures and processes are not described in detail to avoid obscuring the description of this application with unnecessary detail. Therefore, this application is not intended to be limited to the embodiments shown, but is consistent with the broadest scope of the principles and features disclosed in this application.

[0084] This application provides a distribution center exploration method, apparatus, computer equipment, and readable storage medium, which will be described in detail below.

[0085] like Figure 1 As shown, Figure 1 This is a schematic diagram of a distribution center exploration system provided in an embodiment of this application. The distribution center exploration system may include a computer device 100, which integrates a distribution center exploration device, such as... Figure 1 Computer equipment 100.

[0086] In this embodiment, the computer device 100 can be a terminal or a server. When the computer device 100 is a server, it can be a standalone server, a server network, or a server cluster. For example, the computer device 100 described in this embodiment includes, but is not limited to, computers, network hosts, single network servers, multiple sets of network servers, or cloud servers constructed from multiple servers. The cloud server is constructed from a large number of computers or network servers based on cloud computing.

[0087] It is understood that when the computer device 100 in this embodiment is a terminal, the terminal used can be a device that includes both receiving and transmitting hardware, that is, a device with receiving and transmitting hardware capable of performing bidirectional communication on a bidirectional communication link. Such a device may include: cellular or other communication devices, which have a single-line display, a multi-line display, or a cellular or other communication device without a multi-line display. Specifically, the computer device 100 may be a desktop terminal or a mobile terminal, and the computer device 100 may also be one of a mobile phone, tablet computer, laptop computer, medical auxiliary instrument, etc.

[0088] Those skilled in the art will understand that Figure 1The application environment shown is merely one application scenario for the solution in this application and is not intended to limit the application scenario of the solution in this application. Other application environments may include more than one. Figure 1 The number of computer devices shown is more or less, for example Figure 1 Only one computer device is shown in the diagram. It is understood that the distribution center exploration system may also include one or more other computer devices, which are not specified here.

[0089] Those skilled in the art will understand that Figure 1 The application environment shown is merely one application scenario for the solution in this application and is not intended to limit the application scenario of the solution in this application. Other application environments may include more than one. Figure 1 The number of computer devices shown is more or less, for example Figure 1 Only one computer device is shown in the diagram. It is understood that the distribution center exploration system may also include one or more other computer devices, which are not specified here.

[0090] In addition, such as Figure 1 As shown, the distribution center exploration system may also include a memory 200 for storing data, such as storing the current target tile map within the target area and distribution center exploration data, for example, the distribution center exploration data during the operation of the distribution center exploration system.

[0091] It should be noted that, Figure 1 The schematic diagram of the distribution center exploration system shown is merely an example. The distribution center exploration system and scenario described in this application embodiment are for the purpose of more clearly illustrating the technical solutions of this application embodiment and do not constitute a limitation on the technical solutions provided in this application embodiment. As those skilled in the art will know, with the evolution of the distribution center exploration system and the emergence of new business scenarios, the technical solutions provided in this application embodiment are also applicable to similar technical problems.

[0092] Next, we will introduce the distribution center exploration method provided in the embodiments of this application.

[0093] In the embodiments of the distribution center exploration method of this application, the distribution center exploration device is used as the execution subject. For the sake of simplification and ease of description, the execution subject will be omitted in the subsequent method embodiments. The distribution center exploration device is applied to computer equipment.

[0094] Please see Figures 2 to 6 , Figure 2 This is a schematic flowchart of an embodiment of the distribution center exploration method provided in this application, which includes steps 201 to 204:

[0095] 201. Obtain the target tile map corresponding to the target area;

[0096] In this context, the color parameters of the pixel positions in the target tile image are determined by the trajectory data of the target object group within the target area. The trajectory data includes the positioning information, velocity parameters, and azimuth parameters of multiple trajectory points. The positioning information is the positioning data generated by the trajectory point at its current location, and the azimuth parameters are the azimuth data generated by the trajectory point moving in a certain direction.

[0097] In this embodiment, the target area can be a city-level area, a provincial-level area, or a national-level area, which can be selected according to the actual situation. Preferably, the target area in this embodiment is set to a national-level area. The target tile map includes the target area. If the target geographical range to be observed includes multiple target areas, and at least two target areas are located on different tile maps, then different tile maps need to be used as target tile maps respectively, and then processed according to the solution of this application. The specific embodiment of this application is illustrated using one target tile map as an example.

[0098] The trajectory data can include positioning information, speed parameters, and azimuth parameters of multiple trajectory points generated by the target group. This trajectory data can be obtained in real time by installing corresponding data acquisition devices on the target group, such as existing GPS systems. The target group can be a group of vehicles within a logistics company or a group of vehicles that have signed cooperation agreements with the logistics company, so that the target group can collect data according to preset requirements and facilitate acquisition. The vehicle types corresponding to this group are not limited; specifically, it can be large trucks, medium-sized trucks, and small trucks used for delivery, or vans and electric bicycles used for parcel delivery. It should be noted that with the current era, logistics services have covered a considerable area, and the number of vehicles has reached a certain level, resulting in the total amount of trajectory data of the vehicle group exceeding 15 billion, with a maximum of over 200,000 trajectory data points generated per second.

[0099] The tile map is formed using the existing Web Mercator projection. Each tile level corresponds to a different number of tile maps. That is, a global map image is divided into different numbers of tile maps at different tile levels. For example, in this specific embodiment, the tile level is set to 14 levels, and the size of each tile map can be set to 256*256 PNG, meaning each tile map is divided into 256*256 pixel grids. The ground resolution is approximately 10 meters per pixel, and each pixel grid represents a geographical area of ​​approximately 10 meters * 10 meters. Each pixel grid has one pixel position. This allows for sufficient information storage while saving storage space, ensuring mining efficiency and improving mining accuracy. Of course, those skilled in the art can adjust the level and pixel grid size according to different application scenarios. This specific embodiment is based on the above-mentioned 14-level, 256*256 tile map. The target tile map size in this application can correspond to the size of a city, a province, or a country, depending on the actual situation. The color parameters of the pixel positions in the target tile image correspond to the trajectory data of the target object group. Specifically, the R, G, and B values ​​of the pixel position's color parameters correspond to the velocity parameter, azimuth parameter, and magnitude parameter of the trajectory points, respectively. The R, G, and B values ​​are the values ​​corresponding to the red, green, and blue color channels in the RGB color mode. The pixel position refers to the location of each pixel in the tile image, represented by pixel coordinates. The magnitude parameter of the trajectory points is the total number of trajectory points included at a given pixel position.

[0100] In some embodiments of this application, such as Figure 4 As shown, obtaining the target tile map corresponding to the target area includes:

[0101] 401. For the target area, acquire the trajectory data generated by the target object group within a preset time period. The trajectory data includes the positioning information, velocity parameters, and azimuth parameters of multiple trajectory points.

[0102] In this embodiment, the preset time period can be set according to actual needs; for example, the time period can be set to one hour, one day, or three days. In the preferred embodiment of this application, the time period is set to one day.

[0103] Specifically, when the target group is in motion, its installed equipment, such as a GPS system, will automatically generate positioning information, speed parameters, and azimuth parameters, and will upload the positioning information, speed parameters, azimuth parameters, and time parameters to a preset database. This application can retrieve the data from the preset database to obtain the trajectory data generated by the target group within a preset time period. The positioning information is the positioning data generated by the trajectory point at its location, and the azimuth parameter is the azimuth data generated by the trajectory point moving in a certain direction.

[0104] 402. Based on the positioning information of multiple trajectory points, determine the magnitude parameters of the trajectory points of each pixel position in the initial tile map corresponding to the positioning information.

[0105] In this embodiment, based on the positioning information of the multiple trajectory points, the trajectory point magnitude parameters of each pixel position in the initial tile map corresponding to the positioning information are determined. The initial tile map refers to the tile coordinates and pixel coordinates corresponding to each geographic coordinate obtained by calculating the geographic coordinates of the trajectory points, such as the national standard GCJ02, according to the following tile formula and pixel coordinate formula, thereby forming a corresponding tile map. This tile map is called the initial tile map, and the initial tile map can be constructed according to existing technology, i.e., a conventional tile map. Specifically, it includes: firstly, based on the positioning information of the multiple trajectory points, determining the coordinate information of each trajectory point in the initial tile map. The coordinate information includes tile coordinates and pixel coordinates. Specifically, the coordinates corresponding to the positioning information can be converted into coordinates in the initial tile map. The specific conversion method is as follows:

[0106] Specifically, the tile coordinate transformation formula is as follows, where the tile coordinates can represent which tile the trajectory point is located on:

[0107] n = 2^zoom;

[0108] xtile=n*((lon_deg+180) / 360);

[0109] ytile=n*((1-(log(tan(lat_read)+sec(lat_read)) / π)) / 2;

[0110] Where, zoom is the tile level, and the tile level used in this application is 14. n*n represents the total number of tiles in this level. lon_deg represents the x-coordinate of the trajectory point, which is the longitude. lat_rad represents the y-coordinate of the trajectory point, which is the latitude. xtile represents the tile x-coordinate. ytile represents the tile y-coordinate. (xtile, ytile) represents the tile coordinates.

[0111] Specifically, the pixel coordinate transformation formula is:

[0112]

[0113]

[0114] In this context, "zoom level" refers to the number of tile levels, "Pixels" refers to the number of horizontal segments (pixel grids) each tile is divided into (in this application, it's divided into 256 segments, so "Pixels" represents 256), "x" is the pixel x-coordinate, "y" is the pixel y-coordinate, "λ" is the trajectory x-coordinate (longitude, converted to radians), and "ψ" is the trajectory y-coordinate (latitude, converted to radians). "(x, y)" represents the pixel coordinates. Pixel coordinates characterize the pixel position of each trajectory point in the corresponding tile map; tiles with the same tile coordinates and pixel coordinates are considered to be at the same pixel position.

[0115] Then, based on the coordinate information, the number of trajectory points at each pixel position in the initial tile image is counted to obtain the magnitude parameter of the trajectory points at each pixel position.

[0116] 403. Based on the velocity parameters, azimuth parameters, and magnitude parameters of each trajectory point, determine the color parameters of each pixel position to obtain the target tile map corresponding to the target area. The color parameters are displayed according to the first color system, which is established with reference to the RGB color mode. The color parameters include R value, G value, and B value. The R value, G value, and B value in the color parameters correspond to the velocity parameter, azimuth parameter, and magnitude parameter of the trajectory point, respectively.

[0117] In this embodiment of the application, the creation of the first color system includes:

[0118] Based on the velocity parameters and azimuth parameters of each trajectory point, as well as the magnitude parameters of the trajectory points, the color parameters of each current pixel position are determined to obtain the target tile map corresponding to the target region. The color parameters are displayed according to a first color system, which is established with reference to the RGB color mode. The color parameters include R values, G values, and B values, and the R, G, and B values ​​in the color parameters correspond to the velocity parameters, the azimuth parameters, and the magnitude parameters of the trajectory points, respectively.

[0119] In some embodiments of this application, the creation of the first color system includes:

[0120] (1) Take a preset speed range interval, classify the speed parameters of all trajectory points in the trajectory data according to the speed range interval, and obtain a set of trajectory points in the preset speed range interval, each speed range interval corresponding to a different R value;

[0121] Specifically, two speed ranges can be selected, such as the first speed range being [0, 30] (km / h) and the second speed range being greater than 30 (km / h). By setting the speed ranges to these two ranges, it is possible to effectively distinguish whether the vehicle is traveling at a normal speed. Under normal circumstances, the speed of a vehicle on a normal road is generally higher than 30 (km / h).

[0122] (2) Take a preset number of azimuth range intervals, classify the azimuth parameters of all trajectory points in the trajectory data according to the azimuth range intervals, and obtain a set of trajectory points in the preset number of azimuth range intervals, with each azimuth range interval corresponding to a different G value;

[0123] Specifically, 16 azimuth ranges can be selected, with each azimuth range being 22.5°, that is, 360° is divided into 16 22.5° ranges.

[0124] (3) Take a preset range of trajectory point magnitudes, classify the trajectory point magnitude parameters of all trajectory points in the trajectory data, and obtain a set of trajectory points within a preset range of trajectory point magnitudes, with each of the trajectory point magnitude ranges corresponding to a different B value;

[0125] Specifically, three ranges of trajectory point magnitudes can be selected, namely [0, 100), [100, 1000), and [1000, +∞].

[0126] (4) Based on the set of trajectory points in the preset speed range interval, the set of trajectory points in the preset azimuth range interval, and the set of trajectory points in the preset trajectory point magnitude range interval, determine the color parameters of the pixel positions of all trajectory points in the trajectory data in the initial tile map to obtain the current target tile map. Different combinations of speed range intervals, azimuth range intervals and trajectory point magnitude range intervals correspond to different preset color rendering colors.

[0127] In some embodiments of this application, since multiple trajectory points may exist at each pixel position, and each trajectory point has its own velocity parameter and azimuth parameter, and the R value, G value, and B value in the color parameters correspond to the velocity parameter, the azimuth parameter, and the trajectory point magnitude parameter, respectively, the determination of each RGB value can be calculated according to the actual situation or an appropriate method. For example, the method further includes: for each pixel position, based on the velocity parameter of each trajectory point, counting the number of trajectory points falling into each velocity range interval, and selecting the R value corresponding to the velocity range interval with the largest number of trajectory points as the R value of the corresponding pixel position; for each pixel position, based on the azimuth parameter of each trajectory point, counting the number of trajectory points falling into each azimuth range interval, and selecting the G value corresponding to the azimuth range interval with the largest number of trajectory points as the G value of the corresponding pixel position; for each pixel position, based on the trajectory point magnitude parameter, determining the corresponding trajectory point magnitude range interval and using the B value corresponding to the trajectory point magnitude range interval as the B value of the corresponding pixel position.

[0128] In this embodiment of the application, the RGB color mode can be used to render the trajectory point set of the preset speed range interval, the trajectory point set of the preset speed range interval, and the trajectory point set of the preset trajectory point magnitude range interval to obtain the RGB values ​​of the pixel positions of all trajectory points in the trajectory data in the initial tile map, so as to determine the target tile map corresponding to the target area.

[0129] In one specific implementation, the sets from three categories—a set of trajectory points within a preset speed range, a set of trajectory points within a preset azimuth range, and a set of trajectory points within a preset magnitude range—can be combined to obtain multiple combination results. Each combination result is then rendered using RGB color mode. For example, as shown in the above example, there are 2 speed ranges, 16 azimuth ranges, and 3 trajectory point magnitude ranges. Combining the sets from these three categories results in 2*16*3=96 combination results, which are then rendered using RGB color mode.

[0130] 202. Perform target detection on the target tile map to obtain the first set of suspected distribution centers and the first set of location information corresponding to each suspected distribution center;

[0131] Among them, suspected distribution centers refer to certain areas in the target tile map that are preliminarily identified as potential distribution centers after target detection is performed on the target tile map.

[0132] In some embodiments of this application, a pre-defined object detection model based on a deep learning network can be used to detect objects in the target tile image, thereby obtaining a first set of suspected cluster centers. Preferably, the pre-defined object detection model based on a deep learning network can be the open-source YOLOE model from PaddleDetection. The YOLOE model in this application can be a high-efficiency anchor-free model; the configuration of this model in this application is as follows:

[0133] (1) No anchor point: YOLOE borrows from FCOS, placing an anchor point on each pixel to set upper and lower boundaries for the three detectors and assigning ground truths to the corresponding feature maps. Then, it calculates the center position of the bounding box and selects the nearest pixel as the positive sample. This approach improves the model's processing speed.

[0134] (2) Model backbone and neck: YOLO-V4 and YOLO-V5: CSPNet uses cross-stage Dense Connections to reduce computational burden without sacrificing accuracy; the YOLOE in this application combines residual connections and dense connections for the backbone and neck of YOLOE.

[0135] Furthermore, the YOLOE in this application selects the ET-Head structure; this structure makes positive samples with high IoU contribute relatively more to the loss, which also makes the model focus more on high-quality samples rather than low-quality samples during training. Both use IoU-aware classification score (IACS) as the prediction target, which can effectively obtain a joint representation of classification score and localization quality estimation, making training and inference highly consistent.

[0136] It should be noted that in the target tile image, suspected cluster centers have their own identifying features. The target detection model is mainly used to identify these identifying features to determine whether a suspected cluster center exists. Specifically, the identifying features of a suspected cluster center are the presence of a target pixel region with multiple azimuth angles. That is, there are at least two pixel positions in this pixel region, and the azimuth angle distribution of all pixel positions is dense, multidirectional, and irregular. In other words, multiple pixel positions with multiple azimuth angles are densely distributed within the target pixel region. The coverage area of ​​this region is generally not less than 1 square kilometer, but can be adjusted according to actual needs and experience. Specifically, before using the preset target detection model based on a deep learning network, it may also include training the initial target detection model, where the training samples are shown in the attached figure. Figure 7 As shown, from Figure 7 As can be seen, at the center of the image, there exists a densely packed, irregularly distributed area of ​​points—a region where the points are relatively concentrated and radiate outwards. This area, exhibiting the aforementioned density, multidirectionality, and irregularity in its azimuth distribution, corresponds to the target pixel region mentioned above. Because of these identifying characteristics, it will exhibit an attached... Figure 7 The aforementioned collection area. It should be noted that, due to the format requirements of the accompanying drawings in the specification, Figure 7 The points in the image are all black and white, without any other color. However, in reality, the colors of the points within the set of points in the image are diverse. This can be understood as follows: within the distribution center, the vehicle speed, azimuth angle, and number of vehicles at each pixel location will vary, thus causing significant differences in the color parameters of the corresponding pixel locations.

[0137] In some embodiments of this application, obtaining the first location information set corresponding to the suspected distribution centers in the first distribution center set may include: obtaining the pixel position of the suspected distribution centers in the first distribution center set on the target tile map, and then determining the first location information set corresponding to the suspected distribution centers in the first distribution center set based on the pixel position. Specifically, the pixel position in the tile map is determined by tile coordinates and pixel coordinates. Based on the construction of the initial tile map, it is known that the positioning information of each trajectory point of the target object group includes geographic coordinates, such as latitude and longitude information. These geographic coordinates can be converted to each other with the tile coordinates and pixel coordinates. Therefore, by analyzing the target tile map... By performing target detection on a picture, a target pixel region that is suspected to be covered by a distribution center can be obtained. This region generally includes multiple pixel positions. The range and location of the target pixel region are determined by each pixel position. Each pixel position corresponds to tile coordinates and pixel coordinates. The corresponding geographic coordinates can be obtained from each pixel coordinate, and the corresponding geographic range can be obtained. Thus, the tile coordinates, pixel coordinates, and geographic coordinates corresponding to each pixel position can be obtained, which is the first set of location information. Therefore, the first set of location information can be considered as the set of coordinate information (such as tile coordinates and pixel coordinates) or geographic location information (including geographic coordinates) corresponding to each pixel position within the target pixel region.

[0138] 203. Obtain the set of second location information corresponding to the existing distribution centers within the target area;

[0139] In this embodiment of the application, the distribution center that currently exists in the target area is a distribution center that was built by the target manufacturer before the current time.

[0140] Specifically, the second location information set corresponding to the existing distribution centers within the target area can be obtained through the following two methods. The second location information set refers to the set of coordinate or geographical location information of each existing distribution center:

[0141] (1) The target manufacturer provides a second set of location information corresponding to the existing distribution centers in the target area. For example, the target manufacturer can be SF Express, which can provide location information corresponding to all existing distribution centers in the target area (e.g., within China).

[0142] (2) By verifying the geographic information of each subset in the first location information set, it is determined whether there are existing distribution centers. The location information of these existing distribution centers constitutes the second location information set. Specifically, a preset automatic geographic information verification program can be used to verify the geographic information of the first location information set corresponding to the suspected distribution centers in the first distribution center set using a MySQL relational database or a map database. For example, the geographic information corresponding to each location in the first location information set can be queried on a map. This geographic information may include latitude and longitude, geographic use, and land area. For example, if the longitude of the first location information is 116.9689411° and the latitude is 29.7214391°, and there is no park with a target area within a preset range (e.g., within a diameter of 1 kilometer), it can be determined that there is no distribution center at the location corresponding to the first location information. Conversely, if there is, it can be confirmed that a distribution center already exists. For example, a query and comparison of pixel coordinates and tile coordinates can be performed using a relational database to confirm whether a distribution center already exists in the same area of ​​the same tile.

[0143] The first location information set is a summary of the location information of all suspected distribution centers obtained by detecting the target tile map through the target detection model. These suspected distribution centers may include existing distribution centers, newly built or unknown distribution centers, and some areas with the aforementioned identification features but not distribution centers. The second location information set is a summary of the location information of existing distribution centers.

[0144] 204. Based on the first location information set and the second location information set, the first distribution center set is filtered to obtain the filtered second distribution center set.

[0145] In this embodiment of the application, the step of filtering the first set of distribution centers based on the first set of location information and the second set of location information to obtain a filtered second set of distribution centers includes:

[0146] (1) The intersection of the first location information set and the second location information set is used to obtain the third location information set. The purpose of the intersection calculation is to obtain the location information of all currently existing or existing distribution centers in the first location information set, i.e., the third location information set;

[0147] (2) Obtain the original distribution center set corresponding to all location information in the third location information set. The original distribution centers in the original distribution center set are distribution centers that existed before the distribution center exploration was carried out through this scheme, i.e., the distribution centers that currently exist or are already in existence.

[0148] (3) Remove all distribution centers from the original distribution center set from the first distribution center set to obtain the filtered second distribution center set, which includes all newly built or unknown distribution centers.

[0149] In this embodiment of the application, compared with the traditional method, the target tile map is detected to obtain a preliminary set of suspected distribution centers and a first location information set of all suspected distribution centers. Thus, the set of suspected distribution centers can be obtained efficiently, and then filtered to exclude all existing distribution centers in the target area, thereby improving the efficiency and accuracy of distribution center exploration.

[0150] In another embodiment of this application, such as Figure 3 As shown, after filtering the first set of distribution centers based on the first set of location information and the second set of location information to obtain the filtered second set of distribution centers, the method further includes:

[0151] 301. Obtain the fourth location information set of all distribution centers in the second distribution center set. The geographical attributes of each distribution center can be obtained through model detection, or the geographical attributes can be determined through the location information of each distribution center, or other existing methods can be used.

[0152] For example, the method for obtaining the first location information set corresponding to the suspected distribution center in the first distribution center set in step 202 above is the same, and will not be elaborated here.

[0153] 302. Determine the geographic attributes corresponding to each location information in the fourth location information set, which includes the geographic attributes of each distribution center;

[0154] Specifically, one can look up the geographic attributes of each location in the fourth location information set on an electronic map. For example, by entering a location on the electronic map, one can obtain the geographic attributes corresponding to that location, which may be a residential area, a hotel, a street, a park, a gas station, a service station, etc.

[0155] 303. Remove the target distribution centers in the second distribution center set whose geographical attributes are of the preset type, and obtain the third distribution center set, which is all newly built or unknown distribution centers.

[0156] In this application embodiment, the preset geographical attributes may include, but are not limited to, geographical attributes such as gas stations, highway service areas, and industrial parks. Specifically, because geographical attributes such as gas stations, highway service areas, and industrial parks have similar identifying characteristics to the distribution centers in this application, such as a relatively concentrated number of vehicles, diverse vehicle trajectories within the area, and diverse azimuth angles, but they are not distribution centers, they need to be deleted.

[0157] In this embodiment of the application, by further filtering the target distribution centers in the second distribution center set whose geographical attributes are of a preset type based on the geographical attributes corresponding to each location information in the fourth location information set, the accuracy of distribution center exploration is improved.

[0158] To better implement the distribution center exploration method in the embodiments of this application, based on the distribution center exploration method, the embodiments of this application also provide a distribution center exploration device, such as... Figure 5 As shown, the distribution center exploration device 500 includes:

[0159] The first acquisition unit 501 is used to acquire the target tile map corresponding to the target area;

[0160] The first target detection unit 502 is used to perform target detection on the target tile map to obtain a first set of suspected distribution centers and a first set of location information corresponding to each suspected distribution center.

[0161] The second acquisition unit 503 is used to acquire the second location information set corresponding to the currently existing distribution centers within the target area;

[0162] The first filtering unit 504 is used to filter the first distribution center set based on the first location information set and the second location information set to obtain the filtered second distribution center set.

[0163] In some embodiments of this application, the first screening unit 504 is specifically used for:

[0164] The intersection of the first location information set and the second location information set is used to obtain the third location information set;

[0165] Obtain the original distribution center set corresponding to all location information in the third location information set;

[0166] All distribution centers in the original distribution center set are removed from the first distribution center set to obtain the filtered second distribution center set.

[0167] In some embodiments of this application, after filtering the first distribution center set based on the first location information set and the second location information set to obtain a filtered second distribution center set, the device is used to:

[0168] Obtain the fourth location information set of all distribution centers in the second set of distribution centers;

[0169] Determine the geographic attributes corresponding to each location information in the fourth location information set;

[0170] Remove the target distribution centers in the second distribution center set whose geographical attributes are of a preset type to obtain the third distribution center set.

[0171] In some embodiments of this application, the first acquisition unit 501 specifically includes:

[0172] The third acquisition unit is used to acquire trajectory data of a group of target objects generated within a preset time period for the target area. The trajectory data includes positioning information, speed parameters, and azimuth parameters of multiple trajectory points.

[0173] The first determining unit is used to determine the magnitude parameters of the trajectory points of each pixel position in the initial tile map corresponding to the positioning information based on the positioning information of the plurality of trajectory points.

[0174] The second determining unit is used to determine the color parameters of the current pixel position based on the velocity parameters and azimuth parameters of each trajectory point, as well as the magnitude parameters of the trajectory points, to obtain the target tile map corresponding to the target region. The color parameters are displayed according to a first color system, which is established with reference to the RGB color mode. The color parameters include R values, G values, and B values, and the R values, G values, and B values ​​in the color parameters correspond to the velocity parameters, the azimuth parameters, and the magnitude parameters of the trajectory points, respectively.

[0175] In some embodiments of this application, the first determining unit is specifically used for:

[0176] Based on the positioning information of the multiple trajectory points, the coordinate information of each trajectory point in the initial tile map is determined, and the coordinate information includes tile coordinates and pixel coordinates;

[0177] Based on the coordinate information, the number of trajectory points at each pixel position in the initial tile image is counted to obtain the magnitude parameter of the trajectory points at each pixel position.

[0178] In some embodiments of this application, the second determining unit specifically includes:

[0179] The first classification unit is used to take a preset number of speed range intervals and classify the speed parameters of all trajectory points in the trajectory data according to the speed range intervals to obtain a set of trajectory points for the preset number of speed range intervals, with each speed range interval corresponding to a different R value;

[0180] The second classification unit is used to take a preset number of azimuth range intervals and classify the azimuth parameters of all trajectory points in the trajectory data according to the azimuth range intervals to obtain a set of trajectory points for the preset number of azimuth range intervals, with each azimuth range interval corresponding to a different G value.

[0181] The third classification unit is used to take a preset range of trajectory point magnitudes and classify the trajectory point magnitude parameters of all trajectory points in the trajectory data to obtain a set of trajectory points within a preset range of trajectory point magnitudes, with each of the trajectory point magnitude ranges corresponding to a different B value.

[0182] The third determining unit is used to determine the color parameters of the pixel positions of all trajectory points in the trajectory data in the initial tile map based on the trajectory point set of the preset velocity range interval, the trajectory point set of the preset azimuth angle range interval, and the trajectory point magnitude range interval, so as to obtain the current target tile map in the target area. Different combinations of the velocity range interval, azimuth angle range interval, and trajectory point magnitude range interval correspond to different preset color rendering colors.

[0183] In some embodiments of this application, the third determining unit is specifically used for:

[0184] The RGB color mode is used to render the trajectory point set within the preset speed range, the trajectory point set within the preset speed range, and the trajectory point set within the preset trajectory point magnitude range, to obtain the RGB values ​​of the pixel positions of all trajectory points in the trajectory data in the initial tile map, so as to determine the target tile map corresponding to the target region.

[0185] This embodiment of the application uses a first acquisition unit 501 to acquire a target tile map corresponding to a target area; a first target detection unit 502 to perform target detection on the target tile map to obtain a first set of suspected distribution centers and a first set of location information corresponding to each suspected distribution center; a second acquisition unit 503 to acquire a second set of location information corresponding to currently existing distribution centers within the target area; and a first filtering unit 504 to filter the first set of distribution centers based on the first and second set of location information to obtain a filtered second set of distribution centers. Compared to traditional devices, this invention creatively obtains a preliminary set of suspected distribution centers and a first set of location information for all suspected distribution centers by performing target detection on the target tile map. This allows for efficient acquisition of the suspected distribution center set, followed by filtering to exclude all currently existing distribution centers within the target area, thus improving the efficiency and accuracy of distribution center exploration.

[0186] In addition to the above-described methods and apparatus for exploring distribution centers, this application also provides a computer device that integrates any of the distribution center exploration devices provided in this application. The computer device includes:

[0187] One or more processors;

[0188] Memory; and

[0189] One or more applications, wherein the one or more applications are stored in the memory and configured by the processor to perform operations of any of the methods described in any of the embodiments of the above-described distribution center exploration method.

[0190] This application also provides a computer device that integrates any of the distribution center exploration devices provided in this application. For example... Figure 6 As shown, it illustrates a structural schematic diagram of the computer device involved in the embodiments of this application, specifically:

[0191] The computer device may include components such as a processor 601 with one or more processing cores, a storage unit 602 with one or more computer-readable storage media, a power supply 603, and an input unit 604. Those skilled in the art will understand that... Figure 6 The computer device structure shown does not constitute a limitation on the computer device and may include more or fewer components than shown, or combine certain components, or have different component arrangements. Wherein:

[0192] The processor 601 is the control center of the computer device. It connects various parts of the computer device via various interfaces and lines, and performs various functions and processes data by running or executing software programs and / or modules stored in the storage unit 602, and by calling data stored in the storage unit 602, thereby providing overall monitoring of the computer device. Optionally, the processor 601 may include one or more processing cores; preferably, the processor 601 may integrate an application processor and a modem processor, wherein the application processor mainly handles the operating system, user interface, and applications, and the modem processor mainly handles wireless communication. It is understood that the modem processor may not be integrated into the processor 601.

[0193] Storage unit 602 can be used to store software programs and modules. Processor 601 executes various functional applications and data processing by running the software programs and modules stored in storage unit 602. Storage unit 602 may mainly include a program storage area and a data storage area. The program storage area may store the operating system, at least one application program required for a function (such as sound playback function, image playback function, etc.), etc.; the data storage area may store data created according to the use of the computer device, etc. In addition, storage unit 602 may include high-speed random access memory, and may also include non-volatile memory, such as at least one disk storage device, flash memory device, or other volatile solid-state storage device. Accordingly, storage unit 602 may also include a memory controller to provide processor 601 with access to storage unit 602.

[0194] The computer device also includes a power supply 603 that supplies power to the various components. Preferably, the power supply 603 can be logically connected to the processor 601 through a power management system, thereby enabling functions such as charging, discharging, and power consumption management through the power management system. The power supply 603 may also include one or more DC or AC power supplies, recharging systems, power fault detection circuits, power converters or inverters, power status indicators, and other arbitrary components.

[0195] The computer device may also include an input unit 604, which can be used to receive input digital or character information and generate keyboard, mouse, joystick, optical or trackball signal inputs related to user settings and function control.

[0196] Although not shown, the computer device may also include a display unit, etc., which will not be described in detail here. Specifically, in the embodiments of this application, the processor 601 in the computer device loads the executable files corresponding to the processes of one or more applications into the storage unit 602 according to the following instructions, and the processor 601 runs the applications stored in the storage unit 602 to realize various functions, as follows:

[0197] Obtain the target tile map corresponding to the target area; perform target detection on the target tile map to obtain a first set of suspected distribution centers and a first set of location information corresponding to each suspected distribution center; obtain a second set of location information corresponding to the currently existing distribution centers in the target area; based on the first set of location information and the second set of location information, filter the first set of distribution centers to obtain a filtered second set of distribution centers.

[0198] This application embodiment obtains a target tile map corresponding to a target area; performs target detection on the target tile map to obtain a first set of suspected distribution centers and a first set of location information corresponding to each suspected distribution center; obtains a second set of location information corresponding to currently existing distribution centers within the target area; and filters the first set of distribution centers based on the first and second set of location information to obtain a filtered second set of distribution centers. Compared to traditional methods, this innovative approach obtains a preliminary set of suspected distribution centers and a first set of location information for all suspected distribution centers by performing target detection on the target tile map. This allows for efficient acquisition of the suspected distribution center set, which is then filtered to exclude all currently existing distribution centers within the target area, improving the efficiency and accuracy of distribution center exploration.

[0199] Therefore, embodiments of this application provide a computer-readable storage medium, which may include: read-only memory (ROM), random access memory (RAM), a magnetic disk, or an optical disk, etc. The computer-readable storage medium stores multiple instructions, which can be loaded by a processor to execute the steps in any of the distribution center exploration methods provided in embodiments of this application. For example, the instructions can execute the following steps:

[0200] Obtain the target tile map corresponding to the target area; perform target detection on the target tile map to obtain a first set of suspected distribution centers and a first set of location information corresponding to each suspected distribution center; obtain a second set of location information corresponding to the currently existing distribution centers in the target area; based on the first set of location information and the second set of location information, filter the first set of distribution centers to obtain a filtered second set of distribution centers.

[0201] In the above embodiments, the descriptions of each embodiment have different focuses. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions in other embodiments.

[0202] The above provides a detailed description of a distribution center exploration method, apparatus, computer equipment, and readable storage medium provided in the embodiments of this application. Specific examples have been used to illustrate the principles and implementation methods of this application. The descriptions of the above embodiments are only for the purpose of helping to understand the method and core ideas of this application. At the same time, for those skilled in the art, there will be changes in the specific implementation methods and application scope based on the ideas of this application. Therefore, the content of this specification should not be construed as a limitation of this application.

Claims

1. A method of hub exploration, characterized by, The method includes: Obtain the target tile map corresponding to the target area; Target detection is performed on the target tile map to obtain a first set of suspected distribution centers and a first set of location information corresponding to each suspected distribution center; Obtain the second location information set corresponding to the currently existing distribution centers within the target area; Based on the first set of location information and the second set of location information, the first set of distribution centers is filtered to obtain the filtered second set of distribution centers. The step of obtaining the target tile map corresponding to the target region includes: For the target area, acquire trajectory data of the target object group generated within a preset time period. The trajectory data includes the positioning information, velocity parameters, and azimuth parameters of multiple trajectory points. Based on the positioning information of the multiple trajectory points, determine the magnitude parameters of the trajectory points of each pixel position in the initial tile map corresponding to the positioning information. Based on the velocity parameters and azimuth parameters of each trajectory point, as well as the magnitude parameters of the trajectory points, the color parameters of each current pixel position are determined to obtain the target tile map corresponding to the target region. The color parameters are displayed according to a first color system, which is established with reference to the RGB color mode. The color parameters include R values, G values, and B values, and the R, G, and B values ​​in the color parameters correspond to the velocity parameters, the azimuth parameters, and the magnitude parameters of the trajectory points, respectively. The step of filtering the first set of distribution centers based on the first set of location information and the second set of location information to obtain a filtered second set of distribution centers includes: The intersection of the first location information set and the second location information set is used to obtain the third location information set; Obtain the original distribution center set corresponding to all location information in the third location information set; All distribution centers in the original distribution center set are removed from the first distribution center set to obtain the filtered second distribution center set.

2. The hub exploration method of claim 1, wherein, After filtering the first set of distribution centers based on the first set of location information and the second set of location information to obtain a filtered second set of distribution centers, the method further includes: Obtain the fourth location information set of all distribution centers in the second set of distribution centers; Determine the geographic attributes corresponding to each location information in the fourth location information set; Remove the target distribution centers in the second distribution center set whose geographical attributes are of a preset type to obtain the third distribution center set.

3. The hub exploration method of claim 1, wherein, The determination of the trajectory point magnitude parameters of each pixel position in the initial tile image corresponding to the current positioning information based on the positioning information of the plurality of trajectory points includes: Based on the positioning information of the multiple trajectory points, the coordinate information of each trajectory point in the initial tile map is determined, and the coordinate information includes tile coordinates and pixel coordinates; Based on the coordinate information, the number of trajectory points at each pixel position in the initial tile image is counted to obtain the magnitude parameter of the trajectory points at each pixel position.

4. The hub exploration method of claim 1, wherein, The creation of the first color system includes: Take a preset speed range interval, classify the speed parameters of all trajectory points in the trajectory data according to the speed range interval, and obtain a set of trajectory points for the preset speed range interval, with each speed range interval corresponding to a different R value; Take a preset range of azimuth angles, classify the azimuth angle parameters of all trajectory points in the trajectory data according to the range of azimuth angles, and obtain a set of trajectory points for the preset range of azimuth angles, with each range of azimuth angles corresponding to a different G value; Take a preset range of trajectory point magnitudes, classify the trajectory point magnitude parameters of all trajectory points in the trajectory data, and obtain a set of trajectory points within the preset range of trajectory point magnitudes, with each of the trajectory point magnitude ranges corresponding to a different B value; Based on the preset sets of trajectory points within a certain speed range, the preset sets of trajectory points within a certain azimuth range, and the preset sets of trajectory point magnitude ranges, the color parameters of the pixel positions corresponding to all trajectory points in the trajectory data in the initial tile map are determined to obtain the current target tile map within the target area. Different combinations of the speed range, azimuth range, and trajectory point magnitude range correspond to different preset display colors.

5. The hub exploration method of claim 4, wherein, The R, G, and B values ​​in the color parameters correspond to the velocity parameter, the azimuth parameter, and the trajectory point magnitude parameter, respectively. The method further includes: For each pixel position, based on the velocity parameters of each trajectory point, the number of trajectory points falling into each velocity range interval is counted, and the R value corresponding to the velocity range interval with the largest number of trajectory points is selected as the R value of the corresponding pixel position. For each pixel position, based on the azimuth parameters of each trajectory point, the number of trajectory points falling into each azimuth range interval is counted, and the G value corresponding to the azimuth range interval with the largest number of trajectory points is selected as the G value of the corresponding pixel position. For each pixel position, based on the trajectory point magnitude parameter, the corresponding trajectory point magnitude range is determined, and the trajectory point magnitude range is used as the B value for the corresponding pixel position.

6. The hub exploration method of claim 4, wherein, The method, based on the preset set of trajectory points within a preset range of speeds, the preset set of trajectory points within a preset range of azimuth angles, and the preset set of trajectory points within a preset range of magnitude, determines the color parameters of the pixel positions corresponding to all trajectory points in the trajectory data in the initial tile image, thereby obtaining the current target tile image within the target area, including: The RGB color mode is used to render the trajectory point set within the preset speed range, the trajectory point set within the preset speed range, and the trajectory point set within the preset trajectory point magnitude range, to obtain the RGB values ​​of the pixel positions of all trajectory points in the trajectory data in the initial tile map, so as to determine the target tile map corresponding to the target region.

7. A hub discovery apparatus, characterized by comprising: The device includes: The first acquisition unit is used to acquire the target tile map corresponding to the target area; The first target detection unit is used to perform target detection on the target tile map to obtain a first set of suspected distribution centers, and a first set of location information corresponding to the suspected distribution centers in each first set of distribution centers. The second acquisition unit is used to acquire a second set of location information corresponding to the currently existing distribution centers within the target area; The first filtering unit is used to filter the first distribution center set based on the first location information set and the second location information set to obtain the filtered second distribution center set. The first acquisition unit includes: The third acquisition unit is used to acquire trajectory data of a group of target objects generated within a preset time period for the target area. The trajectory data includes positioning information, speed parameters, and azimuth parameters of multiple trajectory points. The first determining unit is used to determine the magnitude parameters of the trajectory points of each pixel position in the initial tile map corresponding to the positioning information based on the positioning information of the plurality of trajectory points. The second determining unit is used to determine the color parameters of the current pixel position based on the velocity parameters and azimuth parameters of each trajectory point, as well as the magnitude parameters of the trajectory points, to obtain the target tile map corresponding to the target region. The color parameters are displayed according to a first color system, which is established with reference to the RGB color mode. The color parameters include R values, G values, and B values, and the R values, G values, and B values ​​in the color parameters correspond to the velocity parameters, the azimuth parameters, and the magnitude parameters of the trajectory points, respectively. The first filtering unit is further configured to take the intersection of the first location information set and the second location information set to obtain a third location information set; obtain the original distribution center set corresponding to all location information in the third location information set; and remove all distribution centers in the original distribution center set from the first distribution center set to obtain the filtered second distribution center set.

8. A computer-readable storage medium, characterized in that, It stores a computer program, which is loaded by a processor to execute the steps in the distribution center exploration method according to any one of claims 1 to 6.

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