Trajectory data fusion method and apparatus, and related device

Abstract

The application provides a trajectory data fusion method and device and related equipment, the method comprising: for the same target area, obtaining first trajectory data and second trajectory data generated by a first target object group and a second target object group in a preset time period respectively; determining a first target tile map of a current first object data center based on the first trajectory data, the color parameter of the pixel position in the first target tile map corresponding to the first trajectory data; obtaining a second target tile map of a current second object data center, the color parameter of the pixel position in the second target tile corresponding to the second trajectory data, wherein the color parameter is used to represent color; and superimposing the first target tile map and the second target tile map to obtain a superimposed first superimposed tile map. The application embodiment improves data security, and indirectly fuses trajectory data of different object data centers, which can effectively improve the accuracy of actual road condition information.

Description

Technical Field

[0001] This application relates to the field of geographic information technology, specifically to a trajectory data fusion method, apparatus, and related equipment. Background Technology

[0002] With the development of mobile internet, real-time traffic information is being used more and more. Currently, several vendors offer real-time traffic services, but all have shortcomings and insufficient accuracy. Furthermore, vendors directly publishing vehicle data raises data security concerns.

[0003] Therefore, improving the accuracy of real-time traffic data analysis and enhancing data security are urgent technical issues that need to be addressed in the field of geographic information technology. Summary of the Invention

[0004] This application provides a trajectory data fusion method, apparatus, and related equipment, aiming to solve the technical problems of how to improve the accuracy of real-time traffic data analysis and enhance data security.

[0005] On the one hand, this application provides a trajectory data fusion method, the method comprising:

[0006] For the same target area, first trajectory data and second trajectory data generated by the first target object group and the second target object group within a preset time period are obtained respectively. The first trajectory data and the second trajectory data both include the positioning information, velocity parameters and azimuth parameters of multiple trajectory points.

[0007] Based on the first trajectory data, a first target tile map of the current first object data center is determined, wherein the color parameters of the pixel positions in the first target tile map correspond to the first trajectory data;

[0008] Obtain the second target tile map of the current second object data center, wherein the color parameters of the pixel positions in the second target tile correspond to the second trajectory data, and the color parameters are used to characterize the color;

[0009] The first target tile image and the second target tile image are superimposed to obtain the first superimposed tile image.

[0010] In one possible implementation of this application, determining the first target tile map of the current first object data center based on the first trajectory data includes:

[0011] Based on the positioning information of each trajectory point, determine the pixel position in the initial tile map corresponding to the positioning information of each trajectory point and the trajectory point magnitude parameters of each pixel position;

[0012] Based on the velocity parameters and azimuth parameters of the multiple trajectory points, as well as the magnitude parameters of the trajectory points, the color parameters of each pixel position are determined to obtain the first target tile map of the current first object data center. The color parameters include H value, V value, and S value. The H value, V value, and S value of the color parameters of each pixel position are determined by the azimuth parameter, velocity parameter, and magnitude parameter of the trajectory point at each pixel position, respectively.

[0013] In one possible implementation of this application, determining the color parameters of each pixel position based on the velocity parameters and azimuth parameters of the plurality of trajectory points, as well as the magnitude parameters of the trajectory points, to obtain the first target tile map of the current first object data center includes:

[0014] Based on the velocity parameters of the multiple trajectory points and the positioning information, determine the average velocity parameter of the trajectory points at each pixel position corresponding to the current positioning information;

[0015] Based on the azimuth parameters of the multiple trajectory points and the positioning information, determine the mean azimuth parameter of the trajectory points at each pixel position corresponding to the current positioning information;

[0016] Based on the average velocity parameter, the average azimuth parameter, and the trajectory point magnitude parameter of the pixel position, the color parameter of each pixel position is determined to obtain the first target tile map of the current first object data center. The H, V, and S values ​​of the color parameter of each pixel position correspond to the average velocity parameter, the average azimuth parameter, and the trajectory point magnitude parameter of the trajectory point at each pixel position, respectively.

[0017] In one possible implementation of this application, the step of overlaying the first target tile image and the second target tile image to obtain a first overlaid tile image includes:

[0018] Based on the trajectory point magnitude parameters of the pixel positions in the first target tile image and the trajectory point magnitude parameters of the pixel positions in the second target tile image, a first weight parameter of the first target tile image and a second weight parameter of the second target tile image are determined.

[0019] Based on the first weight parameter, the second weight parameter, the H value of each pixel position in the first target tile image, and the H value of each pixel position in the second target tile image, the H value of each pixel position in the first overlay tile image is determined.

[0020] Based on the first weight parameter, the second weight parameter, the V value of each pixel position in the first target tile image, and the V value of each pixel position in the second target tile image, the V value of each pixel position in the first overlay tile image is determined.

[0021] Based on the first weight parameter, the second weight parameter, the S value of each pixel position in the first target tile image, and the S value of each pixel position in the second target tile image, the S value of each pixel position in the first overlay tile image is determined.

[0022] Based on the H, V, and S values ​​of each pixel position in the first overlay tile image, the first overlay tile image is determined.

[0023] In one possible implementation of this application, determining the first weight parameter of the first target tile image and the second weight parameter of the second target tile image based on the trajectory point magnitude parameter of the pixel position in the first target tile image and the trajectory point magnitude parameter of the pixel position in the second target tile image includes:

[0024] The trajectory point magnitude parameters of the pixel positions in the first target tile image and the trajectory point magnitude parameters of the pixel positions in the second target tile image are summed to obtain the total trajectory point magnitude parameters.

[0025] The ratio obtained by comparing the trajectory point magnitude parameter of the pixel position in the first target tile image with the total trajectory point magnitude parameter is the first weight parameter of the first target tile image.

[0026] The ratio obtained by comparing the trajectory point magnitude parameter of the pixel position in the second target tile image with the total trajectory point magnitude parameter is the second weight parameter of the second target tile image.

[0027] In one possible implementation of this application, after superimposing the first target tile image and the second target tile image to obtain a superimposed first overlaid tile image, the method further includes:

[0028] Obtain the free flow velocity parameter at each pixel position in the first overlay tile image. The free flow velocity parameter is the median of the V values ​​at each pixel position in the first overlay tile image across multiple historical time periods.

[0029] Obtain the real-time flow velocity parameters of each pixel position in the first overlay tile image, wherein the real-time flow velocity parameters are the V values ​​of each pixel position in the current first overlay tile image;

[0030] Based on the free flow velocity parameter and the real-time flow velocity parameter, the throughput of each pixel position in the first overlay tile image is determined.

[0031] In one possible implementation of this application, after determining the throughput of each pixel position in the first overlay tile image based on the free flow velocity parameter and the real-time flow velocity parameter, the method further includes:

[0032] By aggregating the traffic flow rates at each pixel location in the first overlay tile image, real-time traffic information of the road in the first overlay tile image is obtained.

[0033] In one possible implementation of this application, after superimposing the first target tile image and the second target tile image to obtain a superimposed first overlaid tile image, the method further includes:

[0034] For the same target area, a third target tile map of the current third object data center is obtained, and the color parameters of the pixel positions in the third target tile map correspond to the third trajectory data generated by the third target object group within the preset time period;

[0035] The third target tile image and the first overlaid tile image are superimposed to obtain the second overlaid tile image.

[0036] On the other hand, this application provides a trajectory data fusion device, the device comprising:

[0037] The first acquisition unit is used to acquire, for the same target area, first trajectory data and second trajectory data generated by a first target object group and a second target object group within a preset time period, respectively. The first trajectory data and second trajectory data both include the positioning information, velocity parameters and azimuth parameters of multiple trajectory points.

[0038] The first determining unit is configured to determine a first target tile map of the current first object data center based on the first trajectory data, wherein the color parameters of the pixel positions in the first target tile map correspond to the first trajectory data;

[0039] The second acquisition unit is used to acquire the second target tile map of the current second object data center, wherein the color parameters of the pixel positions in the second target tile correspond to the second trajectory data, and the color parameters are used to characterize the color;

[0040] The first overlay unit is used to overlay the first target tile image and the second target tile image to obtain the overlaid first overlay tile image.

[0041] In one possible implementation of this application, the first determining unit specifically includes:

[0042] The second determining unit is used to determine the pixel position in the initial tile map corresponding to the positioning information of each trajectory point and the trajectory point magnitude parameters of each pixel position based on the positioning information of each trajectory point.

[0043] The third determining unit is used to determine the color parameters of each pixel position based on the velocity parameters and azimuth parameters of the plurality of trajectory points, as well as the magnitude parameters of the trajectory points, to obtain the first target tile map of the current first object data center. The color parameters include H value, V value and S value. The H value, V value and S value of the color parameters of each pixel position are determined by the azimuth parameters, velocity parameters and magnitude parameters of the trajectory points at each pixel position, respectively.

[0044] In one possible implementation of this application, the third determining unit is specifically used for:

[0045] Based on the velocity parameters of the multiple trajectory points and the positioning information, determine the average velocity parameter of the trajectory points at each pixel position corresponding to the current positioning information;

[0046] Based on the azimuth parameters of the multiple trajectory points and the positioning information, determine the mean azimuth parameter of the trajectory points at each pixel position corresponding to the current positioning information;

[0047] Based on the average velocity parameter, the average azimuth parameter, and the trajectory point magnitude parameter of the pixel position, the color parameter of each pixel position is determined to obtain the first target tile map of the current first object data center. The H, V, and S values ​​of the color parameter of each pixel position correspond to the average velocity parameter, the average azimuth parameter, and the trajectory point magnitude parameter of the trajectory point at each pixel position, respectively.

[0048] In one possible implementation of this application, the first superimposed unit is specifically used for:

[0049] Based on the trajectory point magnitude parameters of the pixel positions in the first target tile image and the trajectory point magnitude parameters of the pixel positions in the second target tile image, a first weight parameter of the first target tile image and a second weight parameter of the second target tile image are determined.

[0050] Based on the first weight parameter, the second weight parameter, the H value of each pixel position in the first target tile image, and the H value of each pixel position in the second target tile image, the H value of each pixel position in the first overlay tile image is determined.

[0051] Based on the first weight parameter, the second weight parameter, the V value of each pixel position in the first target tile image, and the V value of each pixel position in the second target tile image, the V value of each pixel position in the first overlay tile image is determined.

[0052] Based on the first weight parameter, the second weight parameter, the S value of each pixel position in the first target tile image, and the S value of each pixel position in the second target tile image, the S value of each pixel position in the first overlay tile image is determined.

[0053] Based on the H, V, and S values ​​of each pixel position in the first overlay tile image, the first overlay tile image is determined.

[0054] In one possible implementation of this application, determining the first weight parameter of the first target tile image and the second weight parameter of the second target tile image based on the trajectory point magnitude parameters of the pixel positions in the first target tile image and the trajectory point magnitude parameters of the pixel positions in the second target tile image specifically includes:

[0055] The trajectory point magnitude parameters of the pixel positions in the first target tile image and the trajectory point magnitude parameters of the pixel positions in the second target tile image are summed to obtain the total trajectory point magnitude parameters.

[0056] The trajectory point magnitude parameter of the pixel position in the first target tile image is compared with the total trajectory point magnitude parameter, and the ratio is used as the first weight parameter of the first target tile image.

[0057] The magnitude parameter of the trajectory points at the pixel positions in the second target tile image is compared with the magnitude parameter of the total trajectory points, and the ratio is used as the second weight parameter of the second target tile image.

[0058] In one possible implementation of this application, after superimposing the first target tile image and the second target tile image to obtain a superimposed first overlay tile image, the device is further configured to:

[0059] Obtain the free flow velocity parameter at each pixel position in the first overlay tile image. The free flow velocity parameter is the median of the V values ​​at each pixel position in the first overlay tile image across multiple historical time periods.

[0060] Obtain the real-time flow velocity parameters of each pixel position in the first overlay tile image, wherein the real-time flow velocity parameters are the V values ​​of each pixel position in the current first overlay tile image;

[0061] Based on the free flow velocity parameter and the real-time flow velocity parameter, the throughput of each pixel position in the first overlay tile image is determined.

[0062] In one possible implementation of this application, after determining the throughput of each pixel position in the first overlay tile image based on the free flow velocity parameter and the real-time flow velocity parameter, the device is further configured to:

[0063] By aggregating the traffic flow rates at each pixel location in the first overlay tile image, real-time traffic information of the road in the first overlay tile image is obtained.

[0064] In one possible implementation of this application, after superimposing the first target tile image and the second target tile image to obtain a superimposed first overlay tile image, the device is further configured to:

[0065] For the same target area, a third target tile map of the current third object data center is obtained, and the color parameters of the pixel positions in the third target tile map correspond to the third trajectory data generated by the third target object group within the preset time period;

[0066] The third target tile image and the first overlaid tile image are superimposed to obtain the second overlaid tile image.

[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 trajectory data fusion 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 execute the steps in the trajectory data fusion method.

[0072] This application embodiment acquires first trajectory data and second trajectory data generated by a first target object group and a second target object group within a preset time period for the same target area. Both the first and second trajectory data include positioning information, speed parameters, and azimuth parameters of multiple trajectory points. Based on the first trajectory data, a first target tile map of the current first object data center is determined, where the color parameters of the pixels in the first target tile map correspond to the first trajectory data. A second target tile map of the current second object data center is acquired, where the color parameters of the pixels in the second target tile map correspond to the second trajectory data, and the color parameters are used to characterize color. The first and second target tile maps are then superimposed to obtain a superimposed first tile map. Compared to traditional methods, this application embodiment does not directly fuse trajectory data from different object data centers. Instead, it superimposes tile maps obtained from data conversion of trajectory data from different object data centers. This effectively prevents the leakage of user privacy information and improves data security. Furthermore, indirectly fusing trajectory data from different object data centers effectively improves the accuracy of actual road condition information. 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 trajectory data fusion scenario provided in an embodiment of this application;

[0075] Figure 2 This is a schematic flowchart of an embodiment of the trajectory data fusion method provided in this application.

[0076] Figure 3 This is a schematic flowchart of an embodiment of determining the first target tile map of the current first object data center provided in this application;

[0077] Figure 4 This is a schematic flowchart of an embodiment of determining the accessibility of each pixel position in the first overlay tile image provided in this application;

[0078] Figure 5 This is a schematic flowchart of an embodiment of obtaining the second overlay tile map provided in this application;

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

[0080] Figure 7 This is a schematic diagram of an embodiment of the computer device provided in 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 trajectory data fusion method, apparatus, and related equipment, which will be described in detail below.

[0085] like Figure 1 As shown, Figure 1This is a schematic diagram of a trajectory data fusion system provided in an embodiment of this application. The trajectory data fusion system may include a computer device 100, which integrates a trajectory data fusion unit, 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 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 trajectory data fusion 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 trajectory data fusion system may also include one or more other computer devices, which are not specified here.

[0090] In addition, such as Figure 1As shown, the trajectory data fusion system may also include a memory 200 for storing data, such as storing the first trajectory data and the first overlay tile map generated by the first target object group within a preset time period, for example, the first overlay tile map when the trajectory data fusion system is running.

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

[0092] Next, we will introduce the trajectory data fusion method provided in the embodiments of this application.

[0093] In the embodiments of the trajectory data fusion method of this application, the trajectory data fusion 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 trajectory data fusion device is applied to a computer device.

[0094] Please see Figures 2 to 7 , Figure 2 This is a schematic flowchart of an embodiment of the trajectory data fusion method provided in this application, which includes steps 201 to 204:

[0095] 201. For the same target area, acquire the first trajectory data and the second trajectory data generated by the first target object group and the second target object group within a preset time period, respectively. The first trajectory data and the second trajectory data both include the positioning information, velocity parameters and azimuth parameters of multiple trajectory points.

[0096] 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. That is, the first trajectory data generated based on the first target object group and the second trajectory data generated based on the second target object group are obtained for the same target area within this day.

[0097] The first trajectory data may include the positioning information, speed parameters, and azimuth parameters of multiple trajectory points generated by the first target object group. This trajectory data can be obtained in real time by installing corresponding acquisition equipment in the target object group, such as the existing GPS system. The first target object group can be a group of vehicles within the first manufacturer or a group of vehicles that have signed a cooperation agreement with the first manufacturer, so that the target object group can collect data according to preset requirements and facilitate acquisition. The vehicle types corresponding to this group are not limited; specifically, they can be large trucks, medium-sized trucks, small trucks, vans, electric bicycles, and private cars. It should be noted that with the current era, logistics services have covered a considerable area, and due to the large number of vehicles, the total trajectory data volume of the vehicle group has exceeded 15 billion, with a maximum of over 200,000 trajectory data points generated per second.

[0098] Specifically, when the target group is in motion, its installed equipment, such as a GPS system, uploads its positioning information, speed parameters, azimuth parameters, and time parameters to a preset database. The positioning information is the positioning data generated by the trajectory point at its location, the speed parameter is the speed data of the trajectory point while it is moving, and the azimuth parameter is the azimuth data generated by the trajectory point moving in a certain direction. This application can retrieve the data from the preset database to obtain the trajectory data of the target group in each of the multiple preset time periods.

[0099] 202. Based on the first trajectory data, determine the first target tile map of the current first object data center, wherein the color parameters of the pixel positions in the first target tile map correspond to the first trajectory data;

[0100] The first object data center is the data center of the first vendor. It can be understood that the first target tile map is obtained based on the first trajectory data, and the first target tile map can be stored in the first object data center.

[0101] Furthermore, the first object data center here differs from the second object data center in step 203 below in that they belong to different vendors. Specifically, the first object data center belongs to the first vendor, while the second object data center belongs to the second vendor. Generally, to ensure data security, data resources between different vendors are not shared. However, the inventors of this application have discovered that because the existing data between different vendors is relatively independent, there are certain gaps in the data used by each. For example, in a certain road section, there are 100 vehicles, of which 60 vehicles use map software provided by the first vendor, while the other 40... The map software used by the vehicles was provided by a second manufacturer. However, the real-time traffic analysis results provided by the first manufacturer were based on the trajectory data of these 60 vehicles. Similarly, the real-time traffic analysis results provided by the second manufacturer were based on the trajectory data of 40 of these vehicles. For example, when performing real-time traffic analysis on the traffic density of a road, it is necessary to calculate based on the trajectory data of the vehicles on that road segment. However, since the trajectory data used for calculation (e.g., the data of 60 vehicles used by the first manufacturer) differs greatly from the actual trajectory data (which is actually the data of 100 vehicles), there is a large error.

[0102] The first target tile map is established for the target area. The tile map is formed using the existing Web Mercator projection. Each tile level corresponds to a different number of tile maps. That is, the global map image is divided into a different number of tile maps at different tile levels. For example, in the specific embodiment of this application, the tile level is set to 14 levels, and the size of each tile map can be set to 256*256 PNG, that is, each tile map is divided into 256*256 pixel grids. The ground resolution is about 10 meters / pixel. Each pixel grid represents a geographical area of ​​about 10 meters * 10 meters. Each pixel grid has a pixel position. Thus, sufficient information can be stored while saving storage space, ensuring mining efficiency and improving mining accuracy. Of course, depending on different application scenarios, those skilled in the art can also adjust the level and divide different pixel grid sizes. The specific embodiment of this application is based on the above-mentioned 14-level, 256*256 tile map for the explanation of the scheme. The color parameters of the pixel positions in the target tile image correspond to the trajectory data of the target object group. Specifically, the V, H, and S values ​​of the pixel position's color parameters correspond to the velocity parameter, azimuth parameter, and magnitude parameter of the trajectory points, respectively. Here, pixel position refers to the location of each pixel in the tile image, represented by pixel coordinates; V, H, and S values ​​correspond to the HSV color mode, where H, S, and V represent hue, saturation, and lightness, respectively. The magnitude parameter of the trajectory points is the total number of trajectory points included at a given pixel position.

[0103] In some embodiments of this application, such as Figure 3 As shown, determining the first target tile map of the current first object data center based on the first trajectory data includes steps 301 and 302:

[0104] 301. Based on the positioning information of each trajectory point, determine the pixel position in the initial tile map corresponding to the positioning information of each trajectory point and the trajectory point magnitude parameter of each pixel position;

[0105] The pixel position refers to the pixel coordinate information in the initial tile image.

[0106] In some embodiments of this application, determining the pixel position in the initial tile image corresponding to the positioning information of each trajectory point and the trajectory point magnitude parameter of each pixel position based on the positioning information of each trajectory point includes: determining the pixel coordinates of each trajectory point in the initial tile image within each preset time period based on the positioning information of each trajectory point; determining the pixel position of each trajectory point based on the pixel coordinates of each trajectory point; counting the number of trajectory points corresponding to each pixel position in the initial tile image to obtain the trajectory point magnitude parameter.

[0107] In some embodiments of this application, based on the positioning information of each trajectory point, the pixel coordinates of each trajectory point within each preset time period in the initial tile map are determined. The initial tile map refers to the tile coordinates and pixel coordinates corresponding to each geographical coordinate obtained by calculating the tile coordinates and pixel coordinates according to the following tile formula and pixel coordinate formula based on the geographical coordinates of the trajectory points, such as the national standard GCJ02, thereby forming a corresponding tile map. This tile map is called the initial tile map, and it can be constructed according to existing technology, i.e., a conventional tile map. Specifically, it may include: converting the geographical coordinates corresponding to the positioning information into coordinates in the initial tile map. The specific conversion method is as follows:

[0108] Specifically, the tile coordinate transformation formula is as follows, where the tile coordinates can represent which tile the trajectory point is located on:

[0109] n = 2^zoom;

[0110] xtile=n*((lon_deg+180) / 360);

[0111] ytile=n*((1-(log(tan(lat_read)+sec(lat_read)) / π)) / 2;

[0112] 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.

[0113] Specifically, the pixel coordinate transformation formula is as follows:

[0114]

[0115]

[0116] 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.

[0117] 302. Based on the velocity parameters, azimuth parameters, and magnitude parameters of multiple trajectory points, determine the color parameters of each pixel position to obtain the first target tile map of the current first object data center. The color parameters include H value, V value, and S value. The H value, V value, and S value of the color parameters of each pixel position are determined by the azimuth parameters, velocity parameters, and magnitude parameters of the trajectory points at each pixel position, respectively.

[0118] In some embodiments of this application, determining the color parameters of each pixel position based on the velocity parameters and azimuth parameters of the plurality of trajectory points, as well as the magnitude parameters of the trajectory points, to obtain the first target tile map of the current first object data center may include the following process:

[0119] First, based on the velocity parameters of the multiple trajectory points and the positioning information, the average velocity parameter of the trajectory points at each pixel position corresponding to the current positioning information is determined. The average velocity parameter is the average of the velocity parameters of all trajectory points corresponding to the same pixel position. Specifically, the positioning information of each trajectory point in the multiple trajectory points can be converted into pixel coordinates to obtain the pixel position. The average velocity parameter of all trajectory points corresponding to the same pixel position is then taken to obtain the average velocity parameter of the trajectory points at the pixel position corresponding to the current positioning information.

[0120] Secondly, based on the azimuth parameters of the multiple trajectory points and the positioning information, the mean azimuth parameters of the trajectory points at each pixel position corresponding to the positioning information are determined. Specifically, the positioning information of each trajectory point in the multiple trajectory points can be converted into pixel coordinates to obtain the pixel position. Then, the azimuth parameters of all trajectory points corresponding to the same pixel position are averaged to obtain the mean azimuth parameters of the trajectory points at the pixel position corresponding to the positioning information.

[0121] Finally, based on the average velocity parameter, the average azimuth parameter, and the trajectory point magnitude parameter of the pixel position, the color parameter of each pixel position is determined to obtain the first target tile map of the current first object data center. The H, V, and S values ​​of the color parameters of each pixel position correspond to the average velocity parameter, the average azimuth parameter, and the trajectory point magnitude parameter of the trajectory point at each pixel position, respectively. Specifically, the average velocity parameter, the average azimuth parameter, and the trajectory point magnitude parameter of the pixel position are used as the V, H, and S values ​​of the color parameters of the corresponding pixel positions, respectively. The color parameters are displayed according to the existing VHS color mode.

[0122] 203. Obtain the second target tile map of the current second object data center. The color parameters of the pixel positions in the second target tile correspond to the second trajectory data. The color parameters are used to characterize the color.

[0123] In this embodiment, the second target data center is the data center of the second vendor. It is understood that the second target tile map is obtained based on the second trajectory data, and this second target tile map can be stored in the second target data center. The tile coordinates of the second target tile map and the first target tile map are the same, meaning they target the same geographical area; both include the target region. The same applies to the third target tile map described below. Both the first and second target tile maps are acquired within the same preset time period, but their corresponding trajectory data differs. If the target geographical area to be observed includes multiple target regions, and at least two target regions are located on different tile maps, then multiple tile coordinates will exist. For each tile coordinate, the first target tile map and the second tile map will be superimposed.

[0124] Specifically, the method for obtaining the second target tile map of the current second object data center is not limited; it can be the same as or different from the method for obtaining the first target tile map described above. The preferred embodiment of this application is that the method for obtaining the second target tile map of the current second object data center is the same as the method for obtaining the first target tile map described above. Please refer to the steps above for the specific process, which will not be repeated here.

[0125] 204. Overlay the first target tile image and the second target tile image to obtain the first overlaid tile image.

[0126] The first overlay image can be used directly to view parameters such as traffic density, driving direction, and driving speed after overlay.

[0127] In some embodiments of this application, the superposition of the first target tile image and the second target tile image to obtain a superimposed first overlaid tile image refers to the superposition of pixels at the same pixel coordinate of the two tile images, which can be performed as follows:

[0128] First, based on the trajectory point magnitude parameters of the pixel positions in the first target tile image and the trajectory point magnitude parameters of the pixel positions in the second target tile image, where the trajectory point magnitude parameters refer to the number of trajectory points at each pixel position, a first weight parameter of the first target tile image and a second weight parameter of the second target tile image are determined. Specifically, this may include: summing the trajectory point magnitude parameters of the pixel positions in the first target tile image and the trajectory point magnitude parameters of the pixel positions in the second target tile image to obtain a summed total trajectory point magnitude parameter.

[0129] The ratio obtained by comparing the trajectory point magnitude parameter of the pixel position in the first target tile image with the total trajectory point magnitude parameter is the first weight parameter of the first target tile image.

[0130] The ratio obtained by comparing the trajectory point magnitude parameter of the pixel position in the second target tile image with the total trajectory point magnitude parameter is the second weight parameter of the second target tile image.

[0131] Assuming the magnitude parameter of the trajectory point at a pixel location in the first target tile image is S1, and the magnitude parameter of the trajectory point at the same pixel location in the second target tile image is S2, the first weight parameter of this pixel location in the first target tile image is q1, and the second weight parameter of this pixel location in the second target tile image is q2, then the specific calculation method is as follows:

[0132] q1=S1 / (S1+S2), q2=S2 / (S1+S2);

[0133] For example, in the first target tile image, a pixel is located at (1,1), and the total number of trajectory points at this pixel is S1, meaning the magnitude parameter of the trajectory points is S1. In the second target tile image, the magnitude parameter of the trajectory points at the same pixel location (1,1) is S2. Therefore, for this pixel location, the first weight parameter is q1, and the second weight parameter is q2. The H, V, and S values ​​for this pixel location can be calculated based on q1 and q2. Other pixel locations are calculated similarly.

[0134] Secondly, based on the first weight parameter, the second weight parameter, the H value of the pixel position in the first target tile image, and the H value of the pixel position in the second target tile image, the H value of the pixel position in the first overlay tile image is determined. Specifically, assuming the H value of a pixel position in the first target tile image is H1, and the H value of the same pixel position in the second target tile image is H2, the calculation method for the H value of each pixel position in the first overlay tile image is as follows:

[0135] H = (H1*q1 + H2*q2), where the value of H ranges from 0 to 360;

[0136] Then, based on the first weight parameter, the second weight parameter, the V value of the pixel position in the first target tile image, and the V value of the pixel position in the second target tile image, the V value of the pixel position in the first overlay tile image is determined. Specifically, assuming the V value of the pixel position in the first target tile image is V1, and the V value of the same pixel position in the second target tile image is V2, the calculation method for the V value of each pixel position in the first overlay tile image is as follows:

[0137] V = (V1*q1 + V2*q2)*100%, and if V exceeds 1, the value is 1;

[0138] Then, based on the first weight parameter, the second weight parameter, the S value of the pixel position in the first target tile image, and the S value of the same pixel position in the second target tile image, the S value of each pixel position in the first overlay tile image is determined. Specifically, the S value of each pixel position in the first overlay tile image is calculated as follows:

[0139] S = (S1*q1 + S2*q2)*100%, and if S exceeds 1, it takes the value 1;

[0140] Finally, based on the H, V, and S values ​​of each pixel position in the first overlay tile image, the first overlay tile image is determined.

[0141] In this embodiment, compared to traditional methods, instead of directly fusing trajectory data from different object data centers, the tile maps obtained by data conversion of trajectory data from different object data centers are overlaid. This can effectively prevent the leakage of user privacy information and improve data security. At the same time, indirectly fusing trajectory data from different object data centers can effectively improve the accuracy of actual road condition information.

[0142] In some embodiments of this application, such as Figure 4As shown, after superimposing the first target tile image and the second target tile image to obtain the superimposed first overlaid tile image, the method further includes:

[0143] 401. Obtain the free flow velocity parameters of each pixel position in the first overlay tile image. The free flow velocity parameters are the median of the V values ​​of each pixel position in the first overlay tile image across multiple historical time periods.

[0144] In this embodiment, the length of the historical time period can be set according to actual needs. For example, the historical time period can be set to one hour, one day, or three days. In the optimal embodiment of this application, the historical time period is set to the same length as the preset time period in step 201. For example, if the preset time period is one day, then the historical time period is also set to one day. Multiple historical time periods can be three or more. Assuming the historical time period is set to one day, then in the optimal embodiment of this application, multiple historical time periods can be set to 30 historical time periods, that is, the past 30 days. This is equivalent to obtaining 30 sets of historical first target tile images and second target tile images, and each set will be superimposed to form a historical first overlay tile image. This results in 30 historical first overlay tile images.

[0145] Specifically, obtaining the free flow velocity parameters of each pixel position in the first overlay tile image can include: obtaining the V values ​​of each pixel position in the first overlay tile images for multiple historical time periods. Specifically, the method of obtaining the first overlay tile image can be referred to the above embodiments, which will not be repeated here. Then, the first overlay tile images for multiple historical time periods are obtained. After obtaining the first overlay tile images for multiple historical time periods, the V values ​​of each pixel position in the first overlay tile image for each historical time period can be read. Finally, the median of the V values ​​of each pixel position in the first overlay tile images for all historical time periods is taken as the free flow velocity parameters of each pixel position in each of the first overlay tile images.

[0146] For example, with 30 historical first-overlay tile images, each pixel location will have 30 V values. The median of these 30 V values ​​will be used as the free flow velocity parameter for each pixel location.

[0147] 402. Obtain the real-time flow velocity parameters of each pixel position in the first overlay tile image. The real-time flow velocity parameters are the V values ​​of each pixel position in the current first overlay tile image, which are the V values ​​of each pixel position.

[0148] In this embodiment of the application, obtaining the real-time flow velocity parameters of each pixel position in the first overlay tile image may include: first obtaining the current first overlay tile image, and then reading the V value of each pixel position in the first overlay tile image, thereby obtaining the real-time flow velocity parameters of each pixel position in the first overlay tile image.

[0149] 403. Based on the free flow velocity parameters and real-time flow velocity parameters, determine the accessibility of each pixel position in the first overlay tile image.

[0150] In this embodiment, the flow rate of each pixel in the first overlay tile image, or the flow rate of each pixel, can be the ratio of the real-time flow velocity parameter to the free flow velocity parameter at that pixel location. Specifically, the correspondence between this ratio and the flow status of the vehicle at the corresponding pixel location is as follows:

[0151] A flow rate of (0, 0.2) at a pixel location indicates severe congestion; a flow rate of (0.2, 0.4) at a pixel location indicates congestion; a flow rate of (0.4, 0.6) at a pixel location indicates slow traffic; a flow rate of (0.6, 0.9) at a pixel location indicates smooth traffic; and a flow rate of (0.9, 1) at a pixel location indicates smooth traffic.

[0152] Based on free flow velocity parameters and real-time flow velocity parameters, this application embodiment determines the accessibility of each pixel position in the first overlay tile image, which can obtain the accessibility of each pixel position in the first overlay tile image in real time, thereby improving the accuracy of real-time traffic analysis.

[0153] In some embodiments of this application, after determining the accessibility rate of each pixel position in the first overlay tile image based on the free flow velocity parameter and the real-time flow velocity parameter, the method further includes: aggregating the accessibility rates of each pixel position in the first overlay tile image to obtain real-time traffic information of the road in the first overlay tile image. Specifically, the accessibility rates of each pixel position in the first overlay tile image can be categorized. For example, on a road, the accessibility rate of the first three consecutive pixel positions and the fifth pixel position is 0.7, while the accessibility rate of the fourth pixel position is 0.4, and the accessibility rates of the sixth to tenth pixel positions are all 0.3. Therefore, the accessibility rate of the fourth pixel position is categorized as 0.7, and the accessibility rate of the fourth pixel position is converted to 0.7, thus completing the aggregation of the accessibility rates of each pixel position in the first overlay tile image to obtain the real-time traffic information of the road in the first overlay tile image.

[0154] In some embodiments of this application, such as Figure 5As shown, after superimposing the first target tile image and the second target tile image to obtain the superimposed first overlaid tile image, the method further includes:

[0155] 501. Obtain the third target tile map of the current third object data center. The color parameters of the pixel positions in the third target tile map correspond to the third trajectory data generated by the third target object group within a preset time period.

[0156] Among them, the third object data center is the data center of the third vendor. It can be understood that the third target tile map is obtained based on the third trajectory data, and the third target tile map can be stored in the third object data center.

[0157] Specifically, the method for obtaining the third target tile map of the current third object data center is not limited; it can be the same as or different from the method for obtaining the first target tile map described above. The preferred embodiment of this application is that the method for obtaining the third target tile map of the current third object data center is the same as the method for obtaining the first target tile map described above. For details, please refer to the steps above, which will not be repeated here.

[0158] 502. Overlay the third target tile image and the first overlay tile image to obtain the second overlay tile image.

[0159] In this embodiment, the method of superimposing the third target tile image and the first overlay tile image is the same as the method of superimposing the first target tile image and the second target tile image in the above steps. For details, please refer to the above embodiments, which will not be repeated here.

[0160] In this embodiment of the application, by superimposing the third target tile map and the first superimposed tile map, a second superimposed tile map can be obtained. This second superimposed tile map is obtained by fusing data from three different manufacturers, thereby further improving the accuracy of the data.

[0161] In some embodiments of this application, data from multiple manufacturers can be merged. The merging method can be the same as in the above embodiments, and will not be elaborated further. Among them, multiple manufacturers are three or more manufacturers. Specifically, it can be four manufacturers, eight manufacturers or twelve manufacturers. There is no specific limitation, and it can be selected and adjusted according to the actual situation.

[0162] To better implement the trajectory data fusion method in the embodiments of this application, based on the trajectory data fusion method, the embodiments of this application also provide a trajectory data fusion device, such as... Figure 6 As shown, the trajectory data fusion device 600 includes:

[0163] The first acquisition unit 601 is used to acquire, for the same target area, first trajectory data and second trajectory data generated by the first target object group and the second target object group within a preset time period, respectively. The first trajectory data and the second trajectory data both include the positioning information, velocity parameters and azimuth parameters of multiple trajectory points.

[0164] The first determining unit 602 is used to determine a first target tile map of the current first object data center based on the first trajectory data, wherein the color parameters of the pixel positions in the first target tile map correspond to the first trajectory data;

[0165] The second acquisition unit 603 is used to acquire the second target tile map of the current second object data center, wherein the color parameters of the pixel positions in the second target tile correspond to the second trajectory data, and the color parameters are used to characterize the color;

[0166] The first overlay unit 604 is used to overlay the first target tile image and the second target tile image to obtain the overlaid first overlay tile image.

[0167] In some embodiments of this application, the first determining unit 602 specifically includes:

[0168] The second determining unit is used to determine the pixel position in the initial tile map corresponding to the positioning information of each trajectory point and the trajectory point magnitude parameters of each pixel position based on the positioning information of each trajectory point.

[0169] The third determining unit is used to determine the color parameters of each pixel position based on the velocity parameters and azimuth parameters of the plurality of trajectory points, as well as the magnitude parameters of the trajectory points, to obtain the first target tile map of the current first object data center. The color parameters include H value, V value and S value. The H value, V value and S value of the color parameters of each pixel position are determined by the azimuth parameters, velocity parameters and magnitude parameters of the trajectory points at each pixel position, respectively.

[0170] In some embodiments of this application, the third determining unit is specifically used for:

[0171] Based on the velocity parameters of the multiple trajectory points and the positioning information, determine the average velocity parameter of the trajectory points at each pixel position corresponding to the current positioning information;

[0172] Based on the azimuth parameters of the multiple trajectory points and the positioning information, determine the mean azimuth parameter of the trajectory points at each pixel position corresponding to the current positioning information;

[0173] Based on the average velocity parameter, the average azimuth parameter, and the trajectory point magnitude parameter of the pixel position, the color parameter of each pixel position is determined to obtain the first target tile map of the current first object data center. The H, V, and S values ​​of the color parameter of each pixel position correspond to the average velocity parameter, the average azimuth parameter, and the trajectory point magnitude parameter of the trajectory point at each pixel position, respectively.

[0174] In some embodiments of this application, the first stacking unit 604 is specifically used for:

[0175] Based on the trajectory point magnitude parameters of the pixel positions in the first target tile image and the trajectory point magnitude parameters of the pixel positions in the second target tile image, a first weight parameter of the first target tile image and a second weight parameter of the second target tile image are determined.

[0176] Based on the first weight parameter, the second weight parameter, the H value of each pixel position in the first target tile image, and the H value of each pixel position in the second target tile image, the H value of each pixel position in the first overlay tile image is determined.

[0177] Based on the first weight parameter, the second weight parameter, the V value of each pixel position in the first target tile image, and the V value of each pixel position in the second target tile image, the V value of each pixel position in the first overlay tile image is determined.

[0178] Based on the first weight parameter, the second weight parameter, the S value of each pixel position in the first target tile image, and the S value of each pixel position in the second target tile image, the S value of each pixel position in the first overlay tile image is determined.

[0179] Based on the H, V, and S values ​​of each pixel position in the first overlay tile image, the first overlay tile image is determined.

[0180] In one possible implementation of this application, determining the first weight parameter of the first target tile image and the second weight parameter of the second target tile image based on the trajectory point magnitude parameters of the pixel positions in the first target tile image and the trajectory point magnitude parameters of the pixel positions in the second target tile image specifically includes:

[0181] The trajectory point magnitude parameters of the pixel positions in the first target tile image and the trajectory point magnitude parameters of the pixel positions in the second target tile image are summed to obtain the total trajectory point magnitude parameters.

[0182] The trajectory point magnitude parameter of the pixel position in the first target tile image is compared with the total trajectory point magnitude parameter, and the ratio is used as the first weight parameter of the first target tile image.

[0183] The magnitude parameter of the trajectory points at the pixel positions in the second target tile image is compared with the magnitude parameter of the total trajectory points, and the ratio is used as the second weight parameter of the second target tile image.

[0184] In some embodiments of this application, after superimposing the first target tile image and the second target tile image to obtain a superimposed first overlay tile image, the apparatus is further configured to:

[0185] Obtain the free flow velocity parameter at each pixel position in the first overlay tile image. The free flow velocity parameter is the median of the V values ​​at each pixel position in the first overlay tile image across multiple historical time periods.

[0186] Obtain the real-time flow velocity parameters of each pixel position in the first overlay tile image, wherein the real-time flow velocity parameters are the V values ​​of each pixel position in the current first overlay tile image;

[0187] Based on the free flow velocity parameter and the real-time flow velocity parameter, the throughput of each pixel position in the first overlay tile image is determined.

[0188] In some embodiments of this application, after determining the throughput of each pixel position in the first overlay tile image based on the free flow velocity parameter and the real-time flow velocity parameter, the device is further configured to:

[0189] By aggregating the traffic flow rates at each pixel location in the first overlay tile image, real-time traffic information of the road in the first overlay tile image is obtained.

[0190] In some embodiments of this application, after superimposing the first target tile image and the second target tile image to obtain a superimposed first overlaid tile image, the apparatus is further configured to:

[0191] For the same target area, a third target tile map of the current third object data center is obtained, and the color parameters of the pixel positions in the third target tile map correspond to the third trajectory data generated by the third target object group within the preset time period;

[0192] The third target tile image and the first overlaid tile image are superimposed to obtain the second overlaid tile image.

[0193] This application embodiment uses a first acquisition unit 601 to acquire first trajectory data and second trajectory data generated by a first target object group and a second target object group within a preset time period for the same target area. Both the first trajectory data and the second trajectory data include positioning information, velocity parameters, and azimuth parameters of multiple trajectory points. A first determination unit 602 is used to determine a first target tile map of the current first object data center based on the first trajectory data. The color parameters of the pixel positions in the first target tile map correspond to the first trajectory data. A second acquisition unit 603 is used to acquire a second target tile map of the current second object data center. The color parameters of the pixel positions in the second target tile map correspond to the second trajectory data. The color parameters are used to characterize color. A first overlay unit 604 is used to overlay the first target tile map and the second target tile map to obtain a first overlay tile map. In this embodiment, compared to traditional methods, instead of directly fusing trajectory data from different object data centers, the tile maps obtained by data conversion of trajectory data from different object data centers are overlaid. This can effectively prevent the leakage of user privacy information and improve data security. At the same time, indirectly fusing trajectory data from different object data centers can effectively improve the accuracy of actual road condition information.

[0194] In addition to the trajectory data fusion methods and apparatus described above, embodiments of this application also provide a computer device that integrates any of the trajectory data fusion apparatuses provided in the embodiments of this application. The computer device includes:

[0195] One or more processors;

[0196] Memory; and

[0197] 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 trajectory data fusion method described above.

[0198] This application also provides a computer device that integrates any of the trajectory data fusion devices provided in this application. For example... Figure 7 As shown, it illustrates a structural schematic diagram of the computer device involved in the embodiments of this application, specifically:

[0199] The computer device may include components such as a processor 701 with one or more processing cores, a storage unit 702 with one or more computer-readable storage media, a power supply 703, and an input unit 704. Those skilled in the art will understand that... Figure 7The 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:

[0200] The processor 701 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 702, and by calling data stored in the storage unit 702, thereby providing overall monitoring of the computer device. Optionally, the processor 701 may include one or more processing cores; preferably, the processor 701 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 also not be integrated into the processor 701.

[0201] Storage unit 702 can be used to store software programs and modules. Processor 701 executes various functional applications and data processing by running the software programs and modules stored in storage unit 702. Storage unit 702 may mainly include a program storage area and a data storage area. The program storage area may store the operating system, application programs required for at least one 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 702 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 702 may also include a memory controller to provide processor 701 with access to storage unit 702.

[0202] The computer device also includes a power supply 703 that supplies power to the various components. Preferably, the power supply 703 can be logically connected to the processor 701 through a power management system, thereby enabling functions such as charging, discharging, and power consumption management through the power management system. The power supply 703 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.

[0203] The computer device may also include an input unit 704, 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.

[0204] 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 701 in the computer device loads the executable files corresponding to the processes of one or more application programs into the storage unit 702 according to the following instructions, and the processor 701 runs the application programs stored in the storage unit 702 to realize various functions, as follows:

[0205] For the same target area, first trajectory data and second trajectory data generated by the first target object group and the second target object group within a preset time period are acquired respectively. Both the first trajectory data and the second trajectory data include the positioning information, velocity parameters, and azimuth parameters of multiple trajectory points. Based on the first trajectory data, a first target tile map of the current first object data center is determined, and the color parameters of the pixel positions in the first target tile map correspond to the first trajectory data. A second target tile map of the current second object data center is acquired, and the color parameters of the pixel positions in the second target tile map correspond to the second trajectory data. The color parameters are used to characterize the color. The first target tile map and the second target tile map are superimposed to obtain a superimposed first overlay tile map.

[0206] This application embodiment acquires first trajectory data and second trajectory data generated by a first target object group and a second target object group within a preset time period for the same target area. Both the first and second trajectory data include positioning information, speed parameters, and azimuth parameters of multiple trajectory points. Based on the first trajectory data, a first target tile map of the current first object data center is determined, where the color parameters of the pixels in the first target tile map correspond to the first trajectory data. A second target tile map of the current second object data center is acquired, where the color parameters of the pixels in the second target tile map correspond to the second trajectory data, and the color parameters are used to characterize color. The first and second target tile maps are then superimposed to obtain a superimposed first tile map. Compared to traditional methods, this application embodiment does not directly fuse trajectory data from different object data centers. Instead, it superimposes tile maps obtained from data conversion of trajectory data from different object data centers. This effectively prevents the leakage of user privacy information and improves data security. Furthermore, indirectly fusing trajectory data from different object data centers effectively improves the accuracy of actual road condition information.

[0207] Therefore, embodiments of this application provide a computer-readable storage medium, which may include: read-only memory (ROM), random access memory (RAM), a 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 trajectory data fusion methods provided in embodiments of this application. For example, the instructions can execute the following steps:

[0208] For the same target area, first trajectory data and second trajectory data generated by the first target object group and the second target object group within a preset time period are acquired respectively. Both the first trajectory data and the second trajectory data include the positioning information, velocity parameters, and azimuth parameters of multiple trajectory points. Based on the first trajectory data, a first target tile map of the current first object data center is determined, and the color parameters of the pixel positions in the first target tile map correspond to the first trajectory data. A second target tile map of the current second object data center is acquired, and the color parameters of the pixel positions in the second target tile map correspond to the second trajectory data. The color parameters are used to characterize the color. The first target tile map and the second target tile map are superimposed to obtain a superimposed first overlay tile map.

[0209] 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.

[0210] The above provides a detailed description of a trajectory data fusion method, apparatus, and related equipment provided in the embodiments of this application. Specific examples have been used to illustrate the principles and implementation methods of this application. The description of the above embodiments is 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 trajectory data fusion method, characterized in that, The method includes: For the same target area, first trajectory data and second trajectory data generated by the first target object group and the second target object group within a preset time period are obtained respectively. The first trajectory data and the second trajectory data both include the positioning information, velocity parameters and azimuth parameters of multiple trajectory points. Based on the first trajectory data, a first target tile map of the current first object data center is determined. The color parameters of the pixel positions in the first target tile map correspond to the first trajectory data. The color parameters include H value, V value and S value. Obtain the second target tile map of the current second object data center, wherein the color parameters of the pixel positions in the second target tile map correspond to the second trajectory data, and the color parameters are used to characterize the color; The first target tile image and the second target tile image are superimposed to obtain the superimposed first overlay tile image; The step of overlaying the first target tile image and the second target tile image to obtain the overlaid first tile image includes: Based on the trajectory point magnitude parameters of the pixel positions in the first target tile image and the trajectory point magnitude parameters of the pixel positions in the second target tile image, a first weight parameter of the first target tile image and a second weight parameter of the second target tile image are determined. Based on the first weight parameter, the second weight parameter, the color parameters of each pixel position in the first target tile image, and the color parameters of each pixel position in the second target tile image, the color parameters of each pixel position in the first overlay tile image are determined; Based on the color parameters of each pixel position in the first overlay tile image, the first overlay tile image is determined.

2. The trajectory data fusion method according to claim 1, characterized in that, The step of determining the first target tile map of the current first object data center based on the first trajectory data includes: Based on the positioning information of each trajectory point, determine the pixel position in the initial tile map corresponding to the positioning information of each trajectory point and the trajectory point magnitude parameters of each pixel position; Based on the velocity parameters and azimuth parameters of the multiple trajectory points, as well as the magnitude parameters of the trajectory points, the color parameters of each pixel position are determined to obtain the first target tile map of the current first object data center. The H, V, and S values ​​of the color parameters of each pixel position are determined by the azimuth parameters, velocity parameters, and magnitude parameters of the trajectory points at each pixel position, respectively.

3. The trajectory data fusion method according to claim 2, characterized in that, The step of determining the color parameters of each pixel position based on the velocity parameters and azimuth parameters of the multiple trajectory points, as well as the magnitude parameters of the trajectory points, to obtain the first target tile map of the current first object data center includes: Based on the velocity parameters of the multiple trajectory points and the positioning information, determine the average velocity parameter of the trajectory points at each pixel position corresponding to the current positioning information; Based on the azimuth parameters of the multiple trajectory points and the positioning information, determine the mean azimuth parameter of the trajectory points at each pixel position corresponding to the current positioning information; Based on the average velocity parameter, the average azimuth parameter, and the trajectory point magnitude parameter of the pixel position, the color parameter of each pixel position is determined to obtain the first target tile map of the current first object data center. The H, V, and S values ​​of the color parameter of each pixel position correspond to the average velocity parameter, the average azimuth parameter, and the trajectory point magnitude parameter of the trajectory point at each pixel position, respectively.

4. The trajectory data fusion method according to claim 1, characterized in that, The step of determining the color parameters of each pixel position in the first overlay tile image based on the first weight parameter, the second weight parameter, the color parameters of each pixel position in the first target tile image, and the color parameters of each pixel position in the second target tile image includes: Based on the first weight parameter, the second weight parameter, the H value of each pixel position in the first target tile image, and the H value of each pixel position in the second target tile image, the H value of each pixel position in the first overlay tile image is determined. Based on the first weight parameter, the second weight parameter, the V value of each pixel position in the first target tile image, and the V value of each pixel position in the second target tile image, the V value of each pixel position in the first overlay tile image is determined. Based on the first weight parameter, the second weight parameter, the S value of each pixel position in the first target tile image, and the S value of each pixel position in the second target tile image, the S value of each pixel position in the first overlay tile image is determined.

5. The trajectory data fusion method according to claim 4, characterized in that, The determination of a first weight parameter for the first target tile image and a second weight parameter for the second target tile image based on the trajectory point magnitude parameters of pixel positions in the first target tile image and the trajectory point magnitude parameters of pixel positions in the second target tile image includes: The trajectory point magnitude parameters of the pixel positions in the first target tile image and the trajectory point magnitude parameters of the pixel positions in the second target tile image are summed to obtain the total trajectory point magnitude parameters. The ratio obtained by comparing the trajectory point magnitude parameter of the pixel position in the first target tile image with the total trajectory point magnitude parameter is the first weight parameter of the first target tile image. The ratio obtained by comparing the trajectory point magnitude parameter of the pixel position in the second target tile image with the total trajectory point magnitude parameter is the second weight parameter of the second target tile image.

6. The trajectory data fusion method according to claim 1, characterized in that, After overlaying the first target tile image and the second target tile image to obtain the overlaid first overlaid tile image, the method further includes: Obtain the free flow velocity parameter at each pixel position in the first overlay tile image. The free flow velocity parameter is the median of the V values ​​at each pixel position in the first overlay tile image across multiple historical time periods. Obtain the real-time flow velocity parameters of each pixel position in the first overlay tile image, wherein the real-time flow velocity parameters are the V values ​​of each pixel position in the current first overlay tile image; Based on the free flow velocity parameter and the real-time flow velocity parameter, the throughput of each pixel position in the first overlay tile image is determined.

7. The trajectory data fusion method according to claim 6, characterized in that, After determining the throughput of each pixel location in the first overlay tile image based on the free flow velocity parameter and the real-time flow velocity parameter, the method further includes: By aggregating the traffic flow rates at each pixel location in the first overlay tile image, real-time traffic information of the road in the first overlay tile image is obtained.

8. The trajectory data fusion method according to claim 1, characterized in that, After overlaying the first target tile image and the second target tile image to obtain the overlaid first overlaid tile image, the method further includes: For the same target area, a third target tile map of the current third object data center is obtained, wherein the color parameters of the pixel positions in the third target tile map correspond to the third trajectory data generated by the third target object group within the preset time period; The third target tile image and the first overlaid tile image are superimposed to obtain the second overlaid tile image.

9. A trajectory data fusion device, characterized in that, The device includes: The first acquisition unit is used to acquire, for the same target area, first trajectory data and second trajectory data generated by a first target object group and a second target object group within a preset time period, respectively. The first trajectory data and second trajectory data both include the positioning information, velocity parameters and azimuth parameters of multiple trajectory points. The first determining unit is configured to determine a first target tile map of the current first object data center based on the first trajectory data, wherein the color parameters of the pixel positions in the first target tile map correspond to the first trajectory data, and the color parameters include H value, V value and S value; The second acquisition unit is used to acquire the second target tile map of the current second object data center, wherein the color parameters of the pixel positions in the second target tile map correspond to the second trajectory data, and the color parameters are used to characterize the color; The first overlay unit is used to overlay the first target tile image and the second target tile image to obtain the overlaid first overlay tile image; The first overlay unit is specifically used to: determine a first weight parameter of the first target tile image and a second weight parameter of the second target tile image based on the trajectory point magnitude parameter of the pixel position in the first target tile image and the trajectory point magnitude parameter of the pixel position in the second target tile image; Based on the first weight parameter, the second weight parameter, the color parameters of each pixel position in the first target tile image, and the color parameters of each pixel position in the second target tile image, the color parameters of each pixel position in the first overlay tile image are determined; Based on the color parameters of each pixel position in the first overlay tile image, the first overlay tile image is determined.

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

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