WO2021189897A1

WO2021189897A1 - Road matching method and apparatus, and electronic device and readable storage medium

Info

Publication number: WO2021189897A1
Application number: PCT/CN2020/131973
Authority: WO
Inventors: 李硕
Original assignee: 平安科技（深圳）有限公司
Priority date: 2020-09-02
Filing date: 2020-11-26
Publication date: 2021-09-30
Also published as: CN112050820B; CN112050820A

Abstract

A road matching method, comprising: segmenting GPS data to obtain segmented GPS data (S1); encoding the segmented GPS data to obtain encoded GPS data (S2); performing association segmentation on map data by utilizing the encoded GPS data, so as to obtain segmented map data (S3); performing first screening, analysis and calculation by utilizing the segmented map data and the segmented GPS data, so as to obtain a target longitude and latitude point set and a candidate start point set (S4); performing second screening, analysis and calculation by utilizing the segmented map data and the target longitude and latitude point set, so as to obtain a candidate end point set (S5); and performing calculation according to the candidate start point set and the candidate end point set, so as to obtain a target path (S6).

Description

Road matching method, device, electronic equipment and readable storage medium

This application claims the priority of a Chinese patent application filed with the Chinese Patent Office on September 02, 2020, the application number is 202010912001.9, and the invention title is "Road matching method, device, electronic equipment and readable storage medium". The entire content of the application is approved The reference is incorporated in this application.

Technical field

This application relates to the field of artificial intelligence, and in particular to a method, device, electronic device, and readable storage medium for road matching.

Background technique

As map navigation is more and more widely used in people's lives, the accuracy of navigation and road matching has attracted more and more attention.

The inventor realized that at present, the main method of global matching is used in navigation. Each matching requires calculating the connected paths of all road nodes on the global map, which consumes a lot of computing resources. When the global map is too large, matching cannot even be performed. Therefore, there is an urgent need for a road matching method that saves more computing resources.

Technical solutions

A road matching method provided by this application includes:

Acquiring GPS data, segmenting the GPS data to obtain segmented GPS data;

Encoding the segmented GPS data to obtain encoded GPS data;

Acquiring map data, using the encoded GPS data to correlate and segment the map data to obtain segmented map data;

Use the segmented map data and the segmented GPS data to perform a first screening analysis and calculation to obtain a target latitude and longitude point set and a candidate starting point set;

Use the segmented map data and the target latitude and longitude point set to perform a second screening analysis and calculation to obtain a candidate end point set;

The target path is obtained by calculation according to the candidate start point set and the candidate end point set.

The application also provides a road matching device, which includes:

A data association module for acquiring GPS data, segmenting the GPS data to obtain segmented GPS data; encoding the segmented GPS data to obtain encoded GPS data; acquiring map data and using the encoded GPS data Performing associated segmentation on the map data to obtain segmented map data;

A starting point generating module, configured to use the segmented map data and the segmented GPS data to perform a first screening analysis and calculation to obtain a target latitude and longitude point set and a candidate starting point set;

An end point generating module, configured to use the segmented map data and the target longitude and latitude point set to perform a second screening analysis and calculation to obtain a candidate end point set;

The target path calculation module is used to calculate the target path according to the candidate start point set and the candidate end point set.

This application also provides an electronic device, which includes:

Memory, storing at least one instruction; and

The processor executes the instructions stored in the memory to implement the road matching method as described below:

Acquiring GPS data, segmenting the GPS data to obtain segmented GPS data;

Encoding the segmented GPS data to obtain encoded GPS data;

This application also provides a computer-readable storage medium, including a storage data area and a storage program area. The storage data area stores data created according to the use of blockchain nodes, and the storage program area stores a computer program, which is readable by the computer. At least one instruction is stored in the storage medium, and the at least one instruction is executed by the processor in the electronic device to implement the road matching method as described below:

Acquiring GPS data, segmenting the GPS data to obtain segmented GPS data;

Encoding the segmented GPS data to obtain encoded GPS data;

Description of the drawings

FIG. 1 is a schematic flowchart of a road matching method provided by an embodiment of this application;

2 is a schematic diagram of modules of a road matching device provided by an embodiment of this application;

3 is a schematic diagram of the internal structure of an electronic device for implementing a road matching method provided by an embodiment of the application;

The realization, functional characteristics, and advantages of the purpose of this application will be further described in conjunction with the embodiments and with reference to the accompanying drawings.

Embodiments of the present invention

It should be understood that the specific embodiments described here are only used to explain the present application, and are not used to limit the present application.

This application provides a road matching method. Referring to FIG. 1, it is a schematic flowchart of a road matching method provided by an embodiment of this application. The method can be executed by a device, and the device can be implemented by software and/or hardware.

In this embodiment, the road matching method includes:

S1. Obtain GPS (Global Positioning System, Global Positioning System) data, and segment the GPS data to obtain segmented GPS data;

In the embodiment of the present application, the GPS data is a collection of GPS points in consecutive time intervals sorted by time, where the GPS points are geographic locations with time, longitude and latitude. The GPS data can be obtained from a navigation data information database of a rental company.

Further, in the embodiment of the present application, the GPS data is relatively large. In order to facilitate the processing of the GPS data, the GPS data is first segmented.

In detail, in the embodiment of the present application, the GPS data is segmented according to a preset time to obtain the segmented GPS data; and/or the GPS data is segmented according to a preset distance to obtain the segmented GPS Data; Preferably, the embodiment of the application divides the GPS data into a segment every 15 minutes or every 10 kilometers. For example, the GPS data is the GPS data in the time period of 2:00-2:30, and the segmentation is performed at 15-minute intervals to obtain the segmented GPS data of 2:00-2:15 and 2:15-2: 30 segmented GPS data.

Further, the above segmentation processing does not change the data attributes in the GPS data. The segmented GPS data is also a collection of GPS points in consecutive time intervals sorted by time, but the collection is only a part of the GPS data. .

In another embodiment of the present application, the segmented GPS data is a data collection of the user's location trajectory. In order to ensure the privacy of the user's location trajectory data, the segmented GPS data may be stored in the blockchain.

S2. Perform encoding processing on the segmented GPS data to obtain encoded GPS data;

In this embodiment of the application, in order to better process the segmented GPS data, the latitude and longitude of each GPS point in the segmented GPS data is converted into one-dimensional data.

In detail, in the embodiment of the present application, encoding the segmented GPS data to obtain the encoded GPS data includes:

S21: Convert the latitude and longitude of each GPS point contained in the segmented GPS data into Geohash values by using the Geohash (geolocation hash coding) algorithm to obtain a GPS Geohash value set;

For example: the latitude and longitude of a GPS point is (39.923201, 116.390705), the range of latitude is (-90, 90), and the middle value is 0. For the latitude 39.923201, in the interval (0, 90), we get a 1; the middle value of the interval (0, 90) is 45 degrees, and the latitude 39.923201 is less than 45, so we get a 0. Calculate in order to get the latitude Binary representation, the final binary representation of latitude is: 10111000110001111001; similarly, the binary representation of longitude 116.390705 can be obtained as: 11010010110001000100. The binary representations of latitude and longitude obtained above are further combined, where longitude occupies even digits and latitude occupies odd digits. For example, for the above latitude and longitude (39.923201, 116.390705), the combined value is 1110011101001000111100000011010101100001. Further, for the above-mentioned combined value, use Base32 for encoding to obtain the Geohash code of the GPS point. One way of the Base32 (reference 32) encoding table is to use 0-9, bz (remove a, i, l, o) These 32 letters are coded. For example, for the above merged value, the Geohash value obtained after Base32 encoding is wx4g0ec1.

S22. Perform duplicate value deletion processing on the data in the GPSGeohash value set to obtain the encoded GPS data.

In the embodiment of the present application, according to the inherent properties of the Geohash algorithm, different GPS points in the segmented GPS data may correspond to the same Geohash value. Therefore, the data in the GPS Geohash value set is deduplicated to obtain encoded GPS data.

S3. Obtain map data, and use the encoded GPS data to correlate and segment the map data to obtain segmented map data;

In the embodiment of the application, the map data is data of an electronic map, and the map data includes, but is not limited to: the starting point ID (Identitydocument, identity identification number) and the end point ID of the road, the starting point latitude and longitude, the end point latitude and longitude of different roads, and Road information description, where the road information description includes: road ID, the name of the road, road data, the direction of the road, and data of multiple latitude and longitude points that make up the road. The map data can be obtained from any electronic map database.

Further, in this embodiment of the present application, the Geohash algorithm is used to process the map data to obtain a map Geohash value set, and each Geohash value in the map Geohash value set represents a piece of map area data in the map data.

Further, in this embodiment of the application, the map data is too large. In order to save computing resources, only the map area data where the segmented GPS data is located is selected, and the coded GPS data is used to associate and cut the map data. Points, including:

S31. Select the Geohash value corresponding to the encoded GPS data in the map Geohash value set to obtain a target Geohash value set;

S32. Select data corresponding to all Geohash values in the target Geohash value set in the map data to obtain the segmented map data.

In the embodiment of the present application, each Geohash value in the map Geohash value set represents a piece of map area data in the map data, so the segmented map data represents data of the map area where the segmented GPS data is located.

S4. Perform a first screening analysis calculation using the segmented map data and the segmented GPS data to obtain a target latitude and longitude point set and a candidate starting point set;

In the embodiment of the present application, in order to find the candidate starting point in the segmented map data, the segmented map data and the segmented GPS data are used for screening, analysis and calculation to obtain a candidate starting point set.

In detail, the embodiment of the present application uses the segmented map data and the segmented GPS data to perform a first screening analysis calculation to obtain a target longitude and latitude point set and a candidate starting point set, including:

S41. Select and sort corresponding latitude and longitude points in the segmented map data according to the segmented GPS data to obtain the target latitude and longitude point set;

In the embodiment of the present application, the segmented GPS data is a collection of GPS points in continuous time intervals sorted by time, where the GPS points are geographic locations with time, latitude and longitude.

Further, in the embodiment of the present application, the selecting and sorting corresponding latitude and longitude points in the segmented map data according to the segmented GPS data to obtain a target latitude and longitude point set includes:

S411: Select latitude and longitude points with the same latitude and longitude included in the segmented map data according to the latitude and longitude of each GPS point included in the segmented GPS data to obtain a latitude and longitude point set;

In the embodiment of the present application, the segmented map data includes multiple latitude and longitude points, and the latitude and longitude points of the same latitude and longitude included in the segmented map data are selected according to the latitude and longitude of each GPS point included in the segmented GPS data For example, if a certain GPS point included in the segmented GPS data is time 11:00 and latitude and longitude (50, 60), then the latitude and longitude point corresponding to the segmented map data is selected as (50, 60).

S412: Sort the longitude and latitude points in the longitude and latitude point set according to the time sequence of the GPS points in the segmented GPS data corresponding to each longitude and latitude point in the longitude and latitude point set to obtain the target longitude and latitude point set.

For example: the 2 GPS points included in the segmented GPS data are point A: time 11:00, latitude and longitude (50, 60), point B: time 11:01, latitude and longitude (50, 61), then get the latitude and longitude The 2 latitude and longitude points included in the point set are (50,60) and (50,61), and the GPS point time corresponding to the latitude and longitude point (50,60) is 11:00, and the GPS point time corresponding to the latitude and longitude point (50,61) It is 11:01, so the latitude and longitude point (50, 60) is sorted as the first latitude and longitude point, and the latitude and longitude point (50, 61) is sorted as the second latitude and longitude point to obtain the target latitude and longitude point set.

S42. Select a road starting point within a preset range of the first latitude and longitude point in the target longitude and latitude point set included in the segmented map data as a candidate starting node to obtain a candidate starting node set;

In the embodiment of the present application, the road starting point is the starting longitude and latitude point of the road in the segmented map data.

Preferably, the preset range is (0-90) m.

S43: Calculate the minimum distance between the first latitude and longitude point and the minimum starting node adjacent edge of the candidate starting node;

In the embodiment of the present application, the minimum distance between adjacent edges of the starting node is the distance from the first latitude and longitude point to the road where the candidate starting node is located.

S44. Calculate the angle between the first directed line segment composed of the first longitude and latitude points and the second longitude and latitude points in the target longitude and latitude points and the adjacent sides of the starting node of the candidate starting node;

In the embodiment of the present application, the angle between the adjacent sides of the starting node is the angle between the first directed line segment and the road direction where the candidate starting node is located.

S45. Calculate the weighted sum of the starting node of the distance between the adjacent edge of the minimum starting node and the angle between the adjacent edge of the starting node;

In detail, the weighted sum of the starting node can be calculated by the following formula:

in,

Represents the weighted sum of the starting node,

Represents the minimum distance between adjacent edges of the starting node,

Indicates the angle between the adjacent sides of the starting node.

S46. Sort the candidate start nodes in the candidate start node set in ascending order according to the corresponding start node weighted sum, and select a predetermined number of candidate start nodes to obtain the candidate start node set.

Preferably, in the embodiment of the present application, the top five candidate starting nodes in the candidate starting node set are selected to obtain the candidate starting point set; further, if the number of candidate starting points in the candidate starting node set is If there are less than 5, the candidate starting points in the candidate starting node set are sorted in ascending order according to the weighted sum of the corresponding starting nodes to obtain the candidate starting point set.

S5. Use the segmented map data and the target latitude and longitude point set to perform a second screening analysis and calculation to obtain a candidate end point set;

In the embodiment of the present application, in order to find candidate end points in the segmented map data, the segmented map data and the target latitude and longitude point set are used for screening, analysis and calculation to obtain a candidate end point set.

In detail, the embodiment of the present application uses the segmented map data and the target latitude and longitude point set to perform screening, analysis and calculation to obtain a candidate end point set, including:

S51. Select a road termination point within the preset range of the first latitude and longitude point in the set of target longitude and latitude points included in the segmented map data as a candidate termination node to obtain a candidate termination node set.

In the embodiment of the present application, the last longitude and latitude point in the target longitude and latitude points is the last longitude and latitude point in the concentrated order of the target longitude and latitude points.

Further, in the embodiment of the present application, the road termination point is the termination latitude and longitude point of the road in the segmented map data.

S52. Calculate the minimum neighboring edge distance of the terminal node from the penultimate latitude and longitude point of the target latitude and longitude points to the candidate terminal node;

In the embodiment of the present application, the minimum neighboring edge distance of the termination node is the distance from the first GPS point from the bottom to the road where the candidate termination node is located.

S53: Calculate the angle between the second directed line segment composed of the penultimate longitude and latitude point and the penultimate longitude and latitude point in the target longitude and latitude points and the adjacent side of the termination node of the candidate termination node;

In the embodiment of the present application, the angle between the adjacent sides of the termination node is the angle between the second directed line segment and the road direction where the candidate termination node is located.

S54: Calculate the weighted sum of the termination node of the minimum distance between the adjacent edges of the termination node and the angle between the adjacent edges of the termination node;

in,

Represents the weighted sum of the termination node,

Represents the distance between the neighboring edges of the minimum terminal node,

Indicates the angle between the adjacent sides of the termination node.

S55: Sort the candidate termination nodes in the candidate termination node set in ascending order according to the corresponding termination node weighted sum, and select a predetermined number of candidate nodes to obtain the candidate termination set.

Preferably, in the embodiment of the present application, the top five candidate termination nodes in the candidate termination node set are selected to obtain the candidate termination node set; further, if the number of candidate termination nodes in the candidate termination node set is less than 5 , Sorting the candidate termination nodes in the candidate termination node set in ascending order according to the corresponding termination node weighted sum to obtain the candidate termination node set.

S6. Perform calculation according to the candidate start point set and the candidate end point set to obtain a target path.

In the embodiment of the present application, the Astar algorithm is used to calculate the path between each candidate starting point in the candidate starting point set and each candidate end point in the candidate ending point set to obtain the path set, and the shortest path in the path set is selected As the shortest path.

Further, in the embodiment of the present application, the shortest path is the best path matched by the segmented GPS data, and the shortest path corresponding to all the segmented GPS data is combined to obtain the target path, that is, the GPS data The best path to match.

In the embodiment of the present application, the GPS data is segmented to obtain segmented GPS data, and the segmented processing improves the calculation speed and saves subsequent road matching calculation resources; the segmented GPS data is encoded to obtain encoded GPS data, Make the data one-dimensional to reduce the amount of subsequent road matching calculations; use the encoded GPS data to correlate and segment the map data, divide the map into blocks, and reduce the amount of subsequent road matching calculations; use the segmented map data and all The segmented GPS data is subjected to a first screening analysis and calculation to obtain a target longitude and latitude point set and a candidate starting point set; the segmented map data and the target longitude and latitude point set are used to perform a second screening analysis and calculation to obtain a candidate end point set; The start point set and the candidate end point set are calculated to obtain the target path. Splitting and matching GPS data with map data improves the calculation speed of road matching and saves calculation resources for road matching.

As shown in Figure 2, it is a functional block diagram of the road matching device of the present application.

The road matching device 100 described in this application can be installed in an electronic device. According to the realized functions, the road matching device may include a data association module 101, a starting point generating module 102, an ending point generating module 103, and a target path calculation module 104. The module described in the present invention can also be called a unit, which refers to a series of computer program segments that can be executed by the processor of an electronic device and can complete fixed functions, and are stored in the memory of the electronic device.

In this embodiment, the functions of each module/unit are as follows:

The data association module 101 is used for acquiring GPS data, segmenting the GPS data to obtain segmented GPS data; encoding the segmented GPS data to obtain encoded GPS data; acquiring map data, using the encoding The GPS data performs correlation and segmentation on the map data to obtain segmented map data.

The starting point generating module 102 is configured to use the segmented map data and the segmented GPS data to perform a first screening analysis calculation to obtain a target latitude and longitude point set and a candidate starting point set.

The end point generating module 103 is configured to use the segmented map data and the target longitude and latitude point set to perform a second screening analysis and calculation to obtain a candidate end point set.

The target path calculation module 104 is configured to calculate a target path according to the candidate start point set and the candidate end point set.

As shown in Fig. 3, it is a schematic diagram of the structure of the electronic device implementing the road matching method of the present application.

The electronic device 1 may include a processor 10, a memory 11, and a bus, and may also include a computer program stored in the memory 11 and running on the processor 10, such as a road matching program.

The memory 11 includes at least one type of readable storage medium, and the readable storage medium may be volatile or nonvolatile. Specifically, the readable storage medium includes flash memory, mobile hard disk, multimedia card, card-type memory (for example: SD or DX memory, etc.), magnetic memory, magnetic disk, optical disk, etc. The memory 11 may be an internal storage unit of the electronic device 1 in some embodiments, for example, a mobile hard disk of the electronic device 1. In other embodiments, the memory 11 may also be an external storage device of the electronic device 1, such as a plug-in mobile hard disk, a smart memory card (SmartMediaCard, SMC), and a secure digital (SecureDigital, SD) equipped on the electronic device 1. Card, flash card (FlashCard), etc. Further, the memory 11 may also include both an internal storage unit of the electronic device 1 and an external storage device. The memory 11 can be used not only to store application software and various data installed in the electronic device 1, such as the code of a road matching program, etc., but also to temporarily store data that has been output or will be output.

The processor 10 may be composed of integrated circuits in some embodiments, for example, may be composed of a single packaged integrated circuit, or may be composed of multiple integrated circuits with the same function or different functions, including one or more Combinations of central processing unit (CentralProcessingunit, CPU), microprocessor, digital processing chip, graphics processor and various control chips, etc. The processor 10 is the control core (ControlUnit) of the electronic device, which uses various interfaces and lines to connect the various components of the entire electronic device, and runs or executes programs or modules (such as road matching) stored in the memory 11 Programs, etc.), and call data stored in the memory 11 to execute various functions of the electronic device 1 and process data.

The bus may be a peripheral component interconnection standard (peripheral component interconnect, referred to as PCI) bus or an extended industry standard architecture (extended industry standard architecture, referred to as EISA) bus, etc. The bus can be divided into address bus, data bus, control bus and so on. The bus is configured to implement connection and communication between the memory 11 and at least one processor 10 and the like.

FIG. 3 only shows an electronic device with components. Those skilled in the art can understand that the structure shown in FIG. 3 does not constitute a limitation on the electronic device 1, and may include fewer or more components than shown in the figure. Components, or a combination of certain components, or different component arrangements.

For example, although not shown, the electronic device 1 may also include a power source (such as a battery) for supplying power to various components. Preferably, the power source may be logically connected to the at least one processor 10 through a power management device, thereby controlling power The device implements functions such as charge management, discharge management, and power consumption management. The power supply may also include any components such as one or more DC or AC power supplies, recharging devices, power failure detection circuits, power converters or inverters, and power status indicators. The electronic device 1 may also include various sensors, Bluetooth modules, Wi-Fi modules, etc., which will not be repeated here.

Further, the electronic device 1 may also include a network interface. Optionally, the network interface may include a wired interface and/or a wireless interface (such as a Wi-Fi interface, a Bluetooth interface, etc.), which is usually used in the electronic device 1 Establish a communication connection with other electronic devices.

Optionally, the electronic device 1 may also include a user interface. The user interface may be a display (Display) and an input unit (such as a keyboard (Keyboard)). Optionally, the user interface may also be a standard wired interface or a wireless interface. Optionally, in some embodiments, the display may be an LED display, a liquid crystal display, a touch-sensitive liquid crystal display, an OLED (Organic Light-Emitting Diode, organic light-emitting diode) touch device, etc. Among them, the display can also be appropriately called a display screen or a display unit, which is used to display the information processed in the electronic device 1 and to display a visualized user interface.

It should be understood that the embodiments are only for illustrative purposes, and are not limited by this structure in the scope of the patent application.

The road matching program 12 stored in the memory 11 in the electronic device 1 is a combination of multiple instructions. When running in the processor 10, it can realize:

Acquiring GPS data, segmenting the GPS data to obtain segmented GPS data;

Encoding the segmented GPS data to obtain encoded GPS data;

Specifically, for the specific implementation method of the above-mentioned instructions by the processor 10, reference may be made to the description of the relevant steps in the embodiment corresponding to FIG. 1, which will not be repeated here.

Further, if the integrated module/unit of the electronic device 1 is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer readable storage medium. The computer-readable storage medium may be volatile or non-volatile. Specifically, the computer-readable storage medium may include: any entity or device capable of carrying the computer program code, recording medium, U disk, mobile hard disk, magnetic disk, optical disk, computer memory, read-only memory (ROM, Read -OnlyMemory).

Further, the computer usable storage medium may mainly include a storage program area and a storage data area, wherein the storage program area may store an operating system, an application program required by at least one function, etc.; the storage data area may store a block chain node Use the created data, etc.

In the several embodiments provided in this application, it should be understood that the disclosed equipment, device, and method may be implemented in other ways. For example, the device embodiments described above are merely illustrative. For example, the division of the modules is only a logical function division, and there may be other division methods in actual implementation.

The modules described as separate components may or may not be physically separated, and the components displayed as modules may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules can be selected according to actual needs to achieve the objectives of the solutions of the embodiments.

In addition, the functional modules in the various embodiments of the present application may be integrated into one processing unit, or each unit may exist alone physically, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware, or may be implemented in the form of hardware plus software functional modules.

For those skilled in the art, it is obvious that the present application is not limited to the details of the foregoing exemplary embodiments, and the present application can be implemented in other specific forms without departing from the spirit or basic characteristics of the application.

Therefore, no matter from which point of view, the embodiments should be regarded as exemplary and non-limiting. The scope of this application is defined by the appended claims rather than the above description, and therefore it is intended to fall into the claims. All changes in the meaning and scope of the equivalent elements of are included in this application. Any associated diagram marks in the claims should not be regarded as limiting the claims involved.

The blockchain referred to in this application is a new application mode of computer technology such as distributed data storage, point-to-point transmission, consensus mechanism, and encryption algorithm. Blockchain, essentially a decentralized database, is a series of data blocks associated with cryptographic methods. Each data block contains a batch of network transaction information for verification. The validity of the information (anti-counterfeiting) and the generation of the next block. The blockchain can include the underlying platform of the blockchain, the platform product service layer, and the application service layer.

In addition, it is obvious that the word "including" does not exclude other units or steps, and the singular does not exclude the plural. Multiple units or devices stated in the system claims can also be implemented by one unit or device through software or hardware. The second class words are used to indicate names, and do not indicate any specific order.

Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the application and not to limit them. Although the application has been described in detail with reference to the preferred embodiments, those of ordinary skill in the art should understand that the technical solutions of the application can be Make modifications or equivalent replacements without departing from the spirit and scope of the technical solution of the present application.

Claims

A road matching method, wherein the method includes:

Acquiring GPS data, segmenting the GPS data to obtain segmented GPS data;

Encoding the segmented GPS data to obtain encoded GPS data;

Acquiring map data, using the encoded GPS data to correlate and segment the map data to obtain segmented map data;

Use the segmented map data and the segmented GPS data to perform a first screening analysis and calculation to obtain a target latitude and longitude point set and a candidate starting point set;

Use the segmented map data and the target latitude and longitude point set to perform a second screening analysis and calculation to obtain a candidate end point set;

The target path is obtained by calculation according to the candidate start point set and the candidate end point set.
The road matching method according to claim 1, wherein said segmenting said GPS data to obtain segmented GPS data comprises:

Segment the GPS data according to a preset time to obtain the segmented GPS data; and/or

The GPS data is segmented according to a preset distance to obtain the segmented GPS data.
The road matching method according to claim 1, wherein said encoding said segmented GPS data to obtain encoded GPS data comprises:

Use the Geohash algorithm to convert the latitude and longitude of each GPS point contained in the segmented GPS data into Geohash values to obtain a GPS Geohash value set;

Performing duplicate value deletion processing on the data in the GPS Geohash value set to obtain the encoded GPS data.
The road matching method according to claim 1, wherein said using said encoded GPS data to associate and segment said map data to obtain segmented map data comprises:

Use Geohash algorithm to process the map data to obtain a map Geohash value set;

Selecting the Geohash value corresponding to the encoded GPS data in the map Geohash value set to obtain a target Geohash value set;

The data corresponding to all Geohash values in the target Geohash value set in the map data is selected to obtain the segmented map data.
5. The road matching method according to claim 1, wherein said using said segmented map data and said segmented GPS data to perform a first screening analysis calculation to obtain a target longitude and latitude point set and a candidate starting point set, comprising:

Selecting and sorting corresponding latitude and longitude points in the segmented map data according to the segmented GPS data to obtain the target latitude and longitude point set;

Selecting a road starting point within a preset range of the first latitude and longitude point in the target longitude and latitude point set included in the segmented map data as a candidate starting node to obtain a candidate starting node set;

Calculating the minimum distance between the first latitude and longitude point and the minimum starting node adjacent edge of the candidate starting node;

Calculating an angle between a first directed line segment composed of a first latitude and longitude point and a second latitude and longitude point in the target longitude and latitude points and the adjacent side of the starting node of the candidate starting node;

Calculating the weighted sum of the starting node of the angle between the minimum distance between the adjacent edges of the starting node and the adjacent edges of the starting node;

The candidate starting nodes in the candidate starting node set are arranged in ascending order according to the weighted sum of the corresponding starting nodes, and the candidate starting nodes with a predetermined ranking are selected to obtain the candidate starting node set.
5. The road matching method according to claim 1, wherein said calculating according to said candidate starting point set and said candidate ending point set to obtain a target path comprises:

Calculating a path between each candidate starting point in the candidate starting point set and each candidate ending point in the candidate ending point set by using an Astar algorithm to obtain a path set;

Selecting the shortest path in the path set as the shortest path;

Combining all the shortest paths corresponding to the segmented GPS data to obtain the target path.
8. The road matching method according to claim 5, wherein said calculating the weighted sum of the starting node of the distance between the adjacent edge of the minimum starting node and the adjacent edge of the starting node comprises:

in,
Represents the weighted sum of the starting node,
Represents the minimum distance between adjacent edges of the starting node,
Indicates the angle between the adjacent sides of the starting node.
A road matching device, wherein the device includes:

A data association module for acquiring GPS data, segmenting the GPS data to obtain segmented GPS data, encoding the segmented GPS data to obtain encoded GPS data, acquiring map data, and using the encoded GPS data Performing associated segmentation on the map data to obtain segmented map data;

A starting point generating module, configured to use the segmented map data and the segmented GPS data to perform a first screening analysis and calculation to obtain a target latitude and longitude point set and a candidate starting point set;

An end point generating module, configured to use the segmented map data and the target longitude and latitude point set to perform a second screening analysis and calculation to obtain a candidate end point set;

The target path calculation module is used to calculate the target path according to the candidate start point set and the candidate end point set.
An electronic device, wherein the electronic device includes:

At least one processor; and,

A memory communicatively connected with the at least one processor; wherein,

The memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor, so that the at least one processor can execute the road matching method as described below:

Acquiring GPS data, segmenting the GPS data to obtain segmented GPS data;

Encoding the segmented GPS data to obtain encoded GPS data;

Acquiring map data, using the encoded GPS data to correlate and segment the map data to obtain segmented map data;

Use the segmented map data and the segmented GPS data to perform a first screening analysis and calculation to obtain a target latitude and longitude point set and a candidate starting point set;

Use the segmented map data and the target latitude and longitude point set to perform a second screening analysis and calculation to obtain a candidate end point set;

The target path is obtained by calculation according to the candidate start point set and the candidate end point set.
9. The electronic device of claim 9, wherein said segmenting said GPS data to obtain segmented GPS data comprises:

Segment the GPS data according to a preset time to obtain the segmented GPS data; and/or

The GPS data is segmented according to a preset distance to obtain the segmented GPS data.
9. The electronic device according to claim 9, wherein said encoding said segmented GPS data to obtain encoded GPS data comprises:

Use the Geohash algorithm to convert the latitude and longitude of each GPS point contained in the segmented GPS data into Geohash values to obtain a GPS Geohash value set;

Performing duplicate value deletion processing on the data in the GPS Geohash value set to obtain the encoded GPS data.
9. The electronic device according to claim 9, wherein said using said encoded GPS data to correlate and segment said map data to obtain segmented map data comprises:

Use Geohash algorithm to process the map data to obtain a map Geohash value set;

Selecting the Geohash value corresponding to the encoded GPS data in the map Geohash value set to obtain a target Geohash value set;

The data corresponding to all Geohash values in the target Geohash value set in the map data is selected to obtain the segmented map data.
9. The electronic device according to claim 9, wherein said using said segmented map data and said segmented GPS data to perform a first screening analysis calculation to obtain a target longitude and latitude point set and a candidate starting point set, comprising:

Selecting and sorting corresponding latitude and longitude points in the segmented map data according to the segmented GPS data to obtain the target latitude and longitude point set;

Selecting a road starting point within a preset range of the first latitude and longitude point in the target longitude and latitude point set included in the segmented map data as a candidate starting node to obtain a candidate starting node set;

Calculating the minimum distance between the first latitude and longitude point and the minimum starting node adjacent edge of the candidate starting node;

Calculating an angle between a first directed line segment composed of a first latitude and longitude point and a second latitude and longitude point in the target longitude and latitude points and the adjacent side of the starting node of the candidate starting node;

Calculating the weighted sum of the starting node of the angle between the minimum distance between the adjacent edges of the starting node and the adjacent edges of the starting node;

The candidate starting nodes in the candidate starting node set are arranged in ascending order according to the weighted sum of the corresponding starting nodes, and the candidate starting nodes with a predetermined ranking are selected to obtain the candidate starting node set.
9. The electronic device according to claim 9, wherein said calculating a target path based on said candidate starting point set and said candidate ending point set comprises:

Calculating a path between each candidate starting point in the candidate starting point set and each candidate ending point in the candidate ending point set by using an Astar algorithm to obtain a path set;

Selecting the shortest path in the path set as the shortest path;

Combining all the shortest paths corresponding to the segmented GPS data to obtain the target path.
A computer-readable storage medium storing a computer program, wherein the computer program is executed by a processor to implement the road matching method as described below:

Acquiring GPS data, segmenting the GPS data to obtain segmented GPS data;

Encoding the segmented GPS data to obtain encoded GPS data;

Acquiring map data, using the encoded GPS data to correlate and segment the map data to obtain segmented map data;

Use the segmented map data and the segmented GPS data to perform a first screening analysis and calculation to obtain a target latitude and longitude point set and a candidate starting point set;

Use the segmented map data and the target latitude and longitude point set to perform a second screening analysis and calculation to obtain a candidate end point set;

The target path is obtained by calculation according to the candidate start point set and the candidate end point set.
15. The computer-readable storage medium of claim 15, wherein said segmenting said GPS data to obtain segmented GPS data comprises:

Segment the GPS data according to a preset time to obtain the segmented GPS data; and/or

The GPS data is segmented according to a preset distance to obtain the segmented GPS data.
15. The computer-readable storage medium according to claim 15, wherein said encoding said segmented GPS data to obtain encoded GPS data comprises:

Use the Geohash algorithm to convert the latitude and longitude of each GPS point contained in the segmented GPS data into Geohash values to obtain a GPS Geohash value set;

Performing duplicate value deletion processing on the data in the GPS Geohash value set to obtain the encoded GPS data.
15. The computer-readable storage medium of claim 15, wherein said using said encoded GPS data to correlate and segment said map data to obtain segmented map data comprises:

Use Geohash algorithm to process the map data to obtain a map Geohash value set;

Selecting the Geohash value corresponding to the encoded GPS data in the map Geohash value set to obtain a target Geohash value set;

The data corresponding to all Geohash values in the target Geohash value set in the map data is selected to obtain the segmented map data.
15. The computer-readable storage medium according to claim 15, wherein the first screening analysis calculation using the segmented map data and the segmented GPS data to obtain a target longitude and latitude point set and a candidate starting point set comprises:

Selecting and sorting corresponding latitude and longitude points in the segmented map data according to the segmented GPS data to obtain the target latitude and longitude point set;

Selecting a road starting point within a preset range of the first latitude and longitude point in the target longitude and latitude point set included in the segmented map data as a candidate starting node to obtain a candidate starting node set;

Calculating the distance between the first latitude and longitude point and the minimum starting node neighboring edge of the candidate starting node;

Calculating an angle between a first directed line segment composed of a first latitude and longitude point and a second latitude and longitude point in the target longitude and latitude points and the adjacent side of the starting node of the candidate starting node;

Calculating the weighted sum of the starting node of the angle between the minimum distance between the adjacent edges of the starting node and the adjacent edges of the starting node;

The candidate starting nodes in the candidate starting node set are arranged in ascending order according to the weighted sum of the corresponding starting nodes, and the candidate starting nodes with a predetermined ranking are selected to obtain the candidate starting node set.
15. The computer-readable storage medium according to claim 15, wherein said calculating a target path based on said candidate start point set and said candidate end point set comprises:

Calculating a path between each candidate starting point in the candidate starting point set and each candidate ending point in the candidate ending point set by using an Astar algorithm to obtain a path set;

Selecting the shortest path in the path set as the shortest path;

Combining all the shortest paths corresponding to the segmented GPS data to obtain the target path.