Vehicle-road portrait construction method and device

Abstract

The present application relates to the field of Internet of Vehicles, and in particular to a method and device for constructing a vehicle-road portrait. A vehicle-road portrait construction method, applied to a processing device, comprises: determining first Internet of Vehicles card data of each target vehicle in a target area, the first Internet of Vehicles card data comprising target vehicle positioning data, target vehicle information data, and target vehicle Internet of Vehicles card traffic data; fusing the first Internet of Vehicles card data of each target vehicle with road network data to determine vehicle-road label data of the target area, the vehicle-road label data comprising road driving details data and vehicle usage status data; determining vehicle-road labels within the target area based on the vehicle-road label data; and generating a vehicle-road portrait within the target area based on the vehicle-road labels.

Description

Technical field

[0001] The present application relates to the field of Internet of Vehicles, and in particular to a method and device for constructing a vehicle-road portrait. [Background Technology]

[0002] Several devices are deployed in a road network system, such as vehicle terminals traveling on the road, roadside equipment deployed on the roadside, and servers for processing specific requests.

[0003] In related technologies, the massive amounts of data generated by various devices within the road network system are transparently transmitted to the business end for direct use, rather than being analyzed and utilized. Furthermore, the spatiotemporal distribution of this massive data is fragmented, and the data sources are heterogeneous, making it difficult to form a comprehensive understanding of the operational relationships and patterns between road sections and vehicles within the road network system. [Summary of the invention]

[0004] In light of this, embodiments of the present invention provide a method and apparatus for constructing a vehicle-road profile. This method distinguishes the massive amount of interactive data within a road network system, identifies the vehicle network card data of a vehicle terminal, and spatially and temporally combines this data with road network data describing the actual road conditions. Features are extracted and abstracted, labeled, and a multi-dimensional stereoscopic profile is constructed. This method addresses the existing difficulty in forming a comprehensive perception of the operational relationships and patterns between road segments and vehicles within a road network system.

[0005] In a first aspect, an embodiment of the present invention provides a method for constructing a vehicle-road profile, the method being applied to a processing device and comprising:

[0006] Determine first vehicle networking card data of each target vehicle in the target area, wherein the first vehicle networking card data includes target vehicle positioning data, target vehicle information data, and target vehicle vehicle networking card traffic data;

[0007] Merging the first vehicle network card data of each target vehicle with the road network data to determine vehicle-road label data of the target area, wherein the vehicle-road label data includes road travel details data and vehicle usage status data;

[0008] Determining the vehicle-road labels within the target area according to the vehicle-road label data;

[0009] A vehicle-road image within the target area is generated based on the vehicle-road label.

[0010] Optionally, determining the first Internet of Vehicles card data of each target vehicle in the target area includes:

[0011] Determining full positioning data within the target area and full vehicle networking card data reported by a vehicle networking card associated with the processing device, wherein the full vehicle networking card data includes full vehicle information data and full vehicle networking card traffic data;

[0012] Performing key field matching on the full positioning data and the full vehicle information data to determine the target vehicle positioning data and the target vehicle information data;

[0013] Key field matching is performed on the target vehicle information data and the full amount of vehicle networking card traffic data to determine the target vehicle vehicle networking card traffic data.

[0014] Optionally, the key field matching includes: SIMID field matching, or IMSI field matching.

[0015] Optionally, fusing the first vehicle network card data of each target vehicle with road network data to determine vehicle-road label data of the target area includes:

[0016] Fusion of the target vehicle's IoV card traffic data with the target vehicle's positioning data to verify whether the positioning information in the target vehicle's positioning data is accurate, and determination of the vehicle's usage status data, including the vehicle's application usage status and the vehicle's driving status;

[0017] The target vehicle positioning data is integrated into the road network data to determine the road travel detail data.

[0018] Optionally, determining the vehicle-road label within the target area according to the vehicle-road label data includes:

[0019] Performing statistical classification on the road network data to determine travel hotspot area labels and road basic attribute labels;

[0020] Statistically classifying the road travel detailed data and inputting the data into a prediction model for prediction to determine traffic operation characteristic labels and vehicle travel characteristic labels;

[0021] Performing statistical classification on the target vehicle information data to determine the basic attribute label of the vehicle;

[0022] The vehicle status data is statistically classified and input into a prediction model for prediction to determine a vehicle start / stop label and an in-vehicle application usage label.

[0023] Optionally, generating a vehicle-road image in the target area according to the vehicle-road label includes:

[0024] The various types of vehicle-road labels are combined to obtain a multi-label vehicle-road portrait, which includes a traffic operation portrait and a vehicle operation portrait.

[0025] Optionally, after generating the vehicle-road portrait within the target area according to the vehicle-road label, the method further includes:

[0026] When the target vehicle meets any of the vehicle-road portraits, the service information corresponding to the vehicle-road portrait is pushed to the target vehicle.

[0027] In a second aspect, an embodiment of the present invention provides a vehicle-road portrait construction device, comprising:

[0028] A first determination module determines first vehicle network card data of each target vehicle in the target area, wherein the first vehicle network card data includes target vehicle positioning data, target vehicle information data, and target vehicle vehicle network card traffic data;

[0029] a fusion module, which fuses the first vehicle network card data of each target vehicle with the road network data to determine the vehicle-road label data of the target area, wherein the vehicle-road label data includes road travel details data and vehicle usage status data;

[0030] A second determining module determines the vehicle-road labels within the target area according to the vehicle-road label data;

[0031] A generation module generates a vehicle-road image in the target area according to the vehicle-road label.

[0032] In a third aspect, an embodiment of the present invention provides an electronic device, including:

[0033] at least one processor; and

[0034] at least one memory in communication with the processor, wherein:

[0035] The memory stores program instructions that can be executed by the processor, and the processor calls the program instructions to execute any method as described in the first aspect.

[0036] In a fourth aspect, an embodiment of the present invention provides a computer-readable storage medium, wherein the computer-readable storage medium includes a stored program, wherein when the program is running, the device where the computer-readable storage medium is located is controlled to execute any of the methods described in the first aspect.

[0037] Through the above solution, we can realize the perception of the operating rules of traffic entities in the road network system, and provide support for refined traffic management and services.

Brief Description of the Drawings

[0038] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for use in the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0039] Figure 1 A flowchart of a vehicle-road portrait construction method provided by an embodiment of the present invention;

[0040] Figure 2 A schematic diagram of the structure of a vehicle-road portrait construction device provided by an embodiment of the present invention;

[0041] Figure 3 A schematic structural diagram of an electronic device provided by an embodiment of the present invention. [Specific implementation method]

[0042] In order to better understand the technical solution of the present invention, the embodiments of the present invention are described in detail below with reference to the accompanying drawings.

[0043] It should be understood that the embodiments described are only a portion of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by persons of ordinary skill in the art without creative work are within the scope of protection of the present invention.

[0044] Within a road network, several types of equipment are included, including vehicles traveling on the road, roadside equipment deployed along the roadside, and servers that process data exchanged within the network. This data can be either request data or information data. Devices within the network exchange request data to enable interoperability and control. Each device also stores information data that identifies its own basic information, which is used in conjunction with the request data to ensure the proper functioning of the network.

[0045] Data exchange between vehicle terminals and other road network equipment is typically achieved through the IoV card, which also records vehicle information data. This means that the interaction data between vehicle terminals is typically IoV card data. Specifically, registered and issued IoV cards correspond one-to-one with vehicle terminals. Each vehicle terminal is equipped with an IoV card, which records its own IoV card data. IoV card data includes vehicle information data, IoV card traffic data, and signaling data.

[0046] The IoV card is a dedicated IoT data flow card that provides connectivity to vehicle terminals. It provides communication services such as vehicle networking, location tracking, data transmission, status monitoring, and emergency response. The IoV card is bound to a processing device and reports the corresponding vehicle's IoV card data to the processing device.

[0047] The vehicle information data in the Internet of Vehicles card data is the basic information registered when the vehicle is delivered, including owner information, vehicle information, Internet of Vehicles card information, etc.

[0048] The Internet of Vehicles card traffic data in the Internet of Vehicles card data is a record of data traffic generated by the mobile communication network, including basic information, start time, duration, data traffic used, etc. each time the vehicle terminal interacts with data through the Internet of Vehicles card.

[0049] Signaling data is the interaction data generated by the vehicle terminal through the Internet of Vehicles card and the transmitting base station or micro station. By calculating this data, the spatial location of the Internet of Vehicles card, that is, the spatial location of the vehicle terminal, can be determined. The signaling data in the Internet of Vehicles card data is calculated to determine the vehicle positioning data in the Internet of Vehicles card data.

[0050] However, in some implementations, interactive data initiated by various road network devices is directly transmitted to the destination device for processing, rather than being actively acquired, utilized, and integrated for analysis. The massive amount of data within the road network system is used solely to ensure stable vehicle operation, rather than to develop a comprehensive understanding of the operational relationships and patterns between road segments and vehicles. Reuse is often difficult when the same data is used across business domains or over time.

[0051] At the same time, in terms of data sources, the data interacting between various road network devices within the road network system is heterogeneous, making it impossible to accurately and effectively distinguish the interaction data of vehicle terminals from various types of road network devices. In other words, it is impossible to determine which interaction data is sent by vehicle terminals and which interaction data is sent by other road network devices. Therefore, the data purity is insufficient, which is not conducive to the targeted acquisition of vehicle driving patterns. At the same time, the data is fragmented in time and space. The interaction data between vehicle terminals and other road network devices are sent separately, and the correlation between different types of data is not strong. The interaction data sent by vehicle terminals is not combined with the actual situation of the vehicle terminals, that is, it is not combined with the actual road sections and road conditions. As a result, the extracted traffic operation patterns are often separated from vehicles and road conditions, making it difficult to form a complete and comprehensive perception of the operation patterns of large-scale traffic entities.

[0052] To solve the above problems, an embodiment of the present invention provides a vehicle-road profile construction method, which is applied to a processing device, such as Figure 1 As shown, the processing steps of the method include:

[0053] 101 , determining first Internet of Vehicles card data of each target vehicle in a target area.

[0054] The first vehicle networking card data includes target vehicle positioning data, target vehicle information data, and target vehicle vehicle networking card traffic data;

[0055] Specifically, all road network equipment in the target area will send interactive data to ensure the stable operation of the target area. In order to accurately obtain the operating rules of the vehicle terminal, the vehicle terminal's Internet of Vehicles card data needs to be extracted and analyzed from the interactive data of all road network equipment, that is, the full amount of original data.

[0056] First, the full amount of original data is obtained, and through the key field matching method, the original data of the target vehicle is extracted from the full amount of original data using the SIMID or IMIS of the Internet of Vehicles card bound to the vehicle.

[0057] Specifically, the full raw data includes full positioning data within the target area and all registered IoV card data associated with the processing device. IoV cards are deployed in vehicles and record their corresponding vehicle information data and IoV card traffic data. The IoV card data collected by each IoV card is aggregated and sent to the processing device to form the full IoV card data. This data consists of the vehicle information data of all vehicles, and the traffic data of all vehicles.

[0058] Full positioning data refers to the positioning data of all road network devices within the target area, such as vehicle terminals, roadside equipment, and mobile phones. Positioning data can be calculated from signaling data between each device and the transmitting base station or micro-station, or determined through Global Navigation Satellite System (GNSS) data, which is the satellite positioning signal of each device.

[0059] Vehicle information data is the information registered upon vehicle delivery and stored in the vehicle network card, including owner information, vehicle type, make, and model. Vehicle network card traffic data is a record of the traffic generated by users using mobile data through the vehicle computer, recording the start time and duration of each traffic session.

[0060] Since the range of data contained in the full positioning data and the full vehicle information data is too large, it is impossible to accurately determine the traffic operation characteristics in the target area. Therefore, key field matching is performed on the full positioning data, the full vehicle information data and the full vehicle network card flow data to extract the target vehicle positioning data, target vehicle information data and target vehicle vehicle network card flow data from the full original data, that is, to extract the positioning data, information data and vehicle network card flow data of each target vehicle in the target area.

[0061] Specifically, key field matching is performed on the full positioning data and the full vehicle information data using the SIMID field or the IMIS field. Specifically, target vehicle positioning data with a specified SIMID or IMIS is determined in the full positioning data, and target vehicle information data containing the specified SIMID or IMIS is determined in the full vehicle information data. The target vehicle information data is obtained by filtering the full vehicle information data using the target area in the full positioning data, and the full positioning data is filtered using the SIMID or IMIS of each vehicle in the target vehicle information data to obtain the target vehicle positioning data.

[0062] Similarly, the target vehicle information data and the full amount of Internet of Vehicles card traffic data are matched with key fields through the SIMID field to determine the target vehicle Internet of Vehicles card traffic data from the full amount of Internet of Vehicles card traffic data.

[0063] To prevent infringement of user information, the first vehicle network card data determined should be desensitized data that does not contain user privacy information. Since the first vehicle network card data is obtained through full positioning data, full vehicle information data, and full vehicle network card traffic data, and the full vehicle information data contains a large amount of user privacy data such as vehicle owner information and vehicle brand, it is necessary to desensitize the obtained full vehicle information data to ensure that the first vehicle network card data subsequently obtained through key field matching is desensitized data.

[0064] 102 , respectively integrate the first vehicle network card data of each target vehicle into the road network data to determine the vehicle-road label data of the target area.

[0065] Among them, the vehicle-road label data includes road driving details data and vehicle usage status data;

[0066] Specifically, the IoV card traffic data of each target vehicle is integrated into its corresponding target vehicle positioning data to verify whether the vehicle information contained in the target vehicle positioning data is accurate and to determine the vehicle usage status data.

[0067] Specifically, the target vehicle's IoV card traffic data is combined with the corresponding positioning data in the target vehicle's positioning data to verify the accuracy of the positioning information in the target vehicle's positioning data. The vehicle's usage status data is then determined based on the positioning information. The vehicle's usage status data specifically includes the usage status of in-vehicle applications and the vehicle's driving status.

[0068] Determine the road network data for the target area from the map data. This road network data is generally static information about the roads within the target area, including their length, name, grade, and category. The target vehicle's location data, verified by its vehicle-to-vehicle network card traffic data, is integrated into the road network data to determine detailed road travel data, thereby integrating the vehicle's location and travel information with the actual road conditions.

[0069] The road network data may be road information of all roads in the target area. Alternatively, the road network data may be road network data of points of interest, that is, road information of a portion of roads selected by the user from all roads.

[0070] Among them, when calculating the signaling data to obtain positioning data, the base station density may be large, resulting in the same device being covered by multiple base stations at the same time. At this time, the device will switch due to the signal strength problem of the overlapping base stations, resulting in the actual position not changing but the signaling data of the position switching, that is, the ping-pong effect, which will have a great impact on the positioning of the device and cause positioning errors.

[0071] Because the target vehicle's positioning data is determined from the full positioning data, and some of the positioning data in the full positioning data is calculated using signaling data, the positioning information in the target vehicle's positioning data may contain errors. Therefore, it is necessary to integrate the target vehicle's IoV card traffic data with the target vehicle's positioning data and perform a spatiotemporal consistency check to determine the start / stop status of the target vehicle's positioning data to determine whether the positioning information in the target vehicle's positioning data is accurate.

[0072] Specifically, the traffic usage of each application in the target vehicle's IoV card traffic data is used to supplement the calculation results of signaling data to determine the vehicle's start-stop status. For example, if signaling data is calculated to determine that the vehicle moved multiple times between 18:00 and 18:05, but the traffic usage recorded in the target vehicle's IoV card traffic data indicates that the number of open sessions between 18:00 and 18:05 is less than one, and no applications consumed traffic, then it can be determined that the vehicle did not move between 18:00 and 18:05, and the positioning information in the target vehicle's positioning data contains errors.

[0073] 103 : Determine the vehicle-road labels within the target area according to the vehicle-road label data.

[0074] The road network data, road travel details data, target vehicle information data and vehicle usage status data are processed separately to obtain the vehicle-road labels in the target area.

[0075] Specifically, since both road network data and target vehicle information data are relatively static and unlikely to change significantly over a short period of time, statistical classification of these data can be performed to determine the corresponding vehicle-road labels. Statistical classification of the road network data is performed to determine travel hotspot labels and basic road attribute labels. Statistical classification of the target vehicle information data is also performed to determine the basic vehicle attribute labels.

[0076] Because detailed road travel data and vehicle usage data are dynamic and can be predicted or extrapolated based on past data, after statistically classifying these data, they must be used in a prediction model to generate corresponding vehicle-road labels. Statistically classify detailed road travel data and input it into the prediction model to determine traffic operation characteristic labels and vehicle travel labels. Statistically classify vehicle usage data and input it into the prediction model to determine vehicle start / stop labels and vehicle application usage labels.

[0077] Each vehicle-road tag includes several sub-tags.

[0078] The travel hotspot area labels include: transportation hub labels, such as stations, airports, ports, logistics stations, etc.; functional area labels, such as hospitals, schools, central business districts, residential areas, large supermarkets, tourist attractions, etc.; vehicle service areas, such as parking lots, charging stations, highway service areas, etc.

[0079] The basic attribute labels of roads include: road usage characteristic labels, such as highways, urban roads, rural roads, tourist roads, dedicated lines, etc.; road grade labels, such as expressways, main roads, etc.; key section labels, such as sections around schools, sections around tourist attractions, etc.

[0080] Traffic operation characteristic labels include: traffic volume time variation characteristic labels, such as holiday peak hours, weekday peak hours, etc.; motor vehicle composition labels, such as the proportion of passenger and freight vehicles, etc.; average vehicle speed labels.

[0081] Vehicle travel characteristic labels include: travel purpose labels, such as commuting, self-driving tours, and operations; road labels, such as frequently traveling on urban roads, frequently traveling on highways, and frequently traveling on village roads; travel characteristic labels, such as traveling across provinces and cities, and long-distance continuous travel; and daily travel distance labels, such as less than 5km, less than 10km, and less than 15km.

[0082] The basic attribute labels of vehicles include: vehicle type labels, such as small passenger cars, large trucks, etc.; vehicle nature labels, such as private cars, commercial vehicles, etc.; power type labels, such as oil, electricity, hybrid, etc.

[0083] The vehicle start-stop label includes: average daily travel time label, including less than 30 minutes, 30-60 minutes, etc.; travel frequency label, such as occasional travel, frequent travel every day, etc.; travel time label, such as frequent travel during peak hours in the morning and evening, frequent travel at night, etc.

[0084] The in-vehicle application usage preference labels include: labels for audio and video entertainment usage while driving, such as rarely used, frequently used, etc.; labels for audio and video entertainment usage when parked, such as rarely used, frequently used, etc.; labels for the degree of dependence on navigation and assisted driving, such as rarely used, frequently used, etc.

[0085] 104 , generating a vehicle-road image in the target area based on the vehicle-road label.

[0086] Specifically, various types of vehicle-road labels are combined to obtain a multi-label vehicle-road portrait, which includes a road traffic operation portrait and a vehicle operation portrait.

[0087] When a target vehicle traveling in the target area meets any preset vehicle-road profile, service information corresponding to the vehicle-road profile, such as safety and road condition reminder travel service information, will be pushed to the target vehicle.

[0088] For example, a preset label combination in a vehicle-road portrait is: expressway, average speed 50-60km / h, small passenger car, and commuting. When the target vehicle meets the conditions of the above vehicle-road portrait, the corresponding service information is pushed to the target vehicle.

[0089] The embodiment of the present invention distinguishes the massive interactive data in the road network system, determines the vehicle network card data of the vehicle terminal, and combines the vehicle network card data with the road network data describing the actual road conditions in time and space, extracts and abstracts the features, labels the features, and constructs a multi-dimensional stereoscopic portrait, thereby realizing the perception of the operating laws of traffic entities in the road network system and providing support for refined traffic management and services.

[0090] Corresponding to the above-mentioned vehicle-road image construction method, an embodiment of the present invention further provides a vehicle-road image construction device, which is applied to a processing device. Figure 2 , is a structural diagram of a vehicle-road image construction device provided by an embodiment of the present invention. Figure 2 As shown, the apparatus may include: a first determination module 201 , a fusion module 202 , a second determination module 203 and a generation module 204 .

[0091] A first determining module 201 determines first vehicle network card data of each target vehicle in a target area, wherein the first vehicle network card data includes target vehicle positioning data, target vehicle information data, and target vehicle vehicle network card traffic data;

[0092] A fusion module 202 is configured to fuse the first vehicle network card data of each target vehicle with the road network data to determine vehicle-road label data of the target area, wherein the vehicle-road label data includes road travel details data and vehicle usage status data;

[0093] A second determining module 203 determines the vehicle-road labels within the target area according to the vehicle-road label data;

[0094] The generating module 204 generates a vehicle-road image in the target area according to the vehicle-road label.

[0095] Figure 2 The vehicle-road portrait construction device provided in the illustrated embodiment can be used to execute the technical solution of the method embodiment shown in this specification. Its implementation principle and technical effects can be further referred to the relevant description in the method embodiment.

[0096] Figure 3 This is a schematic diagram of the structure of an embodiment of the electronic device of this specification. The electronic device can be implemented as a processing device for executing the vehicle-road portrait construction method. Figure 3 As shown, the above-mentioned electronic device may include at least one processor; and at least one memory communicatively connected to the above-mentioned processing unit, wherein: the memory stores program instructions that can be executed by the processing unit, and the above-mentioned processor calls the above-mentioned program instructions to execute the vehicle-road portrait construction method provided in this embodiment.

[0097] The electronic device can be a device that can conduct intelligent dialogue with the user, and the specific form of the electronic device is not limited in the embodiments of this specification. It can be understood that the electronic device here is the machine mentioned in the method embodiment.

[0098] Figure 3 A block diagram is shown of an exemplary electronic device suitable for implementing embodiments of the present description. Figure 3 The electronic device shown is only an example and should not limit the functions and scope of use of the embodiments of this specification.

[0099] like Figure 3 As shown, the electronic device is implemented as a general-purpose computing device. Components of the electronic device may include, but are not limited to, one or more processors 310, a communication interface 320, a memory 330, and a communication bus 340 connecting different system components (including the memory 330, the communication interface 320, and the processor 310).

[0100] Communication bus 340 represents one or more of several types of bus structures, including a memory bus or memory controller, a peripheral bus, an accelerated graphics port, a processor, or a local bus using any of a variety of bus architectures. Examples of these architectures include, but are not limited to, the Industry Standard Architecture (ISA) bus, the Micro Channel Architecture (MAC) bus, the Enhanced ISA bus, the Video Electronics Standards Association (VESA) local bus, and the Peripheral Component Interconnection (PCI) bus.

[0101] Electronic devices typically include a variety of computer system readable media. These media can be any available media that can be accessed by the electronic device, including volatile and non-volatile media, removable and non-removable media.

[0102] Memory 330 may include computer-readable media in the form of volatile memory, such as random access memory (RAM) and / or cache memory. The electronic device may further include other removable / non-removable, volatile / non-volatile computer system storage media. Memory 330 may include at least one program product having a set (e.g., at least one) of program modules configured to perform the functions of various embodiments of this specification.

[0103] A program / utility having a set (at least one) of program modules may be stored in memory 330. Such program modules include, but are not limited to, an operating system, one or more application programs, other program modules, and program data, each of which, or some combination thereof, may include an implementation of a network environment. The program modules generally implement the functions and / or methods of the embodiments described herein.

[0104] The processor 310 executes various functional applications and data processing by running the programs stored in the memory 330, such as implementing the vehicle-road portrait construction method provided in the embodiment shown in this specification.

[0105] An embodiment of this specification provides a non-transitory computer-readable storage medium, which stores computer instructions, and the computer instructions enable the computer to execute the vehicle-road portrait construction method provided by the embodiment shown in this specification.

[0106] The above-mentioned non-transitory computer-readable storage medium can adopt any combination of one or more computer-readable media. The computer-readable medium can be a computer-readable signal medium or a computer-readable storage medium. The computer-readable storage medium can be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or device, or any combination thereof. More specific examples (non-exhaustive list) of computer-readable storage media include: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (Read Only Memory; hereinafter referred to as: ROM), an erasable programmable read-only memory (Erasable Programmable Read Only Memory; hereinafter referred to as: EPROM) or flash memory, an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination thereof. In this document, a computer-readable storage medium can be any tangible medium containing or storing a program that can be used by an instruction execution system, device or device or used in combination with it.

[0107] A computer-readable signal medium may include a data signal propagated in baseband or as part of a carrier wave, which carries computer-readable program code. Such a propagated data signal may take a variety of forms, including, but not limited to, electromagnetic signals, optical signals, or any suitable combination thereof. A computer-readable signal medium may also be any computer-readable medium other than a computer-readable storage medium that can transmit, propagate, or transport a program for use by or in conjunction with an instruction execution system, apparatus, or device.

[0108] Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to wireless, wireline, optical fiber cable, RF, etc., or any suitable combination of the foregoing.

[0109] Computer program code for performing the operations of this specification can be written in one or more programming languages or a combination thereof, including object-oriented programming languages such as Java, Smalltalk, C++, and conventional procedural programming languages such as "C" or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a separate software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving a remote computer, the remote computer can be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or can be connected to an external computer (for example, using an Internet service provider to connect through the Internet).

[0110] The foregoing description of this specification describes specific embodiments. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recited in the claims can be performed in an order different from that described in the embodiments and still achieve the desired results. Furthermore, the processes depicted in the accompanying drawings do not necessarily require the specific order shown or the sequential order to achieve the desired results. In certain embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0111] 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 the technical features being referred to. Thus, a feature specified as "first" or "second" may explicitly or implicitly include at least one such feature. Throughout this specification, "plurality" means at least two, such as two or three, unless otherwise specifically defined.

[0112] Any process or method description in a flowchart or otherwise described herein may be understood to represent a module, segment or portion of code comprising one or more executable instructions for implementing the steps of a custom logical function or process, and the scope of the preferred embodiments of this specification includes alternative implementations in which functions may be performed out of the order shown or discussed, including performing functions in a substantially simultaneous manner or in the reverse order depending on the functions involved, which should be understood by those skilled in the art to which the embodiments of this specification belong.

[0113] The word "if," as used herein, may be interpreted as "at the time of" or "when" or "in response to determining" or "in response to detecting," depending on the context. Similarly, the phrases "if it is determined" or "if (stated condition or event) is detected" may be interpreted as "when it is determined" or "in response to the determination" or "when detecting (stated condition or event)" or "in response to detecting (stated condition or event)," depending on the context.

[0114] It should be noted that the terminals involved in the embodiments of this specification may include but are not limited to personal computers (Personal Computer; hereinafter referred to as: PC), personal digital assistants (Personal Digital Assistant; hereinafter referred to as: PDA), wireless handheld devices, tablet computers (Tablet Computer), mobile phones, car terminals, MP3 players, MP4 players, etc.

[0115] In the embodiments provided in this specification, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For example, the division of the units is merely a logical function division. In actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed may be through some interface, indirect coupling or communication connection of the device or unit, which may be electrical, mechanical or other forms.

[0116] In addition, the functional units in the various embodiments of this specification may be integrated into a single processing unit, each unit may exist physically separately, or two or more units may be integrated into a single unit. The aforementioned integrated units may be implemented in the form of hardware or hardware plus software functional units.

[0117] The above-mentioned integrated unit implemented in the form of a software functional unit can be stored in a computer-readable storage medium. The above-mentioned software functional unit stored in a storage medium includes a number of instructions for causing a computer device (which can be a personal computer, server, or network device, etc.) or a processor to perform some steps of the method described in various embodiments of this specification.

[0118] The above description is only a preferred embodiment of this specification and is not intended to limit this specification. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principles of this specification should be included in the scope of protection of this specification.

Claims

1. A method for constructing a vehicle-road portrait, characterized in that: The method is applied to a processing device, and the method comprises: Determine first vehicle networking card data of each target vehicle in the target area, wherein the first vehicle networking card data includes target vehicle positioning data, target vehicle information data, and target vehicle vehicle networking card traffic data; Merging the first vehicle network card data of each target vehicle with the road network data to determine the vehicle-road label data of the target area, wherein the vehicle-road label data includes road travel details data and vehicle usage status data; Determining the vehicle-road labels within the target area according to the vehicle-road label data; generating a vehicle-road image within the target area according to the vehicle-road label; The step of fusing the first vehicle network card data of each target vehicle with road network data to determine vehicle-road label data of the target area includes: Fusion of the target vehicle's IoV card traffic data with the target vehicle's positioning data to verify whether the positioning information in the target vehicle's positioning data is accurate, and determination of the vehicle's usage status data, including the vehicle's application usage status and the vehicle's driving status; fusing the target vehicle positioning data with the road network data to determine the road travel detail data; The determining the vehicle-road label within the target area according to the vehicle-road label data includes: Performing statistical classification on the road network data to determine travel hotspot area labels and road basic attribute labels; Statistically classifying the road travel detailed data and inputting the data into a prediction model for prediction to determine traffic operation characteristic labels and vehicle travel characteristic labels; Performing statistical classification on the target vehicle information data to determine the basic attribute label of the vehicle; The vehicle usage status data is statistically classified and input into a prediction model for prediction to determine a vehicle start / stop label and an in-vehicle application usage label.

2. The method according to claim 1, characterized in that The determining of the first vehicle network card data of each target vehicle in the target area includes: Determining full positioning data within the target area and full vehicle networking card data reported by a vehicle networking card associated with the processing device, wherein the full vehicle networking card data includes full vehicle information data and full vehicle networking card traffic data; Performing key field matching on the full positioning data and the full vehicle information data to determine the target vehicle positioning data and the target vehicle information data; Key field matching is performed on the target vehicle information data and the full amount of vehicle networking card traffic data to determine the target vehicle vehicle networking card traffic data.

3. The method according to claim 2, characterized in that The key field matching includes: SIMID field matching, or IMSI field matching.

4. The method according to claim 1, wherein Generating a vehicle-road image within the target area according to the vehicle-road label includes: The various types of vehicle-road labels are combined to obtain a multi-label vehicle-road portrait, which includes a traffic operation portrait and a vehicle operation portrait.

5. The method according to claim 1, wherein After generating the vehicle-road image within the target area according to the vehicle-road label, the method further includes: When the target vehicle meets any of the vehicle-road portraits, the service information corresponding to the vehicle-road portrait is pushed to the target vehicle.

6. A vehicle-road image construction device, characterized in that: include: A first determination module determines first vehicle network card data of each target vehicle in the target area, wherein the first vehicle network card data includes target vehicle positioning data, target vehicle information data, and target vehicle vehicle network card traffic data; a fusion module, which fuses the first vehicle network card data of each target vehicle with the road network data to determine the vehicle-road label data of the target area, wherein the vehicle-road label data includes road travel details data and vehicle usage status data; A second determining module determines the vehicle-road labels within the target area according to the vehicle-road label data; A generation module, generating a vehicle-road image within the target area according to the vehicle-road label; The step of fusing the first vehicle network card data of each target vehicle with road network data to determine vehicle-road label data of the target area includes: Fusion of the target vehicle's IoV card traffic data with the target vehicle's positioning data to verify whether the positioning information in the target vehicle's positioning data is accurate, and determination of the vehicle's usage status data, including the vehicle's application usage status and the vehicle's driving status; fusing the target vehicle positioning data with the road network data to determine the road travel detail data; The determining the vehicle-road label within the target area according to the vehicle-road label data includes: Performing statistical classification on the road network data to determine travel hotspot area labels and road basic attribute labels; Statistically classifying the road travel detailed data and inputting the data into a prediction model for prediction to determine traffic operation characteristic labels and vehicle travel characteristic labels; Performing statistical classification on the target vehicle information data to determine the basic attribute label of the vehicle; The vehicle usage status data is statistically classified and input into a prediction model for prediction to determine a vehicle start / stop label and an in-vehicle application usage label.

7. An electronic device, characterized in that: include: at least one processor; as well as at least one memory in communication with the processor, wherein: The memory stores program instructions that can be executed by the processor, and the processor can execute the method according to any one of claims 1 to 5 by calling the program instructions.

8. A computer-readable storage medium, characterized in that The computer-readable storage medium includes a stored program, wherein when the program is executed, the device where the computer-readable storage medium is located is controlled to execute the method according to any one of claims 1 to 5.

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