Vehicle networking data processing method, device and electronic equipment

Abstract

The present disclosure provides a method, device and electronic device for processing vehicle network data, the method comprising: receiving traffic message data through a data interface; processing the traffic message data to obtain basic traffic data; acquiring demand information of a target application according to scheduling information; acquiring target data from the basic traffic data according to the demand information; generating a target data frame based on the target data, and outputting the target data frame to the target application through a data interface.

Description

Technical Field

[0001] The present disclosure relates to the field of computers, and in particular to a method, device and electronic equipment for processing vehicle network data. Background Art

[0002] In Internet of Vehicles applications, each application needs to perform logical processing on traffic message data and then obtain result data. The traffic message data required by some applications are at least partially the same, and the logical processing of some of the required traffic message data is also the same, resulting in redundancy in traffic message data calculation. Summary of the invention

[0003] The present disclosure provides a method, device and electronic device for processing vehicle network data to at least solve the above technical problems existing in the prior art.

[0004] According to a first aspect of the present disclosure, a method for processing Internet of Vehicles data is provided, the method comprising:

[0005] receiving traffic message data via a data interface;

[0006] Processing the traffic message data to obtain basic traffic data;

[0007] Obtaining demand information of the target application based on the scheduling information;

[0008] According to the demand information, acquiring target data from the basic traffic data;

[0009] A target data frame is generated based on the target data, and the target data frame is output to the target application through a data interface.

[0010] In one possible implementation manner, the traffic message data is processed to obtain basic traffic data, including:

[0011] obtaining map data, traffic signal data and traffic participant data based on the traffic message data;

[0012] Performing logical processing on the map data, the traffic signal data and the traffic participant data to obtain traffic logic data;

[0013] The target data is obtained from the map data, the traffic signal data, the traffic participant data and the traffic logic data.

[0014] In one possible implementation manner, the map data, the traffic signal data and the traffic participant data are logically processed to obtain traffic logic data, including:

[0015] Managing the intersections, roads and lanes in the map data respectively through a hash table;

[0016] Convert roads to lanes as the same location;

[0017] The intersection, the road, and the lane are associated by a pointer.

[0018] In one possible implementation, the map data, the traffic signal data and the traffic participant data are logically processed to obtain traffic logic data, further comprising:

[0019] respectively managing the signal light phases and signal lights in the traffic signal data through a hash table;

[0020] According to the intersection identifier of the traffic light, the traffic light is associated with the intersection in the map data.

[0021] In one possible implementation, obtaining map data based on the traffic message data includes: at a first moment, obtaining first map data based on the received traffic message data; at a second moment, obtaining second map data based on the received traffic message data, wherein the second moment is later than the first moment; and when the second map data is different from the first map data, overwriting the first map data with the second map data.

[0022] In one possible implementation, the map data, the traffic signal data and the traffic participant data are logically processed to obtain traffic logic data, further comprising:

[0023] Continuously positioning the target vehicle according to the target vehicle information in the traffic participant data, wherein after the target vehicle is positioned for the first time, subsequent positioning is performed based on the previous positioning information;

[0024] Other traffic participants in the traffic participant data are associated with the target vehicle through distance calculation and geo hash algorithm.

[0025] In one possible implementation, associating other traffic participants in the traffic participant data with the target vehicle through distance calculation and geo hash algorithm includes:

[0026] At least two areas are divided according to the distance from the target vehicle, wherein the processing priority of other traffic participants in the area closer to the target vehicle is higher than the processing priority of other traffic participants in the area farther from the target vehicle.

[0027] In one possible implementation, obtaining the demand information of the target application according to the scheduling information includes:

[0028] Obtain demand frequency and demand data of target applications based on scheduling information;

[0029] Demand data is acquired from the traffic basic data as target data according to the demand frequency, so as to generate the target data frame according to the demand frequency.

[0030] According to a second aspect of the present disclosure, a vehicle networking data processing device is provided, the device comprising:

[0031] A receiving module, which is used to receive traffic message data through a data interface;

[0032] A processing module, which is used to process the traffic message data to obtain basic traffic data;

[0033] A scheduling module is used to obtain demand information of a target application according to scheduling information; obtain target data from the traffic basic data according to the demand information; generate a target data frame based on the target data, and output the target data frame to the target application through a data interface.

[0034] According to a third aspect of the present disclosure, there is provided an electronic device, including:

[0035] at least one processor; and

[0036] a memory communicatively connected to the at least one processor; wherein,

[0037] The memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to perform the method described in the present disclosure.

[0038] According to a fourth aspect of the present disclosure, a non-transitory computer-readable storage medium storing computer instructions is provided, wherein the computer instructions are used to cause the computer to execute the method described in the present disclosure.

[0039] In the vehicle network data processing method disclosed in the present invention, traffic message data received through a data interface is processed to obtain basic traffic data; demand information of a target application is obtained according to scheduling information; target data is obtained from the basic traffic data according to the demand information; a target data frame is generated based on the target data, and the target data frame is output to the target application through a data interface. The data output to the target application is based on the basic traffic data obtained by processing the traffic message data, and the target application only needs to perform personalized processing. The basic processing repeated by each application on the traffic message data is uniformly performed to obtain the basic traffic data, thereby avoiding the repeated basic processing of the traffic message data by each application, reducing computational redundancy, and reducing computing power consumption.

[0040] It should be understood that the content described in this section is not intended to identify the key or important features of the embodiments of the present disclosure, nor is it intended to limit the scope of the present disclosure. Other features of the present disclosure will become easily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] The above and other objects, features and advantages of the exemplary embodiments of the present disclosure will become readily understood by reading the detailed description below with reference to the accompanying drawings. In the accompanying drawings, several embodiments of the present disclosure are shown in an exemplary and non-limiting manner, in which:

[0042] In the drawings, the same or corresponding reference numerals represent the same or corresponding parts.

[0043] Figure 1 A schematic diagram of the implementation process of the vehicle networking data processing method according to an embodiment of the present disclosure is shown;

[0044] Figure 2 A schematic diagram of priority area division in the vehicle networking data processing method according to an embodiment of the present disclosure is shown;

[0045] Figure 3 A schematic diagram showing the composition and structure of a vehicle networking data processing device according to an embodiment of the present disclosure is shown;

[0046] Figure 4 A schematic diagram of the structure of an electronic device according to an embodiment of the present disclosure is shown. DETAILED DESCRIPTION

[0047] In order to make the purpose, features, and advantages of the present disclosure more obvious and easy to understand, the technical solutions in the embodiments of the present disclosure will be clearly and completely described below in conjunction with the drawings in the embodiments of the present disclosure. Obviously, the described embodiments are only part of the embodiments of the present disclosure, not all of the embodiments. Based on the embodiments in the present disclosure, all other embodiments obtained by those skilled in the art without creative work are within the scope of protection of the present disclosure.

[0048] See also Figure 1 The present disclosure provides a method for processing Internet of Vehicles data, the method comprising:

[0049] receiving traffic message data via a data interface;

[0050] Process the traffic message data to obtain basic traffic data;

[0051] Obtaining demand information of the target application based on the scheduling information;

[0052] According to the demand information, target data is obtained from the basic traffic data;

[0053] A target data frame is generated based on the target data, and the target data frame is output to the target application through a data interface.

[0054] In the vehicle network data processing method disclosed in the present invention, the traffic message data received through the data interface is processed to obtain basic traffic data; the demand information of the target application is obtained according to the scheduling information; the target data is obtained from the basic traffic data according to the demand information; the target data frame is generated based on the target data, and the target data frame is output to the target application through the data interface. The data output to the target application is based on the basic traffic data obtained by processing the traffic message data. The target application only needs to perform personalized processing. The basic processing repeated by each application on the traffic message data is uniformly processed by the method of the embodiment of the present invention, and basic logical processing such as locating the road, finding the associated vehicle, and the traffic light ahead is completed to obtain the basic traffic data, avoiding the basic processing repeated by each application on the traffic message data, reducing computational redundancy, and reducing computing power consumption. The application only needs to perform business processing according to the data frame, which reduces redundant calculations, improves performance, and facilitates the expansion and realization of multiple applications.

[0055] In the disclosed embodiment, the target application is an application that needs to configure required data according to the scheduling information. For example, when data needs to be configured for application A according to the scheduling information, application A is the target application, and the target data is obtained from the basic traffic data according to the needs of application A, and a target data frame is generated and output to application A. When data needs to be configured for application B according to the scheduling information, application B is the target application, and the target data is obtained from the basic traffic data according to the needs of application B, and a target data frame is generated and output to application B.

[0056] In one possible implementation mode, traffic message data is processed to obtain basic traffic data, including: obtaining map data, traffic signal data and traffic participant data based on the traffic message data; performing logical processing on the map data, traffic signal data and traffic participant data to obtain traffic logic data; and obtaining target data from the map data, traffic signal data, traffic participant data and traffic logic data.

[0057] Map data includes road data, lane data, intersection data, turn data, speed limit data, etc. Traffic signal data includes the signal and time data of each signal, such as the color of the signal light of each signal at different time periods. Traffic participant data is the data of road traffic participants including the target vehicle. It includes relevant data of the target vehicle and other cars, electric vehicles, motorcycles and pedestrians. For example, traffic participant data includes the distance between other traffic participants and the target vehicle, the lane and speed of the target vehicle, etc. Map data, traffic signal data and traffic participant data are the basic data of the Internet of Vehicles. By processing map data, traffic signal data and traffic participant data accordingly, the result output of the corresponding application can be obtained. For example, based on the current vehicle speed and the speed limit of the current road section, the speeding application can determine whether the target vehicle is speeding.

[0058] Traffic logic data can be obtained by logically processing map data, traffic signal data and traffic participant data. The logical processing of map data, traffic signal data and traffic participant data is the same processing of traffic message data by all or part of the applications. The obtained traffic logic data is used by at least two applications. The basic traffic data includes map data, traffic signal data, traffic participant data and traffic logic data. The basic traffic data is shared by all applications, which reduces computing redundancy and has high application scalability. According to scheduling, it is only necessary to obtain target data from the basic traffic data according to the data required by the newly extended application, and generate target data frames, which are output to the newly extended application, thereby realizing multi-application deployment and iterative upgrades.

[0059] In the disclosed embodiment, the logical processing of the map data, traffic signal data and traffic participant data includes processing the map data, traffic signal data and traffic participant data separately and associating two or three of the map data, traffic signal data and traffic participant data.

[0060] In one possible implementation, the map data, traffic signal data and traffic participant data are logically processed to obtain traffic logic data, including: managing the intersections, roads and lanes in the map data respectively through a hash table; converting roads into lanes as the same location processing; and associating intersections, roads and lanes through pointers. In the disclosed embodiment, the intersections, roads and lanes in the map data are managed respectively using a hash table, and the intersections, roads and lanes in the map data can be directly accessed. Converting roads into lanes as the same location processing can determine the lane where the target vehicle is located, the driving direction, the turn and other information. Associating intersections, roads and lanes through pointers speeds up the search.

[0061] In one possible implementation, the map data, traffic signal data and traffic participant data are logically processed to obtain traffic logic data, including: managing the signal light phase and signal light in the traffic signal data separately through a hash table; and associating the signal light with the intersection in the map data according to the intersection identification of the signal light. In the disclosed embodiment, a hash table is used to separately manage the signal light phase and signal light in the traffic signal data, so that the traffic signal data can be directly accessed to speed up the data search. The signal light data includes the intersection identification of the signal light. According to the intersection identification, the signal light can be associated with the intersection in the map. When the application uses this data, it can determine the control status of the signal light at the intersection ahead. For example, it can be further determined whether the vehicle can pass smoothly or whether it runs a red light based on the current vehicle speed, the distance to the previous intersection and the signal light phase.

[0062] In one possible implementation, map data is obtained based on traffic message data, including: at a first moment, first map data is obtained based on received traffic message data, at a second moment, second map data is obtained based on the received traffic message data, the second moment is later than the first moment, and when the second map data is different from the first map data, the first map data is overwritten with the second map data. In the disclosed embodiment, as the target vehicle moves, new map data is continuously provided, and the map is divided into small areas to provide map data, which has a fast processing speed and occupies a small storage space. When the received map data is still the current map data, the newly received data can be discarded, and the current map data is still used. When the received map data is different from the current map data, the map data is adaptively adjusted according to the travel of the target vehicle, and the received map data is used to update the current map data to provide services for the subsequent travel of the target vehicle.

[0063] In one possible implementation, the map data, traffic signal data and traffic participant data are logically processed to obtain traffic logic data, including: continuously positioning the target vehicle according to the target vehicle information in the traffic participant data, wherein, after the target vehicle is positioned for the first time, the subsequent positioning is based on the previous positioning information for searching and positioning; other traffic participants in the traffic participant data are associated with the target vehicle through distance calculation and geo hash (geographic hash) algorithm. When the target vehicle is positioned for the first time, the target vehicle can be positioned according to the data of the satellite positioning system. Subsequent positioning is based on the previous positioning information for searching and positioning, and the location information of the target vehicle can be quickly obtained through a small amount of data calculation. For example, the current location information can be quickly obtained based on the previous positioning according to the distance increment and driving direction of the target vehicle. By associating other traffic participants with the target vehicle through distance calculation and geo hash (geographic hash) algorithm, the relative information of other traffic participants and the target vehicle can be obtained in a timely and rapid manner, and services such as collision prevention can be provided.

[0064] In one possible implementation, other traffic participants in the traffic participant data are associated with the target vehicle through distance calculation and geo hash algorithm, including: dividing at least two areas according to the distance from the target vehicle, wherein the processing priority of other traffic participants in the area closer to the target vehicle is higher than the processing priority of other traffic participants in the area farther from the target vehicle. In the disclosed embodiment, other traffic participants are divided into at least two areas according to their relative relationship with the target vehicle, and the priority and real-time performance of processing in different areas are different. The area with a relatively close distance to the target vehicle has a higher priority and is processed first. For example, see Figure 2 , when receiving the data of traffic participants, other traffic participants around the target vehicle are divided into core area, concerned area and remote area according to the distance calculation, and the data of other traffic participants in the core area are processed first, followed by the concerned area, and finally the remote area. The lane positioning of other traffic participants in the core area is performed and added to the hash table associated with the target vehicle, and the real-time data processing is guaranteed for the high-priority area. The data of other traffic participants in the concerned area and the remote area can be simply added to a data queue respectively, and the data of other participants in the concerned area and the remote area are processed at a lower frequency than the core area. In a specific implementation, other traffic participants can be divided into multiple intervals according to the distance, and a hash table is generated for the data of other traffic participants in each interval. Multiple hash tables correspond to the above three areas according to the distance, and the corresponding data jumps in the hash table as the motion state of the traffic participants changes. By dividing into different areas and corresponding different processing priorities for the data in different areas, data calculation can be reduced, the timeliness of data processing can be guaranteed, and services such as overtaking, changing lanes and preventing collisions can be provided. For example, when the target vehicle needs to change lanes, it only needs to care about the situation of other traffic participants in the core area and timely process the data of other traffic participants in the core area to accurately determine whether the lane can be changed and the timing of lane change, etc. Other traffic participants in the concerned area and remote areas are far away from the target vehicle and do not affect the target vehicle's lane change. Therefore, the frequency of lane positioning and other processing of other traffic participants in the concerned area and remote areas can be lower than the frequency of data processing of other traffic participants in the core area, reducing the amount of data calculation and ensuring timely processing of data of other traffic participants in the core area.

[0065] In one possible implementation, the demand information of the target application is obtained according to the scheduling information, including: obtaining the demand frequency and demand data of the target application according to the scheduling information; obtaining the demand data from the traffic basic data as the target data according to the demand frequency, so as to generate the target data frame according to the demand frequency. The scheduling information includes the demand frequency and demand data of the target application. Different applications have different demand frequencies for data. According to specific needs, the demand frequency of each application is determined, and each application is scheduled at a different frequency. For example, different applications can have different frequencies such as 3s, 1s, 500ms, 200ms, 50ms, etc., to solve the problem that different applications have different frequencies for data demand.

[0066] In the embodiment of the present disclosure, traffic message data is converted into data frames, and the application only needs to perform business processing according to the data frames, which reduces redundant calculations, improves performance, and facilitates expansion and implementation of multiple applications. Specific data frames are formed according to the business needs of the application. The following is an example of a data frame of an application:

[0067]

[0068] In the above data frame:

[0069] The forward field is a string structure: it indicates the intersection ID in front of the target vehicle's driving direction, which is calculated based on the vehicle's positioning and heading.

[0070] The hv field is a car_msg structure: it represents the driving data of the target vehicle. car_msg is a class, which includes the basic data of the vehicle such as length, width, height, color, license plate, etc. The driving status data of the vehicle includes latitude and longitude, speed, heading, acceleration, etc. The positioning information of the vehicle includes lane markings, road markings, and intersection markings.

[0071] The Paths field is a list table structure: it indicates the recommended driving path for the target vehicle. Path is a class that includes latitude and longitude, speed, acceleration, and time.

[0072] The nearby field is a map table structure: it means dividing a fixed area according to the driving direction of the target vehicle, and putting the vehicle data in the nearby area into the map table according to the position, such as directly in front, left front, right front, etc. See the above for car_msg.

[0073] The remote field is a list structure: it indicates a remotely associated vehicle. See car_msg above.

[0074] The timeStamp field is a long integer: it indicates the traffic light time at the intersection ahead.

[0075] The phases field is a map table structure: it indicates the traffic light information in the four directions of left turn, straight ahead, right turn, and U-turn. Combined with the timeStamp field, it can be known that there are three types of light colors in a control cycle. Through positioning, the traffic light information in front is found, and the phase information of the traffic light on the current road is obtained.

[0076] The frequency field is a type: it indicates the scheduling frequency of this frame.

[0077] The Internet of Vehicles data processing method of the disclosed embodiment can be applied to vehicle terminals and cloud servers. When applied to a cloud server, it can serve multiple vehicles, process the traffic message data of each vehicle separately according to the identity of each vehicle, and obtain a data frame and send it to the corresponding vehicle terminal. The identity of the vehicle can be the registered name of each vehicle. The registered name of the vehicle can be a license plate number, a vehicle identification code, or an engine number, etc. The uniqueness of the license plate number, the vehicle identification code, and the engine number can all be used as the unique identity of the vehicle. Taking the registration of the license plate number as an example, the cloud server receives the registration information of the target vehicle, creates an Internet of Vehicles data processing unit corresponding to the target vehicle according to the license plate number, and the Internet of Vehicles data processing unit processes the traffic message data related to the target vehicle, and generates the data frame required by the application on the terminal of the target vehicle, and provides it to the application of the terminal of the corresponding target vehicle through the data interface.

[0078] The following description will be made by taking the method of the embodiment of the present disclosure applied to a cloud server as an example.

[0079] The cloud server receives the registration information of the target vehicle with the license plate number as the registration name. The cloud server creates a vehicle network data processing unit corresponding to the target vehicle according to the license plate number. The vehicle network data processing unit receives the traffic message data corresponding to the target vehicle. After parsing the received data packets, map data, traffic signal data and traffic participant data can be obtained. The processing of map data can be carried out in a timeout blocking manner, for example, a timeout processing is performed every 5 seconds to determine whether the target vehicle has left the corresponding area. When processing map data, it can be performed in units of nodes (intersections), which is easy to process and has simple logic. After receiving the map data, it can be determined whether there is a map change, such as deletion or addition. If there is a map change, the map data is reprocessed in units of nodes in turn. If it does not exist, no processing is performed, and the data obtained by the previous processing is still used. The processing of the node includes turning calculation on the lane, speed limit calculation, segment calculation, lane matching to the road, as the same positioning processing, to achieve turning calculation on the road, speed limit calculation, road attributes, segment calculation, and side lane processing. Among them, the management of map data is carried out in the form of a linked list and three hash tables. A global linked list is used to maintain all nodes, and a hash table is used to manage intersections, roads, and lanes in the map data for easy search. The roads of each node and the lanes on each road are connected using the linked list method in the standard message. Coordinate calculation of map data can use the Haversine formula (Half-Half-Half-Vector Formula), Vincenty formula (Vincent Formula) and local ENU coordinate system (Northeastern Sky Coordinate System), which are used for general precision calculation, high precision calculation and relative calculation respectively. The ENU coordinate system takes the coordinates of the node as the origin and provides vector calculation methods, angle calculations, etc.

[0080] Traffic signal data comes from traffic lights. Traffic signal data can also be processed by timeout blocking. For example, a timeout is processed every 5 seconds to determine the traffic lights that have not been updated for a long time. The intersection is used as the key value for hash table management. After receiving the traffic signal data, if it is the traffic signal data of the newly added intersection, it is added to the hash table. If it is the update of the traffic signal data of the original intersection, the old data is deleted and the new data is updated to the hash table. The signal light phase and the signal light are managed by a hash table respectively.

[0081] When positioning based on traffic participant data, for the target vehicle that enters the current map area for the first time, the segment segment will be used in the entire map to find the lane closest to the target vehicle. When the distance is less than the lane width, it is considered to be in this lane; when the distance is 3 times greater than the lane width, it is considered not in the map area; otherwise, the vehicle is considered to be driving in the roadside edge area. For the area driving on the right, the right non-motorized lane is the default side lane. Then, you can search nearby based on the last positioning without the need for a global map search. For other traffic participants, when there is no positioning information, you can search based on the positioning information of the target vehicle, because these vehicles are around the target.

[0082] Based on the above data processing, corresponding data frames can be generated according to the data required by different applications at a certain frequency and output through the data interface. Taking three applications A, B, and C as examples, application A is scheduled once every 50ms to provide it with the required data frames, application B is scheduled once every 500ms to provide it with the required data frames, and application C is scheduled once every 1 second to provide it with the required data frames. Scheduling applications at a reference frequency based on the target vehicle can avoid performance loss caused by other traffic participants triggering the provision of data frames. Due to the large number of other traffic participants, triggering them to provide data frames not only causes performance loss, but also causes logic confusion due to the uncertainty of message triggering and reliance on message triggering. For example, the anti-collision application is triggered by the distance between other traffic participants and the target vehicle. Due to the large number of other vehicles around the target vehicle, performance loss and triggering uncertainty will occur.

[0083] See also Figure 3 The embodiment of the present disclosure provides a vehicle network data processing device, which includes a receiving module, a processing module and a scheduling module. The receiving module is used to receive traffic message data through a data interface; the processing module is used to process the traffic message data to obtain basic traffic data; the scheduling module is used to obtain demand information of a target application according to the scheduling information; according to the demand information, obtain target data from the basic traffic data; generate a target data frame based on the target data, and output the target data frame to the target application through the data interface.

[0084] In the Internet of Vehicles data processing device of the disclosed embodiment, the receiving module receives traffic message data through a data interface, the processing module processes the traffic message data to obtain basic traffic data; the scheduling module obtains demand information of the target application according to the scheduling information; obtains target data from the basic traffic data according to the demand information; generates a target data frame based on the target data, and outputs the target data frame to the target application through the data interface. The data output to the target application is based on the basic traffic data obtained by processing the traffic message data, and the target application only needs to perform personalized processing. The basic processing repeated by each application on the traffic message data is uniformly performed to obtain the basic traffic data, avoiding the repeated basic processing of the traffic message data by each application, reducing computational redundancy, and reducing computing power consumption. The application only needs to perform business processing according to the data frame, which reduces redundant calculations, improves performance, and facilitates expansion and realization of multiple applications.

[0085] In one possible implementation mode, the processing module includes a map management unit, which is used to obtain map data, traffic signal data and traffic participant data based on traffic message data; perform logical processing on the map data, traffic signal data and traffic participant data to obtain traffic logic data; and obtain target data from the map data, traffic signal data, traffic participant data and traffic logic data.

[0086] In one embodiment, the processing module includes an intersection management unit, which is used to manage intersections, roads and lanes in map data respectively through hash tables; convert roads into lanes as the same positioning process; and associate intersections, roads and lanes through pointers.

[0087] In one embodiment, the processing module includes a signal management unit, which is used to manage signal light phases and signal lights in traffic signal data respectively through a hash table; and associate the signal light with the intersection in the map data according to the intersection identification of the signal light.

[0088] In one possible implementation, the map management unit obtains map data based on traffic message data, including: at a first moment, the map management unit obtains first map data based on the received traffic message data; at a second moment, the map management unit obtains second map data based on the received traffic message data, the second moment is later than the first moment, and when the second map data is different from the first map data, the map management unit overwrites the first map data with the second map data.

[0089] In one possible implementation mode, the processing module includes a traffic participant management unit, which continuously locates the target vehicle based on the target vehicle information in the traffic participant data, wherein after the first positioning of the target vehicle is completed, subsequent positioning is performed based on the previous positioning information; other traffic participants in the traffic participant data are associated with the target vehicle through distance calculation and geo hash algorithm.

[0090] In one possible implementation, the traffic participant management unit associates other traffic participants in the traffic participant data with the target vehicle through distance calculation and geo hash algorithm, including: dividing at least two areas according to the distance from the target vehicle, wherein the processing priority of other traffic participants in the area closer to the target vehicle is higher than the processing priority of other traffic participants in the area farther from the target vehicle.

[0091] In one possible implementation, the scheduling module obtains demand information of the target application based on the scheduling information, including: obtaining demand frequency and demand data of the target application based on the scheduling information; obtaining demand data from traffic basic data as target data according to the demand frequency to generate a target data frame according to the demand frequency.

[0092] The vehicle networking data processing device of the embodiment of the present disclosure can implement the methods of the above embodiments, and the description of the above method embodiments can be used to understand and explain the device of the embodiment of the present disclosure. For the purpose of brevity and saving space, it will not be repeated here.

[0093] According to an embodiment of the present disclosure, the present disclosure also provides an electronic device and a readable storage medium.

[0094] Figure 4 A schematic block diagram of an example electronic device 400 that can be used to implement an embodiment of the present disclosure is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present disclosure described and / or required herein.

[0095] like Figure 4As shown, the device 400 includes a computing unit 401, which can perform various appropriate actions and processes according to a computer program stored in a read-only memory (ROM) 402 or a computer program loaded from a storage unit 408 into a random access memory (RAM) 403. In the RAM 403, various programs and data required for the operation of the device 400 can also be stored. The computing unit 401, the ROM 402, and the RAM 403 are connected to each other via a bus 404. An input / output (I / O) interface 405 is also connected to the bus 404.

[0096] A number of components in the device 400 are connected to the I / O interface 405, including: an input unit 406, such as a keyboard, a mouse, etc.; an output unit 407, such as various types of displays, speakers, etc.; a storage unit 408, such as a disk, an optical disk, etc.; and a communication unit 409, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 409 allows the device 400 to exchange information / data with other devices through a computer network such as the Internet and / or various telecommunication networks.

[0097] The computing unit 401 may be a variety of general and / or special processing components with processing and computing capabilities. Some examples of the computing unit 401 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various dedicated artificial intelligence (AI) computing chips, various computing units running machine learning model algorithms, digital signal processors (DSPs), and any appropriate processors, controllers, microcontrollers, etc. The computing unit 401 performs the various methods and processes described above, such as the Internet of Vehicles data processing method. For example, in some embodiments, the Internet of Vehicles data processing method may be implemented as a computer software program, which is tangibly contained in a machine-readable medium, such as a storage unit 408. In some embodiments, part or all of the computer program may be loaded and / or installed on the device 400 via the ROM 402 and / or the communication unit 409. When the computer program is loaded into the RAM 403 and executed by the computing unit 401, one or more steps of the Internet of Vehicles data processing method described above may be performed. Alternatively, in other embodiments, the computing unit 401 may be configured to perform the Internet of Vehicles data processing method in any other appropriate manner (e.g., by means of firmware).

[0098] Various implementations of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chips (SOCs), complex programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various implementations can include: being implemented in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which can be a special purpose or general purpose programmable processor that can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.

[0099] The program code for implementing the method of the present disclosure may be written in any combination of one or more programming languages. These program codes may be provided to a processor or controller of a general-purpose computer, a special-purpose computer, or other programmable data processing device, so that the program code, when executed by the processor or controller, enables the functions / operations specified in the flow chart and / or block diagram to be implemented. The program code may be executed entirely on the machine, partially on the machine, partially on the machine and partially on a remote machine as a stand-alone software package, or entirely on a remote machine or server.

[0100] In the context of the present disclosure, a machine-readable medium may be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, device, or equipment. A machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, device, or equipment, or any suitable combination of the foregoing. A more specific example of a machine-readable storage medium may include an electrical connection based on one or more lines, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (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 of the foregoing.

[0101] To provide interaction with a user, the systems and techniques described herein can be implemented on a computer having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user can provide input to the computer. Other types of devices can also be used to provide interaction with the user; for example, the feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including acoustic input, voice input, or tactile input).

[0102] The systems and techniques described herein may be implemented in a computing system that includes back-end components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes front-end components (e.g., a user computer with a graphical user interface or a web browser through which a user can interact with implementations of the systems and techniques described herein), or a computing system that includes any combination of such back-end components, middleware components, or front-end components. The components of the system may be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: a local area network (LAN), a wide area network (WAN), and the Internet.

[0103] A computer system may include a client and a server. The client and the server are generally remote from each other and usually interact through a communication network. The relationship of client and server is generated by computer programs running on respective computers and having a client-server relationship with each other. The server may be a cloud server, a server of a distributed system, or a server combined with a blockchain.

[0104] It should be understood that the various forms of processes shown above can be used to reorder, add or delete steps. For example, the steps recorded in this disclosure can be executed in parallel, sequentially or in different orders, as long as the desired results of the technical solutions disclosed in this disclosure can be achieved, and this document does not limit this.

[0105] In addition, the terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one of the features. In the description of the present disclosure, the meaning of "plurality" is two or more, unless otherwise clearly and specifically defined.

[0106] The above is only a specific embodiment of the present disclosure, but the protection scope of the present disclosure is not limited thereto. Any person skilled in the art who is familiar with the technical field can easily think of changes or substitutions within the technical scope disclosed in the present disclosure, which should be included in the protection scope of the present disclosure. Therefore, the protection scope of the present disclosure should be based on the protection scope of the claims.

Claims

1. A method for processing Internet of Vehicles data, characterized in that: The method comprises: receiving traffic message data via a data interface; Processing the traffic message data to obtain basic traffic data, wherein the processing of the traffic message data includes basic processing repeated by each application program on the traffic message data; Obtaining demand information of the target application based on the scheduling information; According to the demand information, acquiring target data from the basic traffic data; Generate a target data frame based on the target data, and output the target data frame to the target application through a data interface, so that the target application performs personalized processing; The traffic message data is processed to obtain basic traffic data, including: obtaining map data, traffic signal data and traffic participant data based on the traffic message data; Performing logical processing on the map data, the traffic signal data and the traffic participant data to obtain traffic logic data; The target data is obtained from the map data, the traffic signal data, the traffic participant data and the traffic logic data; The logical processing of the map data, the traffic signal data and the traffic participant data includes: The map data, the traffic signal data and the traffic participant data are processed separately and two or three of the map data, the traffic signal data and the traffic participant data are processed in association.

2. The method according to claim 1, characterized in that Logically processing the map data, the traffic signal data, and the traffic participant data to obtain traffic logic data includes: Managing the intersections, roads and lanes in the map data respectively through a hash table; Convert roads to lanes as the same location; The intersection, the road, and the lane are associated by a pointer.

3. The method according to claim 2, characterized in that The map data, the traffic signal data and the traffic participant data are logically processed to obtain traffic logic data, further comprising: respectively managing the signal light phases and signal lights in the traffic signal data through a hash table; According to the intersection identifier of the traffic light, the traffic light is associated with the intersection in the map data.

4. The method according to claim 1, characterized in that: Obtaining map data based on the traffic message data includes: at a first moment, obtaining first map data according to the received traffic message data; at a second moment, obtaining second map data according to the received traffic message data, wherein the second moment is later than the first moment; and when the second map data is different from the first map data, overwriting the first map data with the second map data.

5. The method according to claim 1, characterized in that The map data, the traffic signal data and the traffic participant data are logically processed to obtain traffic logic data, further comprising: Continuously positioning the target vehicle according to the target vehicle information in the traffic participant data, wherein after the target vehicle is positioned for the first time, subsequent positioning is performed based on the previous positioning information; Other traffic participants in the traffic participant data are associated with the target vehicle through distance calculation and geo hash algorithm.

6. The method according to claim 5, characterized in that Associating other traffic participants in the traffic participant data with the target vehicle through distance calculation and geo hash algorithm, including: At least two areas are divided according to the distance from the target vehicle, wherein the processing priority of other traffic participants in the area closer to the target vehicle is higher than the processing priority of other traffic participants in the area farther from the target vehicle.

7. The method according to claim 1, characterized in that Obtain the target application's demand information based on the scheduling information, including: Obtain demand frequency and demand data of target applications based on scheduling information; Demand data is acquired from the traffic basic data as target data according to the demand frequency, so as to generate the target data frame according to the demand frequency.

8. A vehicle networking data processing device, characterized in that: The device comprises: A receiving module, which is used to receive traffic message data through a data interface; A processing module, which is used to process the traffic message data to obtain basic traffic data, wherein the processing of the traffic message data includes repeated basic processing of the traffic message data by each application program; A scheduling module, which is used to obtain demand information of a target application according to the scheduling information; obtain target data from the traffic basic data according to the demand information; generate a target data frame based on the target data, and output the target data frame to the target application through a data interface, so that the target application performs personalized processing; The traffic message data is processed to obtain basic traffic data, including: obtaining map data, traffic signal data and traffic participant data based on the traffic message data; Performing logical processing on the map data, the traffic signal data and the traffic participant data to obtain traffic logic data; The target data is obtained from the map data, the traffic signal data, the traffic participant data and the traffic logic data; The logical processing of the map data, the traffic signal data and the traffic participant data includes: The map data, the traffic signal data and the traffic participant data are processed separately and two or three of the map data, the traffic signal data and the traffic participant data are processed in association.

9. An electronic device, characterized in that: include: at least one processor; as well as a memory communicatively connected to the at least one processor; wherein, The memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to perform the method according to any one of claims 1 to 7.

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