Data processing method and device for driving scene, storage medium and program product

Abstract

The application relates to a driving scene data processing method and device, a storage medium and a program product, and relates to the technical field of automobile data analysis. The method is executed by a mobile device, the mobile device is bound to a vehicle, and the method comprises the following steps: acquiring position data of the vehicle through a vehicle data acquisition platform; the position data comprises real-time position data and historical position data; acquiring traffic road hub data through a cloud server; the traffic road hub data is data of a driving road corresponding to the position data of the vehicle; the traffic road hub data comprises real-time traffic road hub data corresponding to the real-time position data and historical traffic road hub data corresponding to the historical position data; acquiring driving habit information corresponding to a user based on the position data of the vehicle and the traffic road hub data; and displaying target information on a vehicle device corresponding to the vehicle based on the driving habit information. The above scheme can improve the data processing efficiency of the driving scene.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of automobile data analysis, and particularly relates to a driving scene data processing method and device, a storage medium and a program product. BACKGROUND

[0002] With the rapid development of the Internet, big data application technology is applied to multiple fields, especially the field of automobile data analysis.

[0003] In the related art, a user can analyze driving habits of a driver user through big data application technology. A computer device obtains historical driving behavior data of the driver user through big data, and then classifies the historical driving behavior of the driver user to determine the driving style of the driver user, and further determine the driving habits of the driver user.

[0004] However, the method for obtaining the driving habits of the driver user in the above solution is relatively single, and the accuracy of the user driving habit analysis is low. SUMMARY

[0005] Embodiments of the present application provide a driving scene data processing method and device, a storage medium and a program product, which can improve the accuracy of driving scene data processing. The technical solution is as follows:

[0006] On the one hand, a driving scene data processing method is provided, which is executed by a mobile device, the mobile device is bound to a vehicle, and the method comprises:

[0007] acquiring position data of the vehicle through a vehicle data acquisition platform; the position data comprises real-time position data and historical position data;

[0008] acquiring traffic road hub data through a cloud server; the traffic road hub data is data of a driving road corresponding to the position data of the vehicle; the traffic road hub data comprises real-time traffic road hub data corresponding to the real-time position data, and historical traffic road hub data corresponding to the historical position data;

[0009] acquiring driving habit information corresponding to the user based on the position data of the vehicle and the traffic road hub data;

[0010] displaying target information on a vehicle device corresponding to the vehicle based on the driving habit information.

[0011] On the other hand, a driving scene data processing device is provided, which comprises:

[0012] The position data acquisition module is configured to acquire position data of the vehicle through a vehicle data collection platform; the position data comprises real-time position data and historical position data;

[0013] The hub data acquisition module is configured to acquire traffic road hub data through a cloud server; the traffic road hub data is data of a travel road corresponding to the position data of the vehicle; the traffic road hub data comprises real-time traffic road hub data corresponding to the real-time position data and historical traffic road hub data corresponding to the historical position data;

[0014] The habit information acquisition module is configured to acquire driving habit information corresponding to the user based on the position data of the vehicle and the traffic road hub data;

[0015] The target information display module is configured to display target information on a vehicle machine device corresponding to the vehicle based on the driving habit information.

[0016] In a possible implementation, the habit information acquisition module comprises at least one of the following:

[0017] The instant habit information acquisition module is configured to acquire instant driving habit information based on the real-time position data and the real-time traffic road hub data;

[0018] The long-term habit information acquisition module is configured to acquire long-term driving habit information based on the historical position data and the historical traffic road hub data.

[0019] In a possible implementation, the instant habit information acquisition module is configured to input the real-time position data and the real-time traffic road hub data into a first model, and obtain instant driving habit analysis information output by the first model;

[0020] The first model is a machine learning model trained by real-time position data samples, real-time traffic road hub data samples, and labeled instant driving habit information.

[0021] In a possible implementation, the long-term habit information acquisition module is configured to input the historical position data and the historical traffic road hub data into a second model, and obtain long-term driving habit analysis information output by the second model;

[0022] The second model is a machine learning model trained by historical position data samples, historical traffic road hub data samples, and labeled long-term driving habit information.

[0023] In a possible implementation, the displaying, based on the driving habit information, of the target information on the vehicle corresponding infotainment device comprises at least one of the following:

[0024] In response to the instant driving habit information satisfying a first specified condition, a first prompt information is displayed on a terminal interface of the infotainment device; the first prompt information is used to indicate that the user currently has dangerous driving behavior;

[0025] In response to the long-term driving habit information satisfying a second specified condition, and the vehicle being about to travel to a road section matching the second specified condition, a second prompt information is displayed on a terminal interface of the infotainment device; the second prompt information is used to prompt the user that the vehicle is about to travel to a dangerous road section.

[0026] In a possible implementation, the apparatus further comprises:

[0027] The analysis report display module is configured to display, based on the driving habit information, a driving habit analysis report on the infotainment device.

[0028] In another aspect, a computer device is provided, which comprises a processor and a memory, and the memory stores at least one instruction, at least one program, a code set or an instruction set, which is loaded and executed by the processor to implement the driving scene data processing method as described above.

[0029] In another aspect, a computer readable storage medium is provided, which stores at least one instruction, at least one program, a code set or an instruction set, which is loaded and executed by a processor to implement the driving scene data processing method as described above.

[0030] In yet another aspect, a computer program product is provided, which comprises a computer program stored in a computer readable storage medium. A processor of a computer device reads the computer program from the computer readable storage medium, and the processor executes the computer program to enable the computer device to perform the driving scene data processing method provided in the various optional implementation manners described above.

[0031] The technical solution provided in the present application can have the following beneficial effects:

[0032] In the embodiment of the present application, after the mobile device binds the car machine device corresponding to the user's car, the driving habit information corresponding to the user can be obtained based on the location data of the car and the traffic road hub data, and the driving habit information includes the instant driving information or the long-term driving information of the user. Through the location data of the car and the traffic road hub data, the user can obtain different types of driving habit analysis information, obtain different types of driving habit information corresponding to the user, expand the driving habit analysis mode of the user, and improve the accuracy of data processing of the mobile device for the driving scene.

[0033] It should be understood that the above general description and the following detailed description are only exemplary and explanatory, and cannot limit the present application. BRIEF DESCRIPTION OF DRAWINGS

[0034] The accompanying drawings, which are incorporated into and form part of the specification, illustrate embodiments consistent with the present application and, together with the description, serve to explain the principles of the application.

[0035] Figure 1 is an application environment schematic diagram of a driving scene data processing method related to an embodiment of the present application;

[0036] Figure 2 is a flowchart of a driving scene data processing method provided by an embodiment of the present application;

[0037] Figure 3 is a flowchart of a new energy vehicle driving habit analysis system provided by an embodiment of the present application;

[0038] Figure 4 is a block diagram of a driving scene data processing apparatus provided by an exemplary embodiment of the present application;

[0039] Figure 5 is a structural schematic diagram of a computer device provided by an exemplary embodiment of the present application. DETAILED DESCRIPTION

[0040] The exemplary embodiments will be described in detail herein with reference to the drawings. When the following description refers to the drawings, the same numbers in different drawings represent the same or similar elements unless otherwise indicated. The implementations described in the following exemplary embodiments do not represent all implementations consistent with the present application. Instead, they are merely examples of apparatuses and methods consistent with some aspects of the present application as detailed in the appended claims.

[0041] The application provides a scheme for processing driving scene data. The scheme can determine the driving habit information of the user by analyzing the vehicle location information and traffic road hub data, thereby effectively improving the data processing efficiency of the driving scene. For ease of understanding, some concepts related to the application are explained below.

[0042] 1) Artificial intelligence is a computer system that simulates and imitates human intelligent behavior. It can perform tasks similar to human thinking and decision-making, including learning, reasoning, problem-solving, perception, understanding natural language, and communication. Artificial intelligence systems continuously improve and optimize their performance through learning and analysis of large amounts of data, as well as pattern recognition and reasoning based on these data. The field of artificial intelligence encompasses a variety of technologies and methods, including machine learning, deep learning, natural language processing, computer vision, expert systems, etc. These technologies and methods enable computer systems to learn from data and make intelligent decisions and behaviors based on the learned knowledge.

[0043] 2) Machine learning model is a mathematical model designed to learn rules and patterns from data in order to make predictions or decisions on new data. It automatically learns a mapping relationship between input and output from input data using statistical and optimization methods. This mapping relationship can be linear or non-linear, depending on the algorithm and model type used.

[0044] 3) Car navigation system is an electronic device installed in the car interior, which provides navigation and route guidance functions. Car navigation systems usually consist of hardware and software, which can help drivers plan routes, navigate to destinations, and provide navigation instructions and traffic information. The main components of a car navigation system include:

[0045] GPS (Global Positioning System) receiver: The GPS receiver is used to receive satellite signals to determine the current position coordinates of the vehicle.

[0046] Display screen: Car navigation systems usually have a display screen to display maps, navigation instructions, intersection turns, and other information.

[0047] Control unit: The control unit is the core component of the navigation system, responsible for processing route planning, navigation instructions, user input, and other functions.

[0048] Map data: Map data is the foundation of the navigation system, including road networks, geographic landmarks, points of interest, and other information. Map data is usually stored in the system's storage device and updated over time.

[0049] Voice prompts: Navigation systems often provide voice prompt functions that guide drivers through directions, turn at intersections, and other information.

[0050] 4) Intelligent sensing technology is a technology that uses sensors, data processing, pattern recognition, and other methods to enable computer systems to simulate human perception, thereby enabling more intelligent behavior and interaction. In the field of automotive technology, applications include vehicle perception of the surrounding environment, recognition of traffic signs, vehicle following, autonomous driving, and other technologies.

[0051] 5) TSP (Transportation Service Provider) refers to companies or organizations that provide transportation services in the field of logistics and supply chain management. These companies usually provide various types of transportation services, including freight transportation, freight forwarding, freight distribution, and transportation management. TSPs can be large international transportation companies or regional or specialized service providers.

[0052] It should be noted that before collecting the relevant data of the user (such as the account information of the user logging into the vehicle navigation system) and during the process of collecting the relevant data of the user, the application can display a prompt interface, a pop-up window, or output voice prompt information to prompt the user that the relevant data is currently being collected. The relevant steps for obtaining the relevant data of the user are only started after the application obtains the confirmation operation of the user on the prompt interface or the pop-up window, otherwise (i.e. without obtaining the confirmation operation of the user on the prompt interface or the pop-up window), the relevant steps for obtaining the relevant data of the user are ended, i.e. the relevant data of the user is not obtained. In other words, all user data collected by the application is collected with the consent and authorization of the user, and the collection, use, and processing of relevant user data need to comply with relevant laws, regulations, and standards in relevant countries and regions.

[0053] Figure 1 is an application environment schematic diagram of the data processing method of the driving scene involved in an embodiment of the application. As shown in Figure 1 The system includes a mobile device 110 for data processing of a driving scene, an in-vehicle device 120, and a server 130.

[0054] The device type of the mobile device 110 includes at least one of a smartphone, a tablet computer, an e-book reader, an MP3 player, an MP4 player, a laptop computer, and a desktop computer.

[0055] The in-vehicle device 120 can be an in-vehicle system built into a car, which can be any one of a vehicle navigation system, a vehicle entertainment system, a vehicle communication system, a smart home replication system, and a vehicle safety system.

[0056] The server 130 can be a single server, or composed of several servers, or a virtualization platform, or a cloud computing service center.

[0057] The car machine device 120 and the server 130 can be connected to each other through a communication network 140. Optionally, the communication network is a wired network or a wireless network.

[0058] The mobile device 110 and the server 130 can be connected to each other through a communication network 140. Optionally, the communication network is a wired network or a wireless network.

[0059] Optionally, the wireless network or wired network described above uses vehicle communication technology and / or protocol. The network is usually the Internet, but can also be any network, including but not limited to any of the following:

[0060] 1) Controller Area Network (CAN): CAN is a common internal communication network in vehicles, used to connect various electronic control units inside the vehicle, such as engine control units, brake system control units, air conditioning control units, etc. CAN bus has high transmission speed and reliability, and is used to transmit real-time data and commands inside the vehicle.

[0061] 2) Local Area Network (LAN): LAN is a local area network used to connect various electronic devices and systems inside the vehicle, such as multimedia systems, navigation systems, in-vehicle entertainment systems, etc. LAN is usually based on Ethernet technology, providing high-speed data transmission and multi-device connection functions.

[0062] 3) Vehicle-to-Infrastructure communication (V2I): Vehicle-to-Infrastructure communication refers to the communication between vehicles and road infrastructure, such as traffic lights, road signs, road facilities, etc. V2I can be used for traffic management, intelligent transportation systems, vehicle navigation, etc. to improve traffic efficiency and safety.

[0063] 4) Vehicle-to-Vehicle communication (V2V): Vehicle-to-Vehicle communication refers to direct communication between vehicles, used to exchange vehicle location, speed, direction, etc. information to improve traffic safety and efficiency. V2V communication can be used for collision prevention, traffic congestion avoidance, etc. between vehicles.

[0064] 5) Wireless Local Area Networks (WLAN): Wireless Local Area Networks refer to the function of providing wireless network connectivity within the vehicle, allowing drivers and passengers to connect to the Internet, download data, use online services, etc.

[0065] 6) Vehicle-mounted mobile communication network: Vehicle-mounted mobile communication network refers to the mobile communication module integrated within the vehicle, used to connect to mobile communication networks (such as 3G, 4G, 5G networks), providing vehicle-mounted Internet, voice calls, SMS, and other communication functions.

[0066] 7) Vehicle-mounted satellite communication: Vehicle-mounted satellite communication refers to communication through satellite connection by the vehicle, used to provide communication services in remote areas or places without ground mobile communication network coverage.

[0067] The above communication networks can be used alone or in combination to provide various data transmission, communication, and Internet connection functions for the vehicle, thereby achieving more intelligent and convenient driving and riding experiences.

[0068] In some other embodiments, custom and / or dedicated data communication technologies can also be used instead of or in addition to the above data communication technologies.

[0069] For example, Figure 1 The mobile device 110 for data processing of driving scenarios includes a display 110a, a processor 110b, and a database 110c.

[0070] The display 110a can display the data processing results of the driving scenarios, such as the driving habit information corresponding to the driver user.

[0071] The processor 110b can process the data of the driver scenarios.

[0072] The database 110c can store the processed data of the driving scenarios.

[0073] For example, Figure 1 The car device 120 for data processing of driving scenarios includes a display 120a, a processor 120b, and a database 120c.

[0074] The display 120a can display the data processing results of the driving scenarios, such as the driving habit information corresponding to the driver user and the prompt information corresponding to the driving habit information, etc.

[0075] The processor 120b can process the input information of the driver user and the information inside / outside the vehicle. The processor 120b can handle multiple tasks, such as map data processing, route planning, navigation guidance generation, and real-time traffic information processing.

[0076] Among them, the database 120c can provide data support for the car terminal to ensure the accuracy of the navigation function. For example, it can store the map information required by the navigation system, search for navigation target location information in the database, and set different route plans based on the driver's preferences.

[0077] For example, Figure 1 The server 130 used to display navigation information includes a database 130a, which can store and manage various data such as map data, route planning information, and real-time traffic information, providing necessary data support for data processing in driving scenarios.

[0078] exist Figure 1 In the system shown, the driver can input a data processing request to the mobile device 110. The mobile device 110 can obtain data corresponding to the vehicle device 120 and the server 130 through the communication network 140, and generate driving habit information and target information corresponding to the driver based on the obtained data. The generated driving habit information and target information are then sent to the vehicle device 120, so that the target information is displayed on the display 120a.

[0079] Figure 2 This is a flowchart of a data processing method for a driving scenario provided in one embodiment of this application. The data processing method can be executed by a mobile device, which is bound to the vehicle; for example, the mobile device can be the one described above. Figure 1 The mobile device 110 shown. The data processing method for the above driving scenario may include the following steps:

[0080] Step 210: Obtain vehicle location data through the vehicle data acquisition platform; the location data includes real-time location data and historical location data.

[0081] The aforementioned real-time location data refers to the location data corresponding to the current vehicle. Real-time can refer to the current moment, a specified time period from a specified time in the past to the current moment (such as the time period within 1 minute before the current moment), or a specified time period from the current moment to a specified time in the future (such as the time period within 1 minute before and after the current moment). The aforementioned historical location data refers to the location data of the vehicle in the past specified time period or moment (such as the time period within 1 month before the current moment).

[0082] In the embodiments of the present application, the position data of the vehicle can be position coordinates, indicating the longitude and latitude of the current vehicle, and the position data can also include but is not limited to:

[0083] 1) Basic information of the vehicle: vehicle name, license plate number, time stamp (time of recording position data);

[0084] 2) Driving information of the vehicle: longitude, latitude, speed, direction;

[0085] 3) Status information of the vehicle: whether it is malfunctioning.

[0086] In the embodiments of the present application, the position data of the vehicle can be saved in a table of a database (such as MySQL) of the vehicle data collection platform, and there are at least two records in the table, each record saves one position data, and the position data saved in the table can be real-time position data of the vehicle or historical position data.

[0087] In the embodiments of the present application, the user can call the interface of the vehicle data collection platform through the mobile device, register the account of the platform and obtain the corresponding key and authorization, get the access right of the vehicle data collection platform, and then send the built request to the interface endpoint of the platform through the HTTP request method (such as GET or POST). After receiving the request, the vehicle data collection platform acquires the real-time position information of the vehicle through real-time collection, and the terminal device acquires the historical position information of the vehicle by querying the database of the vehicle data collection platform, and sends at least one of the two kinds of information to the mobile device. The vehicle data collection platform can acquire the real-time position data of the vehicle through the sensor of the vehicle, and the sensor can be but not limited to at least one of the following:

[0088] 1) Global Positioning System (GPS) receiver: the GPS receiver is used to receive positioning signals from satellites and determine the longitude and latitude coordinates of the vehicle. The longitude and latitude coordinates can be used to determine the current position of the vehicle.

[0089] 2) Inertial Navigation System (INS): the inertial navigation system uses sensors such as accelerometers and gyroscopes to measure the acceleration and angular velocity of the vehicle, and analyzes the position, speed and direction of the vehicle. INS can work in the case of losing GPS signals.

[0090] 3) Vehicle speed sensor: the speed sensor can directly measure the speed of the vehicle and provide it as part of the position data.

[0091] In the embodiment of the present application, the vehicle data collection platform comprises at least two databases, each database comprises at least two tables, each table comprises at least two records, each record records the position data of a vehicle, and a timestamp corresponding to the collection time of the position data of the vehicle is recorded on each record. The vehicle data collection platform responds to the historical position information acquisition request issued by the terminal device, acquires the corresponding time information in the request, compares the timestamp information in the database, matches the record corresponding to the request, and feeds back the position information to the mobile device.

[0092] It should be noted that in the embodiment of the present application, the vehicle data collection platform will keep connected with the vehicle and periodically acquire the latest position information. Real-time position information can also exist in the database of the vehicle data collection platform, and the embodiment is exemplified by the way of real-time collection.

[0093] Step 220: acquiring traffic road hub data through a cloud server; the traffic road hub data is the data of the driving road corresponding to the position data of the vehicle; the traffic road hub data comprises real-time traffic road hub data corresponding to real-time position data and historical traffic road hub data corresponding to historical position data.

[0094] The real-time traffic road hub data is the road data corresponding to the current driving position of the vehicle, specifically, it can be the road information where the position coordinates of the current vehicle are located. The real-time can be the current time, a time period from a specified past time to the current time, or a time period from the current time to a specified future time. The historical traffic road hub data is the road information data corresponding to the driving position of the vehicle in a specified past time period, or the road data corresponding to the driving position at a specified past time. Specifically, it can be the road information where the vehicle is located at a specified past time or in a specified past time period.

[0095] The traffic road hub data can include but is not limited to:

[0096] 1) Basic information of road hub: name of road hub, specific position (longitude and latitude) of road hub;

[0097] 2) State information of road: traffic flow, congestion degree, accident information, etc. of road;

[0098] 3) Other descriptive information: facility list of road, etc.

[0099] In the embodiments of the present application, the traffic road hub data described above can be stored in a table of a database (such as Elasticsearch) of a cloud server, and at least two documents can be contained in the Elasticsearch, and one traffic road hub data is saved in each document, and the traffic road hub data is stored in the form of a document.

[0100] In the embodiments of the present application, the user can send a traffic road hub data acquisition request to the cloud server through a mobile device, and the cloud server can acquire real-time traffic road hub information by collecting data corresponding to the driving road of the vehicle in real time after receiving the request, or acquire historical traffic road hub information by querying the database of the cloud server for historical data corresponding to the driving road of the vehicle, and send at least one of the two kinds of information to the mobile device.

[0101] In the embodiments of the present application, the cloud server can access a real-time traffic data source to acquire real-time traffic road hub information, and the real-time traffic data source can be a traffic monitoring camera, a traffic signal control system, a vehicle-mounted sensor, etc.; after the cloud server receives the real-time traffic data, the real-time traffic data can be matched with the unique identifier (such as the license plate number, the vehicle-mounted device ID, etc.) or the location information of the vehicle to determine the real-time traffic road hub information corresponding to the vehicle specified in the request.

[0102] In the embodiments of the present application, the cloud server can search its own database according to the information in the request sent by the mobile device to acquire historical traffic road hub information corresponding to the request information, and specifically, each piece of historical traffic road hub information saved in the database contains a time stamp, which indicates the specific time when the traffic road hub information is acquired, and the cloud server responds to the historical traffic road hub information acquisition request, extracts the time information (such as a specified time point or a specified time period) in the request, then finds the historical traffic road hub information in the database whose time stamp corresponds to the time information in the request, and sends the search result to the mobile device.

[0103] It should be noted that in the embodiments of the present application, the cloud server will maintain a connection with the real-time data source to periodically acquire the latest traffic information to reflect the changes of the road network and the traffic state. The real-time traffic road hub information can also exist in the database of the cloud server, and the present embodiment is exemplified by the way of obtaining from the real-time traffic data source.

[0104] Step 230: Acquire the driving habit information corresponding to the user based on the location data of the vehicle and the traffic road hub data.

[0105] The driving habit information is corresponding driving habit information obtained based on position data of a vehicle driven by the driver user and traffic road hub data.

[0106] Optionally, the mobile device can obtain the corresponding driving habit information of the user based on one of the position data of the vehicle or the traffic road hub data.

[0107] Optionally, the mobile device can obtain the corresponding driving habit information of the user based on both the position data of the vehicle and the traffic road hub data.

[0108] Optionally, the mobile device can obtain the corresponding driving habit information of the user based on one of historical position data, real-time position data, historical traffic road hub data, or real-time traffic road hub data of the vehicle.

[0109] Optionally, the mobile device can obtain the corresponding driving habit information of the user based on at least two of the historical position data, the real-time position data, the historical traffic road hub data, or the real-time traffic road hub data of the vehicle.

[0110] In the embodiments of the present application, the driving habit information includes at least one of instant driving habit information and long-term driving habit information. The instant driving habit information represents the current driving habit information of the user, and the long-term driving habit information represents the driving habit information of the user in a specified time period in the past or the future driving habit information inferred based on the driving habit of the user in the specified time period in the past.

[0111] The driving habit information includes, but is not limited to, at least one of a driving posture of the user, a driving style of the user (conservative or aggressive), a driving time period preference of the user (frequency of travel in different time periods of a day), a driving time length of the user, a driving environment preference of the driver (driving behavior characteristics of the driver in different weather and traffic conditions), and a violation situation (speeding or running a red light).

[0112] In some embodiments, the mobile device can take the position data of the vehicle and the traffic road hub data as input, and obtain the corresponding driving habit information of the user through a machine learning model.

[0113] It should be noted that the embodiments of the present application take the mobile device as an example of the execution subject, and in other embodiments, the vehicle corresponding to the vehicle device, the cloud server corresponding to the vehicle, and the TSP vehicle data collection platform corresponding to the vehicle can also obtain the corresponding driving habit information of the user based on the position data of the vehicle and the traffic road hub data.

[0114] Step 240: displaying target information on the vehicle corresponding to the vehicle device based on the driving habit information.

[0115] In the embodiment of the present application, the mobile device displays target information on the terminal interface of the vehicle corresponding to the vehicle device in response to obtaining the driving habit information, and different driving habit information corresponds to different target information.

[0116] The target information can be prompt information that is beneficial to the user driving the vehicle, such as prompting the user that the driving behavior is dangerous, or prompting that the road on which the current vehicle is traveling is dangerous. The target information can be displayed in at least one of a picture, a text, a video, and a voice.

[0117] It should be noted that before the mobile device in the embodiment of the present application obtains the position data of the vehicle and the traffic road hub data, it needs to be bound with the corresponding vehicle. The user can complete the binding operation with the vehicle by inputting the identification code of the vehicle, scanning a two-dimensional code, or performing other forms of identity verification in the mobile device, specifically a mobile phone APP (Application). The sensors on the vehicle continuously collect the state information of the vehicle, such as position, speed, fuel consumption, engine state, etc. These information are sent to the mobile phone APP through the vehicle communication module, and the vehicle information is obtained by communicating with the vehicle gateway module and the vehicle communication module. When the vehicle starts, the OBC (On-Board Computer) automatically sends the vehicle state information to the vehicle gateway module, and these information are packaged and sent to the server corresponding to the vehicle through the cellular network. At the same time, the APP can also send instructions to the OBC to control some functions of the vehicle.

[0118] In the embodiment of the present application, after the mobile device binds the vehicle corresponding to the vehicle device of the user, the driving habit information corresponding to the user can be obtained based on the position data of the vehicle and the traffic road hub data. The driving habit information includes the instant driving information or the long-term driving information of the user. Through the position data of the vehicle and the traffic road hub data, the user can obtain different types of driving habit information, obtain different types of driving habit information corresponding to the user, expand the method of processing the driving habit information of the user, i.e. expand the data processing method of the driving scene, and improve the accuracy of data processing of the mobile device for the driving scene.

[0119] In some embodiments, the mobile device obtains instant driving habit information based on real-time position data of the vehicle and real-time traffic road hub data.

[0120] In some embodiments, the mobile device obtains long-term driving habit information based on historical position data of the vehicle and corresponding historical traffic road hub data.

[0121] In some embodiments, the mobile device can obtain the instant driving habit information of the user based on one of the real-time location data of the vehicle or the real-time traffic road hub data.

[0122] In some embodiments, the mobile device can obtain the instant driving habit information of the user based on both the real-time location data of the vehicle and the real-time traffic road hub data.

[0123] In some embodiments, the mobile device can obtain the long-term driving habit information of the user based on one of the historical location data of the vehicle or the corresponding historical traffic road hub data.

[0124] In some embodiments, the mobile device can obtain the long-term driving habit information of the user based on both the historical location data of the vehicle and the corresponding historical traffic road hub data.

[0125] In the embodiments of the present application, the terminal device can obtain the instant driving habit information of the user through a machine learning model based on the real-time location data of the vehicle and the real-time traffic road hub data, and obtain the long-term driving habit information of the user through a machine learning model based on the historical location data of the vehicle and the corresponding historical traffic road hub data. The machine learning model has driving habit analysis capability.

[0126] In the embodiments of the present application, the terminal device can obtain the instant and long-term driving habit information based on the location data of the vehicle and the traffic road hub data, which can more comprehensively reflect the driving habit of the user and further improve the accuracy of driving habit analysis.

[0127] In some embodiments, the mobile device inputs the real-time location data and the real-time traffic road hub data into a first model to obtain instant driving habit analysis information output by the first model. The first model is a machine learning model trained by real-time location data samples, real-time traffic road hub data samples, and labeled instant driving habit information.

[0128] The first model can be trained based on real-time location data samples, real-time traffic road hub data samples, and labeled instant driving habit information. For example, the first model is a real-time stream processing algorithm, which is used for instant analysis of real-time data streams obtained from vehicle sensors and other data sources. The real-time stream algorithm can process high-speed data streams and perform analysis immediately when data arrives.

[0129] In the training process, the mobile device inputs the real-time position data sample and the real-time traffic road hub data sample into the real-time stream processing algorithm as inputs of the real-time stream processing algorithm, obtains the predicted instant driving habit information output by the real-time stream processing algorithm, then calculates a loss function value through a difference between the predicted instant driving habit information and the labeled instant driving habit information, updates parameters of the real-time stream processing algorithm through the loss function value, so as to complete the training of the real-time stream processing algorithm, and the trained real-time stream processing algorithm is taken as the first model.

[0130] In the embodiments of the present application, the mobile device can input the real-time position data and the real-time traffic road hub data into the first model with instant driving habit information generation capability, and the first model obtains the instant driving habit information corresponding to the input information after processing the input information. For example, the real-time position data and the real-time traffic road hub data of the vehicle can be input into the real-time stream processing algorithm, and the instant driving habit information output based on the real-time stream processing algorithm is obtained.

[0131] For example, the terminal device inputs the driving position coordinates of the vehicle and the road hub road conditions corresponding to the position coordinates into the real-time stream processing algorithm, and obtains the instant driving habit information output by the real-time stream processing algorithm. The real-time stream processing algorithm is a machine learning model trained by the driving position coordinates of the vehicle, the road hub road conditions corresponding to the position coordinates, and the labeled instant driving habit information.

[0132] In other embodiments, the first model described above can also be a machine learning classification algorithm, such as a decision tree, a support vector machine, or a random forest, which can be used for real-time classification of driving behavior. These algorithms can classify real-time data according to a pre-trained algorithm model.

[0133] For example, the terminal device inputs the driving position coordinates of the vehicle and the road hub road conditions corresponding to the position coordinates into the machine learning classification algorithm, and obtains the instant driving habit information output by the machine learning classification algorithm. The real-time stream processing algorithm is a machine learning model trained by the driving position coordinates of the vehicle, the road hub road conditions corresponding to the position coordinates, and the labeled instant driving habit information.

[0134] It should be noted that in the embodiments of the present application, the first model can be an algorithm model built-in the terminal device, or an algorithm model in the server corresponding to the vehicle, or an algorithm model in the vehicle-mounted device corresponding to the vehicle.

[0135] In the embodiments of the present application, the mobile device can obtain the instant driving habit information of the user based on the real-time position data and the real-time traffic road hub data through the first model. The two kinds of real-time data can more accurately reflect the driving state of the current vehicle, thereby improving the analysis accuracy of the instant driving habit information of the user.

[0136] In some embodiments, the mobile device inputs the historical location data and the historical traffic road junction data into a second model to obtain long-term driving habit analysis information output by the second model; wherein the second model is a machine learning model trained by the historical location data samples, the historical traffic road junction data samples, and the labeled long-term driving habit information.

[0137] The second model can be trained based on the historical location data samples, the historical traffic road junction data samples, and the labeled long-term driving habit information of the vehicle, for example, the second model is a clustering algorithm, which is used to group the long-term collected data according to similarity. These algorithms can find patterns and trends hidden in large amounts of data.

[0138] During the training process, the terminal device inputs the historical location data samples and the historical traffic road junction data samples of the vehicle into the clustering algorithm as input, obtains predicted long-term driving habit information output by the clustering algorithm, and then calculates a loss function value based on the difference between the predicted long-term driving habit information and the labeled long-term driving habit information. The parameters of the clustering algorithm are updated through the loss function value to complete the training of the clustering algorithm, and the trained clustering algorithm is used as the second model.

[0139] For example, the terminal device can input the location coordinate data of the vehicle in the past month and the user's violation behavior at the road junction corresponding to the location coordinates into the clustering algorithm to obtain long-term driving habit information output by the clustering algorithm; wherein the long-term driving habit information is a machine learning model trained by the location coordinate data samples of the vehicle in the past month, the violation behavior samples of the user at the road junction corresponding to the location coordinate samples, and the labeled long-term driving habit information.

[0140] In other embodiments, the second model described above can also be a time series analysis algorithm, which is used to analyze driving data that changes over time. These algorithms can reveal trends and periodic patterns in driving habits over time. Predicting future driving behavior of the user, such as travel time prediction based on historical data. The terminal device can input the location information of the vehicle of the user in the past week on a special road segment and the congestion situation of the road junction corresponding to the location information into the time series analysis algorithm to obtain long-term driving habit information output by the time series analysis algorithm; wherein the long-term driving habit information is a machine learning model trained by the location information samples of the vehicle of the user in the past week on a special road segment, the congestion situation samples of the road junction corresponding to the location information samples, and the labeled long-term driving habit information.

[0141] In the embodiment of the present application, the mobile device can obtain the long-term driving habit information of the user based on the historical position data of the vehicle and the historical traffic road hub through the second model. The two kinds of historical data can comprehensively reflect the habit information of the user driving the vehicle in the past specified period / time. Based on the analysis of the two kinds of data, the analysis accuracy of the long-term driving habit information can be effectively improved.

[0142] In some embodiments, the mobile device displays first prompt information on the terminal interface of the vehicle device in response to the instant driving habit information satisfying the first specified condition; the first prompt information is used to indicate that the user currently has dangerous driving behavior.

[0143] In some embodiments, the mobile device displays second prompt information on the terminal interface of the vehicle device in response to the long-term driving habit information satisfying the second specified condition and the vehicle about to travel to the road section matched with the second specified condition; the second prompt information is used to prompt the user that the vehicle is about to travel to the dangerous road section.

[0144] The first specified condition / second specified condition can be preset by the developer or set by the user of the mobile device in the application.

[0145] The first prompt information / second prompt information can be displayed in at least one of text, picture, video, and voice.

[0146] In the embodiment of the present application, the instant driving habit information at least contains classification information of the current driving behavior of the user, such as the current driving behavior of the user belongs to safe driving or the current driving behavior of the user belongs to dangerous driving. The dangerous driving behavior can be, but is not limited to, vehicle driving overspeed, changing lane without turning on the turn signal, reverse driving, answering the phone during driving, and not wearing a safety belt.

[0147] For example, the mobile device displays the speed limit information of the current lane on the terminal interface of the vehicle device in response to the current vehicle overspeed driving and satisfying the dangerous driving; the speed limit information is used to prompt the user that the current driving behavior is overspeed.

[0148] In the embodiment of the present application, the long-term driving habit information at least contains classification information of the long-term driving behavior of the user, such as the user is used to decelerate driving in the dangerous road section or the user is used to accelerate driving in the dangerous road section. The deceleration driving in the dangerous road section belongs to safe driving, and the acceleration driving in the dangerous road section belongs to dangerous driving. The dangerous road section can be, but is not limited to, at least one of the turning port, the up and down slope, the road intersection, and the pedestrian crossing.

[0149] For example, the mobile device displays a turn deceleration prompt on the terminal interface of the vehicle device in response to the user habit of accelerating at a turn, and the vehicle is about to travel to the turn.

[0150] In some embodiments, the mobile device displays a high violation road segment prompt on the terminal interface of the vehicle device in response to the user's instant driving habit information or the long-term driving habit information containing violation information, and the vehicle is about to travel to the high violation road segment.

[0151] It should be noted that in the embodiments of the present application, the mobile device can include at least one prompt information database for storing different prompt information, each prompt information corresponding to a specified condition, and in response to the acquired instant driving habit information or long-term driving habit information satisfying the specified condition, the prompt information corresponding to the specified condition is queried from the database and sent to the terminal interface of the vehicle device and displayed on the terminal interface.

[0152] In the embodiments of the present application, the mobile device displays a prompt information on the terminal interface of the vehicle device in response to the user's instant driving habit information or long-term driving habit information satisfying the specified condition, which can effectively remind the driver user whether the current driving behavior is dangerous, and expand the application scenario of the user's driving habit information, i.e. expand the data processing mode of the driving scene, and improve the data processing efficiency of the mobile device for the driving scene.

[0153] In some embodiments, the mobile device displays a driving habit analysis report on the vehicle device based on the driving habit information.

[0154] The driving habit analysis report is used to display various driving habit information of the user, and the report can include various charts, tables and texts to intuitively display the user's driving behavior, problems existing in the driving behavior and improvement suggestions. The driving habit analysis report at least includes at least one of the user's instant driving habit information and long-term driving habit information.

[0155] In the embodiments of the present application, the mobile device can acquire the user's instant driving habit information and long-term driving habit information in response to the user's request operation on the driving habit analysis report, and generate the driving habit analysis report based on the two kinds of driving habit information and send it to the terminal interface of the vehicle device for display.

[0156] In some embodiments, the user can log in to a specified portal website to query the driving habit analysis report of the user.

[0157] In the embodiments of the present application, the terminal device can generate a driving habit analysis report based on the driving habit information, and the user can improve his driving habit through the driving habit analysis report, thereby expanding the application scenario of the driving habit information, i.e., expanding the data processing mode of the driving scene, and improving the data processing efficiency of the mobile device for the driving scene.

[0158] For example, based on Figure 2 In a corresponding embodiment, taking the new energy vehicle driving habit analysis system as an example, please refer to Figure 3 , Figure 3 The flowchart of the new energy vehicle driving habit analysis system provided by the embodiments of the present application is shown.

[0159] In recent years, with the widespread popularity of smart phones, more and more people have begun to realize the importance of driving safety. However, many drivers do not understand their driving habits, which leads to many extreme situations, thereby endangering the safety of the driver himself and other road users, so the software for analyzing the driving habits of car drivers should provide more help to the user. Its main function is to analyze the driving habits of the driver in real time, not only can record the driving record, but also can analyze and evaluate the driver according to the recorded data, and obtain the score of each driver. At the same time, the software can also provide a series of user-friendly functions to help users improve their driving habits, such as providing reminders and guidance during driving, etc., so that the driver can understand his bad habits and improve them, thereby improving the driving safety.

[0160] In order to achieve the above purpose, the application embodiments adopt the following technical solutions:

[0161] Based on the third-party satellite positioning data and the vehicle built-in sensor (including but not limited to the vehicle's own acceleration sensor, gyroscope, etc.) data, the driving habits of the user are analyzed and reminded in real time.

[0162] The design of the system mainly involves five modules. The first is the real-time vehicle data module of the big data platform TSP; the second is the road information and real-time positioning data module of the vehicle; the third is the logical design of the business code of the driving habit analysis; the fourth is the mobile terminal interface function design; and the fifth is the function design of the user driving habit analysis data background management page.

[0163] Among them, the first module and the second module are the basis and foundation for realizing real-time and long-term analysis of driving habits; the third module is the key to realizing the driving habit analysis function of the system; the fourth module is the platform for users to obtain timely reminders of bad driving habits and long-term driving habit analysis reports; and the fifth module is the platform for system administrators to count the analysis results and data.

[0164] (1) Data input / output module: mainly used for providing data input for Flink operation processing (real-time location data of vehicles, traffic road junction data) After data preprocessing (data cleaning, slicing) The result data is stored in Mysql database (dynamic data) or Elasticsearch (static data) for subsequent driving habit analysis.

[0165] Among them, the data stored in MySQL includes the following data:

[0166] Static information of vehicle location data: This includes basic information of vehicles (such as vehicle identification number, license plate number, vehicle model, etc.), owner information, and static attributes related to vehicles (such as color, purchase date, etc.). These data are usually structured data, suitable for storage in relational databases (such as MySQL).

[0167] Static information of traffic road junctions: including the location, name, type (such as highway entrance, bridge, tunnel, etc.), and the road to which the traffic junction belongs. These data are also structured data, which can be stored and managed through the table structure of MySQL.

[0168] Association information between vehicles and traffic junctions: for example, the traffic junctions frequently passed by vehicles, the traffic statistics of traffic junctions, etc. These association information can also be stored in MySQL for complex data analysis and query.

[0169] Among them, the data stored in Elasticsearch includes the following data:

[0170] Dynamic information of vehicle location data: This includes the real-time location, speed, and driving direction of vehicles. Since these data are updated in real time and need to support fast query and analysis (for example, finding all vehicles in a certain area, analyzing the driving trajectory of vehicles, etc.), Elasticsearch is a better choice. The distributed search and analysis capabilities of Elasticsearch can efficiently handle such data.

[0171] Dynamic information of traffic road junctions: for example, real-time traffic conditions, congestion conditions, accident information, etc. of traffic junctions. These data also need to support fast query and analysis, so that the intelligent transportation system can respond to changes in traffic conditions in real time. Therefore, storing such data in Elasticsearch can improve query efficiency.

[0172] The design idea is as follows:

[0173] ①Decoupling: decoupling data input / output, static data of map traffic hub and real-time dynamic data of vehicle, custom driving habit analysis algorithm module, reducing coupling between them, improving robustness and maintainability of the entire system.

[0174] ②Extensibility: using Flink's support for multiple data input / output features, implementing custom data sources using the SourceFunction interface, supporting Cassandra database write / read; Mysql database write / read.

[0175] (2) Custom driving habit analysis algorithm module:

[0176] ①Custom driving habit analysis algorithm: 1. Instant driving habit sharing provides traffic data and real-time vehicle data required by custom algorithms through standardized interfaces of data input / output module, and obtains driving habit results through custom instant driving habit analysis algorithm and shares them with users. 2. Long-term driving habit analysis is analyzed by big data database according to custom long-term driving habit algorithm, for example, during vehicle driving, due to different road conditions, climate, terrain, speed and other factors, driving behavior will also change, therefore, software will record the behavior data of the driver according to these changes.

[0177] The software uses machine learning technology for analysis, which can automatically identify and classify various driving behaviors such as sudden braking, speeding, and fatigue driving, and finally obtain the driver's score and save the results to the MYSQL database for user query.

[0178] ②Replaceability: due to the standardized interfaces provided by the data input / output module and the background management display module, the custom road planning algorithm module can obtain the required data and provide standardized parameters for the background management display module for display; only the custom road planning algorithm module needs to be developed according to the standard to achieve replaceability (the entire display deployment can be used).

[0179] Among them, the standardized interface can be the following interface:

[0180] RS-232C interface:

[0181] Function: This is a serial interface standard, used for connection between line terminal equipment (such as modem) and terminal. It specifies electrical conditions, signal types, functions and pin arrangement, so that different devices can communicate through a unified interface standard.

[0182] HTTP / HTTPS interface:

[0183] Function: HTTP (Hypertext Transfer Protocol) and HTTPS (HTTP Secure) are protocols used for web communication. In the background management display module, HTTP / HTTPS interfaces are used for data exchange between the web front-end and the back-end server, such as user login, data query, operation instruction sending, etc.

[0184] RESTful API interface:

[0185] Function: RESTful API (Representational State Transfer) is a design style and architectural constraint based on HTTP protocol. It enables different software applications to communicate and exchange data over the network. In the background management display module, RESTful API interfaces are used to provide access to data resources and services, such as data CRUD (Create, Read, Update, Delete) operations.

[0186] Database interface:

[0187] Function: Database interface is used for connection and data exchange between application and database. Common database interfaces include JDBC (Java Database Connectivity), ODBC (Open Database Connectivity), etc. These interfaces enable applications to easily access and manipulate data in the database.

[0188] File transfer interface:

[0189] Function: File transfer interface is used to implement file transfer between different systems or devices. Common file transfer interfaces include FTP (File Transfer Protocol), SFTP (SSH File Transfer Protocol), etc. These interfaces support file upload, download, deletion, etc., making file management and sharing more convenient.

[0190] Template management interface:

[0191] Function: In the background management display module, template management interface is used to manage the templates of front-end pages. These interfaces provide functions such as template upload, download, edit, delete, etc., enabling developers to easily manage and maintain the layout and style of front-end pages.

[0192] Among them, the function of the road planning algorithm module is as follows:

[0193] 1. Data Acquisition and Integration: Necessary information is acquired from multiple data sources, including map data, real-time traffic conditions, road restrictions, etc., and these data are integrated and processed.

[0194] 2. Path Planning: Based on the user's input of the starting point, destination, and possible constraints (such as avoiding congested sections, choosing highways, etc.), an algorithm is used to calculate one or more optimal travel paths. These paths may consider factors such as time, distance, safety, etc.

[0195] 3. Real-time Update: Based on real-time traffic information and other changing factors, the planned path is updated and optimized in real time to ensure that the user always receives the latest and most accurate travel recommendations.

[0196] 4. User Interface Display: The planned path is displayed to the user in an intuitive way, such as through a map, list, or other visualization. Detailed navigation information and travel recommendations can also be provided.

[0197] 5. Extension and Customization: Users can customize and extend the road planning algorithm according to their own needs. For example, users can set different preferences (such as preferring highways or avoiding toll stations), or add custom constraints (such as avoiding specific road sections or buildings).

[0198] Among them, the background management display module can display the following data:

[0199] 1. Planning Path Data

[0200] This is the core output of the road planning algorithm module. It includes the optimal path from the starting point to the destination, which may include multiple road segments and nodes. This data is usually represented in the form of coordinate points (latitude and longitude) or road segment identifiers.

[0201] 2. Path Detailed Information

[0202] In addition to basic path data, the algorithm can also provide detailed information about the path, such as the length of each road segment, estimated travel time, real-time traffic conditions (such as congestion level, accident reports, etc.).

[0203] 3. Navigation Instructions

[0204] The road planning algorithm can generate a series of navigation instructions, such as turn indications, entry and exit of highways, passage through tunnels or bridges, etc. These instructions help users follow the planned path in actual travel.

[0205] 4. Cost Estimation

[0206] The algorithm can evaluate the total cost of the planned path based on different cost functions such as time, distance, fuel consumption, etc. These cost estimates can help users choose the path that best suits their needs.

[0207] 5. Alternative paths

[0208] In addition to the main planned path, the algorithm can also generate multiple alternative paths. These paths may differ in terms of cost, time, or safety, providing users with more options.

[0209] 6. Traffic condition prediction

[0210] If the algorithm integrates a traffic prediction model, it can also provide prediction data on future traffic conditions. These data can help users better plan their journey and avoid potential congestion or delays.

[0211] 7. User feedback data

[0212] If the background management display module allows users to provide feedback, the algorithm can also receive and process this feedback data. These feedbacks can be used to improve the performance and accuracy of the road planning algorithm.

[0213] 8. Statistical and analytical data

[0214] The algorithm can generate statistical and analytical data on the planned paths, such as the most frequently chosen paths by users, traffic flow during different time periods, etc. These data help the background management staff better understand user needs and behavior patterns.

[0215] (3) Server remote access and cluster control module: requires to provide an interface for remote login to YARN cluster server using relevant protocols, can submit commands to the server and execute, get execution results; provide the functions of uploading and downloading HDFS file resources; provide the functions of starting and closing YARN cluster, submitting Flink job tasks to the cluster and executing; provide the functions of opening web pages to view the running state of cluster jobs and the storage state of cluster file resources.

[0216] The design idea is as follows:

[0217] ① Bind menu bar events and button events to jump to server operation pages.

[0218] ② Provide form components for filling in address, port number, username, password information, and call library functions to remotely log in to the server.

[0219] ③ Upload Flink task jar package to the server and execute.

[0220] ④ Provide button components and bind HDFS file resource operations.

[0221] ⑤ Provide a button component to bind the start and stop operations of the YARN cluster.

[0222] ⑥ Provide a button component to bind the web page opening action.

[0223] (4) Results Display Module: Provides road planning functions based on the output results of the custom road planning module, and displays the planning results in a map format.

[0224] User usage of the system:

[0225] 1. The user can actively trigger the driving habit analysis software before setting off, or the TSP platform can trigger it when starting the vehicle.

[0226] 2. Users will be alerted if they are detected to have poor driving behavior while driving.

[0227] 3. Users can log in to the designated portal website to view their driving habit analysis report.

[0228] How developers use the system:

[0229] 1. Developers open the software and enter the backend management system.

[0230] 2. Developers can obtain a driving habit analysis report for a specified user through the query function.

[0231] 3. Exit the system.

[0232] In this application embodiment, a software system integrating big data processing workflow is developed, which greatly facilitates the use by developers; by customizing the road planning algorithm, the development efficiency of the road planning algorithm can be effectively improved; and it is of great significance in guiding enterprises to reduce the cost of vehicle road planning algorithm testing.

[0233] Please refer to Figure 4 The diagram illustrates a block diagram of a data processing apparatus for a driving scenario provided in an exemplary embodiment of this application. This data processing apparatus can be implemented as all or part of a computer device through hardware or a combination of hardware and software, to achieve the above-described... Figure 2 All or part of the steps in the illustrated embodiments. For example... Figure 4 As shown, the data processing apparatus includes:

[0234] The location data acquisition module 401 is used to acquire the vehicle's location data through the vehicle data acquisition platform; the location data includes real-time location data and historical location data.

[0235] The hub data acquisition module 402 is configured to acquire traffic road hub data through a cloud server; the traffic road hub data is data of a travel road corresponding to position data of a vehicle; the traffic road hub data includes real-time traffic road hub data corresponding to real-time position data and historical traffic road hub data corresponding to historical position data;

[0236] The habit information acquisition module 403 is configured to acquire driving habit information corresponding to a user based on the position data of the vehicle and the traffic road hub data;

[0237] The target information display module 404 is configured to display target information on a vehicle-mounted device corresponding to the vehicle based on the driving habit information.

[0238] In a possible implementation, the habit information acquisition module 403 includes at least one of the following:

[0239] The instant habit information acquisition module is configured to acquire instant driving habit information based on the real-time position data and the real-time traffic road hub data;

[0240] The long-term habit information acquisition module is configured to acquire long-term driving habit information based on the historical position data and the historical traffic road hub data.

[0241] In a possible implementation, the instant habit information acquisition module is configured to input the real-time position data and the real-time traffic road hub data into a first model to obtain instant driving habit analysis information output by the first model;

[0242] The first model is a machine learning model trained by real-time position data samples, real-time traffic road hub data samples and labeled instant driving habit information.

[0243] In a possible implementation, the long-term habit information acquisition module is configured to input the historical position data and the historical traffic road hub data into a second model to obtain long-term driving habit analysis information output by the second model;

[0244] The second model is a machine learning model trained by historical position data samples, historical traffic road hub data samples and labeled long-term driving habit information.

[0245] In a possible implementation, the target information is displayed on the vehicle-mounted device corresponding to the vehicle based on the driving habit information, including at least one of the following:

[0246] In response to the instant driving habit information satisfying a first specified condition, first prompt information is displayed on a terminal interface of the vehicle-mounted device; the first prompt information is used to indicate that the user currently has dangerous driving behavior;

[0247] In response to the long-term driving habit information satisfying the second specified condition, and the vehicle about to travel to a road section matching the second specified condition, second prompt information is displayed on a terminal interface of the vehicle machine device; the second prompt information is used to prompt the user that the vehicle is about to travel to a dangerous road section.

[0248] In a possible implementation, the apparatus further includes:

[0249] The analysis report display module is configured to display a driving habit analysis report on the vehicle machine device based on the driving habit information.

[0250] Please refer to Figure 5 , Figure 5 is a structural schematic diagram of a computer device provided by an exemplary embodiment of the present application. The computer device 500 includes a central processing unit (CPU) 501, a system memory 504 including a random access memory (RAM) 502 and a read-only memory (ROM) 503, and a system bus 505 connecting the system memory 504 and the central processing unit 501. The computer device 500 further includes a basic input / output system (I / O system) 506 to help transfer information between various devices in the computer, and a mass storage device 507 for storing an operating system 513, application programs 514, and other program modules 515.

[0251] The basic input / output system 506 includes a display 508 for displaying information and an input device 509 such as a mouse, a keyboard, or the like for inputting information by a user. The display 508 and the input device 509 are both connected to the central processing unit 501 through an input / output controller 510 connected to the system bus 505. The basic input / output system 506 can also include the input / output controller 510 for receiving and processing input from a keyboard, a mouse, or an electronic stylus, and the like. Similarly, the input / output controller 510 also provides output to a display screen, a printer, or other types of output devices.

[0252] The mass storage device 507 is connected to the central processing unit 501 through a mass storage controller (not shown) connected to the system bus 505. The mass storage device 507 and its associated computer readable medium provide non-volatile storage for the computer device 500. That is, the mass storage device 507 can include a computer readable medium (not shown) such as a hard disk or a CD-ROM (Compact Disc Read-Only Memory) drive.

[0253] Without loss of generality, the computer readable medium can include computer storage medium and communication medium. The computer storage medium includes volatile and non-volatile, removable and non-removable media implemented in any method or technology for storage of information such as computer readable instructions, data structures, program modules or other data. The computer storage medium includes RAM (Random Access Memory), ROM (Read-Only Memory), EPROM (Erasable Programmable Read-Only Memory), EEPROM (Electrically Erasable Programmable Read-Only Memory), flash memory or other solid state memory technology, CD-ROM, DVD (Digital Video Disc), or other optical storage, magnetic cassettes, magnetic tapes, magnetic disk storage or other magnetic storage devices. Of course, those skilled in the art can know that the computer storage medium is not limited to the above. The system memory 504 and the mass storage device 507 described above can be collectively referred to as memory.

[0254] The computer device 500 can be connected to the Internet or other network devices through the network interface unit 511 connected to the system bus 505.

[0255] The memory further includes one or more programs stored in the memory, and the central processing unit 501 implements Figure 2 all or part of the steps of the method shown in the embodiments.

[0256] In the exemplary embodiments, a chip is also provided, which includes programmable logic circuit and / or program instructions, when the chip is running on a computer device, for implementing all or part of the steps of the method shown in the embodiments of the present application.

[0257] In the exemplary embodiments, a computer program product is also provided, which includes computer instructions stored in a computer readable storage medium. The processor of the computer device reads the computer instructions from the computer readable storage medium, and the processor reads and executes the computer instructions from the computer readable storage medium to implement all or part of the steps of the method shown in the embodiments of the present application.

[0258] In the example embodiments, a computer readable storage medium is also provided, and the computer readable storage medium stores a computer program, which is loaded and executed by a processor to implement all or part of the steps of the method shown in the above embodiments.

[0259] A person of ordinary skill in the art can understand that all or part of the steps of the above embodiments can be completed by hardware, or by program instructing related hardware, and the program can be stored in a computer readable storage medium, and the storage medium can be a read-only memory, a magnetic disk or an optical disk, etc.

[0260] Those skilled in the art should realize that, in the above one or more examples, the functions described in the embodiments of the present application can be implemented by hardware, software, firmware or any combination thereof. When implemented by software, the functions can be stored in a computer readable medium or transmitted as one or more instructions or codes on a computer readable medium. The computer readable medium includes a computer storage medium and a communication medium, and the communication medium includes any medium that facilitates the transfer of computer programs from one place to another. The storage medium can be any available medium that can be accessed by a general or special purpose computer.

[0261] The above description is only optional embodiments of the present application, and is not intended to limit the present application. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application shall be included in the protection scope of the present application.

Claims

1. A data processing method for driving scenarios, characterized in that, The method is executed by a mobile device, which is bound to a vehicle, and the method includes: The vehicle's location data is acquired through a vehicle data acquisition platform; the location data includes real-time location data and historical location data. Traffic road hub data is obtained through a cloud server; the traffic road hub data is the data of the driving road corresponding to the vehicle's location data; the traffic road hub data includes real-time traffic road hub data corresponding to the real-time location data, and historical traffic road hub data corresponding to the historical location data; The real-time location data and the real-time traffic hub data are input into the first model to obtain the instant driving habit information output by the first model. The first model is a machine learning model trained by real-time location data samples, real-time traffic hub data samples and labeled instant driving habit information. The historical location data and the historical traffic road hub data are input into the second model to obtain the long-term driving habit information output by the second model. The second model is a machine learning model trained by historical location data samples, historical traffic road hub data samples and labeled long-term driving habit information. Based on the real-time driving habit information and / or the long-term driving habit information, the target information is displayed on the vehicle's corresponding in-vehicle infotainment device.

2. The method according to claim 1, characterized in that, The step of displaying target information on the vehicle's in-vehicle infotainment system based on the real-time driving habit information and / or the long-term driving habit information includes at least one of the following: In response to the real-time driving habit information meeting the first specified condition, a first prompt message is displayed on the terminal interface of the vehicle-mounted device. The first prompt message is used to indicate to the user that they are currently engaging in dangerous driving behavior; When the long-term driving habit information meets the second specified condition and the vehicle is about to travel to a road segment that matches the second specified condition, a second prompt message is displayed on the terminal interface of the vehicle-mounted device; the second prompt message is used to remind the user that the vehicle is about to travel to a dangerous road segment.

3. The method according to claim 1, characterized in that, The method further includes: Based on the real-time driving habit information and / or the long-term driving habit information, a driving habit analysis report is displayed on the vehicle's infotainment system.

4. A data processing device for driving scenarios, characterized in that, The apparatus is used to perform the method as described in any one of claims 1 to 3, the apparatus comprising: The location data acquisition module is used to acquire the location data of the vehicle through the vehicle data acquisition platform; the location data includes real-time location data and historical location data. The hub data acquisition module is used to acquire traffic road hub data through a cloud server; the traffic road hub data is the data of the driving road corresponding to the vehicle's location data; the traffic road hub data includes real-time traffic road hub data corresponding to the real-time location data, and historical traffic road hub data corresponding to the historical location data; The real-time habit information acquisition module is used to input the real-time location data and the real-time traffic hub data into the first model to obtain the real-time driving habit information output by the first model. The first model is a machine learning model trained by real-time location data samples, real-time traffic hub data samples and labeled real-time driving habit information. The long-term habit information acquisition module is used to input the historical location data and the historical traffic road hub data into the second model to obtain the long-term driving habit information output by the second model. The second model is a machine learning model trained by historical location data samples, historical traffic road hub data samples and labeled long-term driving habit information. The target information display module is used to display target information on the vehicle's corresponding in-vehicle infotainment device based on the real-time driving habit information and / or the long-term driving habit information.

5. A computer device, characterized in that, The computer device includes a processor and a memory, the memory storing at least one computer instruction, which is loaded and executed by the processor to implement the method as described in any one of claims 1 to 3.

6. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores at least one computer instruction, which is loaded and executed by a processor to implement the method as described in any one of claims 1 to 3.

7. A computer program product, characterized in that, The computer program product includes computer instructions stored in a computer-readable storage medium; the computer instructions are read and executed by a processor of a computer device to implement the method as described in any one of claims 1 to 3.

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