Method, device, storage medium and electronic device for processing vehicle network data

Abstract

The present disclosure provides a method, device, storage medium and electronic device for processing Internet of Vehicles data, which can obtain vehicle status data, vehicle control buried point data, vehicle-mounted equipment recorded data and vehicle-related attribute data; use vehicle status data and vehicle control buried point data to obtain travel data corresponding to each preset indicator to be analyzed; store the travel data in a first database; store the vehicle-mounted equipment recorded data in a second database according to a data storage method corresponding to the data type; store the vehicle-related attribute data in a third database; perform data aggregation processing on the data stored in the first database, the second database and the third database according to preset data dimensions, and store the data after data aggregation processing in a fourth database. The present disclosure collects and aggregates Internet of Vehicles data through indicator tags and data dimensions, reduces the early data processing work of Internet of Vehicles data, and meets the data business needs of enterprises.

Description

Technical Field

[0001] The present disclosure relates to the field of data processing technology, and in particular to a method, device, storage medium, and electronic device for processing Internet of Vehicles data. Background Art

[0002] With the development of connected vehicle technology, an increasing amount of data is being generated. Because the data generated by connected vehicles has diverse dimensions, formats, and volumes, enterprises often need to perform complex and repetitive screening and integration processes when using this data, which can ultimately prevent them from effectively using the data.

[0003] Therefore, faced with massive amounts of Internet of Vehicles data, how to effectively process the Internet of Vehicles data has become a technical problem that technical personnel in this field urgently need to solve. Summary of the Invention

[0004] In view of the above problems, the present disclosure provides a method, device, storage medium and electronic device for processing Internet of Vehicles data to overcome or at least partially solve the above problems. The technical solutions are as follows:

[0005] A method for processing Internet of Vehicles data, comprising:

[0006] Obtaining vehicle status data, vehicle control embedded data, vehicle-mounted device recorded data, and vehicle-related attribute data, wherein the vehicle status data is data reflecting the vehicle's performance status, the vehicle control embedded data is data of user interaction with the vehicle, the vehicle-mounted device recorded data is data recorded by electronic devices in the vehicle, and the vehicle-related attribute data is data reflecting the attribute characteristics of the vehicle and the user attribute characteristics of the user to whom the vehicle belongs;

[0007] Using the vehicle status data and vehicle control buried point data, obtaining travel data corresponding to each preset indicator to be analyzed;

[0008] storing the travel data in a first database;

[0009] Storing the data recorded by the vehicle-mounted device in a second database according to a data storage method corresponding to the data type;

[0010] storing the vehicle-related attribute data in a third database;

[0011] According to the preset data dimension, data aggregation processing is performed on the data stored in the first database, the second database and the third database, and the data after the data aggregation processing is stored in the fourth database.

[0012] Optionally, the use of the vehicle status data and vehicle control buried point data to obtain travel data corresponding to each preset indicator to be analyzed includes:

[0013] Performing preset initialization processing on the vehicle state data and the vehicle control buried point data to obtain first vehicle condition buried point data;

[0014] Divide the first vehicle condition buried point data into trips according to a preset trip division method to obtain trip division data for each trip;

[0015] Based on the travel division data, the travel data corresponding to each preset indicator to be analyzed are calculated using the indicator value calculation method corresponding to each preset indicator to be analyzed.

[0016] Optionally, performing preset initialization processing on the vehicle status data and the vehicle control buried point data to obtain first vehicle condition buried point data includes:

[0017] Deleting irregular data from the vehicle status data and the vehicle control buried point data to obtain second vehicle condition buried point data;

[0018] Performing data reset processing on the second vehicle condition buried point data according to a preset data field threshold processing method to obtain third vehicle condition buried point data;

[0019] The third vehicle condition buried point data is filtered according to preset common fields to obtain fourth vehicle condition buried point data, wherein the preset common fields are pre-set with calculation fields, and the calculation fields are common fields carrying preset calculation identifiers, and the preset calculation identifiers are used to mark data to be calculated in the fourth vehicle condition buried point data;

[0020] The first vehicle condition buried point data corresponding to the calculation type field is determined in the fourth vehicle condition buried point data.

[0021] Optionally, after determining the first vehicle condition buried point data corresponding to the calculation type field in the fourth vehicle condition buried point data, the method further includes:

[0022] The other vehicle condition buried point data in the fourth vehicle condition buried point data except the first vehicle condition buried point data is stored in a fifth database.

[0023] Optionally, after calculating the travel data corresponding to each preset indicator to be analyzed based on the travel segmentation data and using an indicator value calculation method corresponding to each preset indicator to be analyzed, the method further includes:

[0024] Aggregate and count the first vehicle condition buried point data and the travel data according to a preset time dimension to obtain a summary statistical result;

[0025] The summary statistical results are stored in a sixth database.

[0026] Optionally, the data recorded by the vehicle-mounted device includes voice data, video data, image data, log data, and transaction data, and storing the data recorded by the vehicle-mounted device in the second database according to a data storage method corresponding to the data type includes:

[0027] extracting feature values ​​from the voice data, the video data, and the image data;

[0028] storing the voice data, the video data, the image data, and the feature value in a second database;

[0029] The log data and the transaction data are stored in the second database.

[0030] Optionally, the obtaining of vehicle status data, vehicle control buried point data, vehicle-mounted equipment recording data, and vehicle-related attribute data includes:

[0031] Obtain vehicle status data and vehicle control point data through the first Kafka middleware;

[0032] Obtaining the vehicle-mounted device recorded data through the second Kafka middleware and / or the first data interface;

[0033] The vehicle-related attribute data is obtained through the second data interface.

[0034] A vehicle network data processing device includes: a first obtaining unit, a second obtaining unit, a first storage unit, a second storage unit, a third storage unit, and a fourth storage unit.

[0035] The first obtaining unit is configured to obtain vehicle status data, vehicle control buried point data, vehicle-mounted device recorded data, and vehicle-related attribute data, wherein the vehicle status data is data reflecting the vehicle performance status, the vehicle control buried point data is data of user control interaction with the vehicle, the vehicle-mounted device recorded data is data recorded by electronic devices in the vehicle, and the vehicle-related attribute data is data reflecting attribute characteristics of the vehicle and user attribute characteristics of the user to whom the vehicle belongs;

[0036] The second obtaining unit is configured to obtain travel data corresponding to each preset indicator to be analyzed by using the vehicle status data and the vehicle control buried point data;

[0037] The first storage unit is used to store the travel data in a first database;

[0038] The second storage unit is used to store the data recorded by the vehicle-mounted device into a second database according to a data storage method corresponding to the data type;

[0039] The third storage unit is used to store the vehicle-related attribute data in a third database;

[0040] The fourth storage unit is used to perform data aggregation processing on the data stored in the first database, the second database and the third database according to a preset data dimension, and store the data after the data aggregation processing in the fourth database.

[0041] A computer-readable storage medium stores a program, which, when executed by a processor, implements any of the above-mentioned vehicle network data processing methods.

[0042] An electronic device comprising at least one processor, and at least one memory and bus connected to the processor; wherein the processor and the memory communicate with each other via the bus; and the processor is configured to call program instructions in the memory to execute any one of the above-described vehicle network data processing methods.

[0043] By means of the above technical solution, the present disclosure provides a method, device, storage medium and electronic device for processing Internet of Vehicles data, which can obtain vehicle status data, vehicle control buried point data, vehicle-mounted equipment recording data and vehicle-related attribute data; use vehicle status data and vehicle control buried point data to obtain travel data corresponding to each preset indicator to be analyzed; store the travel data in a first database; store the vehicle-mounted equipment recording data in a second database according to a data storage method corresponding to the data type; store the vehicle-related attribute data in a third database; perform data aggregation processing on the data stored in the first database, the second database and the third database according to preset data dimensions, and store the data after data aggregation processing in a fourth database. The present disclosure effectively collects and aggregates massive Internet of Vehicles data through indicator tags and data dimensions, reduces the pre-processing work when using Internet of Vehicles data, and quickly supports the data business needs of enterprises.

[0044] The above description is only an overview of the technical solution of the present disclosure. In order to more clearly understand the technical means of the present disclosure, it can be implemented in accordance with the contents of the specification. In order to make the above and other purposes, features and advantages of the present disclosure more obvious and easy to understand, the specific implementation methods of the present disclosure are listed below. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] Various other advantages and benefits will become apparent to those skilled in the art upon reading the detailed description of the preferred embodiment below. The accompanying drawings are for illustration purposes only and are not to be considered as limiting the present disclosure. The same reference symbols are used throughout the drawings to represent the same components. In the drawings:

[0046] Figure 1 A schematic diagram showing a flow chart of an implementation of a method for processing Internet of Vehicles data provided by an embodiment of the present disclosure;

[0047] Figure 2 A flow chart showing another implementation of the method for processing Internet of Vehicles data provided by an embodiment of the present disclosure is shown;

[0048] Figure 3 A flowchart illustrating an implementation of step S200 in the method for processing Internet of Vehicles data provided by an embodiment of the present disclosure is shown;

[0049] Figure 4 A flow chart showing another implementation of step S200 in the method for processing Internet of Vehicles data provided by an embodiment of the present disclosure is shown;

[0050] Figure 5 A flow chart showing another implementation of the method for processing Internet of Vehicles data provided by an embodiment of the present disclosure is shown;

[0051] Figure 6 A flow chart showing another implementation of the method for processing Internet of Vehicles data provided by an embodiment of the present disclosure is shown;

[0052] Figure 7 A system structure diagram of a vehicle networking data processing system provided by an embodiment of the present disclosure is shown;

[0053] Figure 8 A structural diagram of a vehicle network data processing device provided by an embodiment of the present disclosure is shown. DETAILED DESCRIPTION

[0054] Exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. Although exemplary embodiments of the present disclosure are shown in the accompanying drawings, it should be understood that the present disclosure can be implemented in various forms and should not be limited by the embodiments set forth herein. Rather, these embodiments are provided to enable a more thorough understanding of the present disclosure and to fully convey the scope of the present disclosure to those skilled in the art.

[0055] like Figure 1 FIG. 1 is a flow chart of an implementation of a method for processing Internet of Vehicles data provided by an embodiment of the present disclosure. The method for processing Internet of Vehicles data may include:

[0056] S100: Obtain vehicle status data, vehicle control point data, vehicle-mounted equipment recording data, and vehicle-related attribute data.

[0057] The vehicle status data may be referred to as vehicle condition data. The vehicle status data may be data reflecting the vehicle's performance status. Optionally, the vehicle status data may include the vehicle's safety performance data, power performance data, operational performance data, and exhaust emission data.

[0058] Vehicle control tracking data can include data on user interactions with vehicle control. Optionally, vehicle control tracking data can include the number of users who have activated each application on the vehicle's onboard computer and the number of times each application has been activated. It is understood that the vehicle's onboard computer may be installed with applications that control corresponding hardware devices on the vehicle. For example, seat control applications, humidifier control applications, and audio control applications may be installed.

[0059] In-vehicle device recorded data refers to data recorded by electronic devices in the vehicle. Optionally, based on data type, in-vehicle device recorded data may include voice data, video data, image data, log data, and transaction data. Log data may include a collection of data including system logs and operation logs of the in-vehicle device. Transaction data may include database operation sequence data of the in-vehicle device accessing and manipulating data items.

[0060] Vehicle-related attribute data refers to data reflecting the vehicle's attributes and the user attributes of the vehicle's user. This data may include both vehicle attribute data and the user attribute data of the vehicle's user. Optionally, this data may include the vehicle's sales region, usage type, brand, series, model, color, style, configuration, intelligent connectivity type, engine type, option ID, option name, number of seats, platform, and dealer name. Optionally, user attribute data may include the user's gender and age, the user's corresponding customer type, the user's region, and the brand, series, and model of the vehicle the user drives.

[0061] Optional, based on Figure 1 The method shown, such as Figure 2 As shown, a flowchart of another implementation of the method for processing Internet of Vehicles data provided by an embodiment of the present disclosure is provided. Step S100 may include:

[0062] S110. Obtain vehicle status data and vehicle control point data through the first Kafka middleware.

[0063] In actual applications, the amount of vehicle status data and vehicle control data is large. Through real-time access of the first Kafka middleware, batch data access can be achieved, making it convenient and quick to obtain vehicle status data and vehicle control data.

[0064] Optionally, after obtaining the vehicle status data and vehicle control buried point data, the embodiment of the present disclosure can store the vehicle status data and vehicle control buried point data in full, so as to facilitate backtracking when problems arise in the subsequent data processing process, and also facilitate providing data support for subsequent possible data analysis and mining. Optionally, the vehicle status data and vehicle control buried point data can be stored in full in the first database. Optionally, the first database can be a Hive data warehouse tool.

[0065] Optionally, the embodiment of the present disclosure can fully store vehicle status data and vehicle control tracking data for nearly one year.

[0066] S120. Obtain vehicle-mounted device recorded data through the second Kafka middleware and / or the first data interface.

[0067] In the embodiment of the present disclosure, the second Kafka middleware and / or the first data interface may be selected to access the vehicle-mounted device to record data according to the data type.

[0068] Optionally, the embodiment of the present disclosure can access voice data, video data, and image data in real time through a second Kafka middleware, so as to quickly obtain irregular and large-volume voice data, video data, and image data.

[0069] Optionally, the embodiment of the present disclosure may access log data and transaction data through the second Kafka middleware and / or the first data interface to adapt to access requirements of different data volumes and frequencies of log data and transaction data.

[0070] Optionally, the embodiment of the present disclosure may select a second Kafka middleware or the first data interface for access based on the data volume and frequency of the log data and transaction data. Optionally, the embodiment of the present disclosure may use the second Kafka middleware for accessing log data and transaction data whose data volume is greater than a preset data volume threshold or whose frequency is higher than a preset frequency threshold, and vice versa, use the first data interface for access. Optionally, the preset data volume threshold may be 500M. Optionally, the preset frequency threshold may be 300kb / s.

[0071] S130. Obtain vehicle-related attribute data through the second data interface.

[0072] Since the vehicle-related attribute data is structured data and has a small data volume, the vehicle-related attribute data can be obtained synchronously in real time using the second data interface.

[0073] The disclosed embodiments rationally select Kafka middleware and / or data interfaces for data access based on the characteristics of various types of IoV data, which can quickly and in real time obtain various types of IoV data, shorten the data acquisition time for IoV data processing, and improve the efficiency of IoV data processing.

[0074] S200: Utilize vehicle status data and vehicle control point data to obtain travel data corresponding to each preset indicator to be analyzed.

[0075] The preset indicators to be analyzed may be pre-set fields to be analyzed. Optionally, the preset indicators to be analyzed may include mileage, trip time, maximum speed, average speed, average engine speed, median speed, speed fluctuation, engine speed fluctuation, throttle fluctuation, sudden acceleration, sudden deceleration, engine idle time, engine idle time, medium speed and light load time, medium speed and heavy load time, high speed and light load time, and high speed and heavy load time.

[0076] Optionally, the disclosed embodiment can set a corresponding operation method for each indicator to be analyzed. Based on the vehicle status data and vehicle control buried point data, the travel data corresponding to the indicator to be analyzed is calculated according to the corresponding operation method. By setting the operation method for the travel data corresponding to the indicator to be analyzed, the disclosed embodiment can effectively calculate and process the vehicle status data and vehicle control buried point data to obtain the travel data required by the enterprise.

[0077] It is understandable that the indicators to be analyzed can be set according to actual needs. The embodiment of the present disclosure can provide an indicator management client so that relevant technical personnel can manage and maintain indicator labels and calculation methods through the indicator management client.

[0078] Optional, based on Figure 1 The method shown, such as Figure 3 As shown, a flowchart of an implementation of step S200 in the method for processing Internet of Vehicles data provided by an embodiment of the present disclosure is provided. Step S200 may include:

[0079] S210: Perform preset initialization processing on the vehicle status data and the vehicle control buried point data to obtain first vehicle condition buried point data.

[0080] In actual applications, since vehicle status data and vehicle control buried point data may contain irregular data and the data field threshold may not meet the threshold rules, the embodiment of the present disclosure can first perform preset initialization processing on the vehicle status data and vehicle control buried point data to obtain the first vehicle condition buried point data that meets the specifications, prevent subsequent data processing errors, and save data processing resources.

[0081] Optional, based on Figure 3 The method shown, such as Figure 4 As shown, a flowchart of another implementation of step S200 in the method for processing Internet of Vehicles data provided by an embodiment of the present disclosure is provided. Step S210 may include:

[0082] S211. Delete irregular data in the vehicle status data and the vehicle control buried point data to obtain second vehicle condition buried point data.

[0083] Irregular data may be abnormal data in the vehicle status data and vehicle control buried point data that is not normally distributed with other data. Irregular data can be set according to actual needs. The disclosed embodiment can identify irregular data in the vehicle status data and vehicle control buried point data, clean up the irregular data, and obtain the second vehicle condition buried point data.

[0084] S212. Perform data reset processing on the second vehicle condition buried point data according to a preset data field threshold processing method to obtain third vehicle condition buried point data.

[0085] Among them, the data field to be threshold checked and the threshold interval corresponding to the data field are set in the preset data field threshold processing method. The embodiment of the present disclosure can identify the data field in the second vehicle condition buried data, and compare the value of the data field with the corresponding threshold interval to determine whether the value of the data field is within the threshold interval. If not, the value of the data field is reset to the default value, and the reset processing is completed to ensure that the values ​​of the data fields in the obtained third vehicle condition buried data are all within the corresponding threshold interval. The embodiment of the present disclosure can avoid errors caused by the value of the data field exceeding the corresponding threshold interval in subsequent processing by performing data reset processing on the second vehicle condition buried data, thereby ensuring the correctness of data processing.

[0086] S213. Filter the third vehicle condition buried point data according to preset common fields to obtain fourth vehicle condition buried point data, wherein the preset common fields are pre-set with calculation fields.

[0087] Among them, the commonly used fields can be the vehicle status fields and vehicle control point fields commonly used by vehicles. Optionally, common fields may include: vehicle identification code (VIN), reporting time (acquisitionTime), collection time (reportTime), battery quiescent current value (IBATT_QUIESCENT), average fuel consumption (IP_AvgFuelCons), system power mode (SysPowerMod), engine status (EngState), driving range (IP_RemainDistance), tire pressure temperature status (TireTempSts), tire pressure indication status (TirePressIndSts), low remaining fuel alarm status (IP_FuelLvlLowLmpSts), battery module temperature (BMS_BattModuleTe_91), power battery voltage (BMS_PackVolt), air purifier status (ACAIUEnaSts), cabin cleaning enable status (ACCbnClnEnasts), seat heating status (SeatHeatSts), emergency brake deceleration control request (AEB_TgtDecel_Req) ​​and emergency brake deceleration request (AEB_TgtDecel_ReqValue), etc. It is understandable that commonly used fields can be set according to actual needs.

[0088] Calculation fields are commonly used fields that carry a preset calculation identifier. This preset calculation identifier is used to mark the data to be calculated in the fourth vehicle buried point data. By marking the calculation identifier, the disclosed embodiment distinguishes the data to be calculated from the directly stored data in the fourth vehicle condition buried point data, which facilitates subsequent data calculation preparation and data storage.

[0089] S214. Determine the first vehicle condition buried point data corresponding to the calculation type field in the fourth vehicle condition buried point data.

[0090] The embodiment of the present disclosure can identify and extract the first vehicle condition buried point data corresponding to the calculation type field in the fourth vehicle condition buried point data.

[0091] Optionally, after step S214, the embodiment of the present disclosure may store other vehicle condition buried point data in the fourth vehicle condition buried point data except the first vehicle condition buried point data in the fifth database.

[0092] Optionally, the fifth database and the first database may be the same database. In the embodiment of the present disclosure, the vehicle condition buried point data other than the first vehicle condition buried point data in the fourth vehicle condition buried point data is stored in the fifth database, and corresponding data query and data calculation can be provided.

[0093] Optionally, the embodiment of the present disclosure stores the other vehicle condition tracking data for the past three years.

[0094] The embodiment of the present disclosure can send the vehicle status data and vehicle control buried data to the first Flink computing engine for processing through the first Kafka middleware, and determine the first vehicle condition buried data corresponding to the calculation class field and other vehicle condition buried data except the first vehicle condition buried data in the fourth vehicle condition buried data.

[0095] S220: Divide the first vehicle condition buried point data into trips according to a preset trip division method to obtain trip division data for each trip.

[0096] Optionally, the preset trip segmentation method may include using a field match: VIN = vin(ads_com_vehicle) to obtain the vehicle's brand, series, and model. Sorting is performed using both acquisitionTime and reportTime. When brand = 'Brand A', the 'SysPowerMod' field is used for real-time trip segmentation. When SysPowerMod = 2, the trip is considered to have started, and the corresponding value of reportTime is the trip start time. If the interval between two SysPowerMod = 2 data is less than or equal to 30 seconds, the trip is considered to have not yet ended. If SysPowerMod = 0 for 60 consecutive seconds, the trip is considered to have ended. The last SysPowerMod = 2, and the corresponding value of reportTime is the trip end time. If there is no corresponding VIN feedback signal for 300 consecutive seconds, the trip is considered to have ended. If brand! = 'Brand A', the 'EngState' field is used for real-time trip segmentation. When EngStat = 2, the trip is considered to have started, and the corresponding value of reportTime is the trip start time. If two EngState! = 2 data interval > 30 seconds, the trip ends, the last EngState = 2, and the reporttime value corresponds to the trip end time. If there is no corresponding VIN feedback signal for 30 consecutive seconds, the trip ends.

[0097] It is understandable that the itinerary division method can be set according to actual needs. By dividing the first vehicle condition buried point data into itineraries, the disclosed embodiment can specifically classify the data in the first vehicle condition buried point data into each divided itinerary, so as to facilitate subsequent data processing based on the data in each itinerary and improve data processing efficiency.

[0098] S230 , based on the trip division data, using the indicator value calculation method corresponding to each preset indicator to be analyzed, respectively calculate the trip data corresponding to each preset indicator to be analyzed.

[0099] The embodiment of the present disclosure can calculate the index value corresponding to each preset indicator to be analyzed under each trip based on the trip division data under the trip, and determine the calculated index value of each preset indicator to be analyzed as the trip data under the trip.

[0100] It should be noted that the trip data corresponding to the preset indicator to be analyzed for any trip is calculated using the first vehicle condition buried data corresponding to the calculation field in the trip partition data for that trip. To facilitate understanding of the relationship between the preset indicator to be analyzed and the calculation field, an example is provided here: for the preset indicator to be analyzed "trip time" for any trip, the first vehicle condition buried data corresponding to the calculation fields "collection time" and "engine status" in the trip partition data for that trip is substituted into the calculation formula for the indicator value calculation method corresponding to the preset indicator to be analyzed "trip time". The calculated result is the trip data corresponding to the preset indicator to be analyzed "trip time" for that trip.

[0101] In the embodiment of the present disclosure, the first vehicle condition tracking data can be sent to the second Flink computing engine via the third Kafka middleware to perform trip division, and the trip data corresponding to each preset indicator to be analyzed can be calculated.

[0102] The embodiment of the present disclosure calculates the travel data of each trip based on the trip division, and can calculate and organize the data in the trip dimension to facilitate subsequent data processing and analysis, thereby improving data processing efficiency.

[0103] Optionally, the embodiment of the present disclosure can summarize and count the first vehicle condition buried point data and travel data according to a preset time dimension, obtain summary statistical results, and store the summary statistical results in a sixth database.

[0104] Optionally, the preset time dimension may include six time dimensions: day, week, month, quarter, year, and total. The disclosed embodiment may aggregate the first vehicle condition buried point data and the travel data according to the preset time dimensions to obtain summary statistical results corresponding to the six time dimensions.

[0105] Optionally, the embodiment of the present disclosure may store the summary statistical results in the sixth database according to a preset time period. Optionally, the preset time period may be every day to ensure the real-time nature of the data.

[0106] Optionally, the sixth database may be the same database as the first database. Since the amount of data in the summary statistical results is relatively small, the summary statistical results may be stored for a long time.

[0107] S300: Store the travel data in a first database.

[0108] Optionally, the disclosed embodiment may store the travel data for a long period of time.

[0109] S400: Store the data recorded by the vehicle-mounted device in a second database according to a data storage method corresponding to the data type.

[0110] The data recorded by the vehicle-mounted device may include voice data, video data, and image data. Different data types in the vehicle-mounted device record data have corresponding data storage methods. Optionally, the data storage methods may include data feature extraction and direct storage of raw data.

[0111] In the disclosed embodiments, unstructured data recorded by vehicle-mounted devices may be stored using data feature extraction. This process may include extracting feature values ​​from the unstructured data as structured data, and then storing the unstructured data and structured data in a corresponding manner to facilitate querying the unstructured data and further utilizing the structured data.

[0112] In the embodiment of the present disclosure, for the historical record data of events and operations recorded in the vehicle-mounted device, the data storage method adopted may be direct storage of the original data, and the original historical record data may be saved to facilitate subsequent data analysis and troubleshooting.

[0113] Optional, based on Figure 1 The method shown, such as Figure 5 As shown, a flowchart of another implementation of the method for processing Internet of Vehicles data provided by an embodiment of the present disclosure is provided. Step S400 may include:

[0114] S410: Extract feature values ​​from voice data, video data, and image data.

[0115] The eigenvalue is a parameter that reflects the unique characteristics of the data. For example, the eigenvalue of speech data may include the signal spectrum value predicted from the speech data based on the Mel Frequency Cepstrum Coefficient (MFCC). The eigenvalue of video data may include the texture feature value analyzed from the video data using the VVC rate control algorithm. The eigenvalue of image data may include the local eigenvalue calculated from the image data using the Scale Invariant Feature Transform (SIFT) algorithm.

[0116] S420: Store the voice data, video data, image data, and feature values ​​in a second database.

[0117] In the disclosed embodiment, a second Kafka middleware can be used to consume and process voice data, video data, and image data in real time. On the one hand, the voice data, video data, and image data are stored in a second database using either cold storage or hot storage according to time periods. On the other hand, feature values ​​from the voice data, video data, and image data are extracted and stored as structured data.

[0118] Optionally, since the voice data, video data and image data are large in volume and have time-sensitivity, the embodiment of the present disclosure may store voice data, video data and image data for nearly one year and store the feature quantities for a long time.

[0119] S430: Store the log data and transaction data in the second database.

[0120] Optionally, the second database and the first database may be the same database. In the embodiment of the present disclosure, the log data and transaction data are directly stored in the second database, which can facilitate subsequent correlation analysis of the log data and transaction data.

[0121] Optionally, the embodiment of the present disclosure may store log data and transaction data for nearly one year.

[0122] S500: Store the vehicle-related attribute data in a third database.

[0123] Since the vehicle-related attribute data is structured data and has a small data volume, the embodiment of the present disclosure can use the second data interface to directly store the vehicle-related attribute data incrementally in the third database.

[0124] Optionally, the third database may be a MySQL database. The third database is connected to the first database.

[0125] Optionally, the embodiment of the present disclosure may filter and aggregate the data in the third database according to actual business needs and usage requirements to obtain the required data table, and use the data table as the underlying data.

[0126] S600: Perform data aggregation processing on the data stored in the first database, the second database, and the third database according to the preset data dimension, and store the data after the data aggregation processing in the fourth database.

[0127] Optionally, the preset data dimensions may include a primary dimension and a secondary dimension, and one primary dimension may correspond to at least one secondary dimension. It is understandable that the embodiments of the present disclosure may set data dimensions according to actual needs.

[0128] Optionally, the first-level dimensions may include: vehicle data domain, cockpit data domain, basic data domain, marketing data domain and mobile APP data domain.

[0129] Optionally, the secondary dimensions corresponding to the vehicle data domain may include the vehicle condition domain and the location domain. The secondary dimensions corresponding to the cockpit data domain may include the video domain, the voice domain, the user interaction domain, and the traffic domain. The secondary dimensions corresponding to the basic data domain may include the user basic data domain, the vehicle basic data domain, the environment basic data domain, and the dealer basic data domain. The secondary dimensions corresponding to the marketing data domain may include the order domain and the market domain. The secondary dimensions corresponding to the mobile app data domain may include the behavior domain and the business domain.

[0130] Optionally, the fourth database may be a Hive data warehouse tool. The disclosed embodiment uses data aggregation processing to centrally store data stored in different databases in the fourth database according to preset data dimensions, making it convenient for subsequent users to query, download, and analyze data in the fourth database.

[0131] Optional, based on Figure 1 The method shown, such as Figure 6 FIG. 5 is a flow chart of another embodiment of the method for processing Internet of Vehicles data provided by an embodiment of the present disclosure. After step S600, the method for processing Internet of Vehicles data may further include:

[0132] S700: Query, download, or analyze data stored in the fourth database.

[0133] In the fourth database, all data is categorized and stored according to pre-set data dimensions. The disclosed embodiments allow querying, downloading, or analyzing data within any data dimension in the fourth database. The results of the data analysis can also be stored in the fourth database after verification. By analyzing the data stored in the fourth database, the disclosed embodiments can reprocess related algorithms, making subsequent data processing more scientific and accurate.

[0134] Optionally, embodiments of the present disclosure may provide a front-end functional interface system with data visualization capabilities, allowing technicians to query data in the fourth database through the front-end functional interface system, ensuring data real-time and accuracy. The front-end functional interface system can also query other data associated with any data, providing multi-dimensional data display and data download.

[0135] Optionally, embodiments of the present disclosure may provide an external data provision channel. Through the data provision channel, data in the fourth database may be made available to external parties, making it easier for the enterprise's partners to use the data in the fourth database. The data provision channel may be constructed using methods such as a data interface, a data server, and message middleware.

[0136] The disclosed embodiment filters and organizes massive amounts of vehicle network data through indicator tags and data dimensions to obtain valuable data, and processes the data so that the processed data can be directly applied to various departments of automobile manufacturers. The real-time data processing and data service capabilities can help automobile manufacturers solve various emergencies and assist automobile manufacturers in their digital transformation.

[0137] Optionally, the vehicle network data processing method provided in the embodiment of the present disclosure can be applied to a vehicle network data processing system. Optionally, the vehicle network data processing system can be run on a public cloud server. The vehicle network data processing system provided in the embodiment of the present disclosure can be used as follows: Figure 7 shown.

[0138] The present disclosure provides a method for processing Internet of Vehicles data, which can obtain vehicle status data, vehicle control buried point data, vehicle-mounted equipment recording data, and vehicle-related attribute data; use vehicle status data and vehicle control buried point data to obtain travel data corresponding to each preset indicator to be analyzed; store the travel data in a first database; store the vehicle-mounted equipment recording data in a second database according to a data storage method corresponding to the data type; store the vehicle-related attribute data in a third database; perform data aggregation processing on the data stored in the first database, the second database, and the third database according to preset data dimensions, and store the data after data aggregation processing in a fourth database. The present disclosure effectively collects and aggregates massive Internet of Vehicles data through indicator tags and data dimensions, reduces the pre-processing work when using Internet of Vehicles data, and quickly supports the data business needs of enterprises.

[0139] Although the operations are depicted in a particular order, this should not be understood as requiring that the operations be performed in the particular order shown or in a sequential order.Multitasking and parallel processing may be advantageous under certain circumstances.

[0140] It should be understood that the various steps described in the method embodiments of the present disclosure may be performed in different orders and / or in parallel. In addition, the method embodiments may include additional steps and / or omit the steps shown. The scope of the present disclosure is not limited in this respect.

[0141] Corresponding to the above method embodiment, the embodiment of the present disclosure also provides a vehicle network data processing device, the structure of which is as follows: Figure 8 As shown, it may include: a first obtaining unit 100 , a second obtaining unit 200 , a first storage unit 300 , a second storage unit 400 , a third storage unit 500 and a fourth storage unit 600 .

[0142] The first acquisition unit 100 is used to obtain vehicle status data, vehicle control embedded point data, vehicle-mounted equipment recording data and vehicle-related attribute data, wherein the vehicle status data is data reflecting the vehicle performance status, the vehicle control embedded point data is data of the user's control interaction with the vehicle, the vehicle-mounted equipment recording data is data recorded by the electronic equipment in the vehicle, and the vehicle-related attribute data is data reflecting the attribute characteristics of the vehicle and the user attribute characteristics of the user to whom the vehicle belongs.

[0143] The second obtaining unit 200 is used to obtain travel data corresponding to each preset indicator to be analyzed by using the vehicle status data and the vehicle control buried point data.

[0144] The first storage unit 300 is used to store the travel data in the first database.

[0145] The second storage unit 400 is used to store the data recorded by the vehicle-mounted device into the second database according to a data storage method corresponding to the data type.

[0146] The third storage unit 500 is used to store the vehicle-related attribute data in a third database.

[0147] The fourth storage unit 600 is used to perform data aggregation processing on the data stored in the first database, the second database, and the third database according to a preset data dimension, and store the data after the data aggregation processing in the fourth database.

[0148] Optionally, the second obtaining unit 200 may include: a first obtaining subunit, a second obtaining subunit, and a first calculating subunit.

[0149] The first obtaining subunit is used to perform preset initialization processing on the vehicle status data and the vehicle control buried point data to obtain the first vehicle condition buried point data.

[0150] The second obtaining subunit is used to divide the first vehicle condition buried point data into trips according to a preset trip division method, and obtain trip division data under each trip.

[0151] The first calculation subunit is configured to calculate, based on the travel segmentation data, travel data corresponding to each preset indicator to be analyzed by using an indicator value calculation method corresponding to each preset indicator to be analyzed.

[0152] Optionally, the first acquisition sub-unit can be specifically used to delete irregular data in the vehicle status data and the vehicle control buried point data to obtain the second vehicle condition buried point data; reset the second vehicle condition buried point data according to the preset data field threshold processing method to obtain the third vehicle condition buried point data; filter the third vehicle condition buried point data according to the preset common fields to obtain the fourth vehicle condition buried point data, wherein the preset common fields are pre-set with calculation type fields, and the calculation type fields are common fields carrying preset calculation identifiers, and the preset calculation identifiers are used to mark the data to be calculated in the fourth vehicle buried point data; determine the first vehicle condition buried point data corresponding to the calculation type fields in the fourth vehicle condition buried point data.

[0153] Optionally, the Internet of Vehicles data processing device may further include: a fifth storage unit.

[0154] The fifth storage unit is used to store other vehicle condition buried point data except the first vehicle condition buried point data in the fourth vehicle condition buried point data into the fifth database after the first obtaining unit determines the first vehicle condition buried point data corresponding to the calculation class field in the fourth vehicle condition buried point data.

[0155] Optionally, the Internet of Vehicles data processing device may further include: a third obtaining unit and a sixth storage unit.

[0156] The third obtaining unit is used for the first calculating sub-unit to calculate the travel data corresponding to each preset indicator to be analyzed based on the travel division data, and then summarize and count the first vehicle condition buried data and travel data according to the preset time dimension to obtain the summary statistical results.

[0157] The sixth storage unit is used to store the summary statistical results in the sixth database.

[0158] Optionally, the data recorded by the vehicle-mounted device includes voice data, video data, image data, log data and transaction data.

[0159] Optionally, the second storage unit 400 includes: a feature value extraction subunit, a first storage subunit and a second storage subunit.

[0160] The feature value extraction subunit is used to extract feature values ​​from voice data, video data and image data.

[0161] The first storage subunit is used to store the voice data, video data, image data and feature values ​​in the second database.

[0162] The second storage subunit is used to store the log data and transaction data in the second database.

[0163] Optionally, the first obtaining unit 100 includes: a first data obtaining subunit, a second data obtaining subunit, and a third data obtaining subunit.

[0164] The first data acquisition subunit is used to obtain vehicle status data and vehicle control point data through the first Kafka middleware.

[0165] The second data acquisition subunit is used to obtain the vehicle-mounted device recorded data through the second Kafka middleware and / or the first data interface.

[0166] The third data acquisition subunit is used to obtain vehicle-related attribute data through the second data interface.

[0167] Optionally, the Internet of Vehicles data processing device may further include: a data usage unit.

[0168] The data using unit is used to query, download or analyze the data stored in the fourth database after the fourth storage unit 600 stores the data after data aggregation processing in the fourth database.

[0169] The present disclosure provides an Internet of Vehicles data processing device that can obtain vehicle status data, vehicle control buried point data, vehicle-mounted equipment recording data, and vehicle-related attribute data; use the vehicle status data and vehicle control buried point data to obtain travel data corresponding to each preset indicator to be analyzed; store the travel data in a first database; store the vehicle-mounted equipment recording data in a second database according to a data storage method corresponding to the data type; store the vehicle-related attribute data in a third database; perform data aggregation processing on the data stored in the first database, the second database, and the third database according to preset data dimensions, and store the data after data aggregation processing in a fourth database. The present disclosure effectively collects and aggregates massive Internet of Vehicles data through indicator tags and data dimensions, reduces the pre-processing work when using Internet of Vehicles data, and quickly supports the data business needs of enterprises.

[0170] Regarding the apparatus in the above embodiment, the specific manner in which each unit performs operations has been described in detail in the embodiment of the method, and will not be elaborated on here.

[0171] The Internet of Vehicles data processing device includes a processor and a memory. The above-mentioned first obtaining unit 100, second obtaining unit 200, first storage unit 300, second storage unit 400, third storage unit 500 and fourth storage unit 600 are all stored in the memory as program units, and the processor executes the above-mentioned program units stored in the memory to realize corresponding functions.

[0172] The processor contains a kernel, which retrieves the corresponding program unit from memory. One or more kernels can be configured. By adjusting kernel parameters, massive amounts of connected vehicle data can be effectively collected and aggregated using indicator tags and data dimensions. This reduces the initial data processing required to use connected vehicle data and quickly supports enterprise data business needs.

[0173] An embodiment of the present disclosure provides a computer-readable storage medium having a program stored thereon, which implements the Internet of Vehicles data processing method when executed by a processor.

[0174] An embodiment of the present disclosure provides a processor, which is used to run a program, wherein the vehicle network data processing method is executed when the program is run.

[0175] An embodiment of the present disclosure provides an electronic device comprising at least one processor, at least one memory device connected to the processor, and a bus. The processor and the memory device communicate with each other via the bus. The processor is configured to invoke program instructions stored in the memory device to execute the aforementioned method for processing data in an Internet of Vehicles (IoV). The electronic device herein may include a server, a PC, a PAD, a mobile phone, and the like.

[0176] The present disclosure also provides a computer program product, which, when executed on an electronic device, is suitable for executing a program that initializes the steps of the vehicle network data processing method.

[0177] The present disclosure is described with reference to the flowcharts and / or block diagrams of the methods, apparatuses, electronic devices (systems), and computer program products according to the embodiments of the present disclosure. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable device to produce a machine, so that the instructions executed by the processor of the computer or other programmable device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0178] In a typical configuration, an electronic device includes one or more processors (CPUs), a memory, and a bus. The electronic device may also include an input / output interface, a network interface, and the like.

[0179] Memory may include non-permanent memory in a computer-readable medium, random access memory (RAM), and / or non-volatile memory, such as read-only memory (ROM) or flash RAM. Memory includes at least one memory chip. Memory is an example of a computer-readable medium.

[0180] Computer-readable media includes permanent and non-permanent, removable and non-removable media that can be implemented by any method or technology to store information. The information can be computer-readable instructions, data structures, program modules or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, compact disc read-only memory (CD-ROM), digital versatile disc (DVD) or other optical storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices or any other non-transmission media that can be used to store information that can be accessed by a computing device. As defined herein, computer-readable media does not include transitory computer-readable media (transitory media), such as modulated data signals and carrier waves.

[0181] In the description of the present disclosure, it should be understood that if the terms "up", "down", "front", "back", "left" and "right" are used to indicate directions or positional relationships, they are based on the directions or positional relationships shown in the accompanying drawings. They are only for the convenience of describing the present invention and simplifying the description, and do not indicate or imply that the positions or elements referred to must have a specific direction, be constructed and operate in a specific direction. Therefore, they should not be understood as limitations of the present disclosure.

[0182] It should be noted that, in this document, relational terms such as first and second, etc., are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. It should also be noted that the terms "comprises," "comprising," or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, commodity, or device comprising a series of elements includes not only those elements, but also other elements not explicitly listed, or elements inherent to such process, method, commodity, or device. In the absence of further limitations, an element defined by the phrase "comprising a ..." does not exclude the presence of other identical elements in the process, method, commodity, or device comprising the element.

[0183] Those skilled in the art will appreciate that embodiments of the present disclosure may be provided as methods, systems, or computer program products. Thus, the present disclosure may take the form of a fully hardware embodiment, a fully software embodiment, or an embodiment combining software and hardware. Furthermore, the present disclosure may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0184] The above are merely examples of the present disclosure and are not intended to limit the present disclosure. Various modifications and variations are possible for those skilled in the art. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present disclosure are intended to be included within the scope of the claims of the present disclosure.

Claims

1. A method for processing Internet of Vehicles data, characterized in that: include: Obtaining vehicle status data, vehicle control embedded data, vehicle-mounted device recorded data, and vehicle-related attribute data, wherein the vehicle status data is data reflecting the vehicle's performance status, the vehicle control embedded data is data of user interaction with the vehicle, the vehicle-mounted device recorded data is data recorded by electronic devices in the vehicle, and the vehicle-related attribute data is data reflecting the attribute characteristics of the vehicle and the user attribute characteristics of the user to whom the vehicle belongs; Using the vehicle status data and vehicle control buried point data, obtaining travel data corresponding to each preset indicator to be analyzed; storing the travel data in a first database; Storing the data recorded by the vehicle-mounted device in a second database according to a data storage method corresponding to the data type; storing the vehicle-related attribute data in a third database; According to the preset data dimension, data aggregation processing is performed on the data stored in the first database, the second database and the third database, and the data after the data aggregation processing is stored in the fourth database.

2. The method according to claim 1, characterized in that The method of obtaining travel data corresponding to each preset indicator to be analyzed by utilizing the vehicle status data and vehicle control buried point data includes: Performing preset initialization processing on the vehicle state data and the vehicle control buried point data to obtain first vehicle condition buried point data; Divide the first vehicle condition buried point data into trips according to a preset trip division method to obtain trip division data for each trip; Based on the travel division data, the travel data corresponding to each preset indicator to be analyzed are calculated using the indicator value calculation method corresponding to each preset indicator to be analyzed.

3. The method according to claim 2, characterized in that The performing preset initialization processing on the vehicle status data and the vehicle control buried point data to obtain first vehicle condition buried point data includes: Deleting irregular data from the vehicle status data and the vehicle control buried point data to obtain second vehicle condition buried point data; Performing data reset processing on the second vehicle condition buried point data according to a preset data field threshold processing method to obtain third vehicle condition buried point data; The third vehicle condition buried point data is filtered according to preset common fields to obtain fourth vehicle condition buried point data, wherein the preset common fields are pre-set with calculation fields, and the calculation fields are common fields carrying preset calculation identifiers, and the preset calculation identifiers are used to mark data to be calculated in the fourth vehicle condition buried point data; The first vehicle condition buried point data corresponding to the calculation type field is determined in the fourth vehicle condition buried point data.

4. The method according to claim 3, characterized in that After determining the first vehicle condition buried point data corresponding to the calculation type field in the fourth vehicle condition buried point data, the method further includes: The other vehicle condition buried point data in the fourth vehicle condition buried point data except the first vehicle condition buried point data is stored in a fifth database.

5. The method according to claim 3, characterized in that After calculating the travel data corresponding to each preset indicator to be analyzed based on the travel segmentation data and using the indicator value calculation method corresponding to each preset indicator to be analyzed, the method further includes: Aggregate and count the first vehicle condition buried point data and the travel data according to a preset time dimension to obtain a summary statistical result; The summary statistical results are stored in a sixth database.

6. The method according to claim 1, wherein The vehicle-mounted device recorded data includes voice data, video data, image data, log data, and transaction data. The storing of the vehicle-mounted device recorded data in the second database according to a data storage method corresponding to the data type includes: extracting feature values ​​from the voice data, the video data, and the image data; storing the voice data, the video data, the image data, and the feature value in a second database; The log data and the transaction data are stored in the second database.

7. The method according to claim 1, characterized in that The acquisition of vehicle status data, vehicle control buried point data, vehicle-mounted equipment recording data, and vehicle-related attribute data includes: Obtain vehicle status data and vehicle control point data through the first Kafka middleware; Obtaining the vehicle-mounted device recorded data through the second Kafka middleware and / or the first data interface; The vehicle-related attribute data is obtained through the second data interface.

8. A vehicle network data processing device, characterized in that: include: a first obtaining unit, a second obtaining unit, a first storage unit, a second storage unit, a third storage unit, and a fourth storage unit, The first obtaining unit is configured to obtain vehicle status data, vehicle control buried point data, vehicle-mounted device recorded data, and vehicle-related attribute data, wherein the vehicle status data is data reflecting the vehicle performance status, the vehicle control buried point data is data of user control interaction with the vehicle, the vehicle-mounted device recorded data is data recorded by electronic devices in the vehicle, and the vehicle-related attribute data is data reflecting attribute characteristics of the vehicle and user attribute characteristics of the user to whom the vehicle belongs; The second obtaining unit is configured to obtain travel data corresponding to each preset indicator to be analyzed by using the vehicle status data and the vehicle control buried point data; The first storage unit is used to store the travel data in a first database; The second storage unit is used to store the data recorded by the vehicle-mounted device into a second database according to a data storage method corresponding to the data type; The third storage unit is used to store the vehicle-related attribute data in a third database; The fourth storage unit is used to perform data aggregation processing on the data stored in the first database, the second database and the third database according to a preset data dimension, and store the data after the data aggregation processing in the fourth database.

9. A computer-readable storage medium having a program stored thereon, characterized in that: When the program is executed by a processor, the vehicle network data processing method according to any one of claims 1 to 7 is implemented.

10. An electronic device comprising at least one processor, and at least one memory and a bus connected to the processor; wherein: The processor and the memory communicate with each other via the bus; The processor is used to call the program instructions in the memory to execute the vehicle network data processing method according to any one of claims 1 to 7.

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