Data processing method, device and readable storage medium of vehicle

Abstract

This application provides a vehicle data processing method, apparatus, and readable storage medium. The method includes: acquiring operational data generated by the vehicle during operation, wherein the operational data represents the vehicle's operational state; determining initial event data of the vehicle based on the operational data, wherein the initial event data represents events occurring during the vehicle's operation; determining event identification data of the vehicle based on the initial event data, wherein the event identification data identifies the events; adjusting the initial event data based on the vehicle's target configuration file and the event identification data to obtain target event data, wherein the reliability of the target event data relative to the vehicle is higher than the reliability of the initial event data relative to the vehicle; and storing the target event data in a database. This application solves the technical problem of low data processing efficiency for vehicles.

Description

Technical Field

[0001] This application relates to the field of vehicles, and more specifically, to a data processing method, apparatus, and readable storage medium for vehicles. Background Technology

[0002] Currently, with the application of vehicle-to-everything (V2X) technology, vehicle terminals continuously report massive amounts of signal data in V2X big data analytics scenarios. Moreover, this signal data is typically accessed from the cloud in a semi-structured format and ultimately needs to be processed by the cloud into standardized structured data and stored in a database to provide data for upper-level analytical applications.

[0003] However, in related technologies, methods for processing signal data typically employ hard-coded parsing. When the format of the signal data changes, the parsing code in the aforementioned hard-coded parsing needs to be modified, tested, and deployed again, leading to technical problems such as low data processing efficiency for vehicles.

[0004] There is currently no good solution to the technical problem of low data processing efficiency in the aforementioned vehicles. Summary of the Invention

[0005] This application provides a vehicle data processing method, apparatus, and readable storage medium to at least solve the technical problem of low data processing efficiency in vehicles.

[0006] According to one aspect of the embodiments of this application, a vehicle data processing method is provided, comprising: acquiring operational data generated by the vehicle during operation, wherein the operational data is used to represent the operational state of the vehicle during operation; determining initial event data of the vehicle based on the operational data, wherein the initial event data is used to represent events occurring during the operation of the vehicle; determining event identification data of the vehicle based on the initial event data, wherein the event identification data is used to identify events; adjusting the initial event data based on the vehicle's target configuration file and the event identification data to obtain target event data, wherein the reliability of the target event data relative to the vehicle is higher than the reliability of the initial event data relative to the vehicle; and storing the target event data in a database.

[0007] Furthermore, based on the operational data, the initial event data of the vehicle is determined, including: extracting intermediate event data from the operational data, wherein the degree of normalization of the intermediate event data relative to the vehicle is lower than that of the initial event data relative to the vehicle; and determining the initial event data based on the intermediate event data.

[0008] Furthermore, based on the intermediate event data, the initial event data is determined, including: in response to the intermediate event data being of array type, arranging the intermediate event data to obtain the initial event data; in response to the intermediate event data being of structure type, determining the intermediate event data as the initial event data; in response to the intermediate event data being of string type and including data in a preset format, performing data structure conversion on the intermediate event data, and arranging the converted intermediate event data to obtain the initial event data.

[0009] Furthermore, based on the vehicle's target configuration file and event identifier data, the initial event data is adjusted to obtain target event data. This includes: determining event data templates associated with event identifier data from the target configuration file, wherein the target configuration file includes the association between different event identifier data and different event data templates; different event identifier data includes event identifier data; and the event data template includes template event data to be processed, which represents template events that occurred during the vehicle's historical operation, with the historical operation corresponding to an earlier operation time than the current operation; and adjusting the initial event data based on the event data template and the target configuration file to obtain target event data.

[0010] Furthermore, based on the event data template and target configuration file, the initial event data is adjusted to obtain target event data, including: accessing the storage address of the event data to be combined according to the access policy in the event data template and target configuration file to obtain the event data to be combined, and accessing the storage address of the associated event data of the vehicle to obtain associated event data. The access policy refers to the rules for accessing different storage addresses. The event data to be combined is the sub-event data in the initial event data that is identical to the template event data. The associated event data is used to indicate the time of occurrence of the event and the identifier of the vehicle where the event occurred. The event data to be combined and the associated event data are combined to obtain event data to be processed. Based on the target configuration file and the event data to be processed, the target event data is determined.

[0011] Furthermore, based on the target configuration file and the event data to be processed, the target event data is determined, including: performing data type conversion and time conversion on the event data to be processed according to the target configuration file; and performing data cleaning on the type-converted event data to be processed according to the data cleaning strategy in the target configuration file to obtain the target event data, wherein the data cleaning strategy is used to represent the rules for cleaning the event data to be processed.

[0012] Furthermore, according to the target configuration file, the data type and time conversion of the event data to be processed are performed, including: converting the data type of the event data to be processed according to the target data type in the target configuration file; and converting the time of the converted event data according to the time processing strategy in the target configuration file. The time processing strategy is used to represent the rules for processing the event data according to the timestamp of the event data.

[0013] Furthermore, the method also includes: determining a preset storage object in the target configuration file; converting the format of the preset storage object into the object format required by the storage database to obtain the target storage object; and constructing the storage database in the storage data space according to the target storage object.

[0014] According to another aspect of the embodiments of this application, a vehicle data processing apparatus is also provided, comprising: an acquisition unit, configured to acquire operating data generated by the vehicle during operation, wherein the operating data is used to represent the operating state of the vehicle during operation; a first determination unit, configured to determine initial event data of the vehicle based on the operating data, wherein the initial event data is used to represent events occurring during the operation of the vehicle; a second determination unit, configured to determine event identification data of the vehicle based on the initial event data, wherein the event identification data is used to identify events; an adjustment unit, configured to adjust the initial event data based on the vehicle's target configuration file and the event identification data to obtain target event data, wherein the reliability of the target event data relative to the vehicle is higher than the reliability of the initial event data relative to the vehicle; and a writing unit, configured to store the target event data in a database.

[0015] According to another aspect of the embodiments of this application, a computer-readable storage medium is also provided, the computer-readable storage medium including a stored executable program, wherein, when the executable program is running, it controls the device where the computer-readable storage medium is located to perform the methods of various embodiments of this application.

[0016] According to another aspect of the embodiments of this application, an electronic device is also provided, including: a memory storing an executable program; and a processor for running the program, wherein the program executes the methods in various embodiments of this application when it runs.

[0017] According to another aspect of the embodiments of this application, a vehicle is also provided, including: electronic equipment.

[0018] According to another aspect of the embodiments of this application, a computer program product is also provided, including a computer program that, when executed by a processor, implements the methods of various embodiments of this application.

[0019] According to another aspect of the embodiments of this application, a computer program product is also provided, including a non-volatile computer-readable storage medium storing a computer program that, when executed by a processor, implements the methods in various embodiments of this application.

[0020] According to another aspect of the embodiments of this application, a computer program is also provided, which, when executed by a processor, implements the methods of the various embodiments of this application.

[0021] In this embodiment, operational data generated during vehicle operation is acquired; based on the operational data, initial event data of the vehicle is determined; based on the initial event data, event identification data of the vehicle is determined; based on the target configuration file and event identification data of the vehicle, the initial event data is adjusted to obtain target event data; and the target event data is stored in a database. In other words, in this embodiment, based on the acquired vehicle operational data, the initial event data of the vehicle can be determined, that is, the events occurring during vehicle operation can be determined, and based on the aforementioned events, event identification data used to identify the events can be determined. Then, by combining the target configuration file and event identification data, the initial event data is adjusted to obtain the target event data. Finally, the adjusted target event data is stored in a database, thereby achieving the goal of flexible processing of vehicle data, solving the technical problem of low vehicle data processing efficiency, and realizing the technical effect of improving vehicle data processing efficiency. Attached Figure Description

[0022] The accompanying drawings, which are included to provide a further understanding of this application and form part of this application, illustrate exemplary embodiments and are used to explain this application, but do not constitute an undue limitation of this application. In the drawings:

[0023] Figure 1 This is a flowchart of a vehicle data processing method according to an embodiment of this application;

[0024] Figure 2 This is a schematic diagram of an offline data warehouse system architecture according to an embodiment of this application;

[0025] Figure 3 This is a flowchart of a method for parsing vehicle-end signals according to an embodiment of this application;

[0026] Figure 4 This is a flowchart of a vehicle-side data processing method according to an embodiment of this application;

[0027] Figure 5 This is a schematic diagram of a vehicle data processing device according to an embodiment of this application;

[0028] Figure 6 This is a schematic diagram of an electronic device according to an embodiment of this application. Detailed Implementation

[0029] To enable those skilled in the art to better understand the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present application, and not all embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative effort should fall within the scope of protection of the present application.

[0030] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of this application described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0031] According to an embodiment of this application, a method embodiment for processing vehicle data is provided. It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions. Furthermore, although a logical order is shown in the flowchart, in some cases, the steps shown or described may be executed in a different order than that shown here.

[0032] This embodiment provides a vehicle data processing method. Figure 1 This is a flowchart of a vehicle data processing method according to an embodiment of this application, such as... Figure 1 As shown, the process includes the following steps.

[0033] Step S101: Obtain the operating data generated by the vehicle during operation.

[0034] In the technical solution provided in step S101 of this application, the aforementioned operational data can be used to represent the operational status of the vehicle during the aforementioned operation process. Optionally, the operational data can be uploaded through the vehicle terminal and stored in the Operational Data Store (ODS) layer of the data lake. The operational data can be semi-structured data. For example, the semi-structured data can be JavaScript Object Notation (JSON) format data.

[0035] In this embodiment, operational data generated by the vehicle during operation is acquired. Optionally, the operational data can be read from the ODS layer of the data lake using data read instructions in a distributed big data computing engine (e.g., Apache Spark).

[0036] For example, using commands in Apache Spark, the aforementioned runtime data can be read from the ODS layer of the data lake. These commands can also be used to automatically convert semi-structured data into structured data.

[0037] Step S102: Based on the operational data, determine the initial event data of the vehicle.

[0038] In the technical solution provided in step S102 of this application, the initial event data can be used to represent events that occur during the vehicle's operation. Optionally, the initial event data can be normalized event data from the operating data. For example, the normalized event data can be described using a normalized message body (msg) field, which may include, but is not limited to: normalized driving event data of the vehicle, normalized operating event data of the engine, normalized gear change event data, normalized fault code event data, normalized charging event data, and normalized diagnostic service response event data, etc.

[0039] In this embodiment, after obtaining the operational data, the initial event data of the vehicle can be determined based on the operational data. Optionally, the message body fields in the operational data can be normalized according to a preset normalization rule to obtain the initial event data. The preset normalization rule can be used to normalize message body fields of different data types into a unified type.

[0040] For example, when using a distributed big data computing engine to read JSON format data, the distributed big data computing engine can automatically identify the msg field from the JSON format data, and perform normalization processing on the identified msg field according to preset normalization rules to obtain the normalized msg field.

[0041] Step S103: Based on the initial event data, determine the event identification data of the vehicle.

[0042] In the technical solution provided in step S103 of this application, the event identification data can be used to identify the event. Optionally, the event identification data can be a service code automatically added when the vehicle terminal uploads the event data, used to distinguish the type of the event data. For example, the service code can be a transaction ID (TID).

[0043] In this embodiment, after determining the initial event data, the vehicle's event identification data can be determined based on the initial event data. Optionally, the event identification data can be extracted from the initial event data.

[0044] Optionally, after determining the initial event data, in response to data transformation instructions in the distributed big data computing engine, the initial event data can be converted into a distributed structured data table. Then, in response to the "column reference" instruction in the distributed structured data table, the aforementioned event identifier data can be read.

[0045] Step S104: Based on the vehicle's target configuration file and event identifier data, adjust the initial event data to obtain the target event data.

[0046] In the technical solution provided in step S104 of this application, the target event data can be event data obtained by adjusting the initial event data. The credibility of the target event data relative to the vehicle is higher than the credibility of the initial event data relative to the vehicle. This credibility can be described by data quality requirements; for example, if the data quality requirement of the target event data is higher than a quality requirement threshold, it indicates that the credibility of the target event data is higher than the credibility threshold.

[0047] In this embodiment, the target configuration file can record a data processing strategy in a configuration file format, and the data processing strategy can be used to represent the rules for processing the initial event data. For example, the data processing strategy may include, but is not limited to, event data template mapping strategies, access strategies, data cleaning strategies, and time processing strategies. Optionally, the target configuration file can be in YAML format.

[0048] In this embodiment, after determining the vehicle's event identification data, the initial event data is adjusted based on the vehicle's target configuration file and the event identification data to obtain the target event data. Optionally, using the aforementioned event identification data, a data processing strategy for the initial event data can be determined from the aforementioned target configuration file. Following the aforementioned data processing strategy, processing operations are performed on the initial event data to obtain the aforementioned target event data. These processing operations may include, but are not limited to, mapping, transformation, and cleaning.

[0049] Step S105: Store the target event data in the database.

[0050] In the technical solution provided in step S105 of this application, the database can include: a structured table that supports multiple types of data operations and is oriented towards the data lake. For example, the structured table can be a data lake update engine (Hadoop Upserts, Deletes and Incrementals, abbreviated as Hudi) table.

[0051] In this embodiment, after obtaining the target event data, the target event data can be stored in a database. Optionally, the target event data can be converted into the object format required by the database, and then the converted target event data can be stored in the database.

[0052] Optionally, security verification can be performed on the target event data to obtain a security verification result, which can be used to indicate whether the target event data is normal or abnormal. If the security verification result indicates that the target event data is normal, then the target event data is stored in the database. If the security verification result indicates that the target event data is abnormal, then the target event data is intercepted and protected, and the vehicle terminal is accessed again to obtain new target event data.

[0053] In steps S101 to S105 above, the following steps are performed: Operational data generated during vehicle operation is acquired; initial event data of the vehicle is determined based on the operational data; event identification data of the vehicle is determined based on the initial event data; the initial event data is adjusted based on the vehicle's target configuration file and event identification data to obtain target event data; and the target event data is stored in the database. In other words, in this embodiment, based on the acquired vehicle operational data, the initial event data of the vehicle can be determined, that is, the events occurring during vehicle operation can be determined. Based on these events, event identification data used to identify the events can be determined. Then, by combining the target configuration file and event identification data, the initial event data is adjusted to obtain target event data. Finally, the adjusted target event data is stored in the database. This achieves the goal of flexible processing of vehicle data, solves the technical problem of low vehicle data processing efficiency, and realizes the technical effect of improving vehicle data processing efficiency.

[0054] The data processing method for vehicles in the embodiments of this application will be further described below.

[0055] As an optional implementation method, determining the initial event data of the vehicle based on the operational data includes: extracting intermediate event data from the operational data, wherein the normalization degree of the intermediate event data relative to the vehicle is lower than that of the initial event data relative to the vehicle; and determining the initial event data based on the intermediate event data.

[0056] In this embodiment, the intermediate event data can be the original event data from the aforementioned operational data. For example, the original event data can be described using the original msg field. The intermediate event data has a lower degree of standardization relative to the vehicle than the initial event data.

[0057] In this embodiment, the aforementioned original msg field may include, but is not limited to: the vehicle's original driving event data, the engine's original operating event data, the original gear change event data, the original fault code event data, the original charging event data, and the original diagnostic service response event data, etc.

[0058] In this embodiment, after obtaining the runtime data, intermediate event data can be extracted from it. Optionally, when using a distributed big data computing engine to read the runtime data, the distributed big data computing engine can automatically identify the original msg field from the runtime data, separate the original msg field from the runtime data, and obtain the intermediate event data.

[0059] In this embodiment, after obtaining the intermediate event data, the initial event data can be determined based on the intermediate event data. Optionally, the initial event data can be obtained by normalizing the intermediate event data according to a preset normalization rule.

[0060] In this embodiment, by extracting intermediate event data from the running data and standardizing the intermediate event data, the initial event data can be determined, thereby achieving the goal of determining the initial event data and realizing the technical effect of improving the standardization of the initial event data.

[0061] As an optional implementation method, determining initial event data based on intermediate event data includes: arranging the intermediate event data to obtain initial event data in response to the intermediate event data being of array type; determining the intermediate event data as initial event data in response to the intermediate event data being of structure type; and performing data structure conversion on the intermediate event data in response to the intermediate event data being of string type and including data in a preset format, and arranging the converted intermediate event data to obtain initial event data.

[0062] In this embodiment, the array type (ArrayType) described above can be used to represent an ordered, repeatable collection of elements.

[0063] In this embodiment, the above-mentioned structure type can be used to represent a composite data structure type with multiple named fields.

[0064] In this embodiment, the string type (StringType) can be a text data type and can be used to store character sequences of arbitrary length.

[0065] In this embodiment, the data format of the aforementioned preset format data can be a pre-defined format. Optionally, the aforementioned pre-defined format can be JSON format.

[0066] In this embodiment, after obtaining the intermediate event data, in response to the intermediate event data being of array type, the intermediate event data is arranged to obtain the initial event data. Optionally, the data type of the intermediate event data is compared with array type, structure type, and string type. If the comparison shows that the data type of the intermediate event data is array type, the expand function in the distributed big data computing engine is used to expand the intermediate event data into multiple independent rows of data to obtain the expanded intermediate event data. The expanded intermediate event data is then determined as the aforementioned initial event data.

[0067] In this embodiment, after obtaining the intermediate event data, in response to the data type of the intermediate event data being a structure type, the intermediate event data can be determined as the initial event data. Optionally, the data type of the intermediate event data is compared with array type, structure type, and string type. If the comparison shows that the data type of the intermediate event data is a structure type, then there is no need to process the intermediate data type, and the intermediate event data can be directly determined as the initial event data.

[0068] In this embodiment, after obtaining the intermediate event data, in response to the intermediate event data being of string type and including data in a preset format, the intermediate event data undergoes data structure conversion, and the converted intermediate event data is arranged to obtain the initial event data. Optionally, the data type of the intermediate event data is compared with array type, structure type, and string type. If the comparison reveals that the intermediate event data is of string type, it is determined whether the intermediate event data includes data in a preset format. If it is determined that the intermediate event data includes data in JSON format, a JSON parsing function can be used to convert the intermediate event data into a structured array, and the explode function can be used to expand the structured array into multiple independent rows of data to obtain the initial event data.

[0069] In this embodiment, when the data type of the intermediate event data is an array type, a structure type, or a string type, the intermediate event data of different data types can be normalized into initial event data of a unified type by using preset normalization rules. This achieves the purpose of determining the initial event data and thus realizes the technical effect of improving the normalization of the initial event data.

[0070] As an optional embodiment, the initial event data is adjusted based on the vehicle's target configuration file and event identifier data to obtain target event data. This includes: determining event data templates associated with event identifier data from the target configuration file, wherein the target configuration file includes the association between different event identifier data and different event data templates, the different event identifier data includes event identifier data, and the event data template includes template event data to be processed, the template event data being used to represent template events that occurred during the vehicle's historical operation, the operation time corresponding to the historical operation being earlier than the operation time corresponding to the current operation; and adjusting the initial event data based on the event data template and the target configuration file to obtain the target event data.

[0071] In this embodiment, the target configuration file may include the association between different event identifier data and different event data templates. Optionally, the association may be a predefined mapping relationship between different event identifier data and different event data templates.

[0072] In this embodiment, the aforementioned different event identifier data are event identifier data of different types. Optionally, the aforementioned different event identifier data may include different types of TIDs.

[0073] In this embodiment, the aforementioned event data template may include: template event data to be processed. Optionally, the aforementioned template event data may be used to represent template events that occurred during the vehicle's historical operation. The historical operation time is earlier than the current operation time.

[0074] In this embodiment, after obtaining the event identifier data, the event data template associated with the event identifier data can be determined from the target configuration file. Optionally, after obtaining the event identifier data, the mapping relationship between different event identifier data and different event data templates can be determined from the target configuration file. Then, the event data template that satisfies the above mapping relationship can be determined; the determined event data template is the event data template associated with the event identifier data.

[0075] In this embodiment, after obtaining the event data template, the initial event data is adjusted based on the event data template and the target configuration file to obtain the target event data. Optionally, after obtaining the event data template, the initial event data is processed according to the event data template and the processing strategy indicated in the target configuration file to obtain the target event data.

[0076] In this embodiment, by determining the event data template associated with the event identifier data in the target configuration file, and by adjusting the initial event data according to the event data template and the target configuration file, the purpose of determining the target event data is achieved, thereby realizing the technical effect of improving the data quality of the target event data.

[0077] As an optional implementation, the initial event data is adjusted based on the event data template and the target configuration file to obtain target event data. This includes: accessing the storage address of the event data to be combined according to the access policy in the event data template and the target configuration file to obtain the event data to be combined, and accessing the storage address of the associated event data of the vehicle to obtain associated event data. The access policy is a rule for accessing different storage addresses. The event data to be combined is the sub-event data in the initial event data that is the same as the template event data. The associated event data is used to indicate the time of occurrence of the event and the identifier of the vehicle where the event occurred. The event data to be combined and the associated event data are combined to obtain event data to be processed. The target event data is determined based on the target configuration file and the event data to be processed.

[0078] In this embodiment, the access strategy can be a rule for accessing different storage addresses. Optionally, the access strategy can be embodied in a pre-configured access expression. For example, the access expression can be a JSON path expression.

[0079] In this embodiment, the event data to be combined can be sub-event data that is identical to the template event data in the initial event data. Optionally, the sub-event data can be used to represent a set of multiple data that are identical to each data item in the template event data in the initial event data.

[0080] In this embodiment, the aforementioned associated event data can be used to represent the time of occurrence of the event and the identifier of the vehicle in which the event occurred. Optionally, the aforementioned associated event data may include, but is not limited to, vehicle identification number, data creation time, version number, and data source.

[0081] In this embodiment, the aforementioned occurrence time can be the data creation time of the aforementioned intermediate event data, and the vehicle identification can be represented by a vehicle identification number. For example, the aforementioned vehicle identification number can be a Vehicle Identification Number (VIN).

[0082] In this embodiment, the event data to be processed can be event data resulting from merging the event data to be combined and the associated event data. Optionally, the events to be processed can be presented as a set of fields to be processed.

[0083] In this embodiment, after obtaining the event data template, the storage address of the event data to be combined is accessed according to the access policy in the event data template and the target configuration file to obtain the event data to be combined. Similarly, the storage address of the vehicle's associated event data is accessed to obtain the associated event data. Optionally, after obtaining the event data template, the storage address of the event data to be combined in the initial event data is accessed using the JSONPath expression in the target configuration file to obtain the event data to be combined. Furthermore, the storage address of the associated event data in the running data is accessed using the JSONPath expression to obtain the associated event data.

[0084] In this embodiment, after obtaining the event data to be combined and the associated event data, the event data to be combined and the associated event data are combined to obtain the event data to be processed. Optionally, the event data to be combined and the associated event data can be merged using the join operator of the extensible language (Scala) in the distributed big data computing engine to obtain the event data to be processed.

[0085] In this embodiment, after obtaining the event data to be processed, the target event data can be determined based on the target configuration file and the event data to be processed. Optionally, the event data to be processed can be processed according to the processing rules in the target configuration file to obtain the target event data.

[0086] In this embodiment, by following the access policy, event data to be combined and associated event data can be obtained. By merging the event data to be combined and associated event data, event data to be processed can be obtained. Furthermore, based on the target configuration file and the event data to be processed, target event data can be determined. This achieves the goal of determining target event data and thus realizes the technical effect of improving the data quality of target event data.

[0087] As an optional implementation method, the target event data is determined based on the target configuration file and the event data to be processed, including: performing data type conversion and time conversion on the event data to be processed according to the target configuration file; and performing data cleaning on the type-converted event data to be processed according to the data cleaning strategy in the target configuration file to obtain the target event data, wherein the data cleaning strategy is used to represent the rules for cleaning the event data to be processed.

[0088] In this embodiment, the data cleaning strategy described above can be used to represent the rules for cleaning the event data to be processed. Optionally, the data cleaning strategy may include, but is not limited to: null value checking strategy, specific pattern filtering strategy, data range validation strategy, and regular expression matching strategy, etc.

[0089] In this embodiment, after obtaining the event data to be processed, the data type and time can be converted according to the target configuration file. Optionally, after obtaining the event data to be processed, the data type and timestamp of the event data to be processed can be converted according to the processing rules in the target configuration file to obtain the type-converted event data to be processed.

[0090] In this embodiment, after obtaining the type-converted event data to be processed, the data is cleaned according to the data cleaning strategy in the target configuration file to obtain the target event data. Optionally, after obtaining the type-converted event data to be processed, at least one of the following cleaning operations is performed on the type-converted event data to obtain the target event data, according to the above data cleaning strategy: null value checking, specific pattern filtering, data range validation, and regular expression matching, etc.

[0091] In this embodiment, after performing data type conversion and time conversion on the event data to be processed according to the target configuration file, the event data to be processed after type conversion can be obtained. Then, the event data to be processed after type conversion is cleaned according to the data cleaning strategy to obtain the target event data. This achieves the purpose of determining the target event data and thus realizes the technical effect of improving the data quality of the target event data.

[0092] As an optional implementation method, the data type and time conversion of the event data to be processed are performed according to the target configuration file, including: converting the data type of the event data to be processed according to the target data type in the target configuration file; and converting the time of the converted event data according to the time processing strategy in the target configuration file, wherein the time processing strategy is used to represent the rules for processing the event data to be processed according to the timestamp of the event data to be processed.

[0093] In this embodiment, the time processing strategy described above can be used to represent the rules for processing the event data to be processed according to its timestamp. Optionally, the time processing strategy can also be called an intelligent timestamp processing strategy, which can be used to convert the precision of the timestamp.

[0094] In this embodiment, the target data type can be the data type that each piece of data in the target configuration file must satisfy. Optionally, the target data type can be a floating-point number (float) type.

[0095] In this embodiment, after obtaining the event data to be processed, the data type of the event data to be processed can be converted according to the target data type in the target configuration file. Optionally, after obtaining the event data to be processed, each data item in the event data to be processed can be converted from its original data type to the target data type according to the aforementioned target data type to obtain the type-converted event data to be processed.

[0096] For example, when the data in the event to be processed includes integer (int), boolean (bool), floating-point (float), and string data, data type conversion can force the data in the event to be processed from its original data type to float data.

[0097] In this embodiment, after obtaining the type-converted event data to be processed, the time of the type-converted event data can be converted according to the time processing strategy in the target configuration file. Optionally, according to the above time processing strategy, the timestamps in the type-converted event data to be processed can be uniformly converted into millisecond-level precision timestamps.

[0098] For example, when the precision of the timestamps includes seconds, milliseconds, microseconds, and nanoseconds, performing time conversion on the event data to be processed that has undergone the above type conversion can uniformly convert the timestamps to millisecond-precision timestamps.

[0099] In this embodiment, according to the target data type and time processing strategy in the target configuration file, the data to be processed can be converted in type and time, thereby achieving the goal of unifying the data type and timestamp precision in the data to be processed, and thus realizing the technical effect of improving the data processing efficiency of the data to be processed.

[0100] As an optional embodiment, the method further includes: determining a preset storage object in the target configuration file; converting the format of the preset storage object into the object format required by the storage database to obtain the target storage object; and constructing the storage database in the storage data space according to the target storage object.

[0101] In this embodiment, the aforementioned preset storage object can be used to represent predefined table structure parameters in the target configuration file. Optionally, the aforementioned table structure parameters can be Hudi table structure parameters.

[0102] In this embodiment, the aforementioned storage data space can be used to store the aforementioned target storage object. Optionally, the aforementioned storage data space can be a data lake.

[0103] In this embodiment, a preset storage object is determined in the target configuration file. Optionally, the preset storage object can be obtained by extracting the Hudi table structure parameters from the target configuration file.

[0104] In this embodiment, after obtaining the preset storage object, the format of the preset storage object is converted into the object format required by the storage database to obtain the target storage object. Optionally, the Hudi table structure parameters can be converted according to the format required by the Hudi table to obtain the aforementioned target storage object.

[0105] In this embodiment, after obtaining the target storage object, a storage database can be constructed in the storage data space according to the target storage object. Optionally, the Hudi table can be constructed in the data lake according to the format required by the Hudi table.

[0106] In this embodiment, operational data generated during vehicle operation is acquired; based on the operational data, initial event data of the vehicle is determined; based on the initial event data, event identification data of the vehicle is determined; based on the target configuration file and event identification data of the vehicle, the initial event data is adjusted to obtain target event data; and the target event data is stored in a database. In other words, in this embodiment, based on the acquired vehicle operational data, the initial event data of the vehicle can be determined, that is, the events occurring during vehicle operation can be determined, and based on the aforementioned events, event identification data used to identify the events can be determined. Then, by combining the target configuration file and event identification data, the initial event data is adjusted to obtain the target event data. Finally, the adjusted target event data is stored in a database, thereby achieving the goal of flexible processing of vehicle data, solving the technical problem of low vehicle data processing efficiency, and realizing the technical effect of improving vehicle data processing efficiency.

[0107] The above technical solutions of the present application will be further illustrated below with reference to preferred embodiments of the present application.

[0108] Currently, in the context of vehicle-to-everything (V2X) big data analytics, vehicle terminals continuously report massive amounts of signal data. Moreover, this signal data is typically received from the cloud in a semi-structured format and ultimately needs to be processed by the cloud into standardized structured data and stored in a database to provide data for higher-level analytical applications.

[0109] However, in related technologies, the method for processing signal data usually adopts hard-coded parsing. That is, when the format of signal data changes, the parsing code in the above-mentioned hard-coded parsing needs to be modified, tested and deployed again, which leads to the technical problem of low data processing efficiency of vehicles.

[0110] However, this application proposes a vehicle data processing method that acquires operational data generated during vehicle operation; determines initial event data based on the operational data; determines event identification data based on the initial event data; adjusts the initial event data based on the vehicle's target configuration file and event identification data to obtain target event data; and stores the target event data in a database. In other words, in this application embodiment, based on the acquired vehicle operational data, the initial event data of the vehicle can be determined, that is, the events occurring during vehicle operation can be determined. Based on the aforementioned events, event identification data used to identify the events can be determined. Then, by combining the target configuration file and event identification data, the initial event data is adjusted to obtain the target event data. Finally, the adjusted target event data is stored in a database. This achieves the goal of flexible processing of vehicle data, solves the technical problem of low vehicle data processing efficiency, and realizes the technical effect of improving vehicle data processing efficiency.

[0111] Figure 2 This is a schematic diagram of an offline data warehouse system architecture according to an embodiment of this application, such as... Figure 2 As shown, the system architecture may include: cloud data access layer 201, operation data storage layer 202, configuration center 203, dynamic configuration loading module 204, core dynamic parsing engine 205, and data warehouse detail layer 206.

[0112] The cloud data access layer 201 is used to receive operational data reported by the vehicle. This operational data is in JSON format.

[0113] Operational data storage layer 202 is used to store runtime data in JSON format received by cloud data access layer 201.

[0114] Configuration Center 203 is used to configure YAML configuration files. These YAML configuration files can include: basic field mappings, message structure definitions, signal templates and TID mappings, table configurations, and data cleaning rules, etc.

[0115] The dynamic configuration loading module 204 is used to deserialize YAML configuration files into usable structured objects using case class functions (e.g., Scala Case Class functions).

[0116] The core dynamic parsing engine 205 is used to parse the aforementioned runtime data using dynamic parsing functions (e.g., processJsonData). This parsing may include: normalizing the message structure, dynamically extracting required data from the runtime data using JSONPath, applying mapped signal templates using TIDs, data type conversion, and data cleaning.

[0117] The data warehouse detail layer 206 is used to store the data parsed by the core dynamic parsing engine 205 for use by upper-layer analysis applications.

[0118] In this embodiment, a YAML configuration file is configured through a configuration center, and the YAML configuration file is serialized into a usable structured object using a dynamic configuration loading module. Then, the core dynamic parsing engine dynamically parses the runtime data in the operational data storage layer according to the serialized YAML configuration file. Finally, the parsed data is stored in the Data Warehouse Detail (DWD) layer. This achieves the goal of flexibly processing vehicle data, solves the technical problem of low data processing efficiency in vehicles, and realizes the technical effect of improving vehicle data processing efficiency.

[0119] Figure 3 This is a flowchart illustrating a method for parsing vehicle-side signals according to an embodiment of this application. Figure 3 As shown, the method may include the following steps.

[0120] Step S301: Construct a dynamic configuration system.

[0121] In this embodiment, a dynamic configuration system is constructed, which abstracts and encapsulates all business logic, data mapping rules, and data processing parameters through a structured YAML configuration file. The configuration content may include the following parts.

[0122] The business representation configuration uniquely identifies a specific data processing scenario through fields such as service identifier (service_id), model (model), and TID, ensuring the isolation of different business scenarios.

[0123] The data table naming rules adopt a template-based approach to define the naming patterns of input and output tables, and support dynamic replacement of business parameters through placeholders to achieve table-level isolation in a multi-tenant environment.

[0124] The field mapping definition can be divided into three levels: global basic fields, message basic fields, and signal template fields. Global basic fields define metadata fields common to all records, such as data creation time and vehicle unique identifiers, specifying their extraction path in the source data via JSONPath. Message basic fields define common metadata fields within messages, such as TID, version number, and data source; these fields are crucial for understanding the data context. Signal template fields define reusable templates for grouping business-related measurement signals, with each signal field explicitly specifying its data type, data path, and business meaning.

[0125] The template mapping mechanism establishes a mapping relationship between TIDs and signal templates, allowing the same set of processing programs to dynamically select the set of signals to be parsed based on the actual transaction type in the data.

[0126] The target table structure configuration allows for detailed definition of the physical characteristics of the published data tables. These physical characteristics can include: storage engine type, primary key constraints, partitioning strategy, data distribution method, and table-level attribute parameters.

[0127] The data quality cleaning rules provide a set of pluggable data cleaning component configurations, supporting various quality control rules. These cleaning rules can include: prefix matching, suffix matching, regular expression matching, specific pattern filtering, numerical range validation, and null value checking, among others.

[0128] Step 302, System initialization and configuration loading.

[0129] In this embodiment, when the Apache Spark job starts, the offline data warehouse system first reads a YAML-formatted configuration file from a predefined location. This predefined location can include: a distributed file system (Hadoop Distributed File System, HDFS), Amazon Simple Storage Service (S3), and a configuration center, etc. Then, a configuration parsing library is used to deserialize the text configuration into an in-memory configuration object model. This series of configuration classes (e.g., DynamicTableConfig, FieldConfig, etc.) precisely maps the structure of the YAML file, providing type-safe configuration access for subsequent data processing.

[0130] Optionally, to efficiently share configuration information within the cluster, the system encapsulates the parsed configuration object as a broadcast variable. This mechanism ensures that each compute node can access the configuration content locally, avoiding duplicate transmission of configuration information and significantly improving processing performance in large-scale cluster environments.

[0131] Step S303: Raw data reading and preprocessing.

[0132] In this embodiment, the system reads raw JSON-formatted data from the ODS layer of the data lake. This data is typically stored in a Hudi-based dynamic partitioning table, preserving the most original form of the data reported by the vehicle.

[0133] In this embodiment, the reading process filters based on the input table name and partition conditions specified in the configuration to ensure that only valid data within the target time period is processed.

[0134] Step S304: Dynamic identification and unified processing of message structure.

[0135] In this embodiment, the system automatically identifies the actual data type of the msg field through a runtime detection mechanism and adopts corresponding normalization strategies based on different types. Optionally, the above normalization strategies may include the following.

[0136] When the msg field is detected to be of array type (ArrayType), the system uses Spark's explode function to expand it into multiple rows, with each element becoming an independent record.

[0137] When the msg field is detected to be of structure type, the system directly uses the original field, preserving its nested structure.

[0138] When the system detects that the msg field is of string type (StringType) but actually contains JSON content, it will first use a JSON parsing function to convert it into a structured array before performing the expansion operation.

[0139] In this embodiment, through intelligent type recognition and unified processing mechanisms, it can be ensured that no matter what format the upstream data adopts, it can eventually be standardized into a unified flat structure, laying the foundation for subsequent field extraction.

[0140] Step S305: Extract the source data value of the field based on the transaction type field.

[0141] In this embodiment, the system extracts the TID field from the normalized data and then finds the signal template corresponding to the TID based on the predefined mapping relationship in the configuration. This mapping mechanism is key to achieving multi-vehicle adaptation; different vehicle models or business scenarios can reuse the same set of processing logic by configuring different mapping relationships.

[0142] After locating the signal template corresponding to the TID, the system merges field definitions from three levels: global basic fields, message basic fields, and signal template fields, forming a complete set of fields to be processed. For each field in the set, the system uses its configured JSONPath expression to extract the value from the corresponding location in the source data. This declarative field extraction method completely avoids hard coding, allowing field additions, deletions, and modifications to take effect simply by adjusting the configuration file.

[0143] Step S306, Data type conversion and quality enhancement.

[0144] In this embodiment, the system performs a forced type conversion on the extracted raw values ​​according to the target data type specified in the configuration. This process ensures that the output data has a strict and consistent data type, providing a reliability guarantee for subsequent numerical calculations and analysis.

[0145] After performing a type conversion on the extracted raw values, the system performs intelligent processing of the timestamps. By analyzing the bit characteristics of the timestamp values, the system automatically identifies their original precision and converts them to standard millisecond-level precision. Then, based on this standard timestamp, a date partitioning field is generated, facilitating time-range-based data querying and lifecycle management. The aforementioned original precision can include seconds, milliseconds, microseconds, and nanoseconds, etc.

[0146] Step S307, Configurable data quality cleaning.

[0147] In this embodiment, the system performs quality checks and filters on the data during processing according to the data cleaning rules defined in the configuration. These rules are organized in a plug-in manner and can be selectively enabled or disabled according to specific data quality requirements.

[0148] In this embodiment, the system uses cleaning rules such as prefix matching, suffix matching, regular expression matching, specific pattern filtering, numerical range validation, and null value checking to ensure that the data entering the detailed layer of the data warehouse meets the predetermined quality standards.

[0149] Step S308: Dynamic table creation and data import.

[0150] In this embodiment, the system automatically creates or updates Hudi tables in the target data lake based on the table structure parameters defined in the configuration. The creation or update process may include setting appropriate partitioning strategies, defining primary key constraints, configuring data merging parameters, and setting the table's physical storage attributes.

[0151] In this embodiment, the above-described creation or update process is implemented by converting the configuration object into the corresponding option of the Hudi data source, thus avoiding the need to manually write DDL statements.

[0152] Step S309: Deploy and run the job scheduling system.

[0153] In this embodiment, the above steps are packaged into a Spark JAR job and configured to be executed periodically by a job scheduling system (e.g., Apache Airflow or Azure Data Factory). When a new model needs to be processed, simply prepare a new YAML configuration file and submit a Spark job instance pointing to the newly configured one; the core code requires no modification.

[0154] In this embodiment, the system writes the fully processed data into the target Hudi table. The writing process automatically handles data duplication, updates, and deletions, ensuring the accuracy and consistency of the data warehouse detail layer data. The output table is named using a templated rule defined in the configuration, generating business-meaning physical table names through parameter substitution.

[0155] In steps S301 to S309 above, by constructing a destructured configuration file, and based on this configuration file, processing operations such as unification, mapping, merging, data type conversion, and data quality cleaning can be performed on the read raw data to obtain processed data, which is then written into the Hudi table. Finally, the above content is packaged into a wrapper job. When processing raw data for different vehicle models, only a new configuration file needs to be constructed. This achieves the goal of flexible processing of vehicle data, solves the technical problem of low data processing efficiency for vehicles, and realizes the technical effect of improving the efficiency of vehicle data processing.

[0156] Figure 4 This is a flowchart of a vehicle-side data processing method according to an embodiment of this application. Figure 4 As shown, the method includes the following steps.

[0157] Step S401, Configuration definition and loading.

[0158] In this embodiment, the following content can be defined by defining a configuration file and loading the configuration file.

[0159] Basic field mapping, fields common to all data records (e.g., vehicle VIN code, data creation time).

[0160] The message structure definition defines the basic metadata fields in the msg (e.g., TID, version number).

[0161] Signal template definition groups and defines business-related signal fields (such as vehicle speed and torque status) into reusable templates.

[0162] TID mapping establishes the association between TID and signal template, enabling dynamic selection of the signal set to be parsed based on TID.

[0163] Table configuration defines the structure of the output table, such as primary key, partitioning strategy, and distributed method.

[0164] Data cleaning rules define a series of pluggable data quality checks and filtering rules (e.g., null value checks, prefix filtering, regular expression matching, etc.).

[0165] Step S402, dynamic parsing and flattening.

[0166] In this embodiment, the `processJsonData` function can be used to process the raw data to obtain structured data. This processing can include the following:

[0167] Message structure standardization begins with the function intelligently determining the actual data type of the `msg` field in the original data. When the data type is an array, the `explode` function can be used to expand the `msg` field; when the data type is a string, a JSON parsing function is used to parse the `msg` field into a structure, and then the `explode` function is used to expand it. This step ensures consistent processing regardless of changes in the upstream data format.

[0168] Configure the field extraction driver. Based on the "basic field mapping" and "message structure definition" defined in the configuration, use JSONPath to dynamically extract the corresponding values ​​from the JSON structure and create a new data column for each value.

[0169] The TID-driven template application looks up the configuration mapping based on the TID in the data, dynamically loads the corresponding signal template, and also uses JSONPath to extract all signal fields defined in the template.

[0170] Dynamic data type conversion: Based on the data type of each field in the configuration, the extracted string value can be converted into the target data type.

[0171] Intelligent timestamp processing identifies the precision of all timestamps in the original data, converts all timestamps to millisecond precision, and further generates date fields for partitioning.

[0172] Step S403: Automated table management and data writing.

[0173] In this embodiment, based on the configuration in "Table Configuration", the system can dynamically generate Data Definition Language (DDL) statements to create optimized tables with specified partitions, primary keys and attributes in the target data lake (e.g., a Hudi-enabled data lake), and write the processed structured data into them, automatically completing the construction from the ODS layer to the DWD layer.

[0174] In steps S401 to S403 above, by defining and loading the configuration file, the raw data can be parsed and flattened to obtain structured data. Finally, the structured data is written into the Hudi table, thereby achieving the goal of flexibly processing vehicle data, solving the technical problem of low data processing efficiency of vehicles, and realizing the technical effect of improving the data processing efficiency of vehicles.

[0175] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, data stored, data displayed, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties. Furthermore, the collection, use and processing of the relevant data must comply with the relevant laws, regulations and standards of the relevant countries and regions, and corresponding operation entry points are provided for users to choose to authorize or refuse.

[0176] According to an embodiment of this application, a device embodiment for a vehicle data processing apparatus is provided. It should be noted that the apparatus can be used to execute the above-described vehicle data processing method. Figure 5 This is a schematic diagram of a vehicle data processing device according to an embodiment of this application. Figure 5 As shown, the data processing device 500 of the vehicle may include: an acquisition unit 501, a first determination unit 502, a second determination unit 503, an adjustment unit 504, and a writing unit 505.

[0177] The acquisition unit 501 is used to acquire the operating data generated by the vehicle during operation, wherein the operating data is used to represent the operating status of the vehicle during operation.

[0178] The first determining unit 502 is used to determine the initial event data of the vehicle based on the operating data, wherein the initial event data is used to represent the events that occur during the operation of the vehicle.

[0179] The second determining unit 503 is used to determine the event identification data of the vehicle based on the initial event data, wherein the event identification data is used to identify the event.

[0180] The adjustment unit 504 is used to adjust the initial event data based on the vehicle's target configuration file and event identifier data to obtain target event data, wherein the reliability of the target event data relative to the vehicle is higher than that of the initial event data relative to the vehicle.

[0181] The write unit 505 is used to store the target event data into the database.

[0182] Optionally, the first determining unit 502 is further configured to: extract intermediate event data from the operating data, wherein the degree of standardization of the intermediate event data relative to the vehicle is lower than that of the initial event data relative to the vehicle; and determine the initial event data based on the intermediate event data.

[0183] Optionally, the first determining unit 502 is further configured to: in response to the intermediate event data being of array type, arrange the intermediate event data to obtain initial event data; in response to the intermediate event data being of structure type, determine the intermediate event data as initial event data; in response to the intermediate event data being of string type and the intermediate event data including preset format data, perform data structure conversion on the intermediate event data and arrange the structure-converted intermediate event data to obtain initial event data.

[0184] Optionally, the adjustment unit 504 is further configured to: determine, from the target configuration file, an event data template associated with the event identifier data, wherein the target configuration file includes: the association relationship between different event identifier data and different event data templates, the different event identifier data includes: event identifier data, the event data template includes: template event data to be processed, the template event data is used to represent template events that occurred during the vehicle's historical operation, the operation time corresponding to the historical operation is earlier than the operation time corresponding to the current operation; and adjust the initial event data based on the event data template and the target configuration file to obtain the target event data.

[0185] Optionally, the adjustment unit 504 is further configured to: access the storage address of the event data to be combined according to the access strategy in the event data template and the target configuration file to obtain the event data to be combined, and access the storage address of the associated event data of the vehicle to obtain the associated event data, wherein the access strategy is the rule for accessing different storage addresses, the event data to be combined is the sub-event data in the initial event data that is the same as the template event data, and the associated event data is used to indicate the time of occurrence of the event and the identifier of the vehicle where the event occurred; combine the event data to be combined and the associated event data to obtain the event data to be processed; and determine the target event data based on the target configuration file and the event data to be processed.

[0186] Optionally, the adjustment unit 504 is further configured to: perform data type conversion and time conversion on the event data to be processed according to the target configuration file; and perform data cleaning on the type-converted event data to be processed according to the data cleaning strategy in the target configuration file to obtain the target event data, wherein the data cleaning strategy is used to represent the rules for cleaning the event data to be processed.

[0187] Optionally, the adjustment unit 504 is further configured to: convert the data type of the event data to be processed according to the target data type in the target configuration file; and convert the time of the converted event data according to the time processing strategy in the target configuration file, wherein the time processing strategy is used to represent the rules for processing the event data according to the timestamp of the event data.

[0188] Optionally, the vehicle's data processing device 500 can also be used to: determine a preset storage object in a target configuration file; convert the format of the preset storage object into the object format required by the storage database to obtain the target storage object; and construct a storage database in the storage data space according to the target storage object.

[0189] In the vehicle data processing apparatus described in this application embodiment, an acquisition unit is used to acquire operational data generated by the vehicle during operation; a first determination unit is used to determine initial event data of the vehicle based on the operational data; a second determination unit is used to determine event identification data of the vehicle based on the initial event data; an adjustment unit is used to adjust the initial event data based on the vehicle's target configuration file and event identification data to obtain target event data; and a writing unit is used to store the target event data in a database. In other words, in this application embodiment, based on the acquired vehicle operational data, the initial event data of the vehicle can be determined, that is, the events occurring during the vehicle's operation can be determined, and based on the aforementioned events, event identification data used to identify the events can be determined. Then, by combining the target configuration file and event identification data, the initial event data is adjusted to obtain the target event data. Finally, the adjusted target event data is stored in a database, thereby achieving the goal of flexible processing of vehicle data, solving the technical problem of low vehicle data processing efficiency, and realizing the technical effect of improving vehicle data processing efficiency.

[0190] Embodiments of this application also provide a computer-readable storage medium including a stored executable program, wherein, when the executable program is running, it controls the device where the computer-readable storage medium is located to perform the methods of various embodiments of this application.

[0191] Embodiments of this application also provide an electronic device. Figure 6 This is a schematic diagram of an electronic device according to an embodiment of this application. Figure 6 As shown, the electronic device 600 may include a memory 601 and a processor 602. The memory 601 stores an executable program; the processor 602 is used to run the executable program stored in the memory 601, wherein the program executes the methods described in various embodiments of this application during runtime.

[0192] Embodiments of this application also provide a vehicle, including: electronic equipment.

[0193] Embodiments of this application also provide a computer program product, including a computer program that, when executed by a processor, implements the methods of various embodiments of this application.

[0194] Embodiments of this application also provide a computer program product, including a non-volatile computer-readable storage medium for storing a computer program that, when executed by a processor, implements the methods in various embodiments of this application.

[0195] Embodiments of this application also provide a computer program that, when executed by a processor, implements the methods described in the various embodiments of this application.

[0196] In the above embodiments of this application, the descriptions of each embodiment have different focuses. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0197] In the several embodiments provided in this application, it should be understood that the disclosed technical content can be implemented in other ways. The device embodiments described above are merely illustrative; for example, the division of units can be a logical functional division, and in actual implementation, there may be other division methods. For instance, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the displayed or discussed mutual coupling, direct coupling, or communication connection may be through some interfaces; the indirect coupling or communication connection between units or modules may be electrical or other forms.

[0198] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0199] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0200] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, read-only memory (ROM), random access memory (RAM), portable hard drives, magnetic disks, or optical disks.

[0201] The above description is only a preferred embodiment of this application. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the principle of this application, and these improvements and modifications should also be considered within the scope of protection of this application.

Claims

1. A vehicle data processing method, characterized in that, include: Acquire operational data generated during vehicle operation, wherein the operational data is used to represent the operational status of the vehicle during the operation process; Based on the operational data, initial event data of the vehicle is determined, wherein the initial event data is used to represent events that occur in the vehicle during the operation; Based on the initial event data, event identification data of the vehicle is determined, wherein the event identification data is used to identify the event; Based on the target configuration file of the vehicle and the event identification data, the initial event data is adjusted to obtain target event data, wherein the reliability of the target event data relative to the vehicle is higher than that of the initial event data relative to the vehicle. The target event data is stored in the database.

2. The method according to claim 1, characterized in that, Based on the operational data, the initial event data of the vehicle is determined, including: Intermediate event data is extracted from the operational data, wherein the degree of normalization of the intermediate event data relative to the vehicle is lower than that of the initial event data relative to the vehicle. The initial event data is determined based on the intermediate event data.

3. The method according to claim 2, characterized in that, Based on the intermediate event data, the initial event data is determined, including: In response to the fact that the data type of the intermediate event data is an array type, the intermediate event data is arranged to obtain the initial event data; In response to the fact that the data type of the intermediate event data is a structure type, the intermediate event data is determined as the initial event data; In response to the fact that the data type of the intermediate event data is string and the intermediate event data includes data in a preset format, the intermediate event data is subjected to data structure transformation, and the intermediate event data after structure transformation is arranged to obtain the initial event data.

4. The method according to claim 1, characterized in that, Based on the vehicle's target configuration file and the event identifier data, the initial event data is adjusted to obtain target event data, including: From the target configuration file, an event data template associated with the event identifier data is determined. The target configuration file includes the association between different event identifier data and different event data templates. The different event identifier data includes the event identifier data. The event data template includes template event data to be processed. The template event data is used to represent template events that occurred during the vehicle's historical operation. The running time corresponding to the historical operation is earlier than the running time corresponding to the current operation. Based on the event data template and the target configuration file, the initial event data is adjusted to obtain the target event data.

5. The method according to claim 4, characterized in that, Based on the event data template and the target configuration file, the initial event data is adjusted to obtain the target event data, including: According to the access policy in the event data template and the target configuration file, the storage address of the event data to be combined is accessed to obtain the event data to be combined, and the storage address of the associated event data of the vehicle is accessed to obtain the associated event data. The access policy is a rule for accessing different storage addresses. The event data to be combined is the sub-event data in the initial event data that is the same as the template event data. The associated event data is used to indicate the time of occurrence of the event and the identifier of the vehicle in which the event occurred. The event data to be combined and the associated event data are combined to obtain the event data to be processed; The target event data is determined based on the target configuration file and the event data to be processed.

6. The method according to claim 5, characterized in that, Based on the target configuration file and the event data to be processed, the target event data is determined, including: According to the target configuration file, the data type and time of the event data to be processed are converted. According to the data cleaning strategy in the target configuration file, the data to be processed after type conversion is cleaned to obtain the target event data, wherein the data cleaning strategy is used to represent the rules for cleaning the data to be processed.

7. The method according to claim 6, characterized in that, According to the target configuration file, the data type and time conversion of the event data to be processed are performed, including: The data type of the event data to be processed is converted according to the target data type in the target configuration file; According to the time processing strategy in the target configuration file, the time conversion is performed on the type-converted event data to be processed, wherein the time processing strategy is used to represent the rules for processing the event data to be processed according to the timestamp of the event data to be processed.

8. The method according to any one of claims 1 to 7, characterized in that, The method further includes: Determine the preset storage object in the target configuration file; The format of the preset storage object is converted into the object format required by the storage database to obtain the target storage object; In the storage data space, the storage database is constructed according to the target storage object.

9. A data processing device for a vehicle, characterized in that, include: An acquisition unit is used to acquire operating data generated by the vehicle during operation, wherein the operating data is used to represent the operating status of the vehicle during the operation. The first determining unit is configured to determine initial event data of the vehicle based on the operating data, wherein the initial event data is used to represent events that occur in the vehicle during the operation. The second determining unit is configured to determine event identification data of the vehicle based on the initial event data, wherein the event identification data is used to identify the event; An adjustment unit is configured to adjust the initial event data based on the target configuration file of the vehicle and the event identification data to obtain target event data, wherein the reliability of the target event data relative to the vehicle is higher than the reliability of the initial event data relative to the vehicle. The writing unit is used to store the target event data in the database.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium includes a stored executable program, wherein, when the executable program is executed, it controls the device on which the storage medium is located to perform the method according to any one of claims 1 to 8.

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