Data processing method and device and computer readable storage medium

Abstract

The invention discloses a data processing method and device and a computer readable storage medium, and the method comprises the steps: obtaining a query parameter corresponding to to-be-processed data, and obtaining a log tracking identifier corresponding to the query parameter; in log data corresponding to the to-be-processed data, log information corresponding to the log tracking identifier is obtained, and log key information corresponding to the log information is obtained; acquiring database data corresponding to the log tracking identifier; and based on the database data and the log key information, determining a target solution corresponding to the to-be-processed data. According to the method and the device, the corresponding target solution is inquired according to the database data and the log key information, so that the to-be-processed data (after-sales system problems) is automatically checked, the solution is automatically provided, and the processing efficiency of the vehicle after-sales problems is improved.

Description

Technical Field

[0001] This application relates to the field of data processing technology, and in particular to a data processing method, apparatus and computer-readable storage medium. Background Technology

[0002] Currently, when users frequently report vehicle problems, after-sales service personnel typically forward the issues to professional technicians. The technicians then provide their technical analysis results before relaying the information back to the user, which prevents timely resolution of user issues and leads to low efficiency in handling after-sales problems.

[0003] Therefore, improving the efficiency of handling vehicle after-sales issues is an urgent problem that needs to be solved. Summary of the Invention

[0004] The main objective of this application is to provide a data processing method, apparatus, and computer-readable storage medium, which aims to solve the technical problem of how to improve the processing efficiency of vehicle after-sales issues.

[0005] To achieve the above objectives, this application provides a data processing method, the data processing method comprising: Obtain the query parameters corresponding to the data to be processed, and obtain the log tracking identifier corresponding to the query parameters, wherein the query parameters include VIN and / or mobile phone number; In the log data corresponding to the data to be processed, obtain the log information corresponding to the log tracking identifier, and obtain the log key information corresponding to the log information; Obtain the database data corresponding to the log tracking identifier; Based on the database data and the key information in the logs, the target solution corresponding to the data to be processed is determined.

[0006] In one implementation, the step of obtaining the query parameters corresponding to the data to be processed includes: Input the data to be processed into the scene classification engine to obtain the classification result corresponding to the data to be processed; If the classification result is a system error, then the data to be processed is input into the parameter extractor to obtain the query parameters corresponding to the data to be processed.

[0007] In one implementation, the step of obtaining the log tracing identifier corresponding to the query parameter includes: If the query parameters are incomplete, then complete the query parameters; If the query parameters are complete, they are input into the log tracing engine to obtain the log tracing identifier corresponding to the query parameters.

[0008] In one implementation, the step of completing the query parameters includes: If the query parameter does not have a VIN, then the corresponding VIN is queried based on the mobile phone number to complete the query parameter; If the query parameter does not contain a mobile phone number, the corresponding mobile phone number is retrieved based on the VIN to complete the query parameter.

[0009] In one implementation, the step of obtaining log information corresponding to the log tracking identifier from the log data corresponding to the data to be processed, and obtaining log key information corresponding to the log information, includes: Based on a preset time range, obtain the log information corresponding to the log tracking identifier from the business system logs and vehicle terminal logs; Obtain the key log information corresponding to the log information.

[0010] In one implementation, the step of determining the target solution corresponding to the data to be processed based on the database data and the key information in the logs includes: The database data and the key log information are input into the root cause decision model. The root cause decision model performs rule matching on the database data and the key log information to obtain the matching rules corresponding to the database data and the key log information. The target solution corresponding to the data to be processed is determined based on the matching rules.

[0011] In one implementation, the step of determining the target solution corresponding to the data to be processed based on the matching rule includes: Based on the matching rules, determine the solution corresponding to the data to be processed; The solution is evaluated probabilistically to obtain the evaluation probability of the solution; If the evaluated probability is greater than the preset probability, the solution is optimized based on the preset solutions in the associated knowledge base to obtain the target solution.

[0012] In one implementation, after the step of optimizing the solution based on preset solutions in the associated knowledge base to obtain the target solution, the method further includes: Obtain the feedback results corresponding to the target solution; If the feedback result indicates a successful resolution, the associated knowledge base is updated based on the target solution and the data to be processed.

[0013] In addition, to achieve the above objectives, this application also provides a data processing apparatus, which includes: a memory, a processor, and a data processing program stored in the memory and executable on the processor, wherein the data processing program, when executed by the processor, implements the steps of the aforementioned data processing method.

[0014] In addition, to achieve the above objectives, this application also provides a computer-readable storage medium storing a data processing program, which, when executed by a processor, implements the steps of the aforementioned data processing method.

[0015] This application obtains query parameters corresponding to the data to be processed and log tracking identifiers corresponding to the query parameters, wherein the query parameters include VIN and / or mobile phone number; then, it obtains log information corresponding to the log tracking identifier from the log data corresponding to the data to be processed, and obtains key log information corresponding to the log information; then, it obtains database data corresponding to the log tracking identifier; then, based on the database data and the key log information, it determines the target solution corresponding to the data to be processed. By querying the corresponding log information according to the log tracking identifier of the query parameters corresponding to the data to be processed, and thus obtaining the key log information corresponding to the log information, and simultaneously querying the database data corresponding to the log tracking identifier, and querying the corresponding target solution based on the database data and the key log information, this application achieves automatic troubleshooting and automatic solution provision for data to be processed (after-sales system problems), thereby improving the efficiency of handling vehicle after-sales problems. Attached Figure Description

[0016] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application.

[0017] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, for those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0018] Figure 1 This is a flowchart illustrating an embodiment of the data processing method of this application. Figure 2 This is a simplified flowchart of the data processing method in this application; Figure 3 This is a schematic diagram of the module structure of the data processing device according to an embodiment of this application.

[0019] The realization of the purpose, functional features and advantages of this application will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0020] It should be understood that the specific embodiments described herein are merely illustrative of this application and are not intended to limit this application.

[0021] To better understand the technical solution of this application, a detailed description will be provided below in conjunction with the accompanying drawings and specific implementation methods.

[0022] The main solution of this application is as follows: obtain the query parameters corresponding to the data to be processed, and obtain the log tracking identifier corresponding to the query parameters, wherein the query parameters include VIN and / or mobile phone number; in the log data corresponding to the data to be processed, obtain the log information corresponding to the log tracking identifier, and obtain the log key information corresponding to the log information; obtain the database data corresponding to the log tracking identifier; and determine the target solution corresponding to the data to be processed based on the database data and the log key information.

[0023] Currently, when users frequently report vehicle problems, after-sales service personnel typically forward the issues to professional technicians. The technicians then provide their technical analysis results before relaying the information back to the user, which prevents timely resolution of user issues and leads to low efficiency in handling after-sales problems.

[0024] For example, the intelligent driving system of an intelligent driving vehicle generates various kinds of intelligent driving data during operation. These intelligent driving data may include environmental data of the vehicle's surroundings collected by sensors such as cameras, millimeter-wave radar, lidar, and ultrasonic radar, intermediate algorithm results generated during the operation of each intelligent driving module, and various logs generated during the operation of the intelligent driving system.

[0025] When after-sales issues arise with intelligent driving systems, OEMs can analyze sensor data, intermediate algorithm results, and detailed log data at the time of the problem to pinpoint the issue. For example, they can coordinate for the customer to drive the vehicle to a 4S dealership, where after-sales personnel can read the intelligent driving data and upload it to the OEM for analysis. However, the long data transmission distance and time-consuming data processing hinder the timely location of after-sales problems, resulting in low efficiency in handling them. Therefore, improving the efficiency of handling vehicle after-sales issues is a pressing problem that needs to be solved.

[0026] This application retrieves the corresponding log information based on the log tracking identifier of the query parameters corresponding to the data to be processed, thereby obtaining the key log information corresponding to the log information. At the same time, it queries the database data corresponding to the log tracking identifier, and retrieves the corresponding target solution based on the database data and the key log information. This enables automatic troubleshooting of data to be processed (after-sales system problems) and automatic provision of solutions, thereby improving the efficiency of handling vehicle after-sales problems.

[0027] It should be noted that the execution subject in this embodiment can be a data processing device, or a computing service device with data processing, network communication, and program execution functions, such as a tablet computer, personal computer, or mobile phone, or a data processing device capable of performing the above functions. This embodiment does not specifically limit it in this way. The following uses a data processing device as the execution subject as an example to describe this embodiment and the following embodiments.

[0028] Based on this, this application proposes a data processing method according to a first embodiment, please refer to... Figure 1 The data processing method includes steps S110 to S140: Step S110: Obtain the query parameters corresponding to the data to be processed, and obtain the log tracking identifier corresponding to the query parameters, wherein the query parameters include VIN and / or mobile phone number; In this embodiment, the data to be processed is an after-sales system problem reported by a user. Upon receiving the data to be processed, the query parameters corresponding to the data are obtained. For example, if a user reports an after-sales system problem, the after-sales service personnel input the problem into the AI ​​agent corresponding to the data processing method of this application. When the data to be processed (after-sales system problem) is obtained, the AI ​​agent obtains the query parameters corresponding to the data to be processed. In one feasible implementation, step S110 may include steps S111 to S112: Step S111: Input the data to be processed into the scene classification engine to obtain the classification result corresponding to the data to be processed; If the classification result is a system error, then the data to be processed is input into the parameter extractor to obtain the query parameters corresponding to the data to be processed.

[0029] In this embodiment, a classification knowledge base can be pre-established based on historical data. This knowledge base is used to segment data through descriptions and positive examples. Scene classification can be performed by searching the knowledge base, for example, using a scene classification engine. After acquiring the data to be processed, the AI ​​agent inputs it into the scene classification engine. The engine then searches the knowledge base to determine the classification result. The classification result can include system error categories and non-system error categories. If the classification result is a non-system error category, subsequent processes are not executed.

[0030] If the classification result is a system error, the data to be processed is input into the parameter extractor. The parameter extractor extracts the query parameters corresponding to the data to be processed to obtain the query parameters corresponding to the data to be processed. The query parameters may include VIN (Vehicle Identification Number) and / or mobile phone number. VIN is the vehicle identification number of the vehicle corresponding to the after-sales system problem, and mobile phone number is the mobile phone number of the user corresponding to the vehicle with the after-sales system problem.

[0031] like Figure 2 As shown, the AI ​​agent receives the repair description information submitted by the user, obtains the data to be processed, and inputs the repair description information into the scene classification engine to classify the scene and obtain the classification result. If the classification result is a system error, the repair description information is input into the parameter extractor to extract the key information in the repair description information, namely the query parameters.

[0032] After obtaining the query parameters, the AI ​​agent obtains the log tracking identifier corresponding to the query parameters. Specifically, the AI ​​agent queries the corresponding log data based on the query parameters to obtain the log tracking identifier. In one feasible implementation, step S110 may include steps S113-S114: Step S113: If the query parameters are incomplete, complete the query parameters. Step S114: If the query parameters are complete, input the query parameters into the log tracing engine to obtain the log tracing identifier corresponding to the query parameters.

[0033] In this embodiment, the AI ​​agent first performs a completeness check on the query parameters, that is, it determines whether the query parameters include both the VIN and the mobile phone number. If the query parameters only include one of the VIN and the mobile phone number, the query parameters are determined to be incomplete. At this time, the AI ​​agent completes the query parameters. In a feasible implementation, step S113 may include steps S1131~S1132: Step S1131: If the query parameter does not contain a VIN, then query the corresponding VIN based on the mobile phone number; Step S1132: If the query parameter does not contain a mobile phone number, then query the corresponding mobile phone number based on the VIN.

[0034] In some real-time scenarios, if the query parameters are incomplete, such as a missing VIN, the VIN needs to be supplemented. In this case, the AI ​​agent retrieves the corresponding VIN using the phone number, meaning the AI ​​agent uses the phone number to query relevant data to complete the query parameters. If the query parameters do not include a phone number, the phone number needs to be supplemented. In this case, the AI ​​agent retrieves the corresponding phone number using the VIN, meaning the AI ​​agent uses the VIN to query relevant data to complete the query parameters. In this embodiment of the application, if the query parameters are complete, the query parameters are input into the log tracing engine, that is, the query parameters are input into the log tracing engine of the AI ​​agent. The log tracing engine queries the corresponding log tracing identifier (traceId) based on the query parameters. For example, the log tracing engine queries the corresponding log tracing identifier in the business system logs and vehicle terminal logs. Figure 2 As shown, the AI ​​agent inputs the query parameters into the log tracing engine to query the traceId.

[0035] Step S120: In the log data corresponding to the data to be processed, obtain the log information corresponding to the log tracking identifier, and obtain the log key information corresponding to the log information; In this embodiment, after obtaining the log tracking identifier, the AI ​​agent retrieves the log information corresponding to the log tracking identifier from the log data corresponding to the data to be processed, and retrieves the log key information corresponding to the log information. Specifically, the AI ​​agent first determines the business system log and vehicle terminal log corresponding to the data to be processed. The business system log can be the log information of the business system corresponding to the user's vehicle, and the vehicle terminal log is the log information of the user's vehicle. Then, the AI ​​agent queries the log information corresponding to the log tracking identifier in the business system log and the vehicle terminal log.

[0036] like Figure 2 As shown, the AI ​​agent queries the log system through the traceId. The log system queries the log segment (log information) corresponding to the traceId, and the AI ​​agent queries the log segment for key log information.

[0037] In one feasible implementation, step S120 may include steps S121-S122: Step S121: Based on a preset time range, obtain the log information corresponding to the log tracking identifier from the business system log and the vehicle terminal log; Step S122: Obtain the log key information corresponding to the log information.

[0038] In this embodiment, the AI ​​agent first determines the business system log and vehicle terminal log corresponding to the data to be processed. Then, based on a preset time range, it obtains the log information corresponding to the log tracking identifier in the business system log and vehicle terminal log. That is, the AI ​​agent queries the log information corresponding to the log tracking identifier within the preset time range in the business system log and vehicle terminal log. Specifically, the AI ​​agent can first determine the log time corresponding to the log tracking identifier, and then query the log information corresponding to the log tracking identifier within the preset time range corresponding to the log time in the business system log and vehicle terminal log. The preset time range can be reasonably set. For example, the preset time range is 5 seconds, and the preset time range corresponding to the log time is the time range of (log time - 2.5 seconds) to (log time + 2.5 seconds).

[0039] In this embodiment, after obtaining the log information corresponding to the log tracking identifier, the key log information corresponding to the log information is obtained. Specifically, the AI ​​agent executes information extraction code based on the log information to extract the key log information from the log information.

[0040] Step S130: Obtain the database data corresponding to the log tracking identifier; In this embodiment of the application, while obtaining key log information, the database data corresponding to the log tracking identifier is also obtained. Specifically, the AI ​​agent determines the user database corresponding to the data to be processed, and queries the user database based on the log tracking identifier to obtain the database data corresponding to the log tracking identifier.

[0041] Step S140: Based on the database data and the key information in the logs, determine the target solution corresponding to the data to be processed.

[0042] In this embodiment of the application, after obtaining database data and key log information, the AI ​​agent determines the target solution corresponding to the data to be processed based on the database data and key log information. Specifically, the AI ​​agent inputs the database data and key log information into the root cause decision model and determines the target solution corresponding to the data to be processed through the root cause decision model.

[0043] like Figure 2 As shown, the database is queried through the log tracking identifier to obtain the database data corresponding to the log tracking identifier. The database data may include relevant solutions. By inputting the database data and key log information into the root cause decision model, the target solution corresponding to the data to be processed is obtained.

[0044] Furthermore, in one feasible implementation, step S140 may include steps S141-S142: Step S141: Input the database data and the log key information into the root cause decision model, and perform rule matching on the database data and the log key information through the root cause decision model to obtain the matching rules corresponding to the database data and the log key information. Step S142: Determine the target solution corresponding to the data to be processed based on the matching rules.

[0045] In this embodiment, after obtaining database data and key log information, the AI ​​agent inputs the database data and key log information into the root cause decision model. The root cause decision model performs rule matching on the database data and key log information to obtain the matching rules corresponding to the database data and key log information. Specifically, a rule filtering engine of the root cause decision model is pre-set. Based on the relationship between existing solutions and database data and key log information, various preset rules are set in the rule filtering engine. The root cause decision model performs rule matching on the database data and key log information with the various preset rules of the rule filtering engine to determine the preset rules that match the database data and key log information, thus obtaining the matching rules.

[0046] After obtaining the matching rules, the AI ​​agent determines the target solution corresponding to the data to be processed based on the matching rules. The AI ​​agent can simultaneously determine the cause of the problem corresponding to the data to be processed based on the matching rules and generate the corresponding target solution. Further, in a feasible implementation, step S142 may include steps S1421-S1423: Step S1421: Determine the solution corresponding to the data to be processed based on the matching rules; Step S1422: Perform a probability evaluation on the solution to obtain the evaluation probability of the solution; Step S1423: If the evaluation probability is greater than the preset probability, then the solution is optimized based on the preset solution in the associated knowledge base to obtain the target solution.

[0047] In this embodiment of the application, after obtaining the matching rules, the AI ​​agent determines the solution corresponding to the data to be processed based on the matching rules. The solutions corresponding to each preset rule of the rule filtering engine can be preset. After obtaining the matching rules, the solution can be obtained by directly querying the solutions of each preset rule based on the matching rules.

[0048] It should be noted that after the problem of the pending data is solved by executing the solution provided by the AI ​​agent, the solution and the corresponding pending data are updated to the associated database. Of course, if the solution provided by the AI ​​agent cannot solve the problem of the pending data, the solution is optimized through expert experience, and after the problem of the pending data is solved, the optimized solution is associated and stored in the associated database. In other words, the associated database also includes all the optimized solutions.

[0049] After obtaining a solution, the AI ​​agent performs a probability assessment on the solution to obtain the assessment probability of the solution. If the assessment probability is greater than the preset probability, the AI ​​agent optimizes the solution based on the preset solutions in the associated knowledge base to obtain the target solution. Specifically, the AI ​​agent searches for the associated solutions corresponding to the solution in the preset solutions in the associated knowledge base, optimizes the solution through the associated solutions, and obtains the target solution.

[0050] In some embodiments, when a target solution is obtained, the target solution is sent to the user, who can use the target solution to solve the system problem corresponding to the data to be processed and provide feedback on the solution result (feedback result).

[0051] Furthermore, in one feasible implementation, after step S143, the data processing method may further include steps S1424-S1425: Step S1424: Obtain the feedback result corresponding to the target solution; Step S1425: If the feedback result is a successful resolution, then update the associated knowledge base based on the target solution and the data to be processed.

[0052] In this embodiment, after the user uploads the feedback result corresponding to the target solution, the AI ​​agent obtains the feedback result corresponding to the target solution. The feedback result may include successful solution or unsuccessful solution. If the feedback result is successful solution, the associated knowledge base is updated based on the target solution and the data to be processed, so as to realize the timely update of the associated knowledge base.

[0053] In some embodiments, if the feedback indicates a failure to resolve the issue, the target solution is modified, for example, by manual modification. The modified target solution is then sent to the user, and the associated knowledge base is updated based on the modified target solution and the data to be processed, so as to achieve timely updates of the associated knowledge base.

[0054] It should be noted that if the matching rules corresponding to the database data and log key information are not obtained, the AI ​​agent can input the database data and log key information into the neural network of the root cause decision model, and perform LSTM time series analysis and anomaly detection on the database data and log key information through the neural network to obtain the solution corresponding to the database data and log key information; and continue to execute step S1422 and subsequent steps.

[0055] The data processing method proposed in this embodiment obtains query parameters corresponding to the data to be processed and log tracking identifiers corresponding to the query parameters, wherein the query parameters include VIN and / or mobile phone number; then, it obtains log information corresponding to the log tracking identifier from the log data corresponding to the data to be processed and obtains key log information corresponding to the log information; then, it obtains database data corresponding to the log tracking identifier; then, based on the database data and the key log information, it determines the target solution corresponding to the data to be processed. By querying the corresponding log information according to the log tracking identifier of the query parameters corresponding to the data to be processed, the key log information corresponding to the log information is obtained. At the same time, the database data corresponding to the log tracking identifier is queried, and the target solution is queried according to the database data and the key log information. This achieves automatic investigation and automatic provision of solutions for data to be processed (after-sales system problems), improving the efficiency of handling vehicle after-sales problems.

[0056] The data processing apparatus provided in this application, employing the data processing method described in the above embodiments, can solve the technical problem of how to improve the processing efficiency of vehicle after-sales issues. Compared with the prior art, the beneficial effects of the data processing apparatus provided in this application are the same as those of the data processing method described in the above embodiments, and other technical features in the data processing apparatus are the same as those disclosed in the methods of the above embodiments, and will not be repeated here.

[0057] This application provides a data processing apparatus, which includes: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to perform the data processing method in Embodiment 1 above.

[0058] The following is for reference. Figure 3The diagram illustrates a structural schematic of a data processing apparatus suitable for implementing embodiments of this application. The data processing apparatus in the embodiments of this application may include, but is not limited to, mobile terminals such as mobile phones, laptops, digital broadcast receivers, PDAs (Personal Digital Assistants), PADs (Portable Application Description), PMPs (Portable Media Players), in-vehicle terminals (e.g., in-vehicle navigation terminals), and fixed terminals such as digital TVs and desktop computers. Figure 3 The data processing apparatus shown is merely an example and should not impose any limitations on the functionality and scope of use of the embodiments of this application.

[0059] like Figure 3 As shown, the data processing device may include a processing device 1001 (e.g., a central processing unit, a graphics processing unit, etc.), which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 1002 or a program loaded from a storage device 1003 into a random access memory (RAM) 1004. The RAM 1004 also stores various programs and data required for the operation of the data processing device. The processing device 1001, ROM 1002, and RAM 1004 are interconnected via a bus 1005. An input / output (I / O) interface 1006 is also connected to the bus. Typically, the following systems can be connected to the I / O interface 1006: input devices 1007 including, for example, a touchscreen, touchpad, keyboard, mouse, image sensor, microphone, accelerometer, gyroscope, etc.; output devices 1008 including, for example, a liquid crystal display (LCD), speaker, vibrator, etc.; storage devices 1003 including, for example, magnetic tape, hard disk, etc.; and communication devices 1009. Communication device 1009 allows the data processing device to communicate wirelessly or wiredly with other devices to exchange data. Although the figure shows a data processing device with various systems, it should be understood that it is not required to implement or possess all of the systems shown. More or fewer systems may be implemented alternatively.

[0060] Specifically, according to the embodiments disclosed in this application, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments disclosed in this application include a computer program product comprising a computer program carried on a computer-readable medium, the computer program containing program code for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via a communication device, or installed from storage device 1003, or installed from ROM 1002. When the computer program is executed by processing device 1001, it performs the functions defined in the methods of the embodiments disclosed in this application.

[0061] The data processing apparatus provided in this application, employing the data processing method described in the above embodiments, can solve the technical problem of how to improve the processing efficiency of vehicle after-sales issues. Compared with the prior art, the beneficial effects of the data processing apparatus provided in this application are the same as those of the data processing method described in the above embodiments, and other technical features in this data processing apparatus are the same as those disclosed in the method of the previous embodiment, and will not be repeated here.

[0062] It should be understood that the various parts disclosed in this application can be implemented using hardware, software, firmware, or a combination thereof. In the description of the above embodiments, specific features, structures, materials, or characteristics can be combined in any suitable manner in one or more embodiments or examples.

[0063] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

[0064] This application provides a computer-readable storage medium having computer-readable program instructions (i.e., a computer program) stored thereon, the computer-readable program instructions being used to execute the data processing method described in the above embodiments.

[0065] The computer-readable storage medium provided in this application may be, for example, a USB flash drive, but is not limited to, electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices, or any combination thereof. More specific examples of computer-readable storage media may include, but are not limited to: electrical connections having one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof. In this embodiment, the computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, system, or device. The program code contained on the computer-readable storage medium may be transmitted using any suitable medium, including but not limited to: wires, optical cables, RF (Radio Frequency), etc., or any suitable combination thereof.

[0066] The aforementioned computer-readable storage medium may be included in a data processing apparatus or may exist independently without being assembled into a data processing apparatus.

[0067] The aforementioned computer-readable storage medium carries one or more programs. When the one or more programs are executed by a data processing device, the data processing device causes the data processing device to: obtain query parameters corresponding to the data to be processed, and obtain a log tracking identifier corresponding to the query parameters, wherein the query parameters include a VIN and / or a mobile phone number; obtain log information corresponding to the log tracking identifier from the log data corresponding to the data to be processed, and obtain log key information corresponding to the log information; obtain database data corresponding to the log tracking identifier; and determine a target solution corresponding to the data to be processed based on the database data and the log key information.

[0068] Computer program code for performing the operations of this application can be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, and C++, and conventional procedural programming languages ​​such as the "C" language or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving remote computers, the remote computer can be connected to the user's computer via any type of network—including a Local Area Network (LAN) or a Wide Area Network (WAN)—or can be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0069] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of this application. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, can be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0070] The modules described in the embodiments of this application can be implemented in software or hardware. The names of the modules do not necessarily limit the functionality of the unit itself.

[0071] The readable storage medium provided in this application is a computer-readable storage medium that stores computer-readable program instructions (i.e., a computer program) for executing the above-described data processing method, thereby solving the technical problem of how to improve the processing efficiency of vehicle after-sales issues. Compared with the prior art, the beneficial effects of the computer-readable storage medium provided in this application are the same as the beneficial effects of the data processing method provided in the above embodiments, and will not be repeated here.

[0072] This application provides a computer program product, including a computer program that, when executed by a processor, implements the steps of the data processing method described above.

[0073] The computer program product provided in this application can solve the technical problem of how to improve the efficiency of handling vehicle after-sales issues. Compared with the prior art, the beneficial effects of the computer program product provided in the embodiments of this application are the same as the beneficial effects of the data processing method provided in the above embodiments, and will not be repeated here.

[0074] The above are merely preferred embodiments of this application and do not limit the patent scope of this application. Any equivalent structural or procedural transformations made using the content of this application's specification and drawings, or direct or indirect applications in other related technical fields, are similarly included within the patent protection scope of this application.

Claims

1. A data processing method, characterized in that, The data processing method includes: Obtain the query parameters corresponding to the data to be processed, and obtain the log tracking identifier corresponding to the query parameters, wherein the query parameters include VIN and / or mobile phone number; In the log data corresponding to the data to be processed, obtain the log information corresponding to the log tracking identifier, and obtain the log key information corresponding to the log information; Obtain the database data corresponding to the log tracking identifier; Based on the database data and the key information in the logs, the target solution corresponding to the data to be processed is determined.

2. The data processing method as described in claim 1, characterized in that, The steps for obtaining the query parameters corresponding to the data to be processed include: Input the data to be processed into the scene classification engine to obtain the classification result corresponding to the data to be processed; If the classification result is a system error, then the data to be processed is input into the parameter extractor to obtain the query parameters corresponding to the data to be processed.

3. The data processing method as described in claim 1, characterized in that, The step of obtaining the log tracking identifier corresponding to the query parameter includes: If the query parameters are incomplete, then complete the query parameters; If the query parameters are complete, they are input into the log tracing engine to obtain the log tracing identifier corresponding to the query parameters.

4. The data processing method as described in claim 3, characterized in that, The steps for completing the query parameters include: If the query parameter does not have a VIN, then the corresponding VIN is queried based on the mobile phone number to complete the query parameter; If the query parameter does not contain a mobile phone number, the corresponding mobile phone number is retrieved based on the VIN to complete the query parameter.

5. The data processing method as described in claim 1, characterized in that, The steps of obtaining log information corresponding to the log tracking identifier from the log data corresponding to the data to be processed, and obtaining key log information corresponding to the log information, include: Based on a preset time range, obtain the log information corresponding to the log tracking identifier from the business system logs and vehicle terminal logs; Obtain the key log information corresponding to the log information.

6. The data processing method according to any one of claims 1 to 5, characterized in that, The step of determining the target solution corresponding to the data to be processed based on the database data and the key information in the logs includes: The database data and the key log information are input into the root cause decision model. The root cause decision model performs rule matching on the database data and the key log information to obtain the matching rules corresponding to the database data and the key log information. The target solution corresponding to the data to be processed is determined based on the matching rules.

7. The data processing method as described in claim 6, characterized in that, The step of determining the target solution corresponding to the data to be processed based on the matching rule includes: Based on the matching rules, determine the solution corresponding to the data to be processed; The solution is evaluated probabilistically to obtain the evaluation probability of the solution; If the evaluated probability is greater than the preset probability, the solution is optimized based on the preset solutions in the associated knowledge base to obtain the target solution.

8. The data processing method as described in claim 7, characterized in that, After the step of optimizing the solution based on preset solutions in the associated knowledge base to obtain the target solution, the method further includes: Obtain the feedback results corresponding to the target solution; If the feedback result indicates a successful resolution, the associated knowledge base is updated based on the target solution and the data to be processed.

9. A type of device, characterized in that, The data processing apparatus includes: a memory, a processor, and a data processing program stored in the memory and executable on the processor, wherein when the data processing program is executed by the processor, it implements the steps of the data processing method as described in any one of claims 1 to 8.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a data processing program, which, when executed by a processor, implements the steps of the data processing method as described in any one of claims 1 to 8.

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