Vehicle cloud service fault determination method and device, computer equipment, readable storage medium and program product

Abstract

The invention relates to a vehicle cloud service fault determination method and device, computer equipment, a computer readable storage medium and a computer program product. The method comprises the following steps: acquiring vehicle cloud service data, wherein the vehicle cloud service data comprises vehicle sensor data, network communication data and running state data of vehicle cloud service; analyzing the vehicle cloud service data to obtain multiple initial performance parameters corresponding to the vehicle cloud service; for each initial performance parameter, under the condition that the initial performance parameter is not in a corresponding preset parameter range, determining the initial performance parameter as a target performance parameter; and inputting the target performance parameter into the fault detection model, and outputting a fault position and a fault type corresponding to the vehicle cloud service fault. According to the method provided by the invention, the efficiency of determining the vehicle cloud service fault can be effectively improved.

Description

Technical Field

[0001] The present application relates to the field of vehicle networking technology, and in particular to a vehicle cloud service fault determination method, apparatus, computer equipment, computer-readable storage medium, and computer program product. Background Art

[0002] At present, the way to locate vehicle cloud service problems in the industry is to analyze the logs generated by the vehicle cloud service and find abnormal or error information. However, as the overall cloud service link becomes longer and longer, and the front-end, back-end, and database layers become more and more numerous, the traditional problem location method can only check the logs layer by layer, which is slow and time-consuming, resulting in low overall problem location efficiency. Summary of the invention

[0003] Based on this, it is necessary to provide a vehicle cloud service fault determination method, apparatus, computer equipment, computer-readable storage medium and computer program product that can improve the efficiency of fault determination in response to the above technical problems.

[0004] In a first aspect, the present application provides a vehicle cloud service fault determination method, the method comprising:

[0005] Acquire vehicle cloud service data, wherein the vehicle cloud service data includes vehicle sensor data, network communication data, and operation status data of the vehicle cloud service;

[0006] Analyzing the vehicle cloud service data to obtain a plurality of initial performance parameters corresponding to the vehicle cloud service;

[0007] For each initial performance parameter, if the initial performance parameter is not within the corresponding preset parameter range, determining the initial performance parameter as a target performance parameter;

[0008] The target performance parameters are input into the fault detection model, and the fault location and fault type corresponding to the vehicle cloud service fault are output.

[0009] In one embodiment, the obtaining of vehicle cloud service data includes:

[0010] Receiving a data acquisition request for the vehicle cloud service data sent by a user terminal, wherein the data acquisition request includes an identity identifier of the user;

[0011] When the identity identifier is in a preset identity identifier set, sending the data acquisition request to the vehicle terminal;

[0012] Receive the vehicle cloud service data returned by the vehicle terminal.

[0013] In one embodiment, the vehicle cloud service data is analyzed to obtain a plurality of initial performance parameters corresponding to the vehicle cloud service, including:

[0014] Performing data preprocessing on the vehicle cloud service data, wherein the data preprocessing includes data cleaning, data conversion, and data normalization on the vehicle cloud service data;

[0015] The preprocessed data is input into the data analysis model, and the initial performance parameters are output.

[0016] In one of the embodiments, the initial performance parameters include service response time, packet loss rate, service delay, throughput, resource utilization and scalability.

[0017] In one embodiment, the method further comprises:

[0018] An alarm message is issued based on the fault location and fault type, so that the user can eliminate the vehicle cloud service fault based on the alarm message.

[0019] In one embodiment, the method further comprises:

[0020] The vehicle cloud service data is sent to a cloud so that the cloud stores the vehicle cloud service data.

[0021] In a second aspect, the present application further provides a vehicle cloud service fault determination device, the device comprising:

[0022] An acquisition module, used to acquire vehicle cloud service data, wherein the vehicle cloud service data includes vehicle sensor data, network communication data, and operation status data of the vehicle cloud service;

[0023] An analysis module, used to analyze the vehicle cloud service data to obtain a plurality of initial performance parameters corresponding to the vehicle cloud service;

[0024] a determination module, configured to determine, for each initial performance parameter, the initial performance parameter as a target performance parameter if the initial performance parameter is not within a corresponding preset parameter range;

[0025] The input module is used to input the target performance parameter into the fault detection model and output the fault location and fault type corresponding to the vehicle cloud service fault.

[0026] In a third aspect, the present application further provides a computer device, which includes a memory and a processor, wherein the memory stores a computer program, and the processor implements the steps of the method in any one of the above embodiments when executing the computer program.

[0027] In a fourth aspect, the present application further provides a computer-readable storage medium, wherein a computer program is stored on the computer-readable storage medium, and when the computer program is executed by a processor, the steps of the method in any one of the above embodiments are implemented.

[0028] In a fifth aspect, the present application further provides a computer program product, which includes a computer program, and when the computer program is executed by a processor, the steps of the method in any one of the above embodiments are implemented.

[0029] The above-mentioned vehicle cloud service fault determination method, device, computer equipment, computer-readable storage medium and computer program product obtain vehicle cloud service data, which includes vehicle sensor data, network communication data and vehicle cloud service operation status data; analyze the vehicle cloud service data to obtain multiple initial performance parameters corresponding to the vehicle cloud service; for each initial performance parameter, if the initial performance parameter is not within the corresponding preset parameter range, determine the initial performance parameter as the target performance parameter; input the target performance parameter into the fault detection model, and output the fault location and fault type corresponding to the vehicle cloud service fault. The method provided by the present application can effectively improve the efficiency of determining vehicle cloud service faults. BRIEF DESCRIPTION OF THE DRAWINGS

[0030] In order to more clearly illustrate the technical solutions in the embodiments of the present application or related technologies, the drawings required for use in the embodiments of the present application or related technical descriptions will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without paying creative work.

[0031] Figure 1 A schematic diagram of a process of determining a vehicle cloud service fault in one embodiment;

[0032] Figure 2 A schematic diagram of a process of obtaining vehicle cloud service data in one embodiment;

[0033] Figure 3 A business chain diagram of a vehicle cloud service fault determination method in another embodiment;

[0034] Figure 4 It is an architecture diagram of a vehicle cloud service fault determination method in another embodiment;

[0035] Figure 5 It is a structural block diagram of a vehicle cloud service fault determination device in one embodiment;

[0036] Figure 6 FIG. 4 is a diagram showing the internal structure of a computer device in one embodiment. DETAILED DESCRIPTION

[0037] In order to make the purpose, technical solution and advantages of the present application more clearly understood, the present application is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0038] In one embodiment, Figure 1 As shown, a vehicle cloud service fault determination method is provided. This embodiment takes the method applied to a terminal as an example. It can be understood that the method can also be applied to a server, and can also be applied to a system including a terminal and a server, and is implemented through the interaction between the terminal and the server. In this embodiment, the method includes the following steps:

[0039] S102. Acquire vehicle cloud service data, where the vehicle cloud service data includes vehicle sensor data, network communication data, and operating status data of the vehicle cloud service.

[0040] Among them, vehicle cloud service generally refers to the in-vehicle information service system based on cloud computing technology, which provides various functions by integrating vehicle data and services into the cloud platform; vehicle cloud service data can be obtained based on the logs generated by vehicle sensors and vehicle cloud services.

[0041] S104: Analyze the vehicle cloud service data to obtain a plurality of initial performance parameters corresponding to the vehicle cloud service.

[0042] Among them, the initial performance parameters are used to measure the ability of cloud services in providing vehicle-related information and services.

[0043] The vehicle cloud service data can be analyzed and monitored in real time online or offline, and initial performance parameters can be obtained by processing and calculating the vehicle cloud service data.

[0044] S106. For each initial performance parameter, if the initial performance parameter is not within a corresponding preset parameter range, determine the initial performance parameter as a target performance parameter.

[0045] Among them, the preset parameter range is the normal parameter range corresponding to each initial performance parameter. When the initial performance parameter is not within the corresponding preset parameter range, it means that the initial performance parameter is abnormal data, and the initial performance parameter needs to be analyzed to determine whether the data abnormality is caused by an abnormality in the vehicle cloud service link.

[0046] S108. Input the target performance parameters into the fault detection model, and output the fault location and fault type corresponding to the vehicle cloud service fault.

[0047] Among them, in this embodiment, the fault detection model can be a convolutional neural network, a recurrent neural network or a long short-term memory network. In other embodiments, the fault detection model can also be other models, and the embodiments of the present application do not make specific limitations on this.

[0048] In the above vehicle cloud service fault determination method, vehicle cloud service data is obtained, and the vehicle cloud service data includes vehicle sensor data, network communication data, and vehicle cloud service operation status data; the vehicle cloud service data is analyzed to obtain a plurality of initial performance parameters corresponding to the vehicle cloud service; for each initial performance parameter, if the initial performance parameter is not within the corresponding preset parameter range, the initial performance parameter is determined as the target performance parameter; the target performance parameter is input into the fault detection model, and the fault location and fault type corresponding to the vehicle cloud service fault are output. The method provided by the present application can effectively improve the efficiency of determining vehicle cloud service faults.

[0049] In some embodiments, Figure 2 As shown, obtain vehicle cloud service data, including:

[0050] S202: Receive a data acquisition request for vehicle cloud service data sent by a user terminal, where the data acquisition request includes a user's identity.

[0051] S204: When the identity identifier is in a preset identity identifier set, a data acquisition request is sent to the vehicle computer.

[0052] S206. Receive vehicle cloud service data returned by the vehicle terminal.

[0053] The preset identity set includes the identities of all users who have the authority to access the system.

[0054] In this embodiment, the user's identity is verified using a preset identity identification set, which can ensure the security of the system.

[0055] In some embodiments, the vehicle cloud service data is analyzed to obtain multiple initial performance parameters corresponding to the vehicle cloud service, including: preprocessing the vehicle cloud service data, the data preprocessing includes data cleaning, data conversion and data normalization of the vehicle cloud service data; inputting the preprocessed data into a data analysis model to output the initial performance parameters.

[0056] Among them, the purpose of data cleaning is to identify and correct errors or inconsistencies in the original data set and improve data quality, including processing missing values, outliers, duplicate records and noisy data; data conversion refers to processing data to make it more suitable for subsequent analysis; data normalization is a special form of data standardization, which is usually used to scale data so that data can be compared on the same scale.

[0057] In this embodiment, by performing data preprocessing on the vehicle cloud service data, the quality of the vehicle cloud service data can be improved, thereby improving the accuracy of the obtained initial performance parameters.

[0058] In some embodiments, the initial performance parameters include service response time, packet loss rate, service delay, throughput, resource utilization, and scalability.

[0059] Among them, packet loss rate refers to the ratio of data packets that cannot successfully reach the destination due to various reasons during the data transmission process; throughput is an indicator to measure the amount of data successfully transmitted by a data transmission system or network per unit time. It is usually used to describe the amount of data that the system can process or transmit within a given time period. It can be the amount of information, the number of transactions, or the total number of data packets in the network.

[0060] In some embodiments, the method further includes: issuing warning information based on the fault location and the fault type, so that the user can troubleshoot the vehicle cloud service fault based on the warning information.

[0061] Among them, the fault type may include at least one of communication failure, hardware failure, software failure, power failure, safety failure, environmental failure and human failure; the fault location may be located in the on-board equipment, communication network and cloud service platform in the vehicle cloud service link.

[0062] In this embodiment, the alarm information is issued based on the fault location and fault type, so that the user can accurately determine the location and type of the fault, thereby improving the efficiency and accuracy of troubleshooting.

[0063] In some embodiments, the method further includes: sending the vehicle cloud service data to the cloud, so that the cloud stores the vehicle cloud service data.

[0064] Among them, sending vehicle cloud service data to the cloud for storage is to back up the data to ensure data security and availability.

[0065] In this embodiment, the vehicle cloud service data is sent to the cloud, so that the cloud stores the vehicle cloud service data, which can ensure the security and availability of the data.

[0066] In one embodiment, another vehicle cloud service fault determination method is provided, the method comprising the following contents:

[0067] (1) Data collection: Use sensors, log records and other means to collect data related to vehicle cloud services, including vehicle sensor data, network communication data, and the operating status of cloud services.

[0068] (2) Data transmission and storage: The collected data is transmitted to the cloud for storage and backup to ensure data security and availability.

[0069] (3) Data analysis and monitoring: Real-time or offline analysis and monitoring of collected data. By processing and calculating the data, key indicators and performance parameters such as latency, packet loss rate, and service response time can be obtained.

[0070] (4) Anomaly detection and alarm: By formulating a series of rules and algorithms, abnormal behaviors and failure conditions in vehicle cloud services, such as network failures and excessive server load, are monitored, and alarms are issued in a timely manner.

[0071] (5) Problem location and troubleshooting: When an abnormality or failure occurs, use the monitoring and analysis results combined with troubleshooting techniques to locate the specific cause of the problem, which may involve hardware failure, network communication problems, software errors, etc.

[0072] The service link diagram of this embodiment is as follows Figure 3 As shown, this embodiment is based on the full-link monitoring system of the Internet of Vehicles, including three aspects: the application end, the vehicle end, and the cloud end: the application end calls the cloud end to send a request, the cloud end records information and sends instructions to the vehicle end, the vehicle end controls the vehicle and uploads vehicle-related information, the cloud end obtains relevant logs of each link through full-link monitoring and performs abnormal detection and warnings, and quickly locates problems by setting rules and corresponding algorithms.

[0073] This embodiment is based on Figure 3 The working process is as follows:

[0074] (1) Each application sends a request to the cloud.

[0075] (2) The cloud processes the request after receiving it.

[0076] (3) Involving vehicle-side operations, sending relevant instructions to the vehicle-side to control the vehicle, obtain vehicle data and other related data.

[0077] (4) The full-link monitoring system collects logs related to the entire process and uploads them to the cloud. The cloud obtains the error logs in the logs of each link of the entire link by setting rules and corresponding algorithms. After filtering out invalid values, the cloud reversely verifies the logs of each link of the entire business according to the overall business flow, locates the specific link that causes the error, determines the specific links such as the error reporting interface, network, load, middleware, etc., and records them according to primary keys such as VIN code and userid.

[0078] The architecture diagram of this embodiment is as follows Figure 4 As shown, most intelligent connected vehicles have Internet access capabilities. In this embodiment, it is assumed as follows:

[0079] (1) The car computer uses the Android system and TBOX has Internet access capabilities.

[0080] (2) There are data transmission channels and message push channels between the vehicle computer and the cloud.

[0081] (3) Full-link monitoring has the ability to obtain logs of the entire business (including network, load, middleware and other basic services).

[0082] It should be understood that, although the various steps in the flowcharts involved in the above-mentioned embodiments are displayed in sequence according to the indication of the arrows, these steps are not necessarily executed in sequence according to the order indicated by the arrows. Unless there is a clear explanation in this article, the execution of these steps does not have a strict order restriction, and these steps can be executed in other orders. Moreover, at least a part of the steps in the flowcharts involved in the above-mentioned embodiments can include multiple steps or multiple stages, and these steps or stages are not necessarily executed at the same time, but can be executed at different times, and the execution order of these steps or stages is not necessarily to be carried out in sequence, but can be executed in turn or alternately with other steps or at least a part of the steps or stages in other steps.

[0083] Based on the same inventive concept, the embodiment of the present application also provides a vehicle cloud service fault determination device for implementing the vehicle cloud service fault determination method involved above. The implementation scheme for solving the problem provided by the device is similar to the implementation scheme recorded in the above method, so the specific limitations in one or more vehicle cloud service fault determination device embodiments provided below can refer to the limitations of the vehicle cloud service fault determination method above, and will not be repeated here.

[0084] In an exemplary embodiment, Figure 5 As shown, a vehicle cloud service fault determination device 500 is provided, comprising: an acquisition module 501, an analysis module 502, a determination module 503 and an input module 504, wherein:

[0085] The acquisition module 501 is used to acquire vehicle cloud service data, where the vehicle cloud service data includes vehicle sensor data, network communication data, and operating status data of the vehicle cloud service.

[0086] The analysis module 502 is used to analyze the vehicle cloud service data to obtain a plurality of initial performance parameters corresponding to the vehicle cloud service.

[0087] The determination module 503 is configured to determine, for each initial performance parameter, the initial performance parameter as a target performance parameter if the initial performance parameter is not within a corresponding preset parameter range.

[0088] The input module 504 is used to input the target performance parameter into the fault detection model, and output the fault location and fault type corresponding to the vehicle cloud service fault.

[0089] In some embodiments, the acquisition module 501 is also used to receive a data acquisition request for the vehicle cloud service data sent by a user terminal, and the data acquisition request includes an identity identifier of the user; when the identity identifier is in a preset identity identifier set, the data acquisition request is sent to the vehicle terminal; and the vehicle cloud service data returned by the vehicle terminal is received.

[0090] In some embodiments, the analysis module 502 is also used to perform data preprocessing on the vehicle cloud service data, and the data preprocessing includes data cleaning, data conversion and data normalization on the vehicle cloud service data; inputting the preprocessed data into the data analysis model, and outputting the initial performance parameters.

[0091] In some embodiments, the vehicle cloud service fault determination device 500 is specifically used for initial performance parameters including service response time, packet loss rate, service delay, throughput, resource utilization and scalability.

[0092] In some embodiments, the vehicle cloud service fault determination device 500 is further used to issue an alarm message based on the fault location and fault type, so that the user can eliminate the vehicle cloud service fault based on the alarm message.

[0093] In some embodiments, the vehicle cloud service fault determination device 500 is further used to send the vehicle cloud service data to the cloud so that the cloud stores the vehicle cloud service data.

[0094] Each module in the above-mentioned vehicle cloud service fault determination device can be implemented in whole or in part by software, hardware, or a combination thereof. Each of the above-mentioned modules can be embedded in or independent of a processor in a computer device in the form of hardware, or can be stored in a memory in a computer device in the form of software, so that the processor can call and execute the operations corresponding to each of the above modules.

[0095] In an exemplary embodiment, a computer device is provided. The computer device may be a terminal, and its internal structure diagram may be as shown in FIG. Figure 6As shown. The computer device includes a processor, a memory, an input / output interface, a communication interface, a display unit and an input device. Among them, the processor, the memory and the input / output interface are connected through a system bus, and the communication interface, the display unit and the input device are connected to the system bus through the input / output interface. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The input / output interface of the computer device is used to exchange information between the processor and an external device. The communication interface of the computer device is used to communicate with an external terminal in a wired or wireless manner, and the wireless manner can be implemented through WIFI, a mobile cellular network, near field communication (Near Field Communication, NFC) or other technologies. When the computer program is executed by the processor, a vehicle cloud service fault determination method is implemented.

[0096] Those skilled in the art will understand that Figure 6 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0097] In an exemplary embodiment, a computer device is provided, including a memory and a processor, wherein a computer program is stored in the memory, and the processor implements the following steps when executing the computer program: obtaining vehicle cloud service data, wherein the vehicle cloud service data includes vehicle sensor data, network communication data, and operating status data of the vehicle cloud service; analyzing the vehicle cloud service data to obtain a plurality of initial performance parameters corresponding to the vehicle cloud service; for each initial performance parameter, if the initial performance parameter is not within a corresponding preset parameter range, determining the initial performance parameter as a target performance parameter; inputting the target performance parameter into a fault detection model, and outputting a fault location and fault type corresponding to the vehicle cloud service fault.

[0098] In one embodiment, obtaining vehicle cloud service data implemented when the processor executes a computer program includes: receiving a data acquisition request for the vehicle cloud service data sent by a user terminal, the data acquisition request including a user's identity; when the identity is in a preset identity set, sending the data acquisition request to a vehicle terminal; and receiving the vehicle cloud service data returned by the vehicle terminal.

[0099] In one embodiment, the processor executes a computer program to analyze the vehicle cloud service data to obtain multiple initial performance parameters corresponding to the vehicle cloud service, including: performing data preprocessing on the vehicle cloud service data, wherein the data preprocessing includes data cleaning, data conversion, and data normalization on the vehicle cloud service data; inputting the preprocessed data into a data analysis model to output the initial performance parameters.

[0100] In one embodiment, the initial performance parameters achieved when the processor executes the computer program include service response time, packet loss rate, service delay, throughput, resource utilization, and scalability.

[0101] In one embodiment, the method implemented when the processor executes the computer program also includes: issuing warning information based on the fault location and fault type, so that the user can eliminate the vehicle cloud service fault based on the warning information.

[0102] In one embodiment, the method implemented when the processor executes the computer program further includes: sending the vehicle cloud service data to the cloud, so that the cloud stores the vehicle cloud service data.

[0103] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored, and when the computer program is executed by a processor, the following steps are implemented: obtaining vehicle cloud service data, the vehicle cloud service data including vehicle sensor data, network communication data and operating status data of the vehicle cloud service; analyzing the vehicle cloud service data to obtain a plurality of initial performance parameters corresponding to the vehicle cloud service; for each initial performance parameter, if the initial performance parameter is not within a corresponding preset parameter range, determining the initial performance parameter as a target performance parameter; inputting the target performance parameter into a fault detection model, and outputting a fault location and fault type corresponding to the vehicle cloud service fault.

[0104] In one embodiment, the acquisition of vehicle cloud service data implemented when the computer program is executed by the processor includes: receiving a data acquisition request for the vehicle cloud service data sent by a user terminal, the data acquisition request including the user's identity; when the identity is in a preset identity set, sending the data acquisition request to the vehicle terminal; and receiving the vehicle cloud service data returned by the vehicle terminal.

[0105] In one embodiment, the computer program implemented when executed by the processor analyzes the vehicle cloud service data to obtain multiple initial performance parameters corresponding to the vehicle cloud service, including: performing data preprocessing on the vehicle cloud service data, wherein the data preprocessing includes data cleaning, data conversion and data normalization on the vehicle cloud service data; inputting the preprocessed data into a data analysis model to output the initial performance parameters.

[0106] In one embodiment, the initial performance parameters achieved when the computer program is executed by the processor include service response time, packet loss rate, service delay, throughput, resource utilization, and scalability.

[0107] In one embodiment, the method implemented when the computer program is executed by a processor also includes: issuing warning information based on the fault location and fault type, so that the user can eliminate the vehicle cloud service fault based on the warning information.

[0108] In one embodiment, the method implemented when the computer program is executed by a processor further includes: sending the vehicle cloud service data to a cloud, so that the cloud stores the vehicle cloud service data.

[0109] In one embodiment, a computer program product is provided, including a computer program, which, when executed by a processor, implements the following steps: acquiring vehicle cloud service data, the vehicle cloud service data including vehicle sensor data, network communication data, and operating status data of the vehicle cloud service; analyzing the vehicle cloud service data to obtain a plurality of initial performance parameters corresponding to the vehicle cloud service; for each initial performance parameter, if the initial performance parameter is not within a corresponding preset parameter range, determining the initial performance parameter as a target performance parameter; inputting the target performance parameter into a fault detection model, and outputting a fault location and fault type corresponding to the vehicle cloud service fault.

[0110] In one embodiment, the acquisition of vehicle cloud service data implemented when the computer program is executed by the processor includes: receiving a data acquisition request for the vehicle cloud service data sent by a user terminal, the data acquisition request including the user's identity; when the identity is in a preset identity set, sending the data acquisition request to the vehicle terminal; and receiving the vehicle cloud service data returned by the vehicle terminal.

[0111] In one embodiment, the computer program implemented when executed by the processor analyzes the vehicle cloud service data to obtain multiple initial performance parameters corresponding to the vehicle cloud service, including: performing data preprocessing on the vehicle cloud service data, wherein the data preprocessing includes data cleaning, data conversion and data normalization on the vehicle cloud service data; inputting the preprocessed data into a data analysis model to output the initial performance parameters.

[0112] In one embodiment, the initial performance parameters achieved when the computer program is executed by the processor include service response time, packet loss rate, service delay, throughput, resource utilization, and scalability.

[0113] In one embodiment, the method implemented when the computer program is executed by a processor also includes: issuing warning information based on the fault location and fault type, so that the user can eliminate the vehicle cloud service fault based on the warning information.

[0114] In one embodiment, the method implemented when the computer program is executed by a processor further includes: sending the vehicle cloud service data to a cloud, so that the cloud stores the vehicle cloud service data.

[0115] 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, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of relevant data must comply with relevant regulations.

[0116] A person of ordinary skill in the art can understand that all or part of the processes in the above-mentioned embodiment method can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to the memory, database or other medium used in the embodiments provided in the present application can include at least one of non-volatile memory and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. As an illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM). The database involved in each embodiment provided in this application may include at least one of a relational database and a non-relational database. Non-relational databases may include distributed databases based on blockchains, etc., but are not limited to this. The processor involved in each embodiment provided in this application may be a general-purpose processor, a central processing unit, a graphics processor, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, an artificial intelligence (AI) processor, etc., but are not limited to this.

[0117] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this application.

[0118] The above-described embodiments only express several implementation methods of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the present application. It should be pointed out that, for a person of ordinary skill in the art, several variations and improvements can be made without departing from the concept of the present application, and these all belong to the protection scope of the present application. Therefore, the protection scope of the present application shall be subject to the attached claims.

Claims

1. A vehicle cloud service fault determination method, characterized in that: The method comprises: Acquire vehicle cloud service data, wherein the vehicle cloud service data includes vehicle sensor data, network communication data, and operation status data of the vehicle cloud service; Analyzing the vehicle cloud service data to obtain a plurality of initial performance parameters corresponding to the vehicle cloud service; For each initial performance parameter, if the initial performance parameter is not within the corresponding preset parameter range, determining the initial performance parameter as a target performance parameter; The target performance parameters are input into the fault detection model, and the fault location and fault type corresponding to the vehicle cloud service fault are output.

2. The method according to claim 1, characterized in that The obtaining of vehicle cloud service data includes: Receiving a data acquisition request for the vehicle cloud service data sent by a user terminal, wherein the data acquisition request includes an identity identifier of the user; When the identity identifier is in a preset identity identifier set, sending the data acquisition request to the vehicle terminal; Receive the vehicle cloud service data returned by the vehicle terminal.

3. The method according to claim 1, characterized in that The analyzing the vehicle cloud service data to obtain a plurality of initial performance parameters corresponding to the vehicle cloud service includes: Performing data preprocessing on the vehicle cloud service data, wherein the data preprocessing includes data cleaning, data conversion, and data normalization on the vehicle cloud service data; The preprocessed data is input into the data analysis model, and the initial performance parameters are output.

4. The method according to claim 1, characterized in that The initial performance parameters include service response time, packet loss rate, service delay, throughput, resource utilization and scalability.

5. The method according to claim 1, characterized in that The method further comprises: An alarm message is issued based on the fault location and fault type, so that the user can eliminate the vehicle cloud service fault based on the alarm message.

6. The method according to claim 1, characterized in that The method further comprises: The vehicle cloud service data is sent to a cloud so that the cloud stores the vehicle cloud service data.

7. A vehicle cloud service fault determination device, characterized in that: The device comprises: An acquisition module, used to acquire vehicle cloud service data, wherein the vehicle cloud service data includes vehicle sensor data, network communication data, and operation status data of the vehicle cloud service; An analysis module, used to analyze the vehicle cloud service data to obtain a plurality of initial performance parameters corresponding to the vehicle cloud service; a determination module, configured to determine, for each initial performance parameter, the initial performance parameter as a target performance parameter if the initial performance parameter is not within a corresponding preset parameter range; The input module is used to input the target performance parameter into the fault detection model and output the fault location and fault type corresponding to the vehicle cloud service fault.

8. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 6 are implemented.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

10. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

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