Automobile bus data analysis method and device and electronic equipment

Abstract

The invention provides an automobile bus data analysis method and device, electronic equipment and a storage medium, and the method comprises the steps: obtaining a data file of to-be-analyzed automobile bus data; obtaining an expected result index according to the task case; obtaining analysis frame data corresponding to the data file; analyzing the analysis frame data frame by frame to obtain signal message data; and analyzing the signal message data and the expected result index to obtain an analysis result. By implementing the application, the analysis efficiency of the automobile bus data can be improved, the traceability of the data analysis process is realized, the analysis process is simpler and more convenient, operation errors are not easy to occur, the error is smaller, the analysis process and the analysis result are sufficiently comprehensive, and the full coverage of the automobile bus data can be realized.

Description

Technical Field

[0001] The present application relates to the technical field of data analysis, and in particular to a method, device, electronic equipment and storage medium for analyzing automobile bus data. Background Art

[0002] The data collection and analysis process of the prior art is to record the data on the bus into a data file by using a mainstream bus data collection tool, bring the data file back to the laboratory, use relevant analysis software to manually replay and analyze the data, and implement data analysis by filling the analysis results into the test report. However, there are many problems with the above workflow. For example, it is difficult to trace the test cases and the collected data. When the analysis process needs to be reviewed, it can only be re-analyzed, and there will be deviations in each data analysis, making it difficult to restore the initial data; the data collection process has no operating steps and cannot control the operating steps, which is easy to cause operational errors; the data analysis results are not comprehensive enough, and it is impossible to achieve objective analysis of the vehicle bus data. Summary of the invention

[0003] The purpose of the present application is to provide a method, device, electronic device and storage medium for analyzing automobile bus data, which can improve the analysis efficiency of automobile bus data and realize the traceability of the data analysis process. The analysis process is simpler, less prone to operational errors, smaller errors, and the analysis process and analysis results are comprehensive enough to achieve full coverage of automobile bus data.

[0004] In a first aspect, an embodiment of the present application provides a method for analyzing automobile bus data, the method comprising:

[0005] Obtain a data file of vehicle bus data to be analyzed;

[0006] Obtain expected outcome indicators based on task use cases;

[0007] Obtaining analysis frame data corresponding to the data file;

[0008] Analyze the analysis frame data frame by frame to obtain signal message data;

[0009] The signal message data and the expected result indicator are analyzed to obtain an analysis result.

[0010] In the above implementation process, the analysis frame data and expected result indicators are obtained respectively according to the data files and task use cases, and the analysis results are obtained after analysis and comparison. This can improve the analysis efficiency of the automobile bus data and realize the traceability of the data analysis process. The analysis process is simpler, less prone to operational errors, smaller errors, and the analysis process and analysis results are comprehensive enough to achieve full coverage of the automobile bus data.

[0011] Furthermore, the step of obtaining the analysis frame data corresponding to the data file includes:

[0012] Obtaining the file type corresponding to the data file;

[0013] Obtaining original messages corresponding to the data files under different file types;

[0014] The original message is filtered to obtain the analysis frame data.

[0015] In the above implementation process, the original messages corresponding to data files of different file types are filtered, which can quickly and accurately screen different data files, reduce errors in analyzing frame data, and facilitate the preservation and tracing of original data.

[0016] Furthermore, the step of filtering the original message to obtain the analysis frame data includes:

[0017] Obtaining the message type of the original message;

[0018] Merge original messages with the same message type to obtain a first message;

[0019] Extracting the messages with the same message channel from the first messages to obtain a database file;

[0020] The database file is parsed to obtain the analysis frame data.

[0021] In the above implementation process, the original messages are merged according to the message type and the message channel, which can improve the reading efficiency of the original message data, simplify the parsing process of the database file, and make the obtained analysis frame data more real and effective.

[0022] Furthermore, the step of analyzing the analysis frame data frame by frame to obtain signal message data includes:

[0023] Determine whether the time corresponding to the analyzed frame data meets a preset threshold;

[0024] If so, analyze the expression of the analysis frame data to obtain the signal message data.

[0025] In the above implementation process, when the moment of analyzing the frame data satisfies the preset threshold, the expression of the analysis frame data is analyzed to ensure the availability of the analysis frame data, which can improve the accuracy and effectiveness of data analysis.

[0026] Furthermore, the step of analyzing the expression of the analysis frame data to obtain the signal message data includes:

[0027] Extracting the start condition, end condition and analysis condition of the expression;

[0028] Splitting the start condition, the end condition and the analysis condition as an analysis structure to obtain an analysis unit;

[0029] The analysis unit is analyzed according to the analysis type to obtain the signal message data.

[0030] In the above implementation process, the start condition, end condition and analysis condition of the expression are split into analysis structures, which can simplify the expression and thus simplify the analysis process, so that the expression can be quickly and accurately analyzed into signal message data.

[0031] Furthermore, the step of analyzing the signal message data and the expected result indicator to obtain an analysis result includes:

[0032] Extracting the numerical judgment condition, the transition judgment condition, the duration condition and the sending number condition from the expected result indicator;

[0033] The numerical judgment condition, transition judgment condition, duration condition and number of transmission conditions are compared with the corresponding signal numerical value, transition numerical value, time threshold and frequency threshold in the signal message data in turn to obtain the analysis result.

[0034] In the above implementation process, by comparing and analyzing the expected result indicators with multiple values ​​in the signal message data, the numerical difference between the signal message data and the expected result indicators can be effectively obtained, thereby better analyzing the message.

[0035] Furthermore, the step of obtaining the expected result indicator according to the task use case also includes:

[0036] The channel number, message ID, signal and signal parsing file information corresponding to the channel required for analysis are screened out according to the task use case, and the channel number, message ID, signal and signal parsing file information corresponding to the channel are stored.

[0037] In the above implementation process, the channel number, message ID, signal and signal analysis file information corresponding to the channel that may be used in the analysis process are extracted and stored in advance, which can trace the data analysis process and make the analysis process and analysis results more comprehensive.

[0038] In a second aspect, an embodiment of the present application further provides a vehicle bus data analysis device, the device comprising:

[0039] An acquisition module, used for acquiring a data file of the automobile bus data to be analyzed;

[0040] A data acquisition module is used to obtain expected result indicators according to the task use case; and is also used to obtain analysis frame data corresponding to the data file;

[0041] The analysis module is used to analyze the analysis frame data frame by frame to obtain signal message data; and is also used to analyze the signal message data and the expected result indicator to obtain an analysis result.

[0042] In the above implementation process, the analysis frame data and expected result indicators are obtained respectively according to the data files and task use cases, and the analysis results are obtained after analysis and comparison. This can improve the analysis efficiency of the automobile bus data and realize the traceability of the data analysis process. The analysis process is simpler, less prone to operational errors, smaller errors, and the analysis process and analysis results are comprehensive enough to achieve full coverage of the automobile bus data.

[0043] In a third aspect, an embodiment of the present application provides an electronic device, comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the method described in any one of the first aspects when executing the computer program.

[0044] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium, on which instructions are stored. When the instructions are executed on a computer, the computer executes the method as described in any one of the first aspects.

[0045] In a fifth aspect, an embodiment of the present application provides a computer program product, which, when running on a computer, enables the computer to execute the method as described in any one of the first aspects.

[0046] Other features and advantages of the present disclosure will be set forth in the following description, or some features and advantages may be inferred or unambiguously determined from the description, or may be learned by implementing the above-mentioned technology of the present disclosure.

[0047] And it can be implemented according to the contents of the specification. The following is a detailed description of the preferred embodiments of the present application with reference to the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings required for use in the embodiments of the present application will be briefly introduced below. It should be understood that the following drawings only show certain embodiments of the present application and therefore should not be regarded as limiting the range values. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without paying any creative work.

[0049] Figure 1A schematic diagram of a process flow of a vehicle bus data analysis method provided in an embodiment of the present application;

[0050] Figure 2 A schematic diagram of the structure of the vehicle bus data analysis device provided in an embodiment of the present application;

[0051] Figure 3 A schematic diagram of the structural composition of an electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0052] The technical solutions in the embodiments of the present application will be described below in conjunction with the drawings in the embodiments of the present application.

[0053] It should be noted that similar reference numerals and letters represent similar items in the following drawings, so once an item is defined in one drawing, it does not need to be further defined and explained in subsequent drawings. At the same time, in the description of this application, the terms "first", "second", etc. are only used to distinguish the description and cannot be understood as indicating or implying relative importance.

[0054] The specific implementation methods of the present application are further described in detail below in conjunction with the accompanying drawings and examples. The following examples are used to illustrate the present application, but are not used to limit the scope of the present application.

[0055] Embodiment 1

[0056] Figure 1 is a flow chart of the vehicle bus data analysis method provided in the embodiment of the present application, such as Figure 1 As shown, the method includes:

[0057] S1, obtaining a data file of the vehicle bus data to be analyzed;

[0058] S2, obtain expected result indicators based on task use cases;

[0059] S3, obtaining analysis frame data corresponding to the data file;

[0060] S4, analyzing the analysis frame data frame by frame to obtain signal message data;

[0061] S5, analyzing the signal message data and the expected result indicators to obtain analysis results.

[0062] In the above implementation process, the analysis frame data and expected result indicators are obtained respectively according to the data files and task use cases, and the analysis results are obtained after analysis and comparison. This can improve the analysis efficiency of the automobile bus data and realize the traceability of the data analysis process. The analysis process is simpler, less prone to operational errors, smaller errors, and the analysis process and analysis results are comprehensive enough to achieve full coverage of the automobile bus data.

[0063] The vehicle bus data analysis method of the embodiment of the present application is implemented based on the EV automatic analysis system, and can specifically realize the preparation of test cases and case evaluation indicators, the preparation of test task information, the remote issuance and reception of test tasks, real-time intelligent test assistance and execution, test data collection and feedback, test data storage and automatic analysis, automatic generation of test reports and other functions, so as to realize the control of the whole vehicle testing process, digitally manage the whole vehicle testing business, and uniformly manage the test data and test resources, thereby improving the efficiency of the whole vehicle testing.

[0064] The EV automated analysis system mainly consists of two parts: the server and the execution end. The server can achieve the following functions:

[0065] Basic data management: providing basic data support for the entire system operation; test case management: test case basic information, preconditions, test step writing, case evaluation criteria, etc.; test task management: test task information maintenance and scheduling; test data analysis: test data storage, automatic analysis, automatic generation and download of test reports; test problem management: test problem maintenance, query, and statistics; system help: system operating instructions and flow charts can be viewed online.

[0066] The execution end can realize the following functions: test task reception, receiving test tasks issued by the server; test task execution: executing test tasks, recording test data and judging test status; test data collection: test data collection and associating test cases; test data upload: test data packaging and uploading to the server.

[0067] In S1, there are two types of data files that need to be analyzed received by the server. One is the execution end interface to resume uploading the completed file, and the other is the server-side web page to directly upload the file to the server by selecting the file. The successfully uploaded file will be saved to a fixed path on the server before calling the analysis method.

[0068] In S2, illustratively, the expected result indicators are:

[0069] 1. Count the time from when the vehicle enters the fast charging mode to when it reaches the fast full charging state, and display it in the report as (0-100 charging time);

[0070] 2. Count the duration of the vehicle entering the fast cooling mode and display it in the report (fast cooling duration);

[0071] 3. With the vehicle entering the fast charging mode as the starting condition and the fast full charging state as the ending condition, the total number of battery charge / discharge Ah is counted and displayed in the report (charging capacity Ah);

[0072] 4. With the vehicle entering the fast charging mode as the starting condition and the fast full charging state as the ending condition, the range value of fast cooling is counted and displayed in the report (ECP power consumption kWh);

[0073] 5. With the vehicle entering fast charging mode as the starting condition and the fast full charging state as the ending condition, calculate the maximum voltage difference of the battery pack and display it in the report (the maximum voltage difference of the battery cell during the charging process).

[0074] Furthermore, S3 includes:

[0075] Get the file type corresponding to the data file;

[0076] Obtain the original messages corresponding to data files under different file types;

[0077] Filter the original message to obtain the analysis frame data.

[0078] In the above implementation process, the original messages corresponding to data files of different file types are filtered, which can quickly and accurately screen different data files, reduce errors in analyzing frame data, and facilitate the preservation and tracing of original data.

[0079] In an embodiment of the present application, the file types of data files include BLF format and ASC format. When parsing the frame data of the BLF and ASC format files, filtering is performed to select the frame data that needs to be analyzed in this use case, and then the frame data is sent to the kafka channel, and the consumer mode is synchronously turned on to perform frame-by-frame analysis on the frame data of the current file.

[0080] Furthermore, the original message is filtered to obtain the analysis frame data, including:

[0081] Get the message type of the original message;

[0082] Merge original messages with the same message type to obtain a first message;

[0083] Extracting the message with the same message channel from the first message to obtain a database file;

[0084] Parse the database file to obtain analysis frame data.

[0085] In the above implementation process, the original messages are merged according to the message type and the message channel, which can improve the reading efficiency of the original message data, simplify the parsing process of the database file, and make the obtained analysis frame data more real and effective.

[0086] The server of this application supports parsing ASC and BLF format files. After receiving the file, it obtains the original message and channel information and makes a judgment. After the judgment is completed and the database file is determined, the relevant data (analysis frame data) required for the subsequent server to analyze the expected results can be parsed according to the DBC / LDF / ARXML file.

[0087] Parse the original message information in the file according to the file type. The example is as follows:

[0088] {"startTime":1703488612587,"type":"CANFD","time":"0.000100","channelId":"1","messageId":"287","data":"000000fc00"}, which defines the message type type, message channel channelId, etc., obtains the message data of each channel according to the channel, and then parses the signal data message according to the configured database file. The signal data message after parsing is as follows:

[0089] {"startTime":1703488612587,"type":"CANFD","time":"0.000100","channelId":"1","messageId":"287","data":"00 0000fc00","cycle":100,"signals":[{"name":"Checksum","value":"30"},{"name":"LiveCounter","value":"23"}]}.

[0090] Furthermore, S4 includes:

[0091] Determine whether the time corresponding to the analyzed frame data meets the preset threshold;

[0092] If so, analyze the expression of the analysis frame data to obtain the signal message data.

[0093] In the above implementation process, when the moment of analyzing the frame data satisfies the preset threshold, the expression of the analysis frame data is analyzed to ensure the availability of the analysis frame data, which can improve the accuracy and effectiveness of data analysis.

[0094] When analyzing frame by frame, we will first determine whether the moment corresponding to the frame data is within the time range of the condition judgment. If so, we will then distinguish the indicator expression and divide it into three types: one is direct analysis, one is to first determine the success of the start condition and then analyze, and one is to analyze within the start condition and the end condition.

[0095] Further, the step of analyzing the expression of the analysis frame data to obtain the signal message data includes:

[0096] Extract the start condition, end condition and analysis condition of the expression;

[0097] The start condition, the end condition and the analysis condition are split as analysis structures to obtain analysis units;

[0098] The analysis unit is analyzed according to the analysis type to obtain signal message data.

[0099] In the above implementation process, the start condition, end condition and analysis condition of the expression are split into analysis structures, which can simplify the expression and thus simplify the analysis process, so that the expression can be quickly and accurately analyzed into signal message data.

[0100] This application defines the start condition, end condition, and analysis condition as the same analysis structure, which needs to be split. When splitting the analysis structure, it is recursively peeled off layer by layer to split it into the smallest units (analysis units) for analysis one by one. First determine whether there is "{}" in the outermost layer of the expression. If so, remove it and make a judgment.

[0101] The analysis types are divided into the following categories: calculation class, used to calculate the calculation value after the sub-condition value, and the expression contains "+", "-", "*", and " / "; function class, used to obtain the function value of multiple condition values, and the expression contains "Math"; judgment class, used to form a result by combining multiple single judgments, and the expression contains "&&", "||"; comparison class, used to compare after obtaining the sub-condition, and the expression contains ">", "<", "=", ">=", "<="; summation class, used to sum the return value of the sub-condition, and the expression contains "Sum"; number class, used to calculate the number of times the sub-condition succeeds, and the expression contains "Count"; continuous number class, used to calculate the number of times the sub-condition succeeds continuously, and the expression contains "ContinueCount"; duration class, used to calculate the success duration of a certain judgment when it succeeds continuously, and the expression contains "Duration"; end value class, used to calculate The end value when the signal jumps successfully, the expression contains "EndValue"; the jump duration class is used to calculate the duration of a jump completion, and the expression contains "JumpDuration"; the step time class is used to obtain the value of a number of seconds before or after the start or end of a step, which is generally used for calculating the conditions when the duration is calculated; the channel class determines whether the channel is awakened or dormant, and determines the channel cycle; the message class determines whether the message is dormant or awakened, and determines the sending cycle; the single signal class calculates whether the signal value meets the conditions, calculates whether the signal bit meets the conditions, and determines whether the signal value jumps or continues successfully; the signal calculation class obtains the signal value and the fixed value for calculation and evaluation; the multi-signal comparison class calculates and evaluates after obtaining the signal value of the condition; the multi-signal comparison class makes a judgment after obtaining the signal value of the condition; the signal linear judgment uses the "Up" and "Down" keywords in the expression to determine whether it is a linear rise or a linear fall.

[0102] Furthermore, S5 includes:

[0103] Extract the numerical judgment conditions, transition judgment conditions, duration conditions and sending times conditions in the expected result indicators;

[0104] The value judgment condition, transition judgment condition, duration condition and number of transmission conditions are compared with the corresponding signal value, transition value, time threshold and frequency threshold in the signal message data in turn to obtain the analysis result.

[0105] In the above implementation process, by comparing and analyzing the expected result indicators with multiple values ​​in the signal message data, the numerical difference between the signal message data and the expected result indicators can be effectively obtained, thereby better analyzing the message.

[0106] The analysis result requires analysis of the original message data stream and the signal message data stream. The embodiment of the present application analyzes the signal message data and the expected result indicators, and supports the logic of custom parsing messages. The analysis result is to determine whether the data in this data file meets the test case indicator requirements. The above analysis and comparison process can determine the sending cycle of a message ID; determine a channel / message ID, sleep / wake-up; determine the value of the bit; determine the signal value for a single / multiple signals; statistical time, number, maximum value, etc.; ordinary calculations and cumulative calculations between signals, etc.

[0107] By analyzing the data, determine whether the data meets the expected results of the use case. The expected results include numerical judgment conditions, transition judgment conditions, duration conditions, number of transmission conditions, etc. There are also multiple conditions and OR judgments and calculations of multiple signal values.

[0108] Furthermore, the step of obtaining the expected result indicator according to the task use case also includes:

[0109] The channel number, message ID, signal and signal parsing file information corresponding to the channel required for analysis are filtered out according to the task use case, and the channel number, message ID, signal and signal parsing file information corresponding to the channel are stored.

[0110] In the above implementation process, the channel number, message ID, signal and signal analysis file information corresponding to the channel that may be used in the analysis process are extracted and stored in advance, which can trace the data analysis process and make the analysis process and analysis results more comprehensive.

[0111] Through the information of the task use case, the expected result indicators that need to be analyzed are obtained, the channel number, message ID, and signal that need to be analyzed are screened out, and the signal parsing file information corresponding to the channel is stored for reading in the subsequent analysis process.

[0112] This application can be applied to vehicle testing. By visualizing task cases and automating data analysis, it can greatly reduce the technical level requirements for testers, while ensuring that test steps and test content are not missed. Test data is collected with high precision and transmitted back to the server for automated analysis. The analysis results are comprehensive and accurate.

[0113] Embodiment 2

[0114] In order to execute the method corresponding to the above embodiment 1 to achieve the corresponding functions and technical effects, a vehicle bus data analysis device is provided below, such as Figure 2 As shown, the device comprises:

[0115] An acquisition module 1 is used to acquire a data file of the vehicle bus data to be analyzed;

[0116] Data acquisition module 2, used to obtain expected result indicators according to task use cases; also used to obtain analysis frame data corresponding to data files;

[0117] The analysis module 3 is used to analyze the analysis frame data frame by frame to obtain signal message data; it is also used to analyze the signal message data and the expected result indicators to obtain the analysis results.

[0118] In the above implementation process, the analysis frame data and expected result indicators are obtained respectively according to the data files and task use cases, and the analysis results are obtained after analysis and comparison. This can improve the analysis efficiency of the automobile bus data and realize the traceability of the data analysis process. The analysis process is simpler, less prone to operational errors, smaller errors, and the analysis process and analysis results are comprehensive enough to achieve full coverage of the automobile bus data.

[0119] Furthermore, the data acquisition module 2 is also used for:

[0120] Get the file type corresponding to the data file;

[0121] Obtain the original messages corresponding to data files under different file types;

[0122] Filter the original message to obtain the analysis frame data.

[0123] In the above implementation process, the original messages corresponding to data files of different file types are filtered, which can quickly and accurately screen different data files, reduce errors in analyzing frame data, and facilitate the preservation and tracing of original data.

[0124] Furthermore, the data acquisition module 2 is also used for:

[0125] Get the message type of the original message;

[0126] Merge original messages with the same message type to obtain a first message;

[0127] Extracting the message with the same message channel from the first message to obtain a database file;

[0128] Parse the database file to obtain analysis frame data.

[0129] In the above implementation process, the original messages are merged according to the message type and the message channel, which can improve the reading efficiency of the original message data, simplify the parsing process of the database file, and make the obtained analysis frame data more real and effective.

[0130] Furthermore, the analysis module 3 is also used for:

[0131] Determine whether the time corresponding to the analyzed frame data meets the preset threshold;

[0132] If so, analyze the expression of the analysis frame data to obtain the signal message data.

[0133] In the above implementation process, when the moment of analyzing the frame data satisfies the preset threshold, the expression of the analysis frame data is analyzed to ensure the availability of the analysis frame data, which can improve the accuracy and effectiveness of data analysis.

[0134] Furthermore, the analysis module 3 is also used for:

[0135] Extract the start condition, end condition and analysis condition of the expression;

[0136] The start condition, the end condition and the analysis condition are split as analysis structures to obtain analysis units;

[0137] The analysis unit is analyzed according to the analysis type to obtain signal message data.

[0138] In the above implementation process, the start condition, end condition and analysis condition of the expression are split into analysis structures, which can simplify the expression and thus simplify the analysis process, so that the expression can be quickly and accurately analyzed into signal message data.

[0139] Furthermore, the analysis module 3 is also used for:

[0140] Extract the numerical judgment conditions, transition judgment conditions, duration conditions and sending times conditions in the expected result indicators;

[0141] The value judgment condition, transition judgment condition, duration condition and number of transmission conditions are compared with the corresponding signal value, transition value, time threshold and frequency threshold in the signal message data in turn to obtain the analysis result.

[0142] In the above implementation process, by comparing and analyzing the expected result indicators with multiple values ​​in the signal message data, the numerical difference between the signal message data and the expected result indicators can be effectively obtained, thereby better analyzing the message.

[0143] Furthermore, the device also includes a storage module, which is used to:

[0144] The channel number, message ID, signal and signal parsing file information corresponding to the channel required for analysis are filtered out according to the task use case, and the channel number, message ID, signal and signal parsing file information corresponding to the channel are stored.

[0145] In the above implementation process, the channel number, message ID, signal and signal analysis file information corresponding to the channel that may be used in the analysis process are extracted and stored in advance, which can trace the data analysis process and make the analysis process and analysis results more comprehensive.

[0146] The above-mentioned vehicle bus data analysis device can implement the method of the above-mentioned embodiment 1. The options in the above-mentioned embodiment 1 are also applicable to this embodiment and will not be described in detail here.

[0147] The rest of the contents of the embodiments of the present application can refer to the contents of the above-mentioned embodiment 1, and will not be repeated in this embodiment.

[0148] Embodiment 3

[0149] An embodiment of the present application provides an electronic device, including a memory and a processor, wherein the memory is used to store a computer program, and the processor runs the computer program to enable the electronic device to execute the automobile bus data analysis method of embodiment 1.

[0150] Optionally, the above-mentioned electronic device may be a server.

[0151] See also Figure 3 , Figure 3 A schematic diagram of the structure of an electronic device provided in an embodiment of the present application. The electronic device may include a processor 31, a communication interface 32, a memory 33 and at least one communication bus 34. The communication bus 34 is used to realize direct connection and communication between these components. The communication interface 32 of the device in the embodiment of the present application is used to communicate signaling or data with other node devices. The processor 31 may be an integrated circuit chip with signal processing capabilities.

[0152] The processor 31 can be a general-purpose processor, including a central processing unit (CPU), a network processor (NP), etc.; it can also be a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components. The methods, steps and logic block diagrams disclosed in the embodiments of the present application can be implemented or executed. The general-purpose processor can be a microprocessor or the processor 31 can also be any conventional processor, etc.

[0153] The memory 33 may be, but is not limited to, a random access memory (RAM), a read only memory (ROM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), an electrically erasable read-only memory (EEPROM), etc. The memory 33 stores computer-readable instructions. When the computer-readable instructions are executed by the processor 31, the device can execute the above-mentioned Figure 1 The method embodiment involves various steps.

[0154] Optionally, the electronic device may further include a storage controller and an input / output unit. The memory 33, the storage controller, the processor 31, the peripheral interface, and the input / output unit are electrically connected to each other directly or indirectly to achieve data transmission or interaction. For example, these components may be electrically connected to each other via a communication bus 34. The processor 31 is used to execute executable modules stored in the memory 33, such as software function modules or computer programs included in the device.

[0155] The input and output unit is used to provide users with the task creation and to create an optional time period or preset execution time for the task to enable interaction between the user and the server. The input and output unit can be, but is not limited to, a mouse and a keyboard.

[0156] Understandably, Figure 3 The structure shown is for illustration only. The electronic device may also include Figure 3 More or fewer components as shown, or with Figure 3 Different configurations shown. Figure 3 Each component shown in the figure can be implemented by hardware, software or a combination thereof.

[0157] In addition, an embodiment of the present application further provides a computer-readable storage medium storing a computer program, which implements the vehicle bus data analysis method of embodiment 1 when executed by a processor.

[0158] The embodiment of the present application also provides a computer program product, which, when running on a computer, enables the computer to execute the method described in the method embodiment.

[0159] In several embodiments provided in the present application, it should be understood that the disclosed devices and methods can also be implemented in other ways. The device embodiments described above are merely schematic. For example, the flowcharts and block diagrams in the accompanying drawings show the possible architecture, functions and operations of the devices, methods and computer program products according to multiple embodiments of the present application. In this regard, each box in the flowchart or block diagram can represent a module, a program segment or a part of a code, and the module, a program segment or a part of a code contains one or more executable instructions for implementing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in a different order from the order marked in the accompanying drawings. For example, two consecutive boxes can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram and / or the flowchart, and the combination of boxes in the block diagram and / or the flowchart, can be implemented with a dedicated hardware-based device that performs a specified function or action, or can be implemented with a combination of dedicated hardware and computer instructions.

[0160] In addition, the functional modules in the various embodiments of the present application may be integrated together to form an independent part, or each module may exist separately, or two or more modules may be integrated to form an independent part.

[0161] If the functions are implemented in the form of software function modules and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application can be essentially or partly embodied in the form of a software product that contributes to the prior art. The computer software product is stored in a storage medium and includes several instructions for a computer device (which can be a personal computer, server, or network device, etc.) to perform all or part of the steps of the methods described in each embodiment of the present application. The aforementioned storage medium includes: various media that can store program codes, such as USB flash drives, mobile hard drives, ROM, RAM, magnetic disks, or optical disks.

[0162] The above description is only an embodiment of the present application and is not intended to limit the protection scope of the present application. For those skilled in the art, the present application may have various changes and variations. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present application should be included in the protection scope of the present application. It should be noted that similar numbers and letters represent similar items in the following drawings, so once an item is defined in one drawing, it does not need to be further defined and explained in the subsequent drawings.

[0163] The above is only a specific implementation of the present application, but the protection range of the present application is not limited thereto. Any technician familiar with the technical field can easily think of changes or substitutions within the technical range disclosed in the present application, which should be included in the protection range of the present application. Therefore, the protection range of the present application should be based on the protection range of the claims.

[0164] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, the elements defined by the sentence "comprise a ..." do not exclude the existence of other identical elements in the process, method, article or device including the elements.

Claims

1. A method for analyzing automobile bus data, characterized in that: The method comprises: Obtain a data file of vehicle bus data to be analyzed; Obtain expected outcome indicators based on task use cases; Obtaining analysis frame data corresponding to the data file; Analyze the analysis frame data frame by frame to obtain signal message data; The signal message data and the expected result indicator are analyzed to obtain an analysis result.

2. The vehicle bus data analysis method according to claim 1, characterized in that: The step of obtaining the analysis frame data corresponding to the data file comprises: Obtaining the file type corresponding to the data file; Obtaining original messages corresponding to the data files under different file types; The original message is filtered to obtain the analysis frame data.

3. The vehicle bus data analysis method according to claim 2, characterized in that: The step of filtering the original message to obtain the analysis frame data includes: Obtaining the message type of the original message; Merge original messages with the same message type to obtain a first message; Extracting the messages with the same message channel from the first messages to obtain a database file; The database file is parsed to obtain the analysis frame data.

4. The vehicle bus data analysis method according to claim 1, characterized in that: The step of analyzing the analysis frame data frame by frame to obtain signal message data comprises: Determine whether the time corresponding to the analyzed frame data meets a preset threshold; If so, analyze the expression of the analysis frame data to obtain the signal message data.

5. The vehicle bus data analysis method according to claim 4, characterized in that: The step of analyzing the expression of the analysis frame data to obtain the signal message data comprises: Extracting the start condition, end condition and analysis condition of the expression; Splitting the start condition, the end condition and the analysis condition as an analysis structure to obtain an analysis unit; The analysis unit is analyzed according to the analysis type to obtain the signal message data.

6. The automobile bus data analysis method according to claim 1, characterized in that: The step of analyzing the signal message data and the expected result indicator to obtain an analysis result includes: Extracting the numerical judgment condition, the transition judgment condition, the duration condition and the sending number condition from the expected result indicator; The numerical judgment condition, transition judgment condition, duration condition and number of transmission conditions are compared with the corresponding signal numerical value, transition numerical value, time threshold and frequency threshold in the signal message data in turn to obtain the analysis result.

7. The vehicle bus data analysis method according to claim 1, characterized in that: The step of obtaining the expected result indicator according to the task use case also includes: The channel number, message ID, signal and signal parsing file information corresponding to the channel required for analysis are screened out according to the task use case, and the channel number, message ID, signal and signal parsing file information corresponding to the channel are stored.

8. An automobile bus data analysis device, characterized in that: The device comprises: An acquisition module, used for acquiring a data file of the automobile bus data to be analyzed; A data acquisition module is used to obtain expected result indicators according to the task use case; and is also used to obtain analysis frame data corresponding to the data file; The analysis module is used to analyze the analysis frame data frame by frame to obtain signal message data; and is also used to analyze the signal message data and the expected result indicator to obtain an analysis result.

9. An electronic device, characterized in that: The electronic device comprises a memory and a processor, wherein the memory is used to store a computer program, and the processor runs the computer program to enable the electronic device to execute the automobile bus data analysis method according to any one of claims 1 to 7.

10. A storage medium, characterized in that: The device stores a computer program, which, when executed by a processor, implements the vehicle bus data analysis method as claimed in any one of claims 1 to 7.

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