Vehicle fault data processing method and system and vehicle cloud data analysis method
By classifying and counting the fault values at each moment of the vehicle fault signal, the problem of inaccurate fault data processing in the existing technology is solved, and automatic classification and accurate analysis of fault information is realized, providing reliable data support for vehicle fault diagnosis.
Patent Information
- Application Number
- CN202510144111.8
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-02-10
- Publication Date
- 2025-05-13
AI Technical Summary
Existing vehicle fault data processing technology cannot accurately count the number of occurrences of fault signals, resulting in data distortion and affecting fault diagnosis and prevention.
By obtaining the fault values corresponding to each moment of the fault signal, the same fault signal is divided into multiple fault categories according to the fault values, and each fault category is polled and counted in sequence to determine the vehicle fault data.
It realizes automatic classification, statistics and analysis of fault information, ensures the integrity of the analysis foundation, and can accurately capture the frequency, persistence and severity of faults, providing a comprehensive and accurate basis for fault diagnosis.
Smart Images
Figure CN119992686A_ABST
Abstract
Description
Technical Field
[0001] The present invention belongs to the technical field of vehicle data processing, and specifically relates to a vehicle fault data processing method and system and a vehicle cloud data analysis method. Background Art
[0002] With the rapid development and popularization of new energy vehicle technology, vehicle systems are becoming more and more complex. Compared with traditional fuel vehicles, new energy vehicles require more electronic and electrical components to achieve various functions, including but not limited to thermal management systems, power systems, safety systems, etc. The normal operation of these systems is crucial to the performance, safety and user experience of the vehicle.
[0003] However, as the complexity of vehicle systems increases and the number of electronic and electrical components increases, the possibility of failure also increases. In order to reduce the occurrence of large-scale batch failures after mass production of vehicles, vehicle manufacturers need to conduct comprehensive monitoring and analysis of the operating conditions of each system during the development phase. This requires real-time and long-term acquisition and statistics of cloud data failure information of various systems of a large number of test vehicles. By classifying, counting and analyzing this data, potential fault types and problems can be discovered in advance, and timely measures can be taken to resolve them.
[0004] At present, there are some limitations in vehicle fault data processing and analysis. Existing technical solutions can usually only count the number of times a single fault signal occurs per unit time. This simple statistical method may lead to inaccurate statistical results or even data distortion. Such inaccurate statistical results may also affect developers' judgment of actual fault conditions, making it impossible to effectively prevent and solve potential problems.
[0005] In addition, most existing vehicle fault information prompt systems can only provide information after a vehicle fault occurs, lacking prediction and prevention functions. This passive fault handling method may cause batch failures in various vehicle systems, which in turn may lead to a large number of vehicle repairs, which not only increases maintenance costs, but also affects vehicle reliability and user satisfaction. Summary of the invention
[0006] In view of the shortcomings of the prior art described above, an object of the present invention is to provide a vehicle fault data processing method, system and vehicle cloud data analysis method, which can process a large amount of cloud data and realize automatic classification, statistics and analysis of fault information.
[0007] To achieve the above purpose, the present invention adopts the following technical solution.
[0008] A first aspect of the present invention provides a vehicle fault data processing method, comprising the following steps: Obtaining a fault value corresponding to each moment of at least one fault signal; The same fault signal is divided into multiple fault categories according to different fault values; Each fault category is polled in sequence, and the number of occurrences of the corresponding fault value is counted; The vehicle fault data is determined based on the number of occurrences of the fault category and the corresponding fault value.
[0009] As an optional implementation manner, the obtaining of the fault value corresponding to each moment of at least one fault signal includes: Time series data of at least one fault signal is obtained from the vehicle cloud data, wherein the time series data includes preset moments and fault values corresponding to each preset moment.
[0010] As an optional implementation manner, after obtaining the fault value corresponding to each moment of at least one fault signal, the method includes: The blank data in the time series data are padded, wherein the padded value is a valid fault value at a moment before the blank data.
[0011] As an optional implementation manner, the same fault signal is classified into multiple fault categories according to different fault values, including: Traverse all fault values; Data with the same fault value are classified into the same fault category, and data with different fault values are classified into different fault categories.
[0012] As an optional implementation manner, classifying data with the same fault value into the same fault category includes: creating a separate data group for each fault category.
[0013] As an optional implementation, polling each fault category in sequence and counting the number of occurrences of the corresponding fault value includes: Poll and traverse the data of each fault category in chronological order; Record the number of consecutive occurrences of the fault value in each fault category. If the fault value appears consecutively, the number of occurrences of the fault value will be increased by 1. Count the frequency of occurrence of fault values in each category.
[0014] As an optional implementation, the vehicle fault data processing method is applied to a thermal management system of a pure electric vehicle.
[0015] A second aspect of the present invention provides a vehicle fault data processing system, comprising: An acquisition unit, at least used to acquire a fault value corresponding to each moment of at least one fault signal; A classification unit, at least used for classifying the same fault signal into multiple fault categories according to different fault values; The processing unit is at least used to poll each fault category in sequence and count the number of occurrences of the corresponding fault value; the determination unit is at least used to determine the vehicle fault data based on the fault category and the number of occurrences of the corresponding fault value.
[0016] As an optional implementation, the processing unit includes Polling subunit, recording subunit and statistics subunit, where: The polling subunit is at least used to poll and traverse the data of each fault category in chronological order; The recording subunit is at least used to record the number of consecutive occurrences of the fault value in each fault category. If the fault value appears consecutively, the number of occurrences of the fault value is increased by 1. The statistical subunit is at least used to count the occurrence frequency of each category of fault values.
[0017] A third aspect of the present invention provides a vehicle cloud data analysis method, comprising: Select the folder to be analyzed; Select the target Excel file in the folder; Determine the cloud data and the corresponding cloud data folder type; Determine an opening method based on the cloud data folder type, and cyclically open the cloud data in sequence, wherein the cloud data is the original data to be processed for fault data; Get the VIN; Compare the VIN in the folder name with the obtained VIN to see if they are consistent; If they are consistent, get the folder name and create Sheets in a loop; Compare the VIN and file time in the folder name in turn; Determine vehicle fault data based on the original data read by the vehicle fault data processing method according to the first aspect of the present invention; Loop through the process one by one and write the vehicle fault data to the corresponding Sheet.
[0018] A fourth aspect of the present invention provides an electronic device, comprising: At least one processor; and at least one memory in communication with the processor, wherein: the memory stores program instructions executable by the processor, and the processor calls the program instructions to execute the steps of the method described in the first aspect or the third aspect of the present invention.
[0019] A fifth aspect of the present invention provides a readable storage medium storing a computer program, wherein the computer program is executed by a processor to perform the steps of the method described in the first aspect or the third aspect of the present invention.
[0020] In summary, compared with the prior art, the present invention ensures the integrity of the analysis basis by obtaining the number of occurrences of fault categories and corresponding fault values; classifies according to the differences in fault values, and effectively structures the complex fault information; sequentially polls and counts each fault category, which can not only capture the frequency of the fault, but also reflect its persistence and severity; determines the vehicle fault condition based on the classification results and statistical data, providing a comprehensive and accurate basis for fault diagnosis. BRIEF DESCRIPTION OF THE DRAWINGS
[0021] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For those skilled in the art, other drawings can be obtained based on these drawings without creative work.
[0022] Figure 1 It is a flow chart of a vehicle fault data processing method according to a specific embodiment of the present invention.
[0023] Figure 2 It is a flowchart of another vehicle fault data processing method according to a specific embodiment of the present invention.
[0024] Figure 3 It is a block diagram of a vehicle fault data processing system according to a specific embodiment of the present invention.
[0025] Figure 4 It is a flowchart of a vehicle cloud data analysis method according to a specific embodiment of the present invention.
[0026] Figure 5 A statistical diagram of EAC_info compressor failure times according to a specific embodiment of the present invention is shown.
[0027] Figure 6 FIG. 1 is a schematic structural diagram of an electronic device according to an embodiment of the present invention. DETAILED DESCRIPTION
[0028] The technical solutions in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative work are within the scope of protection of the present application. In addition, it should be understood that the specific embodiments described herein are only used to illustrate and explain the present application, and are not used to limit the present application.
[0029] It should be noted that the description order of the following embodiments is not intended to limit the preferred order of the embodiments of the present application. In addition, in the following embodiments, the description of each embodiment has its own emphasis, and for parts not described in detail in one embodiment, please refer to the relevant description of other embodiments.
[0030] Vehicle cloud data refers to various information such as vehicle operation, performance, environment and usage collected through on-board sensors and communication systems. These data are uploaded and stored on cloud servers for analysis, diagnosis, optimization and improvement of vehicle performance, safety and user experience. Fault data is an important subset of vehicle cloud data. Vehicle cloud data contains comprehensive information on vehicle operation, among which fault data specifically refers to specific information related to vehicle abnormalities, errors or failures. Fault data is extracted and analyzed from the overall vehicle cloud data and is used to diagnose problems, predict potential failures and guide maintenance. This relationship enables more comprehensive and accurate identification and handling of vehicle faults by analyzing a wide range of vehicle cloud data.
[0031] like Figure 1 As shown, the first aspect of the present invention provides a vehicle fault data processing method, comprising the following steps.
[0032] Step S100: obtaining a fault value corresponding to each moment of at least one fault signal.
[0033] Specifically, in step S100, the obtaining of the fault value corresponding to each moment of at least one fault signal includes: obtaining time series data of at least one fault signal from vehicle cloud data, wherein the time series data includes preset moments and the fault values corresponding to each preset moment.
[0034] Exemplarily, as shown in Table 1, a time series data expression of a fault signal is shown, wherein in Table 1, the preset time Time(s) can be counted as a time period, such as 0-22 seconds, and EAC_Info is a compressor fault information, that is, a fault signal: Table 1 Time(s) EAC_Info 0 0 1 0 2 1 3 1 4 1 5 0 6 0 7 2 8 9 2 10 0 11 0 12 0 13 3 14 3 15 3 16 3 17 0 18 0 19 3 20 21 3 22 0 .
[0035] In one embodiment of the present invention, after obtaining the fault value corresponding to each moment of at least one fault signal, before classifying the same fault signal into multiple fault categories according to different fault values, the method includes: The blank data in the time series data are padded, wherein the padded value is a valid fault value at a moment before the blank data.
[0036] Specifically, an empty value passes the value of the previous moment. If the value of the previous moment is equal to the corresponding value, the corresponding value is passed; otherwise, 0 is passed. Specifically: Empty value passing method: Empty value passes the value of the previous moment. If the value of the previous moment is equal to 1, 1 is passed; otherwise, 0 is passed. Empty value passing method: Empty value passes the value of the previous moment. If the value of the previous moment is equal to 2, 2 is passed; otherwise, 0 is passed. Empty value passes the value of the previous moment. If the value of the previous moment is equal to 3, 3 is passed; otherwise, 0 is passed.
[0037] Please continue to refer to Table 1. For the signals in Table 1, classify them and store them in variables, and fill the blanks with the values of the previous moment.
[0038] This data processing method uses the valid fault value of the previous moment to fill the gaps in the time series data instead of simply passing the value of 0. It has significant technical advantages. It not only maintains the continuity of the data and better reflects the actual system operation characteristics, but also effectively retains the fault information and avoids data distortion. This method improves the accuracy of subsequent analysis and can more accurately identify fault modes and trends. At the same time, it enhances the robustness of the data, reduces the impact of outliers on the analysis results, and provides a more reliable data basis for fault diagnosis and prediction. This processing method is particularly suitable for complex vehicle systems, such as the thermal management system of pure electric vehicles, and can better capture the dynamic changes of the system, thereby supporting more accurate fault diagnosis and the formulation of preventive maintenance strategies.
[0039] Step S200: Classify the same fault signal into multiple fault categories according to different fault values.
[0040] Specifically, in step S200, the same fault signal is classified into multiple fault categories according to different fault values, including: Traverse all fault values; Data with the same fault value are classified into the same fault category, and data with different fault values are classified into different fault categories.
[0041] Specifically, in step S200, classifying data with the same fault value into the same fault category further includes: A separate data set is created for each fault category, each data set corresponding to a specific value of the fault signal.
[0042] The same fault signal, with different values, represents different fault information under this signal. Therefore, it is necessary to record the fault values of this fault signal at each moment. For example, the data groups shown in Tables 2, 3, and 4 show the fault categories and their information corresponding to the fault values 1, 2, and 3 of the fault signals, respectively.
[0043] The first data set is Table 2, which only stores data with EAC_Info equal to 1: Table 2
[0044] The second data set is Table 3, which only stores data with EAC_Info equal to 2: Table 3
[0045] The third data set is Table 4, which only stores data with EAC_Info equal to 3: Table 4
[0046] Here, by traversing and classifying fault values, efficient and accurate structured processing of fault information is achieved; by classifying the same fault values into one category and creating an independent data group for each category, complex fault data is simplified and the detailed characteristics of the fault are retained. The classification strategy provided by the present invention makes the fault mode clearer and more identifiable, which is conducive to quickly identifying abnormal conditions and recurring problems. At the same time, creating a separate data group for each specific fault value provides a structured data basis for subsequent statistical analysis and pattern recognition, which helps to more deeply understand the nature and distribution of faults.
[0047] Step S300: poll each fault category in sequence and count the number of occurrences of the corresponding fault value.
[0048] Specifically, Figure 2 As shown, each fault category is polled in sequence, and the number of occurrences of the corresponding fault value is counted, including: Step S310: polling and traversing the data of each fault category in chronological order; Step S320: Record the number of consecutive occurrences of the fault value in each fault category. If the fault value appears consecutively, the number of occurrences of the fault value is increased by 1. Step S330: Count the occurrence frequency of each category of fault values.
[0049] For details, please continue to refer to Tables 2 to 4, where Table 2 shows that EAC_Info equals 1 appears continuously at 2-4 seconds; Table 3 shows that EAC_Info equals 2 appears continuously at 7 seconds and 9 seconds respectively; Table 4 shows that EAC_Info equals 3 appears continuously at 13-16 seconds, and EAC_Info equals 3 appears continuously at 19-21 seconds.
[0050] Based on Tables 2 to 5, status polling is performed respectively to obtain the corresponding vehicle fault data when EAC_Info is equal to 1, 2, and 3, as shown in Tables 5 to 7.
[0051] Table 5 shows the data where EAC_Info is equal to 1: Table 5
[0052] Table 6 shows the data of EAC_Info equal to 2: Table 6
[0053] Table 7 shows the data of EAC_Info equal to 3: Table 7
[0054] Step S400: Determine vehicle fault data based on the fault category and the number of occurrences of the corresponding fault value.
[0055] For example, in combination with Tables 2 to 7, the vehicle fault data corresponding to EAC_Info after data processing is shown in Table 8: Table 8 EAC_Info value 1 2 3 frequency 1 time 1 time 2 times
[0056] Combined with Table 1, by comparison, the data of EAC_Info before data processing is shown in Table 9: Table 9
[0057] Correspondingly, the vehicle fault data corresponding to EAC_Info after data processing is shown in Table 10: Table 10 EAC_Info value 1 2 3 frequency 1 time 2 times 3 times
[0058] By comparing Table 8 and Table 10, it can be seen that the fault data with EAC_Info value = 3 is significantly reduced, that is, the present invention reduces the problem of excessive error information statistics of fault information.
[0059] It should be noted that the present invention only schematically provides a fault signal fault processing method. In actual fault processing, a large number of fault processing signals are included. The present invention can count the faults that occur with different values of multiple signals based on the method described in the above embodiment to improve the efficiency of fault signal processing. The present invention only shows a processing method for a signal example EAC_Info. The computer can process all cloud data fault signals on new energy vehicles through parallel processing and adopt the same similar processing logic, so a large amount of cloud data can be processed.
[0060] In one embodiment of the present invention, the vehicle fault data processing method is applied to a thermal management system of a pure electric vehicle.
[0061] Specifically, compared with traditional fuel vehicles, pure electric vehicles including thermal management systems need to use a large number of electrical components. The more electrical components there are, the higher the possibility of failure. The present invention is applied here to achieve classification statistics of a large amount of data, obtain fault type information in advance, and solve faulty electrical components in advance.
[0062] The present invention ensures the integrity of the analysis basis by acquiring the time series data of the fault signal; classifies according to the difference in fault values, effectively structuring the complex fault information; conducts sequential polling and statistics on each fault category, which can not only capture the frequency of the fault, but also reflect its persistence and severity; determines the vehicle fault condition based on the classification results and statistical data, providing a comprehensive and accurate basis for fault diagnosis. In addition, the method provided by the present invention significantly improves the accuracy and efficiency of fault identification, can timely discover potential problems, and provides strong data support for preventive maintenance and fault warning, thereby improving the overall reliability and safety of the vehicle.
[0063] like Figure 3 As shown, the second aspect of the present invention provides a vehicle fault data processing system, comprising: An acquisition unit, at least used to acquire a fault value corresponding to each moment of at least one fault signal; A classification unit, at least used for classifying the same fault signal into multiple fault categories according to different fault values; The processing unit is at least used to poll each fault category in sequence and count the number of occurrences of the corresponding fault value; the determination unit is at least used to determine the vehicle fault data based on the fault category and the number of occurrences of the corresponding fault value.
[0064] In one embodiment of the present invention, the processing unit includes: Polling subunit, recording subunit and statistics subunit, where: The polling subunit is at least used to poll and traverse the data of each fault category in chronological order; The recording subunit is at least used to record the number of consecutive occurrences of the fault value in each fault category. If the fault value appears consecutively, the number of occurrences of the fault value is increased by 1. The statistical subunit is at least used to count the occurrence frequency of each category of fault values.
[0065] Based on the same idea as the method in the above embodiment, the system provided by the present invention can implement the method in the above embodiment. For the convenience of explanation, the structural diagram of the system embodiment only shows the parts related to the embodiment of the present invention. Those skilled in the art can understand that the illustrated structure does not constitute a limitation on the system, and may include more or less components than shown in the figure, or combine certain components, or arrange the components differently.
[0066] like Figure 4 As shown, the third aspect of the present invention provides a vehicle cloud data analysis method, comprising: Select the folder to be analyzed; Select the target Excel file in the folder; Determine the cloud data and the corresponding cloud data folder type; Determine an opening method based on the cloud data folder type, and cyclically open the cloud data in sequence, wherein the cloud data is the original data to be processed for fault data; Get the VIN; Compare the VIN in the folder name with the obtained VIN to see if they are consistent; If they are consistent, get the folder name and create Sheets in a loop; Compare the VIN and file time in the folder name in turn; Reading original data based on the vehicle fault data processing method described in any one of the above embodiments to determine vehicle fault data; looping in sequence to write the determined vehicle fault data into a corresponding Sheet; Open the summary Excel file, run the written VBA (Visual Basic for Applications, a macro language of VisualBasic), summarize the data into SUME_ALL_sheet (an exemplary table name for data summary statistics) and draw corresponding statistical analysis charts, such as bar charts.
[0067] In an application scenario of the present invention, the vehicle code VIN, for example: LSSSS409XXX999999, each vehicle has a unique VIN cloud data file, the name of the cloud data is "Historical LSSSS409XXX999999_2023-12-16_00_00_02.xlsx" with the suffix .xlsx, and the file will appear in the following signal table, as shown in Table 11: Table 11 vin time EAC_info failure LSSSS409XXX999999 2023-12-16 11:07:45 1 LSSSS409XXX999999 2023-12-16 11:07:46 2 LSSSS409XXX999999 2023-12-16 11:07:47 3 LSSSS409XXX999999 2023-12-16 11:07:48 3 LSSSS409XXX999999 2023-12-16 11:07:49 1 LSSSS409XXX999999 2023-12-16 11:07:50 2 LSSSS409XXX999999 2023-12-16 11:07:51 3 LSSSS409XXX999999 2023-12-16 11:07:52 0 LSSSS409XXX999999 2023-12-16 11:07:53 0
[0068] When the file name VIN and the obtained VIN are inconsistent, the problem is recorded in the corresponding problem data list to facilitate the subsequent corresponding problem solving; by comparing the file name VIN and the file time in sequence, the historically processed data can be excluded and only the unprocessed data can be processed; by using VBA, the efficiency and consistency of data analysis can be significantly improved, especially when processing a large amount of vehicle data. It allows complex data processing and analysis tasks to be standardized and automated, thereby supporting a more efficient decision-making process.
[0069] The obtained vin means obtaining the value LSSSS409XXX999999 in the first column of the above table; compare the file name VIN and the obtained vin in turn: the purpose is to ensure that there is an error in the vin in the cloud data, resulting in statistical errors, and compare the file name VIN and the file time in turn: the method is to compare the time in the second column with the file time 2023-12-16_00_00_02 in the file name. The file time 2023-12-16_00_00_02 should be less than the time in the second column. If the file time 2023-12-16_00_00_02 should be greater than the time in the second column, it means that it is historical data. It is necessary to exclude the historically processed data and only process the data that has not been processed; only in this way can the data be valid. Figure 5 A statistical graph of EAC_info compressor failure times is shown.
[0070] The present invention ensures data matching through VIN verification, marks and records empty data and inconsistent data, and avoids repeated processing through time comparison, thereby improving the accuracy of analysis and processing efficiency. By recording problem data in a special list, it provides a clear direction for subsequent problem solving. At the same time, through the automated data aggregation and visualization process, the method can quickly generate intuitive statistical analysis results, providing decision makers with clear data insights, and is suitable for large-scale vehicle data analysis. It can effectively support vehicle performance optimization, fault prediction, and maintenance strategy formulation, thereby significantly improving the efficiency and quality of vehicle management.
[0071] like Figure 6 As shown, the fourth aspect of the present invention provides an electronic device, including: At least one processor; and at least one memory in communication with the processor, wherein: the memory stores program instructions that can be executed by the processor, and the processor calls the program instructions to execute the steps of the method described in any one of the above embodiments.
[0072] A fifth aspect of the present invention discloses a readable storage medium storing a computer program, wherein the computer program is executed by a processor to perform the steps of the method described in any one of the above embodiments.
[0073] Computer-readable storage media may include: any entity or device capable of carrying a computer program, a recording medium, a USB flash drive, a mobile hard disk, a magnetic disk, an optical disk, a computer memory, a read-only memory (ROM), a random access memory (RAM), and a software distribution medium, etc. A computer program includes a computer program code. The computer program code may be in source code form, object code form, an executable file, or some intermediate form, etc. A computer-readable storage medium may include: any entity or device capable of carrying a computer program code, a recording medium, a USB flash drive, a mobile hard disk, a magnetic disk, an optical disk, a computer memory, a read-only memory (ROM), a random access memory (RAM), and a software distribution medium, etc.
[0074] Any process or method description in a flowchart or otherwise described herein may be understood to represent a module, segment or portion of code that includes one or more executable instructions for implementing the steps of a specific logical function or process, and the scope of the preferred embodiments of the present invention includes alternative implementations in which functions may not be performed in the order shown or discussed, including performing functions in a substantially simultaneous manner or in the reverse order depending on the functions involved, which should be understood by those skilled in the art to which the embodiments of the present invention belong.
[0075] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as an ordered list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by an instruction execution system, apparatus or device (such as a computer-based system, a system including a processing module, or other system that can fetch instructions from an instruction execution system, apparatus or device and execute instructions), or used in combination with these instruction execution systems, apparatuses or devices.
[0076] The above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features may be replaced by equivalents. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.
Claims
1. A vehicle fault data processing method, characterized in that: The following steps are involved: Obtaining a fault value corresponding to each moment of at least one fault signal; The same fault signal is divided into multiple fault categories according to different fault values; Each fault category is polled in sequence, and the number of occurrences of the corresponding fault value is counted; The vehicle fault data is determined based on the number of occurrences of the fault category and the corresponding fault value.
2. The vehicle fault data processing method according to claim 1, characterized in that: The obtaining of the fault value corresponding to each moment of at least one fault signal includes: Time series data of at least one fault signal is obtained from the vehicle cloud data, wherein the time series data includes preset moments and fault values corresponding to each preset moment.
3. The vehicle fault data processing method according to claim 2, characterized in that: After obtaining the fault value corresponding to each moment of at least one fault signal, the method includes: The blank data in the time series data are padded, wherein the padded value is a valid fault value at a moment before the blank data.
4. The vehicle fault data processing method according to claim 1, characterized in that: The same fault signal is divided into multiple fault categories according to different fault values, including: Traverse all fault values; Data with the same fault value are classified into the same fault category, and data with different fault values are classified into different fault categories.
5. The vehicle fault data processing method according to claim 4, characterized in that: The data with the same fault value are classified into the same fault category, including: A separate data set is created for each fault category.
6. The vehicle fault data processing method according to claim 1, characterized in that: The method of polling each fault category in sequence and counting the number of occurrences of the corresponding fault value includes: Poll and traverse the data of each fault category in chronological order; Record the number of consecutive occurrences of the fault value in each fault category. If the fault value appears consecutively, the number of occurrences of the fault value will be increased by 1. Count the frequency of occurrence of fault values in each category.
7. The vehicle fault data processing method according to claim 1, characterized in that: The vehicle fault data processing method is applied to a thermal management system of a pure electric vehicle.
8. A vehicle fault data processing system, characterized in that: include: An acquisition unit, at least used to acquire a fault value corresponding to each moment of at least one fault signal; A classification unit, at least used for classifying the same fault signal into multiple fault categories according to different fault values; The processing unit is at least used to poll each fault category in sequence and count the number of occurrences of the corresponding fault value; The determination unit is used to determine the vehicle fault data based on at least the fault category and the number of occurrences of the corresponding fault value.
9. The vehicle fault data processing system according to claim 8, characterized in that: The processing unit includes Polling subunit, recording subunit and statistics subunit, where: The polling subunit is at least used to poll and traverse the data of each fault category in chronological order; The recording subunit is at least used to record the number of consecutive occurrences of the fault value in each fault category. If the fault value appears consecutively, the number of occurrences of the fault value is increased by 1. The statistical subunit is at least used to count the occurrence frequency of each category of fault values.
10. A vehicle cloud data analysis method, characterized in that: include: Select the folder to be analyzed; Select the target Excel file in the folder; Determine the cloud data and the corresponding cloud data folder type; Determine an opening method based on the cloud data folder type, and cyclically open the cloud data in sequence, wherein the cloud data is the original data to be processed for fault data; Get the VIN; Compare the VIN in the folder name with the obtained VIN to see if they are consistent; If they are consistent, get the folder name and create Sheets in a loop; Compare the VIN and file time in the folder name in turn; Determine vehicle fault data based on the original data read by the vehicle fault data processing method according to any one of claims 1 to 7; Loop through the process one by one and write the vehicle fault data to the corresponding Sheet.
11. An electronic device, characterized in that: include: at least one processor; And at least one memory communicatively connected to the processor, wherein: the memory stores program instructions executable by the processor, and the processor calls the program instructions to execute the steps of the vehicle fault data processing method as described in any one of claims 1-7.
12. A readable storage medium storing a computer program, characterized in that: The computer program is executed by a processor to perform the steps of the vehicle fault data processing method as described in any one of claims 1-7.