Vehicle data management methods, devices, computer equipment and storage media

Abstract

This application relates to a vehicle data management method, apparatus, computer equipment, and storage medium. The method includes: acquiring data types of vehicle component performance data and vehicle fault frequencies, wherein the vehicle component performance data is a vehicle component performance data type, and the vehicle fault frequencies are a vehicle fault frequency type; determining whether both the vehicle component performance data type and the vehicle fault frequency type are quantitative; in response to both being quantitative, performing a normality test on the vehicle component performance data and vehicle fault frequencies to obtain a normality test result; determining a first correlation analysis algorithm based on the normality test result; or in response to determining that at least one is not quantitative, determining a second correlation analysis algorithm; and obtaining the correlation between the vehicle component performance data and the vehicle fault frequencies according to the first or second correlation analysis algorithm. This method improves the accuracy of vehicle data correlation analysis.

Description

Technical Field

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

[0002] In the automotive manufacturing and maintenance field, correlation analysis between the performance and failure frequency of vehicle components is an important way to improve production efficiency and vehicle reliability. By analyzing the correlation between performance data of different vehicle components and historical failure data, the system can identify potential faults in advance.

[0003] In related technologies, choosing the appropriate correlation analysis method often depends on the analyst's experience and understanding of the data characteristics. An inappropriate choice may lead to inaccurate or misleading analysis results. Summary of the Invention

[0004] Therefore, it is necessary to provide a vehicle data management method, device, computer equipment, and storage medium that can improve the accuracy of vehicle data correlation analysis in response to the above-mentioned technical problems.

[0005] On the one hand, a vehicle data management method is provided, the method including:

[0006] The data types for obtaining vehicle component performance data and vehicle fault frequency are as follows: the data type for vehicle component performance data is vehicle component performance data type, and the data type for vehicle fault frequency is vehicle fault frequency type.

[0007] Determine whether both vehicle component performance data types and vehicle failure frequency types are quantitative.

[0008] In response to the determination that both the vehicle component performance data and the vehicle fault frequency data are quantitative, a normality test is performed on the vehicle component performance data and the vehicle fault frequency to obtain the normality test results.

[0009] Based on the normality test results, a first correlation analysis algorithm was determined for performing correlation analysis on vehicle component performance data and vehicle failure frequency; or

[0010] In response to the determination that at least one of the vehicle component performance data type and the vehicle failure frequency type is not quantitative, a second correlation analysis algorithm is determined for performing correlation analysis on the vehicle component performance data and the vehicle failure frequency.

[0011] The correlation between vehicle component performance data and vehicle failure frequency is obtained based on either the first or second correlation analysis algorithm.

[0012] In some embodiments, the step of performing a normality test on vehicle component performance data and vehicle failure frequency to obtain the normality test result includes:

[0013] Determine the first fit between the data distribution of vehicle component performance data and the first theoretical normal distribution, and determine the second fit between the data distribution of vehicle fault frequency and the second theoretical normal distribution;

[0014] The first normality test method is determined based on the first fit result, and the second normality test method is determined based on the second fit result; and

[0015] The vehicle component performance data are tested for normality using the first normality test method, and the vehicle failure frequency is tested for normality using the second normality test method, so as to obtain the normality test results.

[0016] In some embodiments, based on the normality test results, a first correlation analysis algorithm step is determined for performing correlation analysis on vehicle component performance data and vehicle failure frequency, including:

[0017] The normality test results are determined by whether the vehicle component performance data and vehicle failure frequency both conform to a normal distribution;

[0018] In response to the determination that the normality test results show that both vehicle component performance data and vehicle fault frequency conform to a normal distribution, a first preset correlation analysis algorithm is selected as the algorithm for performing correlation analysis on vehicle component performance data and vehicle fault frequency; or

[0019] In response to the determination that the normality test results show that the uneven distribution of vehicle component performance data and vehicle fault frequency conforms to a normal distribution, a second preset correlation analysis algorithm is selected as the algorithm for performing correlation analysis on vehicle component performance data and vehicle fault frequency.

[0020] In some embodiments, in response to determining that at least one of the vehicle component performance data type and the vehicle fault frequency type is not quantitative, a second correlation analysis algorithm step is determined for performing a correlation analysis on the vehicle component performance data and the vehicle fault frequency, including:

[0021] In response to the determination that both the vehicle component performance data type and the vehicle fault frequency type are categorical, a third preset correlation analysis algorithm is selected as the algorithm for performing correlation analysis on the vehicle component performance data and the vehicle fault frequency.

[0022] In response to determining that the vehicle component performance data type is categorical and the vehicle fault frequency type is quantitative, a second preset correlation analysis algorithm is determined as the algorithm for performing correlation analysis on the vehicle component performance data and the vehicle fault frequency; or

[0023] The response vehicle component performance data is quantified and the vehicle fault frequency data is categorized. A second preset correlation analysis algorithm is determined as the algorithm for performing correlation analysis on vehicle component performance data and vehicle fault frequency.

[0024] In some embodiments, the step of acquiring data types of vehicle component performance data and vehicle failure frequency includes:

[0025] Vehicle component performance data and vehicle failure frequency are used as the data to be analyzed in turn, and the diversity of the data to be analyzed is obtained to obtain the diversity index.

[0026] Determine whether the diversity index is greater than the preset index threshold;

[0027] In response to the determination that the diversity index is no greater than a preset index threshold, the data type of the data to be analyzed is set to categorized; or

[0028] In response to the determination that the diversity index is greater than the preset index threshold, the data type of the data to be analyzed is set to quantitative.

[0029] In some embodiments, after determining whether the diversity index is greater than a preset index threshold, the method further includes:

[0030] In response to the determination that the diversity index is greater than the preset index threshold, the data to be analyzed is discretized to determine the number of categories of the data to be analyzed;

[0031] Determine if the number of categories exceeds a preset quantity threshold;

[0032] In response to the determination that the number of categories exceeds a preset threshold, the data type of the data to be analyzed is set to quantitative; or

[0033] In response to the determination that the number of categories is not greater than a preset threshold, the data type of the data to be analyzed is set to categorized.

[0034] In some embodiments, the step of discretizing the data to be analyzed and determining the number of categories of the data to be analyzed includes:

[0035] The data to be analyzed is deduplicated to obtain deduplicated data.

[0036] Determine the number of categories of data to be analyzed based on the amount of deduplicated data.

[0037] In some embodiments, the method further includes:

[0038] The correlation analysis algorithm is used to perform correlation analysis on vehicle component performance data and vehicle failure frequency to obtain correlation analysis results. Based on the data types of vehicle component performance and vehicle failure frequency, correlation analysis charts are determined.

[0039] The correlation analysis results are displayed using correlation analysis charts.

[0040] On the other hand, a vehicle data management device is provided, the device comprising:

[0041] The data type acquisition module is used to acquire the data types of vehicle component performance data and vehicle fault frequency. The data type of vehicle component performance data is the vehicle component performance data type, and the data type of vehicle fault frequency is the vehicle fault frequency type.

[0042] The quantitative determination module is used to determine whether both the vehicle component performance data type and the vehicle fault frequency type are quantitative.

[0043] The normality test module is used to perform a normality test on the vehicle component performance data and vehicle fault frequency in response to the determination that both the vehicle component performance data and vehicle fault frequency data are quantitative, and to obtain the normality test results.

[0044] The first correlation analysis module is used to determine the first correlation analysis algorithm for performing correlation analysis on vehicle component performance data and vehicle failure frequency based on the normality test results; or

[0045] The second correlation analysis module is used to determine a second correlation analysis algorithm for performing correlation analysis on vehicle component performance data and vehicle fault frequency in response to determining that at least one of the vehicle component performance data and vehicle fault frequency is not quantitative.

[0046] The data correlation analysis module is used to obtain the correlation between vehicle component performance data and vehicle fault frequency according to the first correlation analysis algorithm or the second correlation analysis algorithm.

[0047] In some embodiments, the normality test module includes:

[0048] The fitting determination submodule is used to determine the first fitting condition of the data distribution of vehicle component performance data with the first theoretical normal distribution, and to determine the second fitting condition of the data distribution of vehicle fault frequency with the second theoretical normal distribution.

[0049] The normality test submodule is used to determine a first normality test method based on a first fit, and a second normality test method based on a second fit; and

[0050] The result acquisition submodule is used to perform a normality test on the vehicle component performance data according to the first normality test method, and to perform a normality test on the vehicle fault frequency according to the second normality test method, so as to obtain the normality test result.

[0051] In some embodiments, the first correlation analysis module includes:

[0052] The normality determination submodule is used to determine whether the normality test results, namely vehicle component performance data and vehicle failure frequency, both conform to a normal distribution.

[0053] The first correlation analysis algorithm determination submodule is used to determine, in response to the determination that the normality test results show that both the vehicle component performance data and the vehicle fault frequency conform to a normal distribution, a first preset correlation analysis algorithm is used as the algorithm for performing correlation analysis on the vehicle component performance data and the vehicle fault frequency; or

[0054] The second correlation analysis algorithm determination submodule, in response to determining that the normality test result shows that the vehicle component performance data and vehicle fault frequency do not conform to a normal distribution, determines the second preset correlation analysis algorithm as the algorithm for performing correlation analysis on the vehicle component performance data and vehicle fault frequency.

[0055] In some embodiments, the second correlation analysis module includes:

[0056] The first determining submodule is used to determine a third preset correlation analysis algorithm as the algorithm for performing correlation analysis on vehicle component performance data and vehicle fault frequency in response to the determination that both vehicle component performance data and vehicle fault frequency are classified.

[0057] The second determining submodule is used to determine, in response to determining that the vehicle component performance data type is categorical and the vehicle fault frequency type is quantitative, a second preset correlation analysis algorithm as the algorithm for performing correlation analysis on the vehicle component performance data and the vehicle fault frequency; or

[0058] The second determining submodule is used to determine a second preset correlation analysis algorithm as the algorithm for performing correlation analysis on vehicle component performance data and vehicle fault frequency in response to the vehicle component performance data being quantitative and the vehicle fault frequency being categorical.

[0059] In some embodiments, the data type acquisition module includes:

[0060] The diversity index acquisition submodule is used to sequentially take vehicle component performance data and vehicle failure frequency as the data to be analyzed, and analyze the diversity of the data to be analyzed to obtain the diversity index.

[0061] The index comparison submodule is used to determine whether the diversity index is greater than the preset index threshold;

[0062] The first classification submodule is used to set the data type of the data to be analyzed to a classification level in response to determining that the diversity index is not greater than a preset index threshold; or

[0063] The first quantitative submodule is used to set the data type of the data to be analyzed to quantitative in response to the determination that the diversity index is greater than a preset index threshold.

[0064] In some embodiments, the data type acquisition module further includes:

[0065] The discretization detection submodule is used to perform discretization detection on the data to be analyzed in response to determining that the diversity index is greater than a preset index threshold, thereby determining the number of categories in the data to be analyzed.

[0066] The threshold comparison submodule is used to determine whether the number of categories is greater than a preset threshold.

[0067] The second quantitative submodule is used to set the data type of the data to be analyzed to quantitative in response to the determination that the number of categories is greater than a preset quantity threshold; or

[0068] The second classification submodule is used to set the data type of the data to be analyzed to a classification in response to the determination that the number of categories is not greater than a preset quantity threshold.

[0069] In some embodiments, the discretization detection submodule is used for:

[0070] The data to be analyzed is deduplicated to obtain deduplicated data.

[0071] Determine the number of categories of data to be analyzed based on the amount of deduplicated data.

[0072] In some embodiments, the apparatus further includes:

[0073] The correlation analysis module is used to perform correlation analysis on vehicle component performance data and vehicle failure frequency using a defined correlation analysis algorithm, obtain correlation analysis results, and determine correlation analysis charts based on the vehicle component performance data type and vehicle failure frequency type.

[0074] The analysis results acquisition module is used to display the correlation analysis results through correlation analysis charts.

[0075] In another aspect, a computer device is provided, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to perform the following steps:

[0076] The data types for obtaining vehicle component performance data and vehicle fault frequency are as follows: the data type for vehicle component performance data is vehicle component performance data type, and the data type for vehicle fault frequency is vehicle fault frequency type.

[0077] Determine whether both vehicle component performance data types and vehicle failure frequency types are quantitative.

[0078] In response to the determination that both the vehicle component performance data and the vehicle fault frequency data are quantitative, a normality test is performed on the vehicle component performance data and the vehicle fault frequency to obtain the normality test results.

[0079] Based on the normality test results, a first correlation analysis algorithm was determined for performing correlation analysis on vehicle component performance data and vehicle failure frequency; or

[0080] In response to determining that at least one of the vehicle component performance data type and vehicle fault frequency type is not quantitative, a second correlation analysis algorithm is determined for performing correlation analysis on the vehicle component performance data and vehicle fault frequency.

[0081] In another aspect, a computer-readable storage medium is provided on which a computer program is stored, which, when executed by a processor, performs the following steps:

[0082] The data types for obtaining vehicle component performance data and vehicle fault frequency are as follows: the data type for vehicle component performance data is vehicle component performance data type, and the data type for vehicle fault frequency is vehicle fault frequency type.

[0083] Determine whether both vehicle component performance data types and vehicle failure frequency types are quantitative.

[0084] In response to the determination that both the vehicle component performance data and the vehicle fault frequency data are quantitative, a normality test is performed on the vehicle component performance data and the vehicle fault frequency to obtain the normality test results.

[0085] Based on the normality test results, a first correlation analysis algorithm was determined for performing correlation analysis on vehicle component performance data and vehicle failure frequency; or

[0086] In response to determining that at least one of the vehicle component performance data type and vehicle fault frequency type is not quantitative, a second correlation analysis algorithm is determined for performing correlation analysis on the vehicle component performance data and vehicle fault frequency.

[0087] The aforementioned vehicle data management method, device, computer equipment, and storage medium can determine a correlation analysis algorithm based on the vehicle component performance data and the type and normality test results of vehicle fault frequencies. Then, the correlation between vehicle component performance data and vehicle fault frequencies can be analyzed based on the determined correlation analysis algorithm, thereby improving the accuracy of vehicle data correlation analysis. Attached Figure Description

[0088] Figure 1 This is an application environment diagram of the vehicle data management method provided in the embodiments of the present invention;

[0089] Figure 2A flowchart illustrating the vehicle data management method provided in an embodiment of the present invention;

[0090] Figure 3 This is a structural block diagram of a vehicle data management device provided in an embodiment of the present invention;

[0091] Figure 4 An internal structural diagram of a computer device provided in an embodiment of the present invention. Detailed Implementation

[0092] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0093] The vehicle data management method provided in this application can be applied to, for example... Figure 1 In the application environment shown, vehicle 102 communicates with server 104 via a network. Vehicle 102 forwards vehicle component performance data and vehicle fault frequency to server 104. Server 104 first obtains the data types of the vehicle component performance data and vehicle fault frequency, where the data type of the vehicle component performance data is a vehicle component performance data type, and the data type of the vehicle fault frequency is a vehicle fault frequency type. Next, it determines whether both the vehicle component performance data type and the vehicle fault frequency type are quantitative. In response to determining that both the vehicle component performance data type and the vehicle fault frequency type are quantitative, server 104 performs a normality test on the vehicle component performance data and vehicle fault frequency, obtaining a normality test result. Based on the normality test result, a first correlation analysis algorithm is determined for performing correlation analysis on the vehicle component performance data and vehicle fault frequency. Alternatively, in response to determining that at least one of the vehicle component performance data type and vehicle fault frequency type is not quantitative, server 104 determines a second correlation analysis algorithm for performing correlation analysis on the vehicle component performance data and vehicle fault frequency. The correlation between the vehicle component performance data and vehicle fault frequency is obtained according to either the first or second correlation analysis algorithm. Server 104 can be implemented using a standalone server or a server cluster consisting of multiple servers.

[0094] In one embodiment, such as Figure 2 As shown, a vehicle data management method is provided, which is applied to... Figure 1 Taking the server in the example, the following steps are included:

[0095] Step S101: Obtain the data types of vehicle component performance data and vehicle fault frequency, wherein the data type of vehicle component performance data is vehicle component performance data type, and the data type of vehicle fault frequency is vehicle fault frequency type.

[0096] Vehicle component performance data and vehicle failure frequencies can be acquired from multiple sources, including sensor data, inspection reports, and user feedback, and then pre-stored in a database. The database contains performance indicators and historical failure frequencies of components such as the engine and braking system, including data such as engine oil temperature and fuel consumption.

[0097] In some embodiments, the step of acquiring data types of vehicle component performance data and vehicle failure frequency includes:

[0098] (A1) The vehicle component performance data and vehicle failure frequency are used as the data to be analyzed in turn, and the diversity of the data to be analyzed is analyzed to obtain the diversity index.

[0099] (A2) Determine whether the diversity index is greater than the preset index threshold;

[0100] (A3) In response to the determination that the diversity index is not greater than the preset index threshold, the data type of the data to be analyzed is set to categorized.

[0101] (A4) In response to the determination that the diversity index is greater than the preset index threshold, the data type of the data to be analyzed is set to quantitative.

[0102] Assuming a preset index threshold of 0.7, vehicle component performance data is first used as the data to be analyzed. If the calculated Shannom diversity index for vehicle component performance data is 0.6, which is less than the preset index threshold, the data type of the vehicle component performance data is set to categorical. Next, vehicle failure frequency is used as the data to be analyzed. If the calculated Shannom diversity index for vehicle failure frequency is 0.8, which is greater than the preset index threshold, the data type of the vehicle failure frequency is set to quantitative.

[0103] In some embodiments, after determining whether the diversity index is greater than a preset index threshold, the method further includes:

[0104] (B1) In response to the determination that the diversity index is greater than the preset index threshold, discretization detection is performed on the data to be analyzed to determine the number of categories of the data to be analyzed;

[0105] (B2) Determine whether the number of categories is greater than a preset quantity threshold;

[0106] (B3) In response to the determination that the number of categories is greater than a preset quantity threshold, the data type of the data to be analyzed is set to quantitative; or

[0107] (B4) In response to the determination that the number of categories is not greater than a preset quantity threshold, the data type of the data to be analyzed is set to categorized.

[0108] Furthermore, in response to the determination that the diversity index is greater than a preset threshold, the data to be analyzed can be further analyzed to more accurately determine its data type. Discretization detection is performed on the data to be analyzed to determine whether it has significant discretization characteristics, such as containing only a small number of different values.

[0109] In some embodiments, the step of discretizing the data to be analyzed and determining the number of categories of the data to be analyzed includes:

[0110] (C1) Perform deduplication on the data to be analyzed to obtain deduplicated data;

[0111] (C2) Determine the number of categories of data to be analyzed based on the amount of deduplicated data.

[0112] First, the data to be analyzed is deduplicated, that is, duplicate values ​​are removed to obtain deduplicated data containing unique values. Then, based on the amount of deduplicated data, the number of categories of the data to be analyzed is determined. In one embodiment, the deduplicated data can be sorted, for example, by sorting the unique values ​​in the deduplicated data in ascending order or descending order. Then, the number of unique values ​​is counted, and this number of unique values ​​is set as the number of categories of the data to be analyzed.

[0113] For example, suppose the preset quantity threshold is 10. If the number of categories in the data to be analyzed is 8, since the number of categories is not greater than the preset quantity threshold, the data is determined to have strong discretization characteristics, and therefore the data type is set to categorical. If the number of categories in the data to be analyzed is 12, since the number of categories is greater than the preset quantity threshold, the data is determined to not have strong discretization characteristics, and therefore the data type is set to quantitative.

[0114] This application's embodiments, through analysis and discretization detection of the diversity of vehicle component performance data and vehicle fault frequencies, can automatically and accurately identify the data type. For example, when the vehicle component performance data is engine temperature data, diversity analysis and discretization detection determine whether the engine temperature data is continuous quantitative data or data with discrete characteristics. If the engine temperature data is highly discrete, such as having only a limited number of high-temperature alarms, then the engine temperature data is considered categorized data.

[0115] Step S102: Determine whether both the vehicle component performance data type and the vehicle fault frequency type are quantitative.

[0116] Determine whether both the vehicle component performance data type and the vehicle fault frequency type are quantitative. In response to determining that both the vehicle component performance data type and the vehicle fault frequency type are quantitative, proceed to step S103. In response to determining that at least one of the vehicle component performance data type and the vehicle fault frequency type is not quantitative, proceed to step S105.

[0117] Step S103: Perform a normality test on the vehicle component performance data and vehicle failure frequency to obtain the normality test results.

[0118] By generating QQ (Quantile-Quantile) plots, the distribution characteristics of each performance indicator are examined to determine whether standard statistical methods are suitable. For example, if the engine temperature data follows a roughly linear distribution, conforming to a normal distribution, then the Pearson correlation analysis algorithm is preferred. Otherwise, other suitable algorithms are used.

[0119] In one embodiment, the step of performing a normality test on vehicle component performance data and vehicle failure frequency to obtain the normality test result includes:

[0120] (D1) Determine the first fit between the data distribution of vehicle component performance data and the first theoretical normal distribution, and determine the second fit between the data distribution of vehicle fault frequency and the second theoretical normal distribution;

[0121] (D2) Determine the first normality test method based on the first fit, and determine the second normality test method based on the second fit; and

[0122] (D3) Perform a normality test on the vehicle component performance data according to the first normality test method, and perform a normality test on the vehicle fault frequency according to the second normality test method to obtain the normality test results.

[0123] A QQ plot can be generated using vehicle component performance data to determine the first fit between the data distribution and a theoretical normal distribution. If the points in the QQ plot are roughly distributed along a straight line, the first fit indicates that the vehicle component performance data values ​​are generally normal and close to a normal distribution; otherwise, the first fit indicates that the vehicle component performance data contains outliers.

[0124] Similarly, a QQ (Quantile-Quantile) plot can be generated using vehicle fault frequencies to determine the second fit between the data distribution of vehicle fault frequencies and the second theoretical normal distribution. If the points in the QQ plot are roughly distributed along a straight line, the second fit indicates that the vehicle fault frequency values ​​are roughly normal and close to a normal distribution; otherwise, the second fit indicates that there are outliers in the vehicle fault frequencies.

[0125] Next, based on the first fitting result, a first normality test method is determined for testing the normality of vehicle component performance data, and based on the second fitting result, a second normality test method is determined for testing the normality of vehicle fault frequency.

[0126] If the first fitting condition is that the vehicle component performance data contains outliers, then the first normality test method is determined to be the Shapiro-Wilk test. If the first fitting condition is that the vehicle component performance data values ​​are roughly normal and close to a normal distribution, then the first normality test method is determined to be the Kolmogorov-Smirnov test. Among these, the Shapiro-Wilk test performs better than the Kolmogorov-Smirnov test when dealing with small samples or the presence of outliers.

[0127] Similarly, if the second fitting condition is that there are outliers in the vehicle fault frequency, then the second normality test method is determined to be the Shapiro-Wilk test. If the second fitting condition is that the vehicle fault frequency values ​​are roughly normal and close to a normal distribution, then the second normality test method is determined to be the Kolmogorov-Smirnov test.

[0128] The system can automatically recommend suitable algorithms based on data type and normality detection results. For example, when analyzing the relationship between engine temperature data and fault frequency data, if the data is quantitative and conforms to a normal distribution, the Pearson correlation coefficient is recommended. When fuel consumption is quantitative data and braking status is categorical data, the Spearman rank correlation coefficient is recommended. This approach ensures the applicability of the algorithm and the reliability of the analysis results. A detailed explanation follows:

[0129] Step S104: Based on the normality test results, determine the first correlation analysis algorithm for performing correlation analysis on vehicle component performance data and vehicle fault frequency.

[0130] In some embodiments, based on the normality test results, a first correlation analysis algorithm step is determined for performing correlation analysis on vehicle component performance data and vehicle failure frequency, including:

[0131] (E1) Determine whether the normality test results are both vehicle component performance data and vehicle failure frequency conforming to a normal distribution;

[0132] (E2) In response to the determination that the normality test results show that both the vehicle component performance data and the vehicle fault frequency conform to a normal distribution, a first preset correlation analysis algorithm is selected as the algorithm for performing correlation analysis on the vehicle component performance data and the vehicle fault frequency; or

[0133] (E3) In response to the determination that the normality test results show that the vehicle component performance data and vehicle fault frequency do not conform to a normal distribution, a second preset correlation analysis algorithm is determined as the algorithm for performing correlation analysis on the vehicle component performance data and vehicle fault frequency.

[0134] For example, if the normality test results indicate that the vehicle component performance data and vehicle failure frequency conform to a normal distribution, then the Person correlation analysis algorithm is selected as the first preset correlation analysis algorithm for correlation analysis. If the normality test results indicate that the vehicle component performance data and vehicle failure frequency do not conform to a normal distribution, then the Spearman correlation analysis algorithm is selected as the second preset correlation analysis algorithm for correlation analysis.

[0135] Step S105: Determine a second correlation analysis algorithm for performing correlation analysis on vehicle component performance data and vehicle failure frequency.

[0136] In some embodiments, in response to determining that at least one of the vehicle component performance data type and the vehicle fault frequency type is not quantitative, a second correlation analysis algorithm step is determined for performing a correlation analysis on the vehicle component performance data and the vehicle fault frequency, including:

[0137] (F1) In response to the determination that the vehicle component performance data type and the vehicle fault frequency type are both classified, a third preset correlation analysis algorithm is determined as the algorithm for performing correlation analysis on the vehicle component performance data and the vehicle fault frequency.

[0138] (F2) In response to determining that the vehicle component performance data type is categorical and the vehicle fault frequency type is quantitative, a second preset correlation analysis algorithm is determined as the algorithm for performing correlation analysis on the vehicle component performance data and the vehicle fault frequency; or

[0139] (F3) If the vehicle component performance data type is quantitative and the vehicle fault frequency type is categorical, a second preset correlation analysis algorithm is determined as the algorithm for performing correlation analysis on the vehicle component performance data and the vehicle fault frequency.

[0140] For example, in response to determining that both the vehicle component performance data type and the vehicle fault frequency type are categorical, Kendall's Tau correlation analysis algorithm is selected as the third preset correlation analysis algorithm for correlation analysis. In response to determining that one of the vehicle component performance data type and the vehicle fault frequency type is categorical and the other is quantitative, the Spearman correlation analysis algorithm is selected as the second preset correlation analysis algorithm for correlation analysis.

[0141] Step S106: Obtain the correlation between vehicle component performance data and vehicle fault frequency according to the first correlation analysis algorithm or the second correlation analysis algorithm.

[0142] Through the aforementioned data processing flow and efficient algorithms, significant correlations between different performance indicators and fault frequencies can be quickly identified. For example, if there is a high correlation between engine temperature and brake failure, the vehicle will be marked as high-risk, indicating the need for further inspection. For abnormal data, such as anomalies like excessively high temperatures, alarms or optimization suggestions will be automatically generated to help optimize the design before the vehicle is put on the market.

[0143] In some embodiments, correlation analysis results can also be obtained, and correlation analysis charts can be determined based on vehicle component performance data types and vehicle fault frequency types; then, the correlation analysis results can be displayed through the correlation analysis charts.

[0144] After analyzing vehicle component performance data and vehicle failure frequencies using correlation analysis algorithms, correlation analysis charts can be determined based on the data types of vehicle component performance and vehicle failure frequencies to display the correlation analysis results. These correlation analysis charts include at least one of the following: scatter plots, box plots, bar plots, and / or correlation matrices.

[0145] For example, when both vehicle component performance data and vehicle failure frequency data are quantitative, a scatter plot can be used to visually display the relationship between the two data columns. When presenting the correlation analysis results using a scatter plot, the correlation coefficient and p-value can also be labeled. Conversely, when both vehicle component performance data and vehicle failure frequency data are categorical, a box plot or bar plot can be used. When presenting the correlation analysis results using a box plot, the median, quartiles, and possible outliers can be displayed. When presenting the correlation analysis results using a bar plot, the mean or median of each category can be shown.

[0146] The visualizations described above help engineers intuitively view the relationships between data. By combining regression lines and significance markers, the impact of different data on system performance can be determined, providing a scientific basis for subsequent improvements.

[0147] Furthermore, correlation analysis algorithms can be used to further determine the correlation analysis charts to be displayed. When both vehicle component performance data and vehicle failure frequency data are quantitative, if the correlation analysis algorithm used is the Pearson correlation analysis algorithm, regression lines can be added to the scatter plot to show the linear fit of the data.

[0148] The vehicle data management method provided in this application can determine a correlation analysis algorithm based on the performance data of vehicle components and the type and normality test results of vehicle fault frequencies, and then analyze the correlation between the performance data of vehicle components and vehicle fault frequencies based on the determined correlation analysis algorithm, thereby improving the accuracy of vehicle data correlation analysis.

[0149] It should be understood that, although Figure 2 The steps in the flowchart are shown sequentially as indicated by the arrows, but these steps are not necessarily executed in the order indicated by the arrows. Unless otherwise specified herein, there is no strict order in which these steps are executed, and they can be performed in other orders. Figure 2 At least some of the steps in the process may include multiple sub-steps or multiple stages. These sub-steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these sub-steps or stages is not necessarily sequential, but can be executed in turn or alternately with other steps or at least some of the sub-steps or stages of other steps.

[0150] In one embodiment, such as Figure 3 As shown, a vehicle data management device is provided, including: a data type acquisition module 301, a quantitative determination module 302, a normality test module 303, a first correlation analysis module 304, a second correlation analysis module 305, and a data correlation analysis module 306, wherein:

[0151] The data type acquisition module 301 is used to acquire the data types of vehicle component performance data and vehicle fault frequency, wherein the data type of vehicle component performance data is a vehicle component performance data type, and the data type of vehicle fault frequency is a vehicle fault frequency type; the quantification determination module 302 is used to determine whether both the vehicle component performance data type and the vehicle fault frequency type are quantitative; the normality test module 303 is used to perform a normality test on the vehicle component performance data and vehicle fault frequency in response to determining that both the vehicle component performance data type and the vehicle fault frequency type are quantitative, and obtain a normality test result; the first correlation analysis module 304 is used to determine a first correlation analysis algorithm for performing correlation analysis on the vehicle component performance data and vehicle fault frequency based on the normality test result; or the second correlation analysis module 305 is used to determine a second correlation analysis algorithm for performing correlation analysis on the vehicle component performance data and vehicle fault frequency in response to determining that at least one of the vehicle component performance data type and vehicle fault frequency type is not quantitative; the data correlation analysis module 306 is used to obtain the correlation between the vehicle component performance data and the vehicle fault frequency according to the first correlation analysis algorithm or the second correlation analysis algorithm.

[0152] In some embodiments, the normality test module 303 includes: a fitting determination submodule, a normality test submodule, and a result acquisition submodule. Wherein:

[0153] The fitting determination submodule is used to determine the first fitting condition of the data distribution of vehicle component performance data with the first theoretical normal distribution, and to determine the second fitting condition of the data distribution of vehicle fault frequency with the second theoretical normal distribution; the normality test submodule is used to determine the first normality test method based on the first fitting condition, and to determine the second normality test method based on the second fitting condition; and the result acquisition submodule is used to perform normality test on the vehicle component performance data according to the first normality test method, and to perform normality test on the vehicle fault frequency according to the second normality test method, so as to obtain the normality test result.

[0154] In some embodiments, the first correlation analysis module 304 includes: a normal distribution determination submodule, a first correlation analysis algorithm determination submodule, and a second correlation analysis algorithm determination submodule. Wherein:

[0155] The module for determining the normality distribution is used to determine whether the normality test results for vehicle component performance data and vehicle fault frequency both conform to a normal distribution. The module for determining the first correlation analysis algorithm is used to determine a first preset correlation analysis algorithm as the algorithm for performing correlation analysis on vehicle component performance data and vehicle fault frequency in response to the determination that the normality test results for vehicle component performance data and vehicle fault frequency both conform to a normal distribution. Alternatively, the module for determining the second correlation analysis algorithm is used to determine a second preset correlation analysis algorithm as the algorithm for performing correlation analysis on vehicle component performance data and vehicle fault frequency in response to the determination that the normality test results for vehicle component performance data and vehicle fault frequency do not conform to a normal distribution.

[0156] In some embodiments, the second correlation analysis module 305 includes: a first determination submodule, a second determination submodule, and a second determination submodule. Wherein:

[0157] The first determining submodule is used to determine a third preset correlation analysis algorithm as the algorithm for performing correlation analysis on vehicle component performance data and vehicle fault frequency in response to determining that both the vehicle component performance data type and the vehicle fault frequency type are categorical; the second determining submodule is used to determine a second preset correlation analysis algorithm as the algorithm for performing correlation analysis on vehicle component performance data and vehicle fault frequency in response to determining that both the vehicle component performance data type and the vehicle fault frequency type are categorical; or the second determining submodule is used to determine a second preset correlation analysis algorithm as the algorithm for performing correlation analysis on vehicle component performance data and vehicle fault frequency in response to determining that both the vehicle component performance data type and the vehicle fault frequency type are categorical.

[0158] In some embodiments, the data type acquisition module 301 includes: a diversity index acquisition submodule, an index comparison submodule, a first classification submodule, and a first quantitative submodule. Wherein:

[0159] The diversity index acquisition submodule is used to sequentially use vehicle component performance data and vehicle failure frequency as data to be analyzed, and analyze the diversity of the data to be analyzed to obtain a diversity index; the index comparison submodule is used to determine whether the diversity index is greater than a preset index threshold; the first classification submodule is used to set the data type of the data to be analyzed to classification in response to determining that the diversity index is not greater than the preset index threshold; or the first quantitative submodule is used to set the data type of the data to be analyzed to quantitative in response to determining that the diversity index is greater than the preset index threshold.

[0160] In some embodiments, the data type acquisition module 301 further includes: a discretization detection submodule, a threshold comparison submodule, a second quantitative submodule, and a second classification submodule. Wherein:

[0161] The discretization detection submodule is used to perform discretization detection on the data to be analyzed in response to determining that the diversity index is greater than a preset index threshold, and to determine the number of categories of the data to be analyzed; the threshold comparison submodule is used to determine whether the number of categories is greater than a preset quantity threshold; the second quantitative submodule is used to set the data type of the data to be analyzed to quantitative in response to determining that the number of categories is greater than a preset quantity threshold; or the second classification submodule is used to set the data type of the data to classification in response to determining that the number of categories is not greater than a preset quantity threshold.

[0162] In some embodiments, the discretization detection submodule is used to: perform deduplication processing on the data to be analyzed to obtain deduplicated data; and determine the number of categories of the data to be analyzed based on the amount of deduplicated data.

[0163] In some embodiments, the apparatus further includes: a correlation analysis module and an analysis result acquisition module. Wherein:

[0164] The correlation analysis module is used to perform correlation analysis on vehicle component performance data and vehicle failure frequency using a defined correlation analysis algorithm, obtain correlation analysis results, and determine correlation analysis charts based on the vehicle component performance data type and vehicle failure frequency type; the analysis result acquisition module is used to display the correlation analysis results through correlation analysis charts.

[0165] Specific limitations regarding the vehicle data management device can be found in the limitations of the vehicle data management method described above, and will not be repeated here. Each module in the aforementioned vehicle data management device can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in or independent of the processor in a computer device in hardware form, or stored in the memory of a computer device in software form, so that the processor can call and execute the corresponding operations of each module.

[0166] In one embodiment, a computer device is provided, which may be a server, and its internal structure diagram may be as follows: Figure 4 As shown, the computer device includes a processor, memory, network interface, and database connected via a system bus. The processor provides computing and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system, computer programs, and database. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage media. The database stores vehicle component performance data and vehicle failure frequencies. The network interface communicates with external terminals via a network connection. When executed by the processor, the computer program implements a vehicle data management method.

[0167] Those skilled in the art will understand that Figure 4 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0168] In one embodiment, a computer device is provided, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to perform the following steps:

[0169] The method involves acquiring data types for vehicle component performance data and vehicle fault frequency, where the data type for vehicle component performance data is a vehicle component performance data type, and the data type for vehicle fault frequency is a vehicle fault frequency type; determining whether both the vehicle component performance data type and the vehicle fault frequency type are quantitative; in response to determining that both the vehicle component performance data type and the vehicle fault frequency type are quantitative, performing a normality test on the vehicle component performance data and the vehicle fault frequency, and obtaining the normality test result; based on the normality test result, determining a first correlation analysis algorithm for performing correlation analysis on the vehicle component performance data and the vehicle fault frequency; or in response to determining that at least one of the vehicle component performance data type and the vehicle fault frequency type is not quantitative, determining a second correlation analysis algorithm for performing correlation analysis on the vehicle component performance data and the vehicle fault frequency.

[0170] In one embodiment, the processor, when executing a computer program, also performs the following steps:

[0171] The process involves determining the first fit between the data distribution of vehicle component performance data and a first theoretical normal distribution, and determining the second fit between the data distribution of vehicle fault frequency and a second theoretical normal distribution; determining a first normality test method based on the first fit, and determining a second normality test method based on the second fit; performing a normality test on the vehicle component performance data based on the first normality test method, and performing a normality test on the vehicle fault frequency based on the second normality test method, to obtain the normality test results.

[0172] In one embodiment, the processor, when executing a computer program, also performs the following steps:

[0173] The normality test result is determined to be whether both vehicle component performance data and vehicle fault frequency conform to a normal distribution; in response to determining that both vehicle component performance data and vehicle fault frequency conform to a normal distribution, a first preset correlation analysis algorithm is determined as the algorithm for performing correlation analysis on vehicle component performance data and vehicle fault frequency; or in response to determining that both vehicle component performance data and vehicle fault frequency do not conform to a normal distribution, a second preset correlation analysis algorithm is determined as the algorithm for performing correlation analysis on vehicle component performance data and vehicle fault frequency.

[0174] In one embodiment, the processor, when executing a computer program, also performs the following steps:

[0175] In response to the determination that both the vehicle component performance data type and the vehicle fault frequency type are categorical, a third preset correlation analysis algorithm is determined as the algorithm for performing correlation analysis on the vehicle component performance data and the vehicle fault frequency; in response to the determination that the vehicle component performance data type is categorical and the vehicle fault frequency type is quantitative, a second preset correlation analysis algorithm is determined as the algorithm for performing correlation analysis on the vehicle component performance data and the vehicle fault frequency; or in response that the vehicle component performance data type is quantitative and the vehicle fault frequency type is categorical, a second preset correlation analysis algorithm is determined as the algorithm for performing correlation analysis on the vehicle component performance data and the vehicle fault frequency.

[0176] In one embodiment, the processor, when executing a computer program, also performs the following steps:

[0177] Vehicle component performance data and vehicle failure frequency are used as the data to be analyzed in sequence, and the diversity of the data to be analyzed is analyzed to obtain a diversity index; it is determined whether the diversity index is greater than a preset index threshold; in response to determining that the diversity index is not greater than the preset index threshold, the data type of the data to be analyzed is set to categorical; or in response to determining that the diversity index is greater than the preset index threshold, the data type of the data to be analyzed is set to quantitative.

[0178] In one embodiment, the processor, when executing a computer program, also performs the following steps:

[0179] In response to determining that the diversity index is greater than a preset index threshold, the data to be analyzed is discretized to determine the number of categories of the data to be analyzed; it is then determined whether the number of categories is greater than a preset quantity threshold; in response to determining that the number of categories is greater than the preset quantity threshold, the data type of the data to be analyzed is set to quantitative; or in response to determining that the number of categories is not greater than the preset quantity threshold, the data type of the data to be analyzed is set to categorical.

[0180] In one embodiment, the processor, when executing a computer program, also performs the following steps:

[0181] The data to be analyzed is deduplicated to obtain deduplicated data; based on the amount of deduplicated data, the number of categories of the data to be analyzed is determined.

[0182] In one embodiment, the processor, when executing a computer program, also performs the following steps:

[0183] The correlation analysis algorithm is used to perform correlation analysis on vehicle component performance data and vehicle failure frequency to obtain correlation analysis results. Based on the data types of vehicle component performance and vehicle failure frequency, correlation analysis charts are determined and the correlation analysis results are displayed through the correlation analysis charts.

[0184] In one embodiment, a computer-readable storage medium is provided having a computer program stored thereon, the computer program performing the following steps when executed by a processor:

[0185] The method involves acquiring data types for vehicle component performance data and vehicle fault frequency, where the data type for vehicle component performance data is a vehicle component performance data type, and the data type for vehicle fault frequency is a vehicle fault frequency type; determining whether both the vehicle component performance data type and the vehicle fault frequency type are quantitative; in response to determining that both the vehicle component performance data type and the vehicle fault frequency type are quantitative, performing a normality test on the vehicle component performance data and the vehicle fault frequency, and obtaining the normality test result; based on the normality test result, determining a first correlation analysis algorithm for performing correlation analysis on the vehicle component performance data and the vehicle fault frequency; or in response to determining that at least one of the vehicle component performance data type and the vehicle fault frequency type is not quantitative, determining a second correlation analysis algorithm for performing correlation analysis on the vehicle component performance data and the vehicle fault frequency.

[0186] In one embodiment, when the computer program is executed by a processor, it further performs the following steps:

[0187] The process involves determining the first fit between the data distribution of vehicle component performance data and a first theoretical normal distribution, and determining the second fit between the data distribution of vehicle fault frequency and a second theoretical normal distribution; determining a first normality test method based on the first fit, and determining a second normality test method based on the second fit; performing a normality test on the vehicle component performance data based on the first normality test method, and performing a normality test on the vehicle fault frequency based on the second normality test method, to obtain the normality test results.

[0188] In one embodiment, when the computer program is executed by a processor, it also performs the following steps:

[0189] The normality test result is determined to be whether both vehicle component performance data and vehicle fault frequency conform to a normal distribution; in response to determining that both vehicle component performance data and vehicle fault frequency conform to a normal distribution, a first preset correlation analysis algorithm is determined as the algorithm for performing correlation analysis on vehicle component performance data and vehicle fault frequency; or in response to determining that both vehicle component performance data and vehicle fault frequency do not conform to a normal distribution, a second preset correlation analysis algorithm is determined as the algorithm for performing correlation analysis on vehicle component performance data and vehicle fault frequency.

[0190] In one embodiment, when the computer program is executed by a processor, it also performs the following steps:

[0191] In response to the determination that both the vehicle component performance data type and the vehicle fault frequency type are categorical, a third preset correlation analysis algorithm is determined as the algorithm for performing correlation analysis on the vehicle component performance data and the vehicle fault frequency; in response to the determination that the vehicle component performance data type is categorical and the vehicle fault frequency type is quantitative, a second preset correlation analysis algorithm is determined as the algorithm for performing correlation analysis on the vehicle component performance data and the vehicle fault frequency; or in response that the vehicle component performance data type is quantitative and the vehicle fault frequency type is categorical, a second preset correlation analysis algorithm is determined as the algorithm for performing correlation analysis on the vehicle component performance data and the vehicle fault frequency.

[0192] In one embodiment, when the computer program is executed by a processor, it also performs the following steps:

[0193] Vehicle component performance data and vehicle failure frequency are used as the data to be analyzed in sequence, and the diversity of the data to be analyzed is analyzed to obtain a diversity index; it is determined whether the diversity index is greater than a preset index threshold; in response to determining that the diversity index is not greater than the preset index threshold, the data type of the data to be analyzed is set to categorical; or in response to determining that the diversity index is greater than the preset index threshold, the data type of the data to be analyzed is set to quantitative.

[0194] In one embodiment, when the computer program is executed by a processor, it also performs the following steps:

[0195] In response to determining that the diversity index is greater than a preset index threshold, the data to be analyzed is discretized to determine the number of categories of the data to be analyzed; it is then determined whether the number of categories is greater than a preset quantity threshold; in response to determining that the number of categories is greater than the preset quantity threshold, the data type of the data to be analyzed is set to quantitative; or in response to determining that the number of categories is not greater than the preset quantity threshold, the data type of the data to be analyzed is set to categorical.

[0196] In one embodiment, when the computer program is executed by a processor, it also performs the following steps:

[0197] The data to be analyzed is deduplicated to obtain deduplicated data; based on the amount of deduplicated data, the number of categories of the data to be analyzed is determined.

[0198] In one embodiment, when the computer program is executed by a processor, it also performs the following steps:

[0199] The correlation analysis algorithm is used to perform correlation analysis on vehicle component performance data and vehicle failure frequency to obtain correlation analysis results. Based on the data types of vehicle component performance and vehicle failure frequency, correlation analysis charts are determined and the correlation analysis results are displayed through the correlation analysis charts.

[0200] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium. When executed, the computer program can include the processes of the embodiments of the above methods. Any references to memory, storage, databases, or other media used in the embodiments provided in this application can include non-volatile and / or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), dual data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), RAMbus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and RAMbus dynamic RAM (RDRAM), etc.

[0201] The technical features of the above embodiments can be combined in any way. For the sake of brevity, 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 specification.

[0202] The embodiments described above are merely illustrative of several implementation methods of this application, and while the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the invention patent. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this patent application should be determined by the appended claims.

Claims

1. A vehicle data management method, characterized in that, include: The data types for acquiring vehicle component performance data and vehicle fault frequency are as follows: the data type for vehicle component performance data is a vehicle component performance data type, and the data type for vehicle fault frequency is a vehicle fault frequency type. Determine whether both the vehicle component performance data type and the vehicle fault frequency type are quantitative. In response to the determination that both the vehicle component performance data type and the vehicle fault frequency type are quantitative, a normality test is performed on the vehicle component performance data and the vehicle fault frequency to obtain the normality test result. Based on the normality test results, a first correlation analysis algorithm is determined for performing correlation analysis on the vehicle component performance data and the vehicle failure frequency; or In response to determining that at least one of the vehicle component performance data type and the vehicle fault frequency type is not quantitative, a second correlation analysis algorithm is determined for performing correlation analysis on the vehicle component performance data and the vehicle fault frequency. The correlation between vehicle component performance data and vehicle failure frequency is obtained based on the first correlation analysis algorithm or the second correlation analysis algorithm.

2. The vehicle data management method according to claim 1, characterized in that, The step of performing a normality test on the vehicle component performance data and the vehicle failure frequency to obtain the normality test result includes: Determine the first fit between the data distribution of the vehicle component performance data and the first theoretical normal distribution, and determine the second fit between the data distribution of the vehicle fault frequency and the second theoretical normal distribution; A first normality test method is determined based on the first fitting result, and a second normality test method is determined based on the second fitting result; and The vehicle component performance data are subjected to a normality test according to the first normality test method, and the vehicle fault frequency is subjected to a normality test according to the second normality test method to obtain the normality test result.

3. The vehicle data management method according to claim 1, characterized in that, The step of determining a first correlation analysis algorithm for performing correlation analysis on the vehicle component performance data and the vehicle failure frequency based on the normality test results includes: The normality test result is determined by whether the vehicle component performance data and the vehicle failure frequency both conform to a normal distribution; In response to determining that the normality test result indicates that both the vehicle component performance data and the vehicle fault frequency conform to a normal distribution, a first preset correlation analysis algorithm is determined as the algorithm for performing correlation analysis on the vehicle component performance data and the vehicle fault frequency; or In response to determining that the normality test result shows that the vehicle component performance data and the vehicle fault frequency do not conform to a normal distribution, a second preset correlation analysis algorithm is determined as the algorithm for performing correlation analysis on the vehicle component performance data and the vehicle fault frequency.

4. The vehicle data management method according to claim 1, characterized in that, The step of determining a second correlation analysis algorithm for performing correlation analysis on the vehicle component performance data and the vehicle fault frequency in response to determining that at least one of the vehicle component performance data and the vehicle fault frequency is not quantitative includes: In response to determining that both the vehicle component performance data type and the vehicle fault frequency type are classified, a third preset correlation analysis algorithm is determined as the algorithm for performing correlation analysis on the vehicle component performance data and the vehicle fault frequency. In response to determining that the vehicle component performance data type is categorical and the vehicle fault frequency type is quantitative, a second preset correlation analysis algorithm is determined as the algorithm for performing correlation analysis on the vehicle component performance data and the vehicle fault frequency; or In response to the fact that the vehicle component performance data type is quantitative and the vehicle fault frequency type is categorical, a second preset correlation analysis algorithm is determined as the algorithm for performing correlation analysis on the vehicle component performance data and the vehicle fault frequency.

5. The vehicle data management method according to claim 1, characterized in that, The steps for acquiring vehicle component performance data and vehicle fault frequency data include: The vehicle component performance data and the vehicle failure frequency are used as the data to be analyzed in sequence, and the diversity of the data to be analyzed is obtained to obtain a diversity index. Determine whether the diversity index is greater than a preset index threshold; In response to determining that the diversity index is not greater than a preset index threshold, the data type of the data to be analyzed is set to categorized; or In response to determining that the diversity index is greater than the preset index threshold, the data type of the data to be analyzed is set to quantitative.

6. The vehicle data management method according to claim 5, characterized in that, After determining whether the diversity index is greater than a preset index threshold, the method further includes: In response to determining that the diversity index is greater than the preset index threshold, the data to be analyzed is discretized to determine the number of categories of the data to be analyzed; Determine whether the number of categories is greater than a preset quantity threshold; In response to determining that the number of categories is greater than the preset quantity threshold, the data type of the data to be analyzed is set to quantitative; or In response to determining that the number of categories is not greater than the preset quantity threshold, the data type of the data to be analyzed is set to a fixed category.

7. The vehicle data management method according to claim 6, characterized in that, The step of discretizing the data to be analyzed and determining the number of categories of the data to be analyzed includes: The data to be analyzed is deduplicated to obtain deduplicated data; The number of categories of the data to be analyzed is determined based on the amount of deduplicated data.

8. The vehicle data management method according to any one of claims 1-7, characterized in that, After the step of obtaining the correlation between vehicle component performance data and vehicle failure frequency according to the first correlation analysis algorithm or the second correlation analysis algorithm, the method further includes: Obtain the correlation analysis results and determine the correlation analysis chart based on the vehicle component performance data type and the vehicle fault frequency type; The correlation analysis results are displayed through the correlation analysis chart.

9. A vehicle data management device, characterized in that, The device includes: The data type acquisition module is used to acquire the data types of vehicle component performance data and vehicle fault frequency, wherein the data type of the vehicle component performance data is a vehicle component performance data type, and the data type of the vehicle fault frequency is a vehicle fault frequency type. A quantitative determination module is used to determine whether both the vehicle component performance data type and the vehicle fault frequency type are quantitative. The normality test module is used to perform a normality test on the vehicle component performance data and the vehicle fault frequency in response to determining that both the vehicle component performance data and the vehicle fault frequency are quantitative, and to obtain the normality test result. The first correlation analysis module is used to determine a first correlation analysis algorithm for performing correlation analysis on the vehicle component performance data and the vehicle failure frequency based on the normality test results; or The second correlation analysis module is used to determine a second correlation analysis algorithm for performing correlation analysis on the vehicle component performance data and the vehicle fault frequency in response to determining that at least one of the vehicle component performance data and the vehicle fault frequency is not quantitative. The data correlation analysis module is used to obtain the correlation between vehicle component performance data and vehicle fault frequency according to the first correlation analysis algorithm or the second correlation analysis algorithm.

10. A computer device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the steps of the method according to any one of claims 1 to 8.

11. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 8.

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