Vehicle cabin system log monitoring method and device

Abstract

The invention provides a vehicle cabin system log monitoring method and device, and the method comprises the steps: carrying out the process testing of a cabin system in a current system version in a target vehicle based on test case information, so as to obtain a log analysis data set; based on historical log analysis data sets of the cockpit system under multiple historical system versions, the log analysis data sets and type information corresponding to the target vehicle and the current system version, determining a log line number threshold range of each process under each preset scene under the current system version; and based on the log line number threshold range and the process type of each process, performing anomaly detection on the log analysis data set, and respectively determining a process line number monitoring result and a monitoring anomaly early warning result. By means of the method, the accuracy and efficiency of process testing of the vehicle cabin system are improved, and the operation stability of the cabin system is improved.

Description

Technical Field

[0001] This application relates to the field of vehicle cockpit system testing technology, and in particular to a method and apparatus for monitoring vehicle cockpit system logs. Background Technology

[0002] During the testing of the cockpit system, log data serves as an important information carrier, reflecting key aspects such as the operational status of the cockpit system application, error messages, and user behavior. However, traditional log analysis methods typically rely on manual screening, resulting in low analysis efficiency. This method also fails to identify abnormal information in the logs in real time. When the system application experiences crashes, freezes, or security vulnerabilities, it cannot issue timely warnings, leading to delays in troubleshooting, impacting user experience, and reducing the stability of the cockpit system's operation. Summary of the Invention

[0003] In view of this, the purpose of this application is to provide a method and apparatus for monitoring vehicle cockpit system logs. By performing process testing on the cockpit system of a target vehicle under the current system version based on pre-configured test case information, the method acquires a log analysis dataset of the cockpit system under the current system version. Based on historical log analysis datasets of the cockpit system under multiple historical system versions and the log analysis dataset under the current system version, it determines the threshold range of the number of log lines for each process under the current system version in each preset scenario. Based on the threshold range of the number of log lines and the process type of each process, it performs anomaly detection on the log analysis dataset, determines the monitoring results of the number of process lines for each process and the monitoring anomaly warning results of the cockpit system, thereby improving the accuracy and efficiency of process testing of the vehicle cockpit system and thus improving the stability of the cockpit system operation.

[0004] This application provides a method for monitoring vehicle cockpit system logs, the monitoring method including: Based on pre-configured test case information, process testing is performed on the cockpit system of the target vehicle under the current system version to obtain the log analysis dataset of the cockpit system under the current system version collected through the process testing; Based on the historical log analysis dataset of the cockpit system under multiple historical system versions, the log analysis dataset, and the type information corresponding to the target vehicle and the current system version, the threshold range of the number of log lines for each process under each preset scenario under the current system version is determined. Based on the threshold range of the number of log lines and the process type corresponding to each process, anomaly detection is performed on the log analysis dataset to determine the monitoring result of the number of process lines for each process under each preset scenario in the current system version, as well as the monitoring anomaly warning result of the cockpit system under the current system version.

[0005] Furthermore, based on pre-configured test case information, process testing is performed on the cockpit system of the target vehicle under the current system version to obtain a log analysis dataset of the cockpit system under the current system version collected through the process testing, including: Obtain pre-configured test case information; wherein, the test case information includes test cases corresponding to multiple processes under each of multiple preset scenarios; For the cockpit system in the target vehicle under the current system version, process tests are performed on the cockpit system for each of the test cases to obtain the log dataset of the cockpit system under the current system version collected through the process tests; A tiered analysis is performed on the log dataset to obtain the corresponding log analysis dataset; wherein, the log analysis dataset includes log analysis data of each process under each preset scenario in the current system version.

[0006] Furthermore, the process testing corresponding to each test case information of the cockpit system is performed to obtain the log dataset of the cockpit system under the current system version collected through the process testing, including: Perform process tests on the cockpit system for each test case information to obtain system log datasets and performance monitoring log datasets of the cockpit system under the current system version, collected through the process tests. Based on the process information corresponding to each process in the test case information, process correspondence matching is performed on the system log dataset and the performance monitoring log dataset to obtain the log dataset of each process under the current system version.

[0007] Furthermore, the step of determining the threshold range for the number of log lines for each process in each preset scenario under the current system version, based on the historical log analysis dataset of the cockpit system under multiple historical system versions, the log analysis dataset, and the type information corresponding to the target vehicle and the current system version, includes: Obtain the historical log analysis dataset of the cockpit system under each of the multiple historical system versions; wherein, the historical log analysis dataset includes the historical log analysis data of each process under each preset scenario under each of the historical system versions; Based on the historical log analysis dataset and the log analysis dataset, determine the number of lower quartile log lines, upper quartile log lines, and interquartile distance log lines for each process in each preset scenario. Based on the historical log analysis dataset and the log analysis dataset, determine the standard deviation of the number of log lines for each process; Based on the number of log lines in the upper quartile, the number of log lines in the interquartile range, the standard deviation of the number of log lines, and the type information corresponding to the target vehicle and the current system version, a first log line number threshold corresponding to each process under the current system version is determined. Based on the number of log lines in the lower quartile, the number of log lines in the interquartile range, the standard deviation of the number of log lines, and the type information corresponding to the target vehicle and the current system version, a second log line number threshold corresponding to each process under the current system version is determined. The first log line count threshold is used as the upper limit of the range, and the second log line count threshold is used as the lower limit of the range to determine the log line count threshold range for each process under the current system version.

[0008] Furthermore, determining the number of lower quartile log lines, upper quartile log lines, and interquartile range log lines for each process under each preset scenario based on the historical log analysis dataset and the log analysis dataset includes: For each process in each preset scenario, determine the number of first log lines corresponding to the historical log analysis data of the process in each historical system version, and determine the number of second log lines corresponding to the log analysis data of the process in the current system version; Sort the number of the first log line and the number of the second log line in descending order of quantity to obtain the sorting result, and determine the number of the lower quartile log lines and the upper quartile log lines corresponding to the process in the sorting result; The difference between the number of log lines in the upper quartile and the number of log lines in the lower quartile is determined as the number of log lines in the quartile distance corresponding to the process.

[0009] Furthermore, based on the threshold range of the number of log lines and the process type corresponding to each process, anomaly detection is performed on the log analysis dataset to determine the monitoring result of the number of process lines for each process under each preset scenario in the current system version, and the monitoring anomaly warning result of the cockpit system under the current system version, including: In the log analysis dataset, determine the total number of log lines, the number of information log lines, the number of warning log lines, and the number of error log lines corresponding to each process under each preset scenario; The total number of log lines corresponding to each process in each preset scenario is compared with the threshold range of the number of log lines to obtain the comparison result for each process. Based on the comparison results and the number of error log lines corresponding to each process, determine the monitoring result of the number of process lines corresponding to each process in each preset scenario under the current system version; For each process type, based on the number of information log lines, the number of warning log lines, and the number of error log lines, determine the target number of information log lines, the target number of warning log lines, and the target number of error log lines for each process type. Anomaly detection is performed on the number of target information log lines, the number of target warning log lines, and the number of target error log lines to obtain the percentage detection results of information log lines, warning log lines, and error log lines for each process type. Based on the detection results of the percentage of information log lines, the percentage of warning log lines, and the percentage of error log lines, the monitoring anomaly warning results of the cockpit system under the current system version are determined.

[0010] Furthermore, the step of determining the monitoring result of the number of process lines for each process in each preset scenario under the current system version, based on the comparison result and the number of error log lines corresponding to each process, includes: Determine the proportion of the number of error log lines to the total number of log lines; When the total number of log lines is greater than a preset multiple corresponding to the first log line number threshold in the range of log line number thresholds, and / or the total number of log lines is less than a preset proportion corresponding to the second log line number threshold in the range of log line number thresholds, and / or the proportion value is greater than a preset proportion threshold, the process line number monitoring result for each process under each preset scenario in the current system version is determined to be abnormal in the number of process lines; wherein, the first log line number threshold is greater than the second log line number threshold; When the total number of log lines is less than or equal to a preset multiple corresponding to the first log line number threshold in the log line number threshold range, and the total number of log lines is greater than or equal to a preset proportion corresponding to the second log line number threshold in the log line number threshold range, and the proportion value is greater than the preset proportion threshold, the process line number monitoring result for each process under each preset scenario in the current system version is determined to be normal.

[0011] Furthermore, the process types include core processes, critical processes, and ordinary processes; the determination of the monitoring anomaly warning results of the cockpit system under the current system version based on the detection results of the percentage of information log lines, the percentage of warning log lines, and the percentage of error log lines includes: When the detection result of the percentage of warning log lines corresponding to the process under the core process is abnormal and the detection result of the percentage of error log lines corresponding to the process under the core process is abnormal, or the detection result of the percentage of warning log lines corresponding to the process under the critical process is abnormal, the monitoring abnormality warning result of the cockpit system under the current system version is determined to be a level one abnormality warning. When the detection result of the percentage of warning log lines corresponding to the process under the core process is abnormal, the detection result of the percentage of error log lines corresponding to the process under the critical process is abnormal, and the detection result of the percentage of warning log lines corresponding to the process under the ordinary process is abnormal, the monitoring abnormality warning result of the cockpit system under the current system version is determined to be a level two abnormality warning. When the detection result of the percentage of information log lines corresponding to the process under the core process is abnormal, the detection result of the percentage of warning log lines corresponding to the process under the critical process is abnormal, and the detection result of the percentage of error log lines corresponding to the process under the ordinary process is abnormal, the monitoring anomaly warning result of the cockpit system under the current system version is determined to be a level three anomaly warning.

[0012] This application embodiment also provides a monitoring device for a vehicle cabin system log, the monitoring device comprising: The data acquisition module is used to perform process testing on the cockpit system of the target vehicle under the current system version based on pre-configured test case information, so as to obtain the log analysis dataset of the cockpit system under the current system version collected through the process test; The threshold calibration module is used to determine the threshold range of the number of log lines for each process in each preset scenario under the current system version, based on the historical log analysis dataset of the cockpit system under multiple historical system versions, the log analysis dataset, and the type information corresponding to the target vehicle and the current system version respectively. The anomaly monitoring module is used to perform anomaly detection on the log analysis dataset based on the threshold range of the number of log lines and the process type corresponding to each process, and to determine the monitoring result of the number of process lines for each process under each preset scenario in the current system version, as well as the monitoring anomaly warning result of the cockpit system under the current system version.

[0013] Furthermore, when the data acquisition module performs process testing on the cockpit system of the target vehicle under the current system version based on pre-configured test case information to obtain the log analysis dataset of the cockpit system under the current system version collected through the process test, the data acquisition module is used to: Obtain pre-configured test case information; wherein, the test case information includes test cases corresponding to multiple processes under each of multiple preset scenarios; For the cockpit system in the target vehicle under the current system version, process tests are performed on the cockpit system for each of the test cases to obtain the log dataset of the cockpit system under the current system version collected through the process tests; A tiered analysis is performed on the log dataset to obtain the corresponding log analysis dataset; wherein, the log analysis dataset includes log analysis data of each process under each preset scenario in the current system version.

[0014] Furthermore, when the data acquisition module performs process testing on the cockpit system for each test case information to obtain the log dataset of the cockpit system under the current system version collected through the process testing, the data acquisition module is used to: Perform process tests on the cockpit system for each test case information to obtain system log datasets and performance monitoring log datasets of the cockpit system under the current system version, collected through the process tests. Based on the process information corresponding to each process in the test case information, process correspondence matching is performed on the system log dataset and the performance monitoring log dataset to obtain the log dataset of each process under the current system version.

[0015] Furthermore, when the threshold calibration module is used to determine the threshold range of the number of log lines for each process in each preset scenario under the current system version based on the historical log analysis dataset of the cockpit system under multiple historical system versions, the log analysis dataset, and the type information corresponding to the target vehicle and the current system version, the threshold calibration module is used to: Obtain the historical log analysis dataset of the cockpit system under each of the multiple historical system versions; wherein, the historical log analysis dataset includes the historical log analysis data of each process under each preset scenario under each of the historical system versions; Based on the historical log analysis dataset and the log analysis dataset, determine the number of lower quartile log lines, upper quartile log lines, and interquartile distance log lines for each process in each preset scenario. Based on the historical log analysis dataset and the log analysis dataset, determine the standard deviation of the number of log lines for each process; Based on the number of log lines in the upper quartile, the number of log lines in the interquartile range, the standard deviation of the number of log lines, and the type information corresponding to the target vehicle and the current system version, a first log line number threshold corresponding to each process under the current system version is determined. Based on the number of log lines in the lower quartile, the number of log lines in the interquartile range, the standard deviation of the number of log lines, and the type information corresponding to the target vehicle and the current system version, a second log line number threshold corresponding to each process under the current system version is determined. The first log line count threshold is used as the upper limit of the range, and the second log line count threshold is used as the lower limit of the range to determine the log line count threshold range for each process under the current system version.

[0016] Furthermore, when the threshold calibration module is used to determine the number of lower quartile log lines, upper quartile log lines, and interquartile range log lines for each process under each preset scenario based on the historical log analysis dataset and the log analysis dataset, the threshold calibration module is used to: For each process in each preset scenario, determine the number of first log lines corresponding to the historical log analysis data of the process in each historical system version, and determine the number of second log lines corresponding to the log analysis data of the process in the current system version; Sort the number of the first log line and the number of the second log line in descending order of quantity to obtain the sorting result, and determine the number of the lower quartile log lines and the upper quartile log lines corresponding to the process in the sorting result; The difference between the number of log lines in the upper quartile and the number of log lines in the lower quartile is determined as the number of log lines in the quartile distance corresponding to the process.

[0017] Furthermore, when the anomaly monitoring module performs anomaly detection on the log analysis dataset based on the log line count threshold range and the process type corresponding to each process, and determines the process line count monitoring result for each process under each preset scenario in the current system version, and the monitoring anomaly warning result for the cockpit system in the current system version, the anomaly monitoring module is used to: In the log analysis dataset, determine the total number of log lines, the number of information log lines, the number of warning log lines, and the number of error log lines corresponding to each process under each preset scenario; The total number of log lines corresponding to each process in each preset scenario is compared with the threshold range of the number of log lines to obtain the comparison result for each process. Based on the comparison results and the number of error log lines corresponding to each process, determine the monitoring result of the number of process lines corresponding to each process in each preset scenario under the current system version; For each process type, based on the number of information log lines, the number of warning log lines, and the number of error log lines, determine the target number of information log lines, the target number of warning log lines, and the target number of error log lines for each process type. Anomaly detection is performed on the number of target information log lines, the number of target warning log lines, and the number of target error log lines to obtain the percentage detection results of information log lines, warning log lines, and error log lines for each process type. Based on the detection results of the percentage of information log lines, the percentage of warning log lines, and the percentage of error log lines, the monitoring anomaly warning results of the cockpit system under the current system version are determined.

[0018] Furthermore, when the anomaly monitoring module determines the monitoring result of the number of process lines for each process in each preset scenario under the current system version based on the comparison result and the number of error log lines corresponding to each process, the anomaly monitoring module is used to: Determine the proportion of the number of error log lines to the total number of log lines; When the total number of log lines is greater than a preset multiple corresponding to the first log line number threshold in the range of log line number thresholds, and / or the total number of log lines is less than a preset proportion corresponding to the second log line number threshold in the range of log line number thresholds, and / or the proportion value is greater than a preset proportion threshold, the process line number monitoring result for each process under each preset scenario in the current system version is determined to be abnormal in the number of process lines; wherein, the first log line number threshold is greater than the second log line number threshold; When the total number of log lines is less than or equal to a preset multiple corresponding to the first log line number threshold in the log line number threshold range, and the total number of log lines is greater than or equal to a preset proportion corresponding to the second log line number threshold in the log line number threshold range, and the proportion value is greater than the preset proportion threshold, the process line number monitoring result for each process under each preset scenario in the current system version is determined to be normal.

[0019] Furthermore, the process types include core processes, critical processes, and ordinary processes; when the anomaly monitoring module determines the monitoring anomaly warning result of the cockpit system under the current system version based on the detection results of the percentage of information log lines, the percentage of warning log lines, and the percentage of error log lines, the anomaly monitoring module is used to: When the detection result of the percentage of warning log lines corresponding to the process under the core process is abnormal and the detection result of the percentage of error log lines corresponding to the process under the core process is abnormal, or the detection result of the percentage of warning log lines corresponding to the process under the critical process is abnormal, the monitoring abnormality warning result of the cockpit system under the current system version is determined to be a level one abnormality warning. When the detection result of the percentage of warning log lines corresponding to the process under the core process is abnormal, the detection result of the percentage of error log lines corresponding to the process under the critical process is abnormal, and the detection result of the percentage of warning log lines corresponding to the process under the ordinary process is abnormal, the monitoring abnormality warning result of the cockpit system under the current system version is determined to be a level two abnormality warning. When the detection result of the percentage of information log lines corresponding to the process under the core process is abnormal, the detection result of the percentage of warning log lines corresponding to the process under the critical process is abnormal, and the detection result of the percentage of error log lines corresponding to the process under the ordinary process is abnormal, the monitoring anomaly warning result of the cockpit system under the current system version is determined to be a level three anomaly warning.

[0020] This application also provides an electronic device, including: a processor, a memory, and a bus. The memory stores machine-readable instructions executable by the processor. When the electronic device is running, the processor communicates with the memory via the bus. When the machine-readable instructions are executed by the processor, the steps of the vehicle cockpit system log monitoring method described above are performed.

[0021] This application also provides a computer-readable storage medium storing a computer program that, when executed by a processor, performs the steps of the vehicle cabin system log monitoring method described above.

[0022] The vehicle cockpit system log monitoring method and apparatus provided in this application include: performing process testing on the cockpit system of a target vehicle in the current system version based on pre-configured test case information to obtain a log analysis dataset of the cockpit system under the current system version collected through the process testing; determining a threshold range for the number of log lines corresponding to each process under the current system version in each preset scenario based on the historical log analysis dataset of the cockpit system under multiple historical system versions, the log analysis dataset, and the type information corresponding to the target vehicle and the current system version; performing anomaly detection on the log analysis dataset based on the threshold range for the number of log lines and the process type corresponding to each process, and determining the monitoring result of the number of process lines corresponding to each process under the current system version in each preset scenario, as well as the monitoring anomaly warning result of the cockpit system under the current system version.

[0023] Compared to existing log analysis methods that typically rely on manual screening, this new approach improves the accuracy and efficiency of process testing for vehicle cockpit systems. It acquires a log analysis dataset of the cockpit system under the current system version during process testing of the cockpit system in a target vehicle based on pre-configured test case information. By combining historical log analysis datasets from multiple historical system versions with the current system version's log analysis dataset, a threshold range for the number of log lines for each process under each preset scenario in the current system version is determined. Based on this threshold range and the process type of each process, anomaly detection is performed on the log analysis dataset, determining the monitoring results for the number of process lines for each process and the cockpit system's monitoring anomaly warning results. This enhances the stability of the vehicle cockpit system's operation.

[0024] To make the above-mentioned objectives, features and advantages of this application more apparent and understandable, preferred embodiments are described below in detail with reference to the accompanying drawings. Attached Figure Description

[0025] To more clearly illustrate the technical solutions of the embodiments of this application, the accompanying drawings used in the embodiments will be briefly introduced below. It should be understood that the following drawings only show some embodiments of this application and should not be regarded as a limitation of the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0026] Figure 1 A flowchart illustrating a method for monitoring vehicle cabin system logs provided in an embodiment of this application; Figure 2 A schematic diagram of the structure of a monitoring device for a vehicle cabin system log provided in an embodiment of this application; Figure 3 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application. Detailed Implementation

[0027] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. The components of the embodiments of this application described and shown in the accompanying drawings can generally be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of this application provided in the accompanying drawings is not intended to limit the scope of the claimed application, but merely represents selected embodiments of this application. Based on the embodiments of this application, every other embodiment obtained by those skilled in the art without inventive effort falls within the scope of protection of this application.

[0028] Research has revealed that during cockpit system testing, log data serves as a crucial information carrier, reflecting key aspects such as the operational status of the cockpit system application, error messages, and user behavior. However, traditional log analysis methods typically rely on manual screening, resulting in low efficiency. This approach also fails to identify anomalies in logs in real time. Consequently, it cannot issue timely warnings when system applications experience crashes, freezes, or security vulnerabilities, leading to delays in troubleshooting, impacting user experience, and reducing the stability of the cockpit system.

[0029] Based on this, the present application provides a method for monitoring vehicle cabin system logs (with beneficial effects).

[0030] Please see Figure 1 , Figure 1 This is a flowchart illustrating a method for monitoring vehicle cabin system logs provided in an embodiment of this application. Figure 1 As shown in the embodiment of this application, the method for monitoring vehicle cockpit system logs includes: S101. Based on pre-configured test case information, perform process testing on the cockpit system of the target vehicle under the current system version to obtain the log analysis dataset of the cockpit system under the current system version collected through the process testing.

[0031] In this embodiment of the application, the test case information includes test cases corresponding to multiple processes in each of the multiple preset scenarios.

[0032] The preset scenarios include, but are not limited to, vehicle static scenarios (e.g., stationary charging and sleep standby), vehicle dynamic scenarios (e.g., urban road driving and highway driving), and vehicle environmental scenarios (e.g., low temperature, high temperature, and network connection status).

[0033] Here, each process belongs to a corresponding process category, which includes, but is not limited to, core control, information interaction, multimedia, auxiliary functions, and third-party applications.

[0034] It should be noted that during the testing process of this application embodiment, the target vehicle will have corresponding type information, such as domestic model R11, domestic model MCE, foreign model R11 and foreign model MCE, etc.; the cockpit system will have corresponding type information for the current system version and the historical system version, such as Android system 11, Android system 12 and Android system 13, etc.

[0035] In this embodiment of the application, the log analysis dataset includes log analysis data of each process under each preset scenario in the current system version.

[0036] In one possible implementation of this application, step S101 may include: S1011. Obtain pre-configured test case information.

[0037] For example, the test case information may include test cases for the core control class process A in a static charging scenario.

[0038] S1012. For the cockpit system in the target vehicle under the current system version, perform process testing on the cockpit system for each of the test cases to obtain the log dataset of the cockpit system under the current system version collected through the process testing.

[0039] In one possible implementation of this application, step S1012 may include: S10121. Perform process testing on the cockpit system for each test case information to obtain the system log dataset and performance monitoring log dataset of the cockpit system under the current system version collected through the process testing.

[0040] In the embodiments of this application, system log data typically contains PID (e.g., [PID=1234]), but it is not possible to directly know which process it is. Performance monitoring log data (TOP data) provides information on the resource usage of processes, but it needs to be associated with the PID to know which application is consuming resources. Performance monitoring log data (TOP data) displays system resource usage, including key indicators such as CPU and memory.

[0041] S10122. Based on the process information corresponding to each process in the test case information, perform process correspondence matching on the system log dataset and the performance monitoring log dataset to obtain the log dataset of each process under the current system version.

[0042] For example, the process list is collected by "adb shell ps>processes.txt"; TOP data is collected by "adb shell top -n 1 -m 10>top_data.txt"; the PID column is matched using a text editor or script to match the process to the system log dataset and the performance monitoring log dataset.

[0043] S1013. Perform a level analysis on the log dataset to obtain the log analysis dataset corresponding to the log dataset.

[0044] In this embodiment of the application, the levels corresponding to the log lines in the log dataset include, but are not limited to, information logs (Level I), warning logs (Level W), and error logs (Level E). Furthermore, the levels corresponding to the log lines may also include debug logs (Level D) and complete information logs (Level V).

[0045] For example, an example of log analysis data in the log analysis dataset is shown in the table below.

[0046]

[0047] S102. Based on the historical log analysis dataset of the cockpit system under multiple historical system versions, the log analysis dataset, and the type information corresponding to the target vehicle and the current system version, determine the threshold range of the number of log lines for each process under each preset scenario in the current system version.

[0048] It should be noted that existing log monitoring methods only focus on the content of the log data and ignore the changing patterns of the number of log lines for each process. Abnormal fluctuations in the number of process log lines are usually an early signal of process abnormality. For example, a sudden increase or decrease in the number of log lines within a short period of time. In addition, the printing of cockpit system logs itself consumes resources. If the process logs are excessive for a long period of time, it will cause performance bottlenecks and additional performance losses.

[0049] Based on the above problems, this application embodiment uses historical log analysis datasets and log analysis datasets of the cockpit system under multiple historical system versions, as well as type information corresponding to the target vehicle and the current system version, to determine the threshold range of the number of log lines for each process under the current system version in each preset scenario, so as to monitor the number of log lines based on the changing pattern of the number of log lines for each process.

[0050] In one possible implementation of this application, step S102 may include: S1021. Obtain the historical log analysis dataset for each of the multiple historical system versions of the cockpit system.

[0051] The historical log analysis dataset includes the historical log analysis data of each process in each preset scenario under each historical system version.

[0052] S1022. Based on the historical log analysis dataset and the log analysis dataset, determine the number of lower quartile log lines, upper quartile log lines, and interquartile distance log lines for each process under each preset scenario.

[0053] In one possible implementation of this application, step S1022 may include: S10221. For each process in each preset scenario, determine the number of first log lines corresponding to the historical log analysis data of the process in each historical system version, and determine the number of second log lines corresponding to the log analysis data of the process in the current system version.

[0054] Wherein, the first log line count is the number of log lines corresponding to the historical log analysis data of each process under each historical system version; the second log line count is the number of log lines corresponding to the log analysis data of each process under the current system version.

[0055] S10222. Sort the number of the first log lines and the number of the second log lines in descending order of quantity to obtain a sorting result, and determine the number of the lower quartile log lines and the upper quartile log lines corresponding to the process in the sorting result.

[0056] In this step, based on the sorting results obtained by sorting the number of multiple first log lines and the number of one second log line in descending order of quantity, the number in the 1st / 4th order is selected as the number of log lines in the lower quartile, and the number in the 3rd / 4th order is selected as the number of log lines in the upper quartile.

[0057] For example, when there are N-1 historical system versions, there will be N-1 first log line counts and 1 second log line count, for a total of N sets of data. The number of log lines in the (N+1) / 4th position in these N sets of data is the number of log lines in the lower quartile. The number of log lines in the 3(N+1) / 4th position in these N sets of data is the number of log lines in the lower quartile.

[0058] For example, assuming there are 10 historical system versions, there will be 10 first log line counts and 1 second log line count, for a total of 11 sets of data. The number of log lines in the 3rd position in these 11 sets of data is the number of log lines in the lower quartile, and the number of log lines in the 9th position in these 11 sets of data is the number of log lines in the lower quartile.

[0059] S10223. The difference between the number of log lines in the upper quartile and the number of log lines in the lower quartile is determined as the number of log lines in the quartile distance corresponding to the process.

[0060] In this step, the difference between the number of log lines in the upper quartile and the number of log lines in the lower quartile is determined as the number of log lines in the quartile distance corresponding to the process.

[0061] The interquartile range of log rows is used to characterize data fluctuations in order to avoid the impact of extreme values.

[0062] S1023. Based on the historical log analysis dataset and the log analysis dataset, determine the standard deviation of the number of log lines corresponding to each process.

[0063] In this embodiment of the application, the standard deviation of the number of log lines for each process is calculated using the following formula.

[0064] .

[0065] in, Indicates the first Standard deviation of the number of log lines for each process; Indicates the first The number of first or second log lines corresponding to each process; This represents the average of the number of the first log line count and the number of the second log line count. This represents the sum of the number of the first log line and the number of the second log line.

[0066] S1024. Based on the number of log lines in the upper quartile, the number of log lines in the interquartile range, the standard deviation of the number of log lines, and the type information corresponding to the target vehicle and the current system version, determine the first log line number threshold corresponding to each process under the current system version.

[0067] In this embodiment of the application, the threshold for the number of first log lines for each process under the current system version is determined by the following formula.

[0068] .

[0069] in, This represents the threshold number of the first log line for each process in the current system version; Indicates the number of log lines in the upper quartile; Indicates the number of log lines at the interquartile range; This represents the standard deviation of the number of log lines. The coefficient represents the type information corresponding to the target vehicle; This represents the coefficient corresponding to the type information of the current system version.

[0070] Here, the coefficients corresponding to the type information of the target vehicle vary depending on the model, which uses different chips, has different computing power, and different application configurations. Therefore, the coefficients fluctuate around the constant of 1.64 commonly used in statistics.

[0071] For example, the coefficient corresponding to R11 in domestic car models The coefficient is 1.6; the corresponding coefficient for MCE in domestic car models. It is 1.55; the coefficient corresponding to R11 for foreign models. It is 1.67; the coefficient corresponding to MCE for foreign models. It is 1.65.

[0072] For the coefficients corresponding to the type information of the current system version, taking the Android system as an example, Android 11 added cross-application access restrictions, Android 12 blocked sensitive data in basic scenarios, and Android 13 expanded the scope of blocked sensitive data. With the upgrade of the version, Android privacy and security are gradually strengthened.

[0073] For example, based on these native system characteristics, and using Android 11 as the baseline version, the coefficients corresponding to the type information of the current system version are defined as follows: when the current system version is Android 11, When the current system version is Android 12, When the current system version is Android 13, .

[0074] S1025. Based on the number of log lines in the lower quartile, the number of log lines in the interquartile range, the standard deviation of the number of log lines, and the type information corresponding to the target vehicle and the current system version, determine the second log line number threshold corresponding to each process under the current system version.

[0075] In this embodiment of the application, the threshold for the number of second log lines for each process under the current system version is determined by the following formula.

[0076] .

[0077] in, This represents the threshold number of the second log lines for each process in the current system version. Indicates the number of log lines in the lower quartile; Indicates the number of log lines at the interquartile range; This represents the standard deviation of the number of log lines. The coefficient represents the type information corresponding to the target vehicle; This represents the coefficient corresponding to the type information of the current system version.

[0078] here, and It is the core criterion for identifying outliers in statistics. The critical value used to define "upper limit outliers" The critical value used to define the "lower limit outlier" is based on the standard value algorithm, which adds a threshold to this. The threshold for judging outliers is dynamically adjusted based on data characteristics, making outlier identification more in line with actual needs.

[0079] S1026. Using the first log line number threshold as the upper limit of the range and the second log line number threshold as the lower limit of the range, determine the log line number threshold range corresponding to each process under the current system version.

[0080] For example, when This represents the threshold number of the first log line for each process in the current system version. This indicates the threshold number of the second log line for each process under the current system version. , [] indicates the threshold range for the number of log lines for each process under the current system version.

[0081] S103. Based on the threshold range of the number of log lines and the process type corresponding to each process, perform anomaly detection on the log analysis dataset, and determine the monitoring result of the number of process lines corresponding to each process in each preset scenario under the current system version, and the monitoring anomaly warning result of the cockpit system under the current system version.

[0082] In the embodiments of this application, the process types include core processes, critical processes, and ordinary processes.

[0083] Among them, the core process is the fundamental process that the system absolutely depends on for normal operation. If it crashes, the system will fail to start or become completely out of control. The critical process is the process that is crucial to the core business functions or user experience. If it crashes, the main services will be unavailable, but the system can still run. The normal / general process is a general application or auxiliary process. If it crashes, it will not affect the overall availability of the system.

[0084] In one possible implementation of this application, step S103 may include: S1031. Determine the total number of log lines, the number of information log lines, the number of warning log lines, and the number of error log lines corresponding to each process under each preset scenario in the log analysis dataset.

[0085] S1032. Compare the total number of log lines corresponding to each process in each preset scenario with the log line number threshold range to obtain the comparison result for each process.

[0086] In this step, the total number of log lines for each process under each preset scenario is compared with the first log line number threshold and the second log line number threshold in the log line number threshold range to obtain the comparison result for each process, so as to determine whether the total number of log lines is greater than the preset multiple corresponding to the first log line number threshold, and whether the total number of log lines is less than the preset ratio corresponding to the second log line number threshold.

[0087] S1033. Based on the comparison results and the number of error log lines corresponding to each process, determine the monitoring result of the number of process lines corresponding to each process in each preset scenario under the current system version.

[0088] In one possible implementation of this application, step S1033 may include: S10331. Determine the proportion of the number of error log lines to the total number of log lines.

[0089] In this step, the number of error log lines is divided by the total number of log lines to obtain the proportion of error log lines to the total number of log lines.

[0090] S10332. When the total number of log lines is greater than a preset multiple corresponding to the first log line number threshold in the range of log line number thresholds, and / or the total number of log lines is less than a preset proportion corresponding to the second log line number threshold in the range of log line number thresholds, and / or the proportion value is greater than a preset proportion threshold, the process line number monitoring result corresponding to each process in each preset scenario under the current system version is determined to be abnormal in the number of process lines.

[0091] Wherein, the first log line count threshold is greater than the second log line count threshold.

[0092] For example, the preset multiplier can be set to 2 times, and the preset ratio can be set to 50%.

[0093] S10333 When the total number of log lines is less than or equal to a preset multiple corresponding to the first log line number threshold in the log line number threshold range, and the total number of log lines is greater than or equal to a preset ratio corresponding to the second log line number threshold in the log line number threshold range, and the ratio value is greater than the preset ratio threshold, the process line number monitoring result corresponding to each process in each preset scenario under the current system version is determined to be normal.

[0094] For example, the preset ratio threshold can be set to 20%.

[0095] S1034. For each process type corresponding to each process, based on the number of information log lines, the number of warning log lines, and the number of error log lines, determine the target number of information log lines, the target number of warning log lines, and the target number of error log lines corresponding to each process type.

[0096] In this step, the number of target information log lines, target warning log lines, and target error log lines corresponding to all processes under the core process are determined. The number of target information log lines, target warning log lines, and target error log lines corresponding to all processes under the critical process are also determined. Finally, the number of target information log lines, target warning log lines, and target error log lines corresponding to all processes under the ordinary process are determined.

[0097] S1035. Perform percentage anomaly detection on the number of target information log lines, the number of target warning log lines, and the number of target error log lines respectively, and obtain the percentage detection results of the number of information log lines, warning log lines, and error log lines for each process type.

[0098] In this step, an anomaly detection is performed on the number of target information log lines to determine whether the proportion of the number of target information log lines to the total number of log lines for all processes is greater than the information log line proportion threshold; anomaly detection is performed on the number of target warning log lines to determine whether the proportion of the number of target warning log lines to the total number of log lines for all processes is greater than the warning log line proportion threshold; and anomaly detection is performed on the number of target error log lines to determine whether the proportion of the number of target error log lines to the total number of log lines for all processes is greater than the error log line proportion threshold.

[0099] Here, the threshold values ​​for information log lines, warning log lines, and error log lines can be specifically set according to actual monitoring needs and the actual equipment parameters of the cockpit system.

[0100] S1036. Based on the detection results of the percentage of information log lines, the percentage of warning log lines, and the percentage of error log lines, determine the monitoring anomaly warning result of the cockpit system under the current system version.

[0101] In one possible implementation of this application, step S1036 may include: S10361. When the detection result of the percentage of warning log lines corresponding to the process under the core process is abnormal and the detection result of the percentage of error log lines corresponding to the process under the core process is abnormal, or the detection result of the percentage of warning log lines corresponding to the process under the critical process is abnormal, the monitoring abnormality warning result of the cockpit system under the current system version is determined to be a level one abnormality warning.

[0102] In this embodiment, the Level 1 anomaly warning indicates that the monitoring anomaly warning result of the cockpit system under the current system version is a serious anomaly.

[0103] Here, when the monitoring anomaly warning result is determined to be a Level 1 anomaly warning, it is necessary to stop the release of the cockpit system version and immediately start the cockpit system troubleshooting process.

[0104] S10362. When the detection result of the percentage of warning log lines corresponding to the process under the core process is abnormal, the detection result of the percentage of error log lines corresponding to the process under the critical process is abnormal, and the detection result of the percentage of warning log lines corresponding to the process under the ordinary process is abnormal, the monitoring abnormality warning result of the cockpit system under the current system version is determined to be a level two abnormality warning.

[0105] In this embodiment, the Level 2 anomaly warning indicates that the monitoring anomaly warning result of the cockpit system under the current system version is a general anomaly.

[0106] Here, when the monitoring anomaly warning result is determined to be a level two anomaly warning, it is necessary to remind the developers to conduct anomaly analysis on the cockpit system and develop an optimization plan.

[0107] S10363. When the detection result of the percentage of information log lines corresponding to the process under the core process is abnormal, the detection result of the percentage of warning log lines corresponding to the process under the critical process is abnormal, and the detection result of the percentage of error log lines corresponding to the process under the ordinary process is abnormal, the monitoring abnormality warning result of the cockpit system under the current system version is determined to be a level three abnormality warning.

[0108] In this embodiment, the Level 3 anomaly warning indicates that the monitoring anomaly warning result of the cockpit system under the current system version is a minor anomaly.

[0109] Here, when the monitoring anomaly warning result is determined to be a level three anomaly warning, it is necessary to remind the developers to track and evaluate the anomaly of the cockpit system and further determine whether the cockpit system needs to be optimized.

[0110] The vehicle cockpit system log monitoring method provided in this application improves the accuracy and efficiency of process testing of the cockpit system in the target vehicle under the current system version by acquiring the log analysis dataset of the cockpit system under the current system version during process testing based on pre-configured test case information. Based on the historical log analysis datasets of the cockpit system under multiple historical system versions and the log analysis dataset under the current system version, the method determines the threshold range of the number of log lines for each process under the current system version in each preset scenario. Based on the threshold range of the number of log lines and the process type of each process, the method performs anomaly detection on the log analysis dataset, determines the monitoring results of the number of process lines for each process and the monitoring anomaly warning results of the cockpit system, thereby improving the stability of the cockpit system operation.

[0111] Please see Figure 2 , Figure 2 This is a schematic diagram of the structure of a vehicle cabin system log monitoring device provided in an embodiment of this application. Figure 2 As shown, the monitoring device 200 includes: The data acquisition module 210 is used to perform process testing on the cockpit system of the target vehicle under the current system version based on pre-configured test case information, so as to obtain the log analysis dataset of the cockpit system under the current system version collected through the process test; The threshold calibration module 220 is used to determine the threshold range of the number of log lines for each process in each preset scenario under the current system version based on the historical log analysis dataset of the cockpit system under multiple historical system versions, the log analysis dataset, and the type information corresponding to the target vehicle and the current system version respectively. The anomaly monitoring module 230 is used to perform anomaly detection on the log analysis dataset based on the threshold range of the number of log lines and the process type corresponding to each process, and to determine the monitoring result of the number of process lines for each process under each preset scenario in the current system version, as well as the monitoring anomaly warning result of the cockpit system under the current system version.

[0112] Furthermore, when the data acquisition module 210 performs process testing on the cockpit system of the target vehicle under the current system version based on pre-configured test case information to obtain the log analysis dataset of the cockpit system under the current system version collected through the process test, the data acquisition module 210 is used to: Obtain pre-configured test case information; wherein, the test case information includes test cases corresponding to multiple processes under each of multiple preset scenarios; For the cockpit system in the target vehicle under the current system version, process tests are performed on the cockpit system for each of the test cases to obtain the log dataset of the cockpit system under the current system version collected through the process tests; A tiered analysis is performed on the log dataset to obtain the corresponding log analysis dataset; wherein, the log analysis dataset includes log analysis data of each process under each preset scenario in the current system version.

[0113] Furthermore, when the data acquisition module 210 performs process testing on the cockpit system for each test case information to obtain the log dataset of the cockpit system under the current system version collected through the process testing, the data acquisition module 210 is used to: Perform process tests on the cockpit system for each test case information to obtain system log datasets and performance monitoring log datasets of the cockpit system under the current system version, collected through the process tests. Based on the process information corresponding to each process in the test case information, process correspondence matching is performed on the system log dataset and the performance monitoring log dataset to obtain the log dataset of each process under the current system version.

[0114] Furthermore, when the threshold calibration module 220 is used to determine the threshold range of the number of log lines for each process in each preset scenario under the current system version based on the historical log analysis dataset of the cockpit system under multiple historical system versions, the log analysis dataset, and the type information corresponding to the target vehicle and the current system version, the threshold calibration module 220 is used to: Obtain the historical log analysis dataset of the cockpit system under each of the multiple historical system versions; wherein, the historical log analysis dataset includes the historical log analysis data of each process under each preset scenario under each of the historical system versions; Based on the historical log analysis dataset and the log analysis dataset, determine the number of lower quartile log lines, upper quartile log lines, and interquartile distance log lines for each process in each preset scenario. Based on the historical log analysis dataset and the log analysis dataset, determine the standard deviation of the number of log lines for each process; Based on the number of log lines in the upper quartile, the number of log lines in the interquartile range, the standard deviation of the number of log lines, and the type information corresponding to the target vehicle and the current system version, a first log line number threshold corresponding to each process under the current system version is determined. Based on the number of log lines in the lower quartile, the number of log lines in the interquartile range, the standard deviation of the number of log lines, and the type information corresponding to the target vehicle and the current system version, a second log line number threshold corresponding to each process under the current system version is determined. The first log line count threshold is used as the upper limit of the range, and the second log line count threshold is used as the lower limit of the range to determine the log line count threshold range for each process under the current system version.

[0115] Furthermore, when the threshold calibration module 220 is used to determine the number of lower quartile log lines, upper quartile log lines, and interquartile range log lines for each process under each preset scenario based on the historical log analysis dataset and the log analysis dataset, the threshold calibration module 220 is used to: For each process in each preset scenario, determine the number of first log lines corresponding to the historical log analysis data of the process in each historical system version, and determine the number of second log lines corresponding to the log analysis data of the process in the current system version; Sort the number of the first log line and the number of the second log line in descending order of quantity to obtain the sorting result, and determine the number of the lower quartile log lines and the upper quartile log lines corresponding to the process in the sorting result; The difference between the number of log lines in the upper quartile and the number of log lines in the lower quartile is determined as the number of log lines in the quartile distance corresponding to the process.

[0116] Furthermore, when the anomaly monitoring module 230 performs anomaly detection on the log analysis dataset based on the log line count threshold range and the process type corresponding to each process, and determines the process line count monitoring result for each process under each preset scenario in the current system version, and the monitoring anomaly warning result for the cockpit system in the current system version, the anomaly monitoring module 230 is used to: In the log analysis dataset, determine the total number of log lines, the number of information log lines, the number of warning log lines, and the number of error log lines corresponding to each process under each preset scenario; The total number of log lines corresponding to each process in each preset scenario is compared with the threshold range of the number of log lines to obtain the comparison result for each process. Based on the comparison results and the number of error log lines corresponding to each process, determine the monitoring result of the number of process lines corresponding to each process in each preset scenario under the current system version; For each process type, based on the number of information log lines, the number of warning log lines, and the number of error log lines, determine the target number of information log lines, the target number of warning log lines, and the target number of error log lines for each process type. Anomaly detection is performed on the number of target information log lines, the number of target warning log lines, and the number of target error log lines to obtain the percentage detection results of information log lines, warning log lines, and error log lines for each process type. Based on the detection results of the percentage of information log lines, the percentage of warning log lines, and the percentage of error log lines, the monitoring anomaly warning results of the cockpit system under the current system version are determined.

[0117] Furthermore, when the anomaly monitoring module 230 determines the process line count monitoring result for each process in each preset scenario under the current system version based on the comparison result and the number of error log lines corresponding to each process, the anomaly monitoring module 230 is used to: Determine the proportion of the number of error log lines to the total number of log lines; When the total number of log lines is greater than a preset multiple corresponding to the first log line number threshold in the range of log line number thresholds, and / or the total number of log lines is less than a preset proportion corresponding to the second log line number threshold in the range of log line number thresholds, and / or the proportion value is greater than a preset proportion threshold, the process line number monitoring result for each process under each preset scenario in the current system version is determined to be abnormal in the number of process lines; wherein, the first log line number threshold is greater than the second log line number threshold; When the total number of log lines is less than or equal to a preset multiple corresponding to the first log line number threshold in the log line number threshold range, and the total number of log lines is greater than or equal to a preset proportion corresponding to the second log line number threshold in the log line number threshold range, and the proportion value is greater than the preset proportion threshold, the process line number monitoring result for each process under each preset scenario in the current system version is determined to be normal.

[0118] Furthermore, the process types include core processes, critical processes, and ordinary processes; when the anomaly monitoring module 230 determines the monitoring anomaly warning result of the cockpit system under the current system version based on the detection results of the percentage of information log lines, the percentage of warning log lines, and the percentage of error log lines, the anomaly monitoring module 230 is used to: When the detection result of the percentage of warning log lines corresponding to the process under the core process is abnormal and the detection result of the percentage of error log lines corresponding to the process under the core process is abnormal, or the detection result of the percentage of warning log lines corresponding to the process under the critical process is abnormal, the monitoring abnormality warning result of the cockpit system under the current system version is determined to be a level one abnormality warning. When the detection result of the percentage of warning log lines corresponding to the process under the core process is abnormal, the detection result of the percentage of error log lines corresponding to the process under the critical process is abnormal, and the detection result of the percentage of warning log lines corresponding to the process under the ordinary process is abnormal, the monitoring abnormality warning result of the cockpit system under the current system version is determined to be a level two abnormality warning. When the detection result of the percentage of information log lines corresponding to the process under the core process is abnormal, the detection result of the percentage of warning log lines corresponding to the process under the critical process is abnormal, and the detection result of the percentage of error log lines corresponding to the process under the ordinary process is abnormal, the monitoring anomaly warning result of the cockpit system under the current system version is determined to be a level three anomaly warning.

[0119] The vehicle cockpit system log monitoring device provided in this application improves the accuracy and efficiency of process testing of the cockpit system in the target vehicle under the current system version by acquiring the log analysis dataset of the cockpit system under the current system version during process testing based on pre-configured test case information. Based on the historical log analysis datasets of the cockpit system under multiple historical system versions and the log analysis dataset under the current system version, it determines the threshold range of the number of log lines for each process under the current system version in each preset scenario. Based on the threshold range of the number of log lines and the process type of each process, it performs anomaly detection on the log analysis dataset, determines the monitoring result of the number of process lines for each process and the monitoring anomaly warning result of the cockpit system, thereby improving the stability of the cockpit system operation.

[0120] Please see Figure 3 , Figure 3 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application. Figure 3 As shown, the electronic device 300 includes a processor 310, a memory 320, and a bus 330.

[0121] The memory 320 stores machine-readable instructions executable by the processor 310. When the electronic device 300 is running, the processor 310 and the memory 320 communicate via the bus 330. When the machine-readable instructions are executed by the processor 310, they can perform the operations described above. Figure 1 The steps of the vehicle cockpit system log monitoring method in the illustrated method embodiment can be found in the method embodiment for specific implementation, and will not be repeated here.

[0122] This application also provides a computer-readable storage medium storing a computer program, which, when executed by a processor, can perform the above-described actions. Figure 1 The steps of the vehicle cockpit system log monitoring method in the illustrated method embodiment can be found in the method embodiment for specific implementation, and will not be repeated here.

[0123] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0124] In the several embodiments provided in this application, it should be understood that the disclosed systems, apparatuses, and methods can be implemented in other ways. The apparatus embodiments described above are merely illustrative. For example, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. Furthermore, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Additionally, the shown or discussed mutual couplings, direct couplings, or communication connections may be through some communication interfaces; indirect couplings or communication connections between devices or units may be electrical, mechanical, or other forms.

[0125] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0126] In addition, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit.

[0127] If the aforementioned functions are implemented as software functional units and sold or used as independent products, they can be stored in a processor-executable, non-volatile, computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0128] Finally, it should be noted that the above-described embodiments are merely specific implementations of this application, used to illustrate the technical solutions of this application, and not to limit them. The scope of protection of this application is not limited thereto. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that any person skilled in the art can still modify or easily conceive of changes to the technical solutions described in the foregoing embodiments, or make equivalent substitutions for some of the technical features, within the scope of the technology disclosed in this application. Such modifications, changes, or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application, and should all be covered within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A method for monitoring vehicle cockpit system logs, characterized in that, The monitoring method includes: Based on pre-configured test case information, process testing is performed on the cockpit system of the target vehicle under the current system version to obtain the log analysis dataset of the cockpit system under the current system version collected through the process testing; Based on the historical log analysis dataset of the cockpit system under multiple historical system versions, the log analysis dataset, and the type information corresponding to the target vehicle and the current system version, the threshold range of the number of log lines for each process under each preset scenario under the current system version is determined. Based on the threshold range of the number of log lines and the process type corresponding to each process, anomaly detection is performed on the log analysis dataset to determine the monitoring result of the number of process lines for each process under each preset scenario in the current system version, as well as the monitoring anomaly warning result of the cockpit system under the current system version.

2. The method according to claim 1, characterized in that, Based on pre-configured test case information, process testing is performed on the cockpit system of the target vehicle under the current system version to obtain a log analysis dataset of the cockpit system under the current system version collected through the process testing, including: Obtain pre-configured test case information; wherein, the test case information includes test cases corresponding to multiple processes under each of multiple preset scenarios; For the cockpit system in the target vehicle under the current system version, process tests are performed on the cockpit system for each of the test cases to obtain the log dataset of the cockpit system under the current system version collected through the process tests; A tiered analysis is performed on the log dataset to obtain the corresponding log analysis dataset; wherein, the log analysis dataset includes log analysis data of each process under each preset scenario in the current system version.

3. The method according to claim 2, characterized in that, The process testing of the cockpit system for each test case information, to obtain the log dataset of the cockpit system under the current system version collected through the process testing, includes: Perform process tests on the cockpit system for each test case information to obtain system log datasets and performance monitoring log datasets of the cockpit system under the current system version, collected through the process tests. Based on the process information corresponding to each process in the test case information, process correspondence matching is performed on the system log dataset and the performance monitoring log dataset to obtain the log dataset of each process under the current system version.

4. The method according to claim 1, characterized in that, The method of determining the threshold range of the number of log lines for each process in each preset scenario under the current system version, based on the historical log analysis dataset of the cockpit system under multiple historical system versions, the log analysis dataset, and the type information corresponding to the target vehicle and the current system version, includes: Obtain the historical log analysis dataset of the cockpit system under each of the multiple historical system versions; wherein, the historical log analysis dataset includes the historical log analysis data of each process under each preset scenario under each of the historical system versions; Based on the historical log analysis dataset and the log analysis dataset, determine the number of lower quartile log lines, upper quartile log lines, and interquartile distance log lines for each process in each preset scenario. Based on the historical log analysis dataset and the log analysis dataset, determine the standard deviation of the number of log lines for each process; Based on the number of log lines in the upper quartile, the number of log lines in the interquartile range, the standard deviation of the number of log lines, and the type information corresponding to the target vehicle and the current system version, a first log line number threshold corresponding to each process under the current system version is determined. Based on the number of log lines in the lower quartile, the number of log lines in the interquartile range, the standard deviation of the number of log lines, and the type information corresponding to the target vehicle and the current system version, a second log line number threshold corresponding to each process under the current system version is determined. The first log line count threshold is used as the upper limit of the range, and the second log line count threshold is used as the lower limit of the range to determine the log line count threshold range for each process under the current system version.

5. The method according to claim 4, characterized in that, The step of determining the number of lower quartile log lines, upper quartile log lines, and interquartile range log lines for each process under each preset scenario based on the historical log analysis dataset and the log analysis dataset includes: For each process in each preset scenario, determine the number of first log lines corresponding to the historical log analysis data of the process in each historical system version, and determine the number of second log lines corresponding to the log analysis data of the process in the current system version; Sort the number of the first log line and the number of the second log line in descending order of quantity to obtain the sorting result, and determine the number of the lower quartile log lines and the upper quartile log lines corresponding to the process in the sorting result; The difference between the number of log lines in the upper quartile and the number of log lines in the lower quartile is determined as the number of log lines in the quartile distance corresponding to the process.

6. The method according to claim 1, characterized in that, Based on the threshold range of the number of log lines and the process type corresponding to each process, anomaly detection is performed on the log analysis dataset to determine the monitoring result of the number of process lines for each process under each preset scenario in the current system version, and the monitoring anomaly warning result of the cockpit system under the current system version, including: In the log analysis dataset, determine the total number of log lines, the number of information log lines, the number of warning log lines, and the number of error log lines corresponding to each process under each preset scenario; The total number of log lines corresponding to each process in each preset scenario is compared with the threshold range of the number of log lines to obtain the comparison result for each process. Based on the comparison results and the number of error log lines corresponding to each process, determine the monitoring result of the number of process lines corresponding to each process in each preset scenario under the current system version; For each process type, based on the number of information log lines, the number of warning log lines, and the number of error log lines, determine the target number of information log lines, the target number of warning log lines, and the target number of error log lines for each process type. Anomaly detection is performed on the number of target information log lines, the number of target warning log lines, and the number of target error log lines to obtain the percentage detection results of information log lines, warning log lines, and error log lines for each process type. Based on the detection results of the percentage of information log lines, the percentage of warning log lines, and the percentage of error log lines, the monitoring anomaly warning results of the cockpit system under the current system version are determined.

7. The method according to claim 6, characterized in that, The step of determining the monitoring result of the number of process lines for each process in each preset scenario under the current system version, based on the comparison result and the number of error log lines corresponding to each process, includes: Determine the proportion of the number of error log lines to the total number of log lines; When the total number of log lines is greater than a preset multiple corresponding to the first log line number threshold in the range of log line number thresholds, and / or the total number of log lines is less than a preset proportion corresponding to the second log line number threshold in the range of log line number thresholds, and / or the proportion value is greater than a preset proportion threshold, the process line number monitoring result for each process under each preset scenario in the current system version is determined to be abnormal in the number of process lines; wherein, the first log line number threshold is greater than the second log line number threshold; When the total number of log lines is less than or equal to a preset multiple corresponding to the first log line number threshold in the log line number threshold range, and the total number of log lines is greater than or equal to a preset proportion corresponding to the second log line number threshold in the log line number threshold range, and the proportion value is greater than the preset proportion threshold, the process line number monitoring result for each process under each preset scenario in the current system version is determined to be normal.

8. The method according to claim 6, characterized in that, The process types include core processes, critical processes, and ordinary processes; the determination of the monitoring anomaly warning results of the cockpit system under the current system version based on the detection results of the percentage of information log lines, the percentage of warning log lines, and the percentage of error log lines includes: When the detection result of the percentage of warning log lines corresponding to the process under the core process is abnormal and the detection result of the percentage of error log lines corresponding to the process under the core process is abnormal, or the detection result of the percentage of warning log lines corresponding to the process under the critical process is abnormal, the monitoring abnormality warning result of the cockpit system under the current system version is determined to be a level one abnormality warning. When the detection result of the percentage of warning log lines corresponding to the process under the core process is abnormal, the detection result of the percentage of error log lines corresponding to the process under the critical process is abnormal, and the detection result of the percentage of warning log lines corresponding to the process under the ordinary process is abnormal, the monitoring abnormality warning result of the cockpit system under the current system version is determined to be a level two abnormality warning. When the detection result of the percentage of information log lines corresponding to the process under the core process is abnormal, the detection result of the percentage of warning log lines corresponding to the process under the critical process is abnormal, and the detection result of the percentage of error log lines corresponding to the process under the ordinary process is abnormal, the monitoring anomaly warning result of the cockpit system under the current system version is determined to be a level three anomaly warning.

9. A monitoring device for a vehicle cabin system log, characterized in that, The monitoring device includes: The data acquisition module is used to perform process testing on the cockpit system of the target vehicle under the current system version based on pre-configured test case information, so as to obtain the log analysis dataset of the cockpit system under the current system version collected through the process test; The threshold calibration module is used to determine the threshold range of the number of log lines for each process in each preset scenario under the current system version, based on the historical log analysis dataset of the cockpit system under multiple historical system versions, the log analysis dataset, and the type information corresponding to the target vehicle and the current system version respectively. The anomaly monitoring module is used to perform anomaly detection on the log analysis dataset based on the threshold range of the number of log lines and the process type corresponding to each process, and to determine the monitoring result of the number of process lines for each process under each preset scenario in the current system version, as well as the monitoring anomaly warning result of the cockpit system under the current system version.

10. An electronic device, characterized in that, include: The device includes a processor, a memory, and a bus. The memory stores machine-readable instructions executable by the processor. When the electronic device is running, the processor communicates with the memory via the bus. The machine-readable instructions are executed by the processor to perform the steps of the monitoring method for the vehicle cockpit system log as described in any one of claims 1 to 8.

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