CN114971326A - Method and system for monitoring and counting reliability test process of whole vehicle - Google Patents

Abstract

A monitoring and counting method and a monitoring and counting system for a finished automobile reliability test process relate to the technical field of automobile reliability tests and solve the problems of long test period, high manual processing cost, large test data volume, complex process monitoring and high error rate. The method comprises the following steps: s1, establishing a process monitoring evaluation index; s2, managing the reliability test data of the whole vehicle: collecting test data aiming at the content covered by the process monitoring evaluation index, and standardizing the test data; s3, preprocessing data; s4, counting vehicle driving information data, and analyzing according to a data analysis rule; and S5, generating an automatic test report. The system comprises a data management module, a data configuration module, a data analysis module, a graph drawing module and a report export module. The invention can be applied to the whole vehicle reliability test process, saves manpower and improves working efficiency.

Description

A Statistical Method and System for Monitoring Process of Vehicle Reliability Test

technical field

The invention relates to the technical field of vehicle reliability test, in particular to a method and system for monitoring and statistics of a reliability test process of a complete vehicle.

Background technique

The vehicle reliability test is mainly composed of two parts: test mileage and test operation items. In the process of completing the above two parts of the test, the faults of the complete vehicle are found and evaluated. Existing vehicle reliability test engineers need to perform daily statistics, periodic statistics, summary statistics, and test report generation operations on the test process, which have long test cycles, high labor costs, large volume of test data, complex process monitoring, and other problems. high error rate.

SUMMARY OF THE INVENTION

In order to solve the above technical problems, the present invention proposes a statistical method and system for monitoring the reliability test process of a complete vehicle.

The technical scheme of the present invention is as follows:

A statistical method for monitoring a vehicle reliability test process, the method comprising the following steps:

S1. Establish process monitoring and evaluation indicators;

S2. Vehicle reliability test data management: collect test data for the content covered by the process monitoring and evaluation index, and standardize the test data;

S3, data preprocessing;

S4. Statistical vehicle driving information data, and analyze according to the data analysis rules;

S5, automatic test report generation.

Preferably, the process monitoring and evaluation indicators described in step S1 include test progress, man-hour management, completion of test operation items, vehicle failure status, operating status of key component systems, and monitoring content input from the designer and specified in the test program.

Preferably, the source of the test data in step S2 includes the man-hour content data reported by the driver and the test data obtained by the test vehicle data collection device.

Preferably, the working hour content data reported by the driver includes: employee unit, working date, test project name, project cost number, vehicle number, test content, working hour amount, working hour type, responsible engineer, test location, start time, end Time, starting mileage, ending mileage, fuel and electricity consumption records, supporting pictures and engineer-customized content; the working hour content data needs to be fixed in a unified template.

Preferably, the data acquisition device has wireless transmission capability, and the acquisition objects of the data acquisition device include power cans, hybrid cans, comfort cans, chassis cans, instrument cans, diagnostic cans, lin lines, Ethernet, and audio and video, The test data obtained by the test vehicle data acquisition equipment specifically includes: vehicle number and data acquisition equipment binding information, working hours, GPS vehicle speed, longitude and latitude, body equipment status signals, electronic and electrical function status signals, and vehicle key components fault signals. and status signal and temperature signal; the body equipment status signal, electronic and electrical function status signal, vehicle key component fault signal and status signal and temperature signal are the data after DBC analysis, and the data name is used for the DBC analysis. Standardized processing.

Preferably, the step S3 further comprises:

(1) Formulate filter conditions to delete and modify the wrong time record sheet;

(2) Formulate filter conditions to delete data drifts caused by unstable data equipment.

Preferably, the vehicle driving information data in step S4 includes test progress information data and operation item frequency statistics;

The data analysis rules for the test progress information data are: (1) The mileage on the day: group the mileage reporting data according to the working date, and obtain the maximum value minus the minimum value in the group; (2) Accumulated mileage: on the current day As the target date, the previous mileage is accumulated and summed; (3) Cumulative fuel consumption per 100 kilometers = accumulated fuel consumption/accumulated test mileage × 100;

The data analysis rule for the frequency statistics of the operation item is to establish a new column based on the signal state value of the next frequency, and horizontally compare whether the state value of the current moment and the next moment are consistent to determine whether the signal state value has changed.

Preferably, the data analysis of the frequency statistics of the operation items specifically includes: creating a sequence time column with a statistical frequency state signal column, and obtaining the signal state value at the next moment; determining whether the signal column is a single-valued variable, and executing data analysis rules;

Preferably, the execution data analysis rules further include:

(1) If there are only two state values, execute the following formula:

Among them, N represents the frequency of signal switching, V _i represents the current time value of the signal, V _i+1 represents the next time value of the signal, i represents the time, T represents the total number of times of the signal, P ₁ represents the state value 1, and P ₂ represents the state value 2. C represents the number of state cycles of P ₁ → P ₂ → P ₁ ;

The execution steps are as follows:

(1) Calculate the ratio between the absolute value of the difference between the next moment value of the signal sequence and the current moment value and the absolute value of the difference between the state value. At this time, if the state value changes, the ratio is 1. If the state value does not change , then the ratio is 0;

(2) Find the ratio;

(3) The number of signal switching times is obtained by summing the ratio sequence obtained by the signal sequence;

(4) Calculate the number of state cycles according to the number of signal switching;

(2) If there are multiple state values, execute the following formula:

Among them, N represents the frequency of signal switching, V _i represents the current time value of the signal, V _i+1 represents the next time value of the signal, i represents the time, T represents the total number of times of the signal, P{P ₁ , P ₂ , P ₃ , P ₄ , P _n } represents the state value set, P _y →P _z represents the state value switching from y to z, P _y , P _z ∈P, C represents P ₁ →P ₂ →…→P _n →…→P ₂ → P ₁ state cycle number;

The execution steps are as follows:

(1) Enumerate all possible combinations of state signal switching to determine the number of possible combinations: if the signal is a sequential closed-loop signal, the number of switching combinations is 2 (n-1), and if the signal is an unordered switching state signal, the switching combination is The number is n(n-1), where n represents the number of signal values;

(2) Calculate the difference between the next moment value of the signal sequence and the current moment value, if the state value has not changed, the difference value is 0;

(3) If the state value changes and the difference is not zero, then sum up to the switching times of the signal group according to the formula;

(4) If the signal is a sequential closed-loop signal, the value with the least number of switching times in all combinations is taken as the number of state cycles of the signal. If the signal is an unordered switching signal, the switching times of each group of signals are enumerated;

(3) Statistical content of vehicle fault monitoring: According to the operating status signals of each component collected by the data acquisition equipment, count the frequency and duration of faults of each main component, and count when the workingst and errorst values of the components are out of the normal range to obtain the fault. frequency, and calculate the fault running time;

The formula for the failure frequency is:

The fault duration formula:

Among them, N: fault frequency, V _i : signal current time value, V _i+1 : signal next time value, i: time, T: total signal time, V _r : signal normal limit, E: error state operation Duration, f: sampling frequency of data acquisition equipment;

(4) System monitoring of key components: Based on the status signals of each component collected by the data acquisition equipment, and according to the definition of the communication matrix, the running time, fault frequency, state value distribution and extreme value statistics of the components are calculated through the signal values;

(5) Custom monitoring content: Based on the temperature signal and energy consumption signal of each component, according to the distribution interval or integral statistics, energy consumption statistics and temperature extreme value monitoring are carried out.

The present invention also provides a monitoring and statistics system for the reliability test process of a vehicle, which is used to realize the above-mentioned method for monitoring and statistics of the reliability test process of the vehicle. The system includes a data management module, a data configuration module, a data analysis module, a graph Drawing module and report export module;

The data management module is used to manage the durability test data after the data standardization and statistical rules are defined, including the creation, opening, modification and saving of the durability test data, and the collected man-hour statistics report is obtained by connecting to the enterprise database. Data and durability test data, and support to display the opened data in the form of a list for proofreading; the data configuration module configures the project vehicle-data table-monitoring module-monitoring items-monitoring rules, with item number, vehicle number, work The date field is used as the benchmark for data connection; the data analysis module is used to connect with the enterprise database, and the data analysis module is configured based on SQL for big data processing, and the data configuration information is referenced to complete the frequency, working time, extreme value and average value statistics, cutting Data segmentation completes interval distribution statistics; the graphics drawing module is used to call the original data table and the operation result table, call the two-dimensional and three-dimensional drawing functions and map API, and draw the driving track, frequency interval distribution diagram, time domain curve diagram, Pie charts, scatter charts and heat maps; the report export module is used to export and generate reports with one click and store them in a specific path. At the same time, it supports selecting time periods to generate daily, monthly, and full-cycle test reports.

Compared with the prior art, the specific beneficial effects of the present invention are:

The invention automatically processes the key information required for monitoring the reliability test process, completes the statistics of the evaluation indicators in an automated form, establishes a data processing template and automatically generates a test report, and the evaluation indicators can be analyzed from multiple dimensions. The whole vehicle reliability test is used to monitor the process. The frequency statistics method can improve the computing efficiency, and the automatic statistics method can greatly reduce the manual processing cost, cycle and error rate, and can customize the analysis process and generate templates. reports to improve work efficiency.

Description of drawings

Fig. 1 is the schematic flow chart of the monitoring and statistical method of the whole vehicle reliability test process according to the present invention;

Fig. 2 is the data analysis process flow diagram of the operation item frequency statistics described in Embodiment 9;

FIG. 3 is a flow chart of the automatic statistical generation of the test report of the system described in Embodiment 10. FIG.

Detailed ways

In order to make the technical solutions of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings of the description of the present invention. It should be noted that the following embodiments are only used for better understanding The technical solutions of the present invention should not be construed as limitations of the present invention.

Example 1.

This embodiment provides a statistical method for monitoring the reliability test process of a vehicle, as shown in FIG. 1 , the method includes the following steps:

S1. Establish process monitoring and evaluation indicators;

S2. Vehicle reliability test data management: collect test data for the content covered by the process monitoring and evaluation index, and standardize the test data;

S3, data preprocessing;

S4. Statistical vehicle driving information data, and analyze according to the data analysis rules;

S5, automatic test report generation.

Example 2.

This embodiment is a further illustration of Embodiment 1. The process monitoring and evaluation indicators described in step S1 include test progress, man-hour management, completion of test operation items, vehicle failure status, operating status of key components and systems, and information from designers. Input and monitoring content specified in the test program.

The present embodiment will be specifically explained below:

Test progress: The purpose is to monitor the running progress of the whole vehicle and control the overall completion of the test every day. The monitoring contents include: planned mileage, test mileage on the day, accumulated test mileage, work content, fuel and electricity consumption statistics, etc. Examples are as follows:

Working hours management: The purpose is to monitor the usage of working hours, whether the reporting of working hours is correct, the usage of working hours, and to avoid the problems of subsequent working hours management. The monitoring content includes: employee unit, project expense number, working hours of the day, and working hour type. Examples are as follows:

Electronic fence: The purpose is to monitor whether there are violations such as speeding violation monitoring, test route deviation from offline and other violations.

Statistics of test operation items: The test operation items include body equipment operation and electronic and electrical function operation. The purpose is to monitor whether the test personnel complete the test operation items as required, and focus on whether the content is faulty during the completion process. The number of operations is determined by the test outline, and the completion of the test is calculated from the test data. Examples are as follows:

Vehicle fault signal statistics (CAN): In the test, there are often sporadic test faults that are fleeting. In order to fully expose the test fault, the daily automatic monitoring of the test fault signal is carried out, in order to not miss the occasional fault. According to the communication matrix, the frequency of workingst and errorst of each component system is counted. If the platform can integrate diagnostic CAN data and fault code compilation files, the frequency of diagnostic fault codes can also be counted. Examples are as follows:

Failure frequency per 10,000 kilometers = cumulative failure frequency / cumulative test mileage * 10000

Operating status of key parts system: For the entire vehicle system, classify the test signals for each system, and count the operating status of key parts and components. After the corresponding system has a test failure, the operating status data can be directly called to provide designers with fault analysis. Supported by data, the statistical indicators of the operation status are input by the designer or formulated according to the test outline. An example is as follows:

Example 3.

This embodiment is a further illustration of Embodiment 1, and the source of the test data in step S2 includes the working hour content data reported by the driver and the test data obtained by the test vehicle data collection device.

Example 4.

This embodiment is a further illustration of Embodiment 3. The working hour content data filled in by the driver includes: employee unit, working date, test project name, project cost number, vehicle number, test content, working hour amount, working hour type, responsible Engineer, test site, start time, end time, start mileage, end mileage, fuel and electricity consumption records, and documentation pictures and engineer-defined content; the working hour content data needs to be listed in a unified template.

Example 5.

This embodiment is a further illustration of Embodiment 3. The data acquisition device has wireless transmission capability, and the acquisition objects of the data acquisition device include power can, hybrid can, comfort can, chassis can, instrument can, and diagnostic can. , lin line, Ethernet and audio and video, the test data obtained by the test vehicle data acquisition equipment specifically includes: vehicle number and data acquisition equipment binding information, working time, GPS speed, longitude and latitude, body equipment status signal, electronic and electrical functions Status signal, fault signal, status signal and temperature signal of key parts of the vehicle; the status signal of body equipment, electronic and electrical function status signal, fault signal and status signal of key parts of the vehicle, and temperature signal are the data analyzed by DBC , the data names are standardized during the DBC parsing.

Example 6.

This embodiment is a further illustration of Embodiment 1, and the step S3 further includes:

(1) Formulate filter conditions to delete and modify the wrong time record sheet;

(2) Formulate filter conditions to delete data drifts caused by unstable data equipment.

For further example, formulate filter conditions to delete and modify the incorrectly filled time records: such as vehicle number does not match the project, the employee unit does not match the driver, the mileage is beyond the specified range, the work date is beyond the specified range, repeated filling, etc. Extraction and identification and modify. Formulate filter conditions to delete data drifting points caused by unstable data equipment: such as vehicle speed exceeding the maximum speed, latitude and longitude exceeding the limit value, the data is invalid value of the DBC communication matrix, etc.

Example 7.

This embodiment is a further illustration of Embodiment 1, and the vehicle driving information data in step S4 includes test progress information data and operation item frequency statistics;

The data analysis rules for the test progress information data are: (1) The mileage on the day: group the mileage reporting data according to the working date, and obtain the maximum value minus the minimum value in the group; (2) Accumulated mileage: on the current day As the target date, the previous mileage is accumulated and summed; (3) Cumulative fuel consumption per 100 kilometers = accumulated fuel consumption/accumulated test mileage × 100;

The data analysis rule for the frequency statistics of the operation item is to establish a new column based on the signal state value of the next frequency, and horizontally compare whether the state value of the current moment and the next moment are consistent to determine whether the signal state value has changed.

Example 8.

This embodiment is a further illustration of Embodiment 1. The data analysis of the frequency statistics of the operation items specifically includes: creating a sequence time column with a statistical frequency state signal column, and obtaining the signal state value at the next time; Whether the signal column is a single-valued variable and executes data analysis rules;

Example 9.

This embodiment is a further illustration of Embodiment 8. As shown in FIG. 2 , the execution data analysis rule further includes:

(1) If there are only two state values, execute the following formula:

Among them, N represents the frequency of signal switching, V _i represents the current time value of the signal, V _i+1 represents the next time value of the signal, i represents the time, T represents the total number of times of the signal, P ₁ represents the state value 1, and P ₂ represents the state value 2. C represents the number of state cycles of P ₁ → P ₂ → P ₁ ;

The execution steps are as follows:

(1) Calculate the ratio between the absolute value of the difference between the next moment value of the signal sequence and the current moment value and the absolute value of the difference between the state value. At this time, if the state value changes, the ratio is 1. If the state value does not change , then the ratio is 0;

(2) Find the ratio;

(3) The number of signal switching times is obtained by summing the ratio sequence obtained by the signal sequence;

(4) Calculate the number of state cycles according to the number of signal switching;

(2) If there are multiple state values, execute the following formula:

Among them, N represents the frequency of signal switching, V _i represents the current time value of the signal, V _i+1 represents the next time value of the signal, i represents the time, T represents the total number of times of the signal, P{P ₁ , P ₂ , P ₃ , P ₄ , P _n } represents the state value set, P _y →P _z represents the state value switching from y to z, P _y , P _z ∈P, C represents P ₁ →P ₂ →…→P _n →…→P ₂ → P ₁ state cycle number;

The execution steps are as follows:

(1) Enumerate all possible combinations of state signal switching to determine the number of possible combinations: if the signal is a sequential closed-loop signal, the number of switching combinations is 2 (n-1), and if the signal is an unordered switching state signal, the switching combination is The number is n(n-1), where n represents the number of signal values;

(2) Calculate the difference between the next moment value of the signal sequence and the current moment value, if the state value has not changed, the difference value is 0;

(3) If the state value changes and the difference is not zero, then sum up to the switching times of the signal group according to the formula;

(4) If the signal is a sequential closed-loop signal, the value with the least number of switching times in all combinations is taken as the number of state cycles of the signal. If the signal is an unordered switching signal, the switching times of each group of signals are enumerated;

(3) Statistical content of vehicle fault monitoring: According to the operating status signals of each component collected by the data acquisition equipment, count the frequency and duration of faults of each main component, and count when the workingst and errorst values of the components are out of the normal range to obtain the fault. frequency, and calculate the fault running time;

The formula for the failure frequency is:

The fault duration formula:

Among them, N: fault frequency, V _i : signal current time value, V _i+1 : signal next time value, i: time, T: total signal time, V _r : signal normal limit, E: error state operation Duration, f: sampling frequency of data acquisition equipment;

(4) System monitoring of key components: Based on the status signals of each component collected by the data acquisition equipment, and according to the definition of the communication matrix, the running time, fault frequency, state value distribution and extreme value statistics of the components are calculated through the signal values;

(5) Custom monitoring content: Based on the temperature signal and energy consumption signal of each component, according to the distribution interval or integral statistics, energy consumption statistics and temperature extreme value monitoring are carried out.

Example 10.

This embodiment provides a monitoring and statistical system for the reliability test process of a vehicle, as shown in FIG. 3 , which is used to implement the monitoring and statistics method for the reliability test process of a vehicle described in any one of Embodiments 1-9. The system Including data management module, data configuration module, data analysis module, graphic drawing module and report export module;

The data management module is used to manage the durability test data after the data standardization and statistical rules are defined, including the creation, opening, modification and saving of the durability test data, and the collected man-hour statistics report is obtained by connecting to the enterprise database. Data and durability test data, and support to display the opened data in the form of a list for proofreading; the data configuration module configures the project vehicle-data table-monitoring module-monitoring items-monitoring rules, with item number, vehicle number, work The date field is used as the benchmark for data connection; the data analysis module is used to connect with the enterprise database, and the data analysis module is configured based on SQL for big data processing, and the data configuration information is referenced to complete the frequency, working time, extreme value and average value statistics, cutting Data segmentation completes interval distribution statistics; the graphics drawing module is used to call the original data table and the operation result table, call the two-dimensional and three-dimensional drawing functions and map API, and draw the driving track, frequency interval distribution diagram, time domain curve diagram, Pie charts, scatter charts and heat maps; the report export module is used to export and generate reports with one click and store them in a specific path. At the same time, it supports selecting time periods to generate daily, monthly, and full-cycle test reports.

Claims (10)

1. a vehicle reliability test process monitoring statistical method, is characterized in that, described method comprises the following steps: S1. Establish process monitoring and evaluation indicators; S2. Vehicle reliability test data management: collect test data for the content covered by the process monitoring and evaluation index, and standardize the test data; S3, data preprocessing; S4. Statistical vehicle driving information data, and analyze according to the data analysis rules; S5, automatic test report generation. 2. The vehicle reliability test process monitoring statistical method according to claim 1, wherein the process monitoring and evaluation indicators described in step S1 include test progress, man-hour management, completion of test operation items, vehicle failure status, System operation status of key components and monitoring content from designer input and test program regulations. 3. The method for monitoring and statistic of the whole vehicle reliability test process according to claim 1, wherein the source of the test data described in step S2 includes the man-hour content data reported by the driver and the test data obtained by the test vehicle data acquisition equipment. 4. The vehicle reliability test process monitoring statistical method according to claim 3, wherein the working hours content data reported by the driver comprises: employee unit, work date, test item name, item cost number, vehicle number, Test content, working hours, type of working hours, engineer in charge, test location, start time, end time, start mileage, end mileage, fuel and electricity consumption records and pictures for certification and the content customized by the engineer; the content data of the working hours should be unified Template fixed column names. 5 . The method for monitoring and statistics of a vehicle reliability test process according to claim 3 , wherein the data acquisition equipment has wireless transmission capability, and the acquisition objects of the data acquisition equipment include power can, hybrid can, comfortable can, chassis can, instrument can, diagnostic can, lin line, Ethernet and audio and video, the test data obtained by the test vehicle data acquisition equipment specifically includes: vehicle number and data acquisition equipment binding information, working time, GPS speed, Longitude and latitude, body equipment status signal, electronic and electrical function status signal, fault signal and status signal of key components of the vehicle, and temperature signal; said body equipment status signal, electronic and electrical function status signal, fault signal and status signal of key vehicle components And the temperature signal is the data after DBC analysis, and the data name is standardized during the DBC analysis. 6. The vehicle reliability test process monitoring statistical method according to claim 1, wherein the step S3 further comprises: (1) Formulate filter conditions to delete and modify the wrong time record sheet; (2) Formulate filter conditions to delete data drifts caused by unstable data equipment. 7. The method for monitoring and statistic of the whole vehicle reliability test process according to claim 1, wherein the vehicle driving information data in step S4 includes test progress information data and operation item frequency statistics; The data analysis rules for the test progress information data are: (1) The mileage on the day: group the mileage reporting data according to the working date, and obtain the maximum value minus the minimum value in the group; (2) Accumulated mileage: on the current day As the target date, the previous mileage is accumulated and summed; (3) Cumulative fuel consumption per 100 kilometers = accumulated fuel consumption/accumulated test mileage × 100; The data analysis rule for the frequency statistics of the operation item is to establish a new column based on the signal state value of the next frequency, and horizontally compare whether the state value of the current moment and the next moment are consistent to determine whether the signal state value has changed. 8. The method for monitoring and statistics of a vehicle reliability test process according to claim 1, wherein the data analysis of the operation item frequency statistics specifically comprises: creating a sequence time column with a statistical frequency status signal column, obtaining the following Signal state value at a moment; determine if the signal column is a single-valued variable, and execute data analysis rules. 9. The vehicle reliability test process monitoring statistical method according to claim 8, wherein the execution data analysis rule further comprises: (1) If there are only two state values, execute the following formula:

Among them, N represents the frequency of signal switching, V _i represents the current time value of the signal, V _i+1 represents the next time value of the signal, i represents the time, T represents the total number of times of the signal, P ₁ represents the state value 1, and P ₂ represents the state value 2. C represents the number of state cycles of P ₁ → P ₂ → P ₁ ; The execution steps are as follows: (1) Calculate the ratio between the absolute value of the difference between the next moment value of the signal sequence and the current moment value and the absolute value of the difference between the state value. At this time, if the state value changes, the ratio is 1. If the state value does not change , then the ratio is 0; (2) Find the ratio; (3) The number of signal switching times is obtained by summing the ratio sequence obtained by the signal sequence; (4) Calculate the number of state cycles according to the number of signal switching; (2) If there are multiple state values, execute the following formula:

Among them, N represents the frequency of signal switching, V _i represents the current time value of the signal, V _i+1 represents the next time value of the signal, i represents the time, T represents the total number of times of the signal, P{P ₁ , P ₂ , P ₃ , P ₄ , P _n } represents the state value set, P _y →P _z represents the state value switching from y to z, P _y , P _z ∈P, C represents P ₁ →P ₂ →…→P _n →…→P ₂ → P ₁ state cycle number; The execution steps are as follows: (1) Enumerate all possible combinations of state signal switching to determine the number of possible combinations: if the signal is a sequential closed-loop signal, the number of switching combinations is 2 (n-1), and if the signal is an unordered switching state signal, the switching combination is The number is n(n-1), where n represents the number of signal values; (2) Calculate the difference between the next moment value of the signal sequence and the current moment value, if the state value has not changed, the difference value is 0; (3) If the state value changes and the difference is not zero, then sum up to the switching times of the signal group according to the formula; (4) If the signal is a sequential closed-loop signal, the value with the least number of switching times in all combinations is taken as the number of state cycles of the signal. If the signal is an unordered switching signal, the switching times of each group of signals are enumerated; (3) Statistical content of vehicle fault monitoring: According to the operating status signals of each component collected by the data acquisition equipment, count the frequency and duration of faults of each main component, and count when the workingst and errorst values of the components are out of the normal range to obtain the fault. frequency, and calculate the fault running time; The formula for the failure frequency is:

The fault duration formula:

Among them, N: fault frequency, V _i : signal current time value, V _i+1 : signal next time value, i: time, T: total signal time, V _r : signal normal limit, E: error state operation Duration, f: sampling frequency of data acquisition equipment; (4) System monitoring of key components: Based on the status signals of each component collected by the data acquisition equipment, and according to the definition of the communication matrix, the running time, fault frequency, state value distribution and extreme value statistics of the components are calculated through the signal values; (5) Custom monitoring content: Based on the temperature signal and energy consumption signal of each component, according to the distribution interval or integral statistics, energy consumption statistics and temperature extreme value monitoring are carried out. 10. A vehicle reliability test process monitoring and statistics system, characterized in that, for realizing the vehicle reliability test process monitoring and statistics method according to any one of claims 1-9, the system comprises a data management module, Data configuration module, data analysis module, graph drawing module and report export module; The data management module is used to manage the durability test data after the data standardization and statistical rules are defined, including the creation, opening, modification and saving of the durability test data, and the collected working hours statistics and reporting are obtained by connecting to the enterprise database. Data and durability test data, and support to display the opened data in the form of a list for proofreading; the data configuration module configures the project vehicle-data table-monitoring module-monitoring items-monitoring rules, with item number, vehicle number, work The date field is used as the benchmark for data connection; the data analysis module is used to connect with the enterprise database, and the data analysis module is configured based on SQL for big data processing, and the data configuration information is referenced to complete the frequency, working time, extreme value and average value statistics, cutting Data segmentation completes interval distribution statistics; the graphics drawing module is used to call the original data table and the operation result table, call the two-dimensional and three-dimensional drawing functions and map API, and draw the driving track, frequency interval distribution diagram, time domain curve diagram, Pie charts, scatter charts and heat maps; the report export module is used to export and generate reports with one click and store them in a specific path. At the same time, it supports selecting time periods to generate daily, monthly, and full-cycle test reports.

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