CN114662620A - Automobile endurance load data processing method and device for market users - Google Patents

Automobile endurance load data processing method and device for market users Download PDF

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CN114662620A
CN114662620A CN202210566586.2A CN202210566586A CN114662620A CN 114662620 A CN114662620 A CN 114662620A CN 202210566586 A CN202210566586 A CN 202210566586A CN 114662620 A CN114662620 A CN 114662620A
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丁鼎
韩广宇
张永仁
卢放
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Lantu Automobile Technology Co Ltd
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Abstract

The invention discloses a method and a device for processing automobile endurance load data of market users, wherein the method comprises the following steps: the method comprises the steps of defining key durable load signals of new energy automobile market user data, and introducing selection and basis of the durable load signals by taking a motor driving system assembly as an example; defining a sub-working condition segment of a market user endurance load signal, and in the proposal, a motor driving system assembly is taken as an example to introduce an algorithm for compressing and cutting market user endurance load data into sub-working conditions in detail; clustering operation is carried out on the market user endurance load data sub-working condition segments, a mean vector and a covariance matrix of a probability density function of the market user endurance load data are solved on the basis of a multi-step iteration method, dispersion distances from different sub-working conditions to a center working condition are calculated on the basis of the mean vector and the covariance matrix, and multi-step iteration is carried out on the basis of the dispersion distances, so that a market user endurance load data sub-working condition segment set is generated through clustering and is used for guiding the formulation of finished automobile and rack endurance test specifications of automobiles.

Description

Automobile endurance load data processing method and device for market users
Technical Field
The invention relates to the technical field of automobile durability, in particular to an automobile durable load data processing method and device for market users.
Background
The endurance test of the automobile is generally established according to the development experience of a host factory, or the service condition of a user is investigated according to the questionnaire of a market user. However, the endurance test of the automobile is actually inseparable from the development process of the host factory and the actual use situation of the user.
That is to say, the result of the car endurance test verification in the related art is one-sided, and only depends on the development experience of the host factory, or only depends on the user experience collected from the user using process in the questionnaire to implement the car endurance test, so that the accuracy of the car endurance test is low, and the optimal balance of the whole car cost and the safety and reliability of the whole car cannot be realized.
Disclosure of Invention
The embodiment of the application provides a method and a device for processing automobile endurance load data of market users, solves the technical problems of one-sidedness and low accuracy of test results of automobile endurance tests in the prior art, realizes combination of actual use working conditions of users, obtains automobile working condition data of the users in the use process, and improves the comprehensiveness of the automobile endurance tests and the technical effect of verifying the accuracy.
In a first aspect, the present application provides a method for processing data of automobile endurance load of market users, the method comprising:
acquiring key durable load signal data of a target system assembly of an automobile;
dividing various types of data in the key durable load signal data into F sub-working condition segments respectively according to the time sequence of the last parking vehicle speed, the maximum peak value of the vehicle speed and the time sequence of the next parking time, wherein F is a positive integer;
and clustering the F sub-working condition fragments to obtain K types of target working conditions, wherein the K types of target working conditions are used for carrying out an endurance test on the automobile.
Further, when the target system assembly is a motor drive system assembly, acquiring critical endurance load signal data of the target system assembly of the automobile, comprising:
obtaining key durable load signal data of the motor driving system assembly, wherein the key durable load signal data comprises driving motor torque signal data, driving motor rotating speed signal data, motor water temperature signal data and differential signal data.
Further, acquiring differential signal data of the motor drive system assembly comprises:
acquiring left wheel speed signal data and right wheel speed signal data of an automobile;
and determining differential signal data according to the difference value of the left wheel speed signal data and the right wheel speed signal data.
Further, after obtaining the critical endurance load signal data of the target system assembly of the automobile, before dividing each kind of data in the critical endurance load signal data into F sub-operating condition segments, the method further includes:
acquiring monitoring signal data of the automobile from an automobile networking system of the automobile, wherein the monitoring signal data comprises speed signal data, steering wheel signal data, brake pedal signal data and wheel speed signal data;
identifying whether abnormal data exist in the key durable load signal data according to the variation trend of the monitoring signal data;
and when abnormal data exists in the key durable load signal data, correcting the abnormal data, and updating the key durable load signal data according to the corrected abnormal data.
Further, after updating the critical endurance load signal data depending on the corrected abnormal data, before dividing each kind of data in the critical endurance load signal data into F sub-condition segments, the method further includes:
identifying key point data of the key durable load signal data according to a preset key point data screening condition;
acquiring a preset threshold value of a data channel of a weight-related signal of a target system assembly;
and on the premise of reserving all key point data in the key durable load signal data, removing the signal data smaller than the preset threshold value of the critical durable load signal data channel from the key durable load signal data to obtain the compressed key durable load signal data.
Further, clustering the F sub-working condition segments to obtain K types of target working conditions, including:
step 1, determining a mean vector and a covariance matrix of each sub-working condition segment in F sub-working condition segments;
step 2, randomly selecting K sub-working condition segments from the F sub-working condition segments as the central working condition of the K-type target working condition;
step 3, determining the dispersion distance between each non-central working condition and each central working condition according to the mean vector and the covariance matrix of each non-central working condition fragment in the F sub-working condition fragments and the mean vector and the covariance matrix of each central working condition in the K central working conditions;
step 4, dividing each non-central working condition into data sets of K types of target working conditions according to the dispersion distance between each non-central working condition and each central working condition to obtain K data sets;
step 5, aiming at each data set, connecting the sub-working condition segments in the data set into new data segments according to the time sequence, determining the mean vector and covariance matrix of the new data segments, and taking the mean vector and covariance matrix of the new data segments as the mean vector and covariance matrix after the central working condition corresponding to the data set is updated;
and 6, repeating the steps 3-5 aiming at the updated mean vector and covariance matrix of each central working condition until the variation amplitude of the mean vector and covariance matrix of each data set in the K data sets is smaller than a preset amplitude threshold value.
In a second aspect, the present application provides a market consumer's automobile endurance load data processing apparatus, comprising:
the key durable load signal data acquisition module is used for acquiring key durable load signal data of a target system assembly of the automobile;
the sub-working condition segment dividing module is used for dividing various types of data in the key durable load signal data into F sub-working condition segments according to the time sequence of the last parking vehicle speed, the time of the maximum peak value of the vehicle speed and the time of the next parking vehicle, wherein F is a positive integer;
and the clustering module is used for clustering the F sub-working condition fragments to obtain K types of target working conditions, and the K types of target working conditions are used for carrying out an endurance test on the automobile.
Further, the key endurance load signal data acquisition module is specifically configured to:
when the target system assembly is the motor driving system assembly, key durable load signal data of the motor driving system assembly are obtained, wherein the key durable load signal data comprise driving motor torque signal data, driving motor rotating speed signal data, motor water temperature signal data and differential signal data.
In a third aspect, the present application provides an electronic device, comprising:
a processor;
a memory for storing processor-executable instructions;
wherein the processor is configured to execute to implement a market user's automobile endurance load data processing method as provided in the first aspect.
In a fourth aspect, the present application provides a non-transitory computer-readable storage medium, wherein instructions in the storage medium, when executed by a processor of an electronic device, enable the electronic device to perform a method of processing automobile endurance load data for a market user as provided in the first aspect.
One or more technical solutions provided in the embodiments of the present application have at least the following technical effects or advantages:
the method comprises the steps of firstly defining key durable load signals of new energy automobile market user data, and specifically, taking a motor driving system assembly of a new energy automobile as an example to describe selection and basis of the durable load signals in detail; then, defining a sub-working condition segment of the market user durable load signal, and specifically, describing an algorithm for compressing and cutting market user durable load data into sub-working conditions in detail by taking a motor driving system assembly of the new energy automobile as an example; finally, clustering operation is carried out on the market user durable load data sub-working condition segments, specifically, taking a motor driving system of a new energy automobile as an example, a mean vector and a covariance matrix of a probability density function for solving the market user durable load data based on a multi-step iteration method are introduced in detail, the dispersion distance from different sub-working conditions to a central working condition is calculated based on the mean vector and the covariance matrix, multi-step iteration is carried out based on the dispersion distance, so that a market user durable load data sub-working condition segment set is generated through clustering, and the obtained sub-working condition segment set can be used for guiding the formulation of the finished automobile and rack durable test specifications of the automobile.
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In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings needed to be used in the description of the embodiments are briefly introduced below, and it is obvious that the drawings in the following description are some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings based on the drawings without creative efforts.
FIG. 1 is a schematic flow chart of a method for processing automobile endurance load data of market users according to the present application;
FIG. 2 is a schematic structural diagram of an automobile endurance load data processing apparatus for market users according to the present application;
fig. 3 is a schematic structural diagram of an electronic device provided in the present application.
Detailed Description
The embodiment of the application provides a method for processing automobile endurance load data of market users, and solves the technical problems that in the prior art, the test result of an automobile endurance test has one-sidedness and low accuracy.
In order to solve the technical problems, the general idea of the embodiment of the application is as follows:
a method for processing automobile endurance load data of market users comprises the following steps: acquiring key durable load signal data of a target system assembly of an automobile; dividing various types of data in the key durable load signal data into F sub-working condition segments respectively according to the time sequence of the last parking vehicle speed, the maximum peak value of the vehicle speed and the time sequence of the next parking time, wherein F is a positive integer; and clustering the F sub-working condition fragments to obtain K types of target working conditions, wherein the K types of target working conditions are used for carrying out an endurance test on the automobile.
The method comprises the steps of firstly defining a key endurance load signal of new energy automobile market user data, and specifically, taking a motor driving system assembly of a new energy automobile as an example to describe selection and basis of the endurance load signal in detail; then, defining a sub-working condition segment of the market user durable load signal, and specifically, describing an algorithm for compressing and cutting market user durable load data into sub-working conditions in detail by taking a motor driving system assembly of the new energy automobile as an example; finally, clustering operation is carried out on the market user durable load data sub-working condition segments, specifically, taking a motor driving system of a new energy automobile as an example, a mean vector and a covariance matrix of a probability density function for solving the market user durable load data based on a multi-step iteration method are introduced in detail, the dispersion distance from different sub-working conditions to a central working condition is calculated based on the mean vector and the covariance matrix, multi-step iteration is carried out based on the dispersion distance, so that a market user durable load data sub-working condition segment set is generated through clustering, and the obtained sub-working condition segment set can be used for guiding the formulation of the finished automobile and rack durable test specifications of the automobile.
In order to better understand the technical solution, the technical solution will be described in detail with reference to the drawings and the specific embodiments.
First, it is stated that the term "and/or" appearing herein is merely one type of associative relationship that describes an associated object, meaning that three types of relationships may exist, e.g., a and/or B may mean: a exists alone, A and B exist simultaneously, and B exists alone. In addition, the character "/" herein generally indicates that the former and latter related objects are in an "or" relationship.
The new energy automobiles delivered to market users are all provided with TBOX systems (Telematics Box, Internet of vehicles, TBOX for short), the TBOX can provide services such as driving data acquisition, remote query and control, fault monitoring and the like for the automobiles by using remote communication and information science technologies, and can also provide services such as fault diagnosis, road rescue, remote unlocking, air conditioner control and the like for the users. The TBOX stores the internal signal data of the automobile in the driving process, and automobile manufacturers can call the data in the TBOX through the big data platform under the permission of users so as to read the internal signal data of the automobile in the driving process.
The automobile comprises a plurality of different subsystems, each subsystem is subjected to different endurance load excitations, so that relevant data of the corresponding subsystem are acquired from different signal data channels of TBOX aiming at endurance tests of the different subsystems. The types of the automobile subsystems and the corresponding key signal data channels of the subsystems can be referred to table 1.
TABLE 1
Figure 373062DEST_PATH_IMAGE001
Figure 495739DEST_PATH_IMAGE002
The method for processing the automobile endurance load data provided by the embodiment is mainly taken as an example of a motor driving system, and the method for processing the automobile endurance load data is explained.
Specifically, the present embodiment provides a method for processing automobile endurance load data of a market user as shown in fig. 1, which includes steps S11-S13.
And step S11, acquiring key durable load signal data of the target system assembly of the automobile.
The target system assembly may be a suspension system, a steering system, a braking system, a vehicle body system, a power system, etc. in table 1, or may be other system assemblies in an automobile, which is not limited in this embodiment. In the following description, a motor driving system of a new energy vehicle is taken as an example to illustrate a method for processing vehicle endurance load data of a market user.
When the target system assembly is a motor drive system assembly, obtaining key endurance load signal data of the target system assembly of the automobile, comprising: obtaining key durable load signal data of the motor driving system assembly, wherein the key durable load signal data comprises driving motor torque signal data, driving motor rotating speed signal data, motor water temperature signal data and differential signal data.
New energy automobile's motor drive system assembly includes: motor assembly, reduction gear assembly, differential mechanism assembly.
For a motor assembly and a reducer assembly, a signal data channel within the TBOX comprises: the driving motor torque signal data, the driving motor rotating speed signal data and the motor water temperature signal data, wherein the 3 signal data can represent mechanical loads (namely the driving motor torque load and the driving motor rotating speed load) and environmental loads (namely the motor water temperature) borne by the motor assembly and the reducer assembly.
For the differential assembly, in addition to the torque and rotation speed load input by the driving motor, the differential assembly is also subjected to differential load of left and right wheel speeds, and a signal data channel in the TBOX has no differential signal data of the left and right wheel speeds, but contains single wheel speed signal data, so that the differential signal data of the left and right wheel speeds can be obtained by subtracting the right wheel speed signal data from the left wheel speed signal data.
Specifically, left wheel speed signal data and right wheel speed signal data of the automobile can be acquired from the TBOX; and determining differential signal data according to the difference value of the left wheel speed signal data and the right wheel speed signal data.
According to the structure composition and the load composition of the motor driving system assembly, the torque signal data of the driving motor, the rotating speed signal data of the driving motor, the water temperature signal data of the motor and the differential signal data of the left wheel speed and the right wheel speed are taken as the key durable load signal data of the motor driving system assembly of the automobile, so as to explain the processing method of durable load data of users in the automobile market.
And step S12, dividing each kind of data in the key durable load signal data into F sub-working condition segments respectively according to the time of the last parking vehicle speed, the time of the maximum peak value of the vehicle speed and the time sequence of the next parking time, wherein F is a positive integer.
In the actual processing, the acquired torque signal data of the driving motor, the rotational speed signal data of the driving motor, the water temperature signal data of the driving motor, and the differential signal data of the left and right wheel speeds may be defined as the following expressions:
Figure 862129DEST_PATH_IMAGE003
Figure 44849DEST_PATH_IMAGE004
Figure 751817DEST_PATH_IMAGE005
Figure 819130DEST_PATH_IMAGE006
Figure 164661DEST_PATH_IMAGE007
wherein:
Figure 642916DEST_PATH_IMAGE008
-representing a new energy vehicle market user data matrix;
Figure 441107DEST_PATH_IMAGE009
time series data representing a new energy vehicle market user data matrix, e.g. A1The matrix includes the number of T time slices;
Figure 312111DEST_PATH_IMAGE010
-drive motor torque signal data representative of new energy vehicle market users;
Figure 512149DEST_PATH_IMAGE011
-data values representing time series of i instants in the drive motor torque signal data;
Figure 161305DEST_PATH_IMAGE012
representing the driving motor rotating speed signal data of the new energy automobile market user;
Figure 446792DEST_PATH_IMAGE013
-data values representing a time series of i instants in the drive motor speed signal data;
Figure 855908DEST_PATH_IMAGE014
the motor water temperature signal data of the new energy automobile market user are represented;
Figure 910452DEST_PATH_IMAGE015
-data values representing a time series of i instants in the motor water temperature signal data;
Figure 996088DEST_PATH_IMAGE016
differential signal data representing left and right wheel speeds of new energy vehicle market users;
Figure 644239DEST_PATH_IMAGE017
-data values at time i in time series in differential signal data representing left and right wheel speeds.
For the new energy automobile market user data matrix a, the following operations 1 and 2 may be implemented.
[ operation 1 ]
Based on monitored signal data inside the TBOX of the new energy market consumer vehicle, for example: the data matrix A comprises vehicle speed signal data, steering wheel signal data, brake pedal signal data, wheel speed signal data and the like, and the data matrix A is identified to determine whether peak value abnormality, mean value drift and the like exist so as to ensure the validity of the data.
The method specifically comprises the following steps:
step 1, after obtaining key durable load signal data of a target system assembly (a motor driving system assembly is taken as an example for explanation here) of an automobile, before dividing various types of data in the key durable load signal data into F sub-working condition segments respectively, obtaining monitoring signal data of the automobile from an automobile networking system of the automobile, wherein the monitoring signal data comprises speed signal data, steering wheel signal data, brake pedal signal data and wheel speed signal data;
step 2, identifying whether abnormal data exist in the key durable load signal data according to the variation trend of the monitoring signal data;
and 3, when abnormal data exist in the key durable load signal data, correcting the abnormal data, and updating the key durable load signal data according to the corrected abnormal data.
For example, the vehicle speed and the motor speed are in a correlation, and in a normal case, the vehicle speed is high, and the motor speed is also high, so that whether the driving motor speed signal data is abnormal or not can be determined through the variation trend of the vehicle speed, and if the driving motor speed signal data is abnormal, the abnormal data is processed, specifically, the abnormal data can be removed, corrected or supplemented, and a proper processing mode can be adopted according to the actual situation.
[ operation 2 ]
For the critical durable load signal data of the target system assembly (the motor driving system assembly is taken as an example for explanation), a threshold value can be set, and when the critical durable load signal data is smaller than the threshold value, the data does not participate in subsequent calculation, so that the purpose of compressing the data scale is realized, and the subsequent calculation amount is reduced. In the data compression, the data size of the data matrix a should be compressed as much as possible without reducing the critical point data to reduce the amount of subsequent calculation. The method can be specifically decomposed into the following steps:
step 1, after updating key endurance load signal data depending on corrected abnormal data, identifying key point data of the key endurance load signal data according to a preset key point data screening condition before dividing various types of data in the key endurance load signal data into F sub-working condition segments respectively;
step 2, acquiring a preset threshold value of a data channel of a weight-off signal of the motor driving system assembly;
and 3, removing the signal data smaller than the preset threshold of the critical durable load signal data channel from the critical durable load signal data on the premise of keeping all the critical point data in the critical durable load signal data to obtain the compressed critical durable load signal data.
The preset key point data filtering conditions may include the following conditions 1 and 2. If the data point in the critical endurance load signal data (i.e., user data matrix A)
Figure 716100DEST_PATH_IMAGE018
And if the following two conditions are met, the data is defined as key point data:
condition 1:
Figure 749784DEST_PATH_IMAGE019
condition 2:
Figure 881688DEST_PATH_IMAGE020
on the premise that the condition 1 is satisfied,
Figure 751555DEST_PATH_IMAGE021
Figure 627107DEST_PATH_IMAGE022
Figure 515297DEST_PATH_IMAGE023
wherein:
Figure 818103DEST_PATH_IMAGE024
-data values representing the j-th signal data in the critical endurance load signal data at time instant i in time series,
Figure 440845DEST_PATH_IMAGE025
wherein j =1 is represented as drive motor torque signal data, wherein j =2 is represented as drive motor rotational speed signal data, wherein j =3 is represented as motor water temperature signal data, and wherein j =4 is represented as differential signal data of left and right wheel speeds;
Figure 854509DEST_PATH_IMAGE026
、
Figure 862785DEST_PATH_IMAGE027
、
Figure 336492DEST_PATH_IMAGE028
、
Figure 180951DEST_PATH_IMAGE029
-data values of the jth signal data in the critical endurance load signal data at time sequences (i-1), (i + 1), i, e, respectively;
Figure 398306DEST_PATH_IMAGE030
、
Figure 526668DEST_PATH_IMAGE031
、
Figure 46642DEST_PATH_IMAGE032
、
Figure 503031DEST_PATH_IMAGE033
respectively representing time values corresponding to the time series
Figure 400709DEST_PATH_IMAGE030
、
Figure 524523DEST_PATH_IMAGE031
、
Figure 215398DEST_PATH_IMAGE032
、
Figure 549297DEST_PATH_IMAGE033
；
Figure 842875DEST_PATH_IMAGE034
-a data vector representing the j-th signal data in the critical endurance load signal data at time instant i in time series;
Figure 696561DEST_PATH_IMAGE035
-a data vector representing the j-th signal data in the critical endurance load signal data at time instance e in time series;
Figure 682972DEST_PATH_IMAGE036
、
Figure 973008DEST_PATH_IMAGE037
-representing the intermediate calculation vector;
Figure 70277DEST_PATH_IMAGE038
-representing a vector multiplication;
Figure 778470DEST_PATH_IMAGE039
、
Figure 325995DEST_PATH_IMAGE040
-a modulus representing a vector;
Figure 978693DEST_PATH_IMAGE041
-representing a constant threshold parameter of the value,
Figure 755019DEST_PATH_IMAGE042
；
Figure 442352DEST_PATH_IMAGE043
-representing a constant threshold parameter of the device,
Figure 160778DEST_PATH_IMAGE044
；
Figure 300773DEST_PATH_IMAGE045
-representing a constant threshold parameter;
Figure 615211DEST_PATH_IMAGE046
-representing a time constant delta parameter.
Screening key point data in the key durable load signal data according to the two conditions, removing signal data smaller than a preset threshold value of a key important signal data channel from the key durable load signal data on the premise of keeping all key point data in the key durable load signal data to obtain compressed key durable load signal data, and finally obtaining a compressed new energy automobile market user data matrix
Figure 157050DEST_PATH_IMAGE047
The expression is defined as follows:
Figure 780799DEST_PATH_IMAGE048
wherein:
Figure 408089DEST_PATH_IMAGE049
-representing a compressed new energy vehicle market user data matrix;
Figure 526218DEST_PATH_IMAGE050
-representing compressed drive motor torque signal data for new energy vehicle market users;
Figure 312777DEST_PATH_IMAGE051
representing compressed driving motor rotating speed signal data of new energy automobile market users;
Figure 248372DEST_PATH_IMAGE052
the compressed motor water temperature signal data of the new energy automobile market user are represented;
Figure 238325DEST_PATH_IMAGE053
differential signal data representing left and right wheel speeds of the compressed new energy automobile market users.
According to the variation trend of the vehicle speed signal data V, namely from the time close to the parking vehicle speed (for example, the time when the vehicle speed is 0) to the time when the next vehicle speed is maximum to the time close to the parking vehicle speed (for example, the time when the vehicle speed is 0), the data matrix
Figure 284778DEST_PATH_IMAGE054
Dividing the operation condition into F sub-operation condition segments, and defining an expression as follows:
Figure 925844DEST_PATH_IMAGE055
Figure 642127DEST_PATH_IMAGE056
Figure 244010DEST_PATH_IMAGE057
Figure 959068DEST_PATH_IMAGE058
Figure 330007DEST_PATH_IMAGE059
Figure 217191DEST_PATH_IMAGE060
wherein:
Figure 571949DEST_PATH_IMAGE061
-representing a compressed new energy vehicle market user data matrix;
Figure 819260DEST_PATH_IMAGE062
-representing the g-th segment of the new energy automobile market user data matrix;
f represents the total number of the sub-working conditions of the new energy automobile market user data matrix;
Figure 185650DEST_PATH_IMAGE063
-a data timing vector representing the g-th segment of the drive motor torque signal data;
Figure 633949DEST_PATH_IMAGE064
-a data timing vector representing the g-th segment of drive motor speed signal data;
Figure 69478DEST_PATH_IMAGE065
when data representing the g-th segment of the water temperature signal data of the motorAn order vector;
Figure 995846DEST_PATH_IMAGE066
-a time sequence vector of the g-th piece of differential signal data representing left and right wheel speeds;
Figure 482322DEST_PATH_IMAGE067
-a value representing the h data of the g segment of the drive motor torque signal data;
Figure 835943DEST_PATH_IMAGE068
-a value representing the h data of the g segment of the drive motor speed signal data;
Figure 758769DEST_PATH_IMAGE069
-a value representing the h data of the g segment of motor water temperature signal data;
Figure 488827DEST_PATH_IMAGE070
-a value of the h data of the g segment of differential signal data representing left and right wheel speeds;
Figure 564231DEST_PATH_IMAGE071
-representing the total number of data of the g-th segment of the market user data matrix.
And step S13, clustering the F sub-working condition fragments to obtain K types of target working conditions, wherein the K types of target working conditions are used for carrying out an endurance test on the automobile.
Step S13 can be specifically decomposed into the following steps:
step 1, determining a mean vector and a covariance matrix of each sub-working condition segment in F sub-working condition segments;
step 2, randomly selecting K sub-working condition segments from the F sub-working condition segments as the central working condition of the K-type target working condition;
step 3, determining the dispersion distance between each non-central working condition and each central working condition according to the mean vector and the covariance matrix of each non-central working condition fragment in the F sub-working condition fragments and the mean vector and the covariance matrix of each central working condition in the K central working conditions;
step 4, dividing each non-central working condition into data sets of K types of target working conditions according to the dispersion distance between each non-central working condition and each central working condition to obtain K data sets;
step 5, aiming at each data set, connecting the sub-working condition segments in the data set into new data segments according to the time sequence, determining the mean vector and covariance matrix of the new data segments, and taking the mean vector and covariance matrix of the new data segments as the mean vector and covariance matrix after the central working condition corresponding to the data set is updated;
and 6, repeating the steps 3-5 aiming at the updated mean vector and covariance matrix of each central working condition until the variation amplitude of the mean vector and covariance matrix of each data set in the K data sets is smaller than a preset amplitude threshold value.
The method for determining the mean vector and the covariance matrix of each of the F sub-regime segments may specifically include:
probability density function for appointing new energy automobile market user data matrix variable
Figure 88753DEST_PATH_IMAGE072
The expression of (a) is as follows:
Figure 764454DEST_PATH_IMAGE073
Figure 907990DEST_PATH_IMAGE074
wherein:
Figure 962534DEST_PATH_IMAGE075
-a probability density function representing a variable of the data matrix;
Figure 313750DEST_PATH_IMAGE076
-representing a variable vector, wherein: variables of
Figure 86534DEST_PATH_IMAGE077
Data variable representing torque signal of driving motor
Figure 768182DEST_PATH_IMAGE078
Data variable representing speed signal of driving motor
Figure 942811DEST_PATH_IMAGE079
Data variable, variable representing motor water temperature signal
Figure 933770DEST_PATH_IMAGE080
Differential signal data variables representing left and right wheel speeds;
Figure 334795DEST_PATH_IMAGE081
the proposal totally has 4 data channels, namely torque signal data of a driving motor, rotating speed signal data of the driving motor, water temperature signal data of the motor and differential signal data of left and right wheel speeds, so that the value is 4;
Figure 944768DEST_PATH_IMAGE082
-representing a variable vector
Figure 98538DEST_PATH_IMAGE076
The mean vector of (2);
Figure 401343DEST_PATH_IMAGE083
-representing a variable vector
Figure 758506DEST_PATH_IMAGE076
The covariance matrix of (a);
Figure 172170DEST_PATH_IMAGE084
-representing a variable vector
Figure 440166DEST_PATH_IMAGE076
The inverse of the covariance matrix of (a);
Figure 913873DEST_PATH_IMAGE085
-representing a variable vector
Figure 23911DEST_PATH_IMAGE076
The determinant of covariance matrix of (a);
upper corner mark
Figure 100321DEST_PATH_IMAGE086
-a transpose operation of the representation matrix.
Data matrix for the g-th segment of the F sub-condition segments
Figure 104049DEST_PATH_IMAGE087
Performing a probability density function
Figure 624023DEST_PATH_IMAGE088
Solving, i.e. solving the data matrix
Figure 80412DEST_PATH_IMAGE087
Corresponding mean vector
Figure 960512DEST_PATH_IMAGE089
And covariance matrix
Figure 553168DEST_PATH_IMAGE090
The corresponding solving expression is as follows:
Figure 244043DEST_PATH_IMAGE091
Figure 187728DEST_PATH_IMAGE092
Figure 137099DEST_PATH_IMAGE093
Figure 849840DEST_PATH_IMAGE094
Figure 446037DEST_PATH_IMAGE095
Figure 267232DEST_PATH_IMAGE096
solving according to the constraint condition:
Figure 364501DEST_PATH_IMAGE097
order to
Figure 72694DEST_PATH_IMAGE098
Order to
Figure 964426DEST_PATH_IMAGE099
Order to
Figure 7337DEST_PATH_IMAGE100
Figure 173877DEST_PATH_IMAGE101
Solving according to iteration:
1、
Figure 470997DEST_PATH_IMAGE102
and
Figure 799210DEST_PATH_IMAGE103
is not changed, is updated
Figure 594997DEST_PATH_IMAGE104
：
Figure 440593DEST_PATH_IMAGE105
=
Figure 716853DEST_PATH_IMAGE106
；
2、
Figure 606181DEST_PATH_IMAGE107
And
Figure 374417DEST_PATH_IMAGE103
is not changed, is updated
Figure 351600DEST_PATH_IMAGE108
：
Figure 144019DEST_PATH_IMAGE109
=
Figure 814034DEST_PATH_IMAGE110
；
3、
Figure 538408DEST_PATH_IMAGE111
And
Figure 319282DEST_PATH_IMAGE112
is not changed, is updated
Figure 960348DEST_PATH_IMAGE113
：
Figure 801265DEST_PATH_IMAGE114
=
Figure 544093DEST_PATH_IMAGE115
；
Iterate until
Figure 863079DEST_PATH_IMAGE105
、
Figure 358651DEST_PATH_IMAGE109
、
Figure 370469DEST_PATH_IMAGE114
If the variation of the threshold value is smaller than the preset threshold value or the maximum iteration number is reached, the iteration is stopped.
Wherein:
Figure 69435DEST_PATH_IMAGE116
-representing the number of iterations;
Figure 457691DEST_PATH_IMAGE117
、
Figure 807770DEST_PATH_IMAGE118
、
Figure 990490DEST_PATH_IMAGE119
、
Figure 442331DEST_PATH_IMAGE120
-representing a mean vector
Figure 368698DEST_PATH_IMAGE121
Each row value of (a);
Figure 573284DEST_PATH_IMAGE122
-representing the mean of the solution data;
Figure 192484DEST_PATH_IMAGE123
-representing the middleCalculating a matrix;
Figure 131621DEST_PATH_IMAGE124
-representing the total number of data of the g-th segment of the market user data matrix;
Figure 861680DEST_PATH_IMAGE125
-representing an intermediate calculation matrix;
Figure 920771DEST_PATH_IMAGE126
-representing a covariance matrix
Figure 710873DEST_PATH_IMAGE127
The inverse matrix of (d);
Figure 871727DEST_PATH_IMAGE128
-representing a covariance matrix
Figure 139897DEST_PATH_IMAGE127
The inverse matrix of (d);
Figure 319075DEST_PATH_IMAGE129
-expressing a minimum operation;
Figure 14498DEST_PATH_IMAGE130
-representing a determinant solving a matrix;
Figure 662648DEST_PATH_IMAGE131
the sum of the elements on the main diagonal (diagonal from top left to bottom right) of the solution matrix is represented;
Figure 734509DEST_PATH_IMAGE132
-represents isThen the parameter matrix is transformed;
Figure 299352DEST_PATH_IMAGE133
-representing an intermediate calculation matrix;
Figure 41043DEST_PATH_IMAGE134
-representation matrix
Figure 301123DEST_PATH_IMAGE132
And matrix
Figure 318887DEST_PATH_IMAGE133
Is a Hadamard product, which is a type of operation of a matrix if A = (a =)ij) And B = (B)ij) Are two matrices of the same order, if cij=aij×bijThen, the matrix C = (C)ij) The Hadamard product or base product of A and B;
Figure 488968DEST_PATH_IMAGE135
the 1-norm representing the solution matrix, for example: matrix A, the 1-norm of matrix A being represented as
Figure 526194DEST_PATH_IMAGE136
，
Figure 398204DEST_PATH_IMAGE137
I.e. the maximum of the sum of the absolute values of all matrix column vectors;
Figure 811868DEST_PATH_IMAGE138
、
Figure 836456DEST_PATH_IMAGE139
-representing an intermediate calculation function;
Figure 44583DEST_PATH_IMAGE140
-representing a calculation function;
Figure 403889DEST_PATH_IMAGE141
-representing an intermediate calculation matrix;
Figure 90086DEST_PATH_IMAGE142
-representing a threshold coefficient;
Figure 969180DEST_PATH_IMAGE143
-representing the F-norm of the solution matrix, the F-norm of the matrix being the number of the roots after summing the squares of each element in a matrix;
Figure 348209DEST_PATH_IMAGE144
-representing the F-norm of the solution matrix
Figure 929232DEST_PATH_IMAGE145
The number of squares of (c).
Through the steps, the market user data matrix can be obtained
Figure 684698DEST_PATH_IMAGE146
The mean vector and covariance matrix corresponding to the probability density function of any segment data, for example: g-th fragment data matrix
Figure 418299DEST_PATH_IMAGE147
Function of probability density
Figure 233808DEST_PATH_IMAGE148
Corresponding mean vector
Figure 302127DEST_PATH_IMAGE149
And covariance matrix
Figure 533388DEST_PATH_IMAGE150
Thereby making a needleClustering the sub-working conditions of the new energy automobile market user data matrix, wherein the specific clustering process is as follows:
according to the foregoing, the user data matrix for the new energy automobile market
Figure 980550DEST_PATH_IMAGE146
Data matrix
Figure 91595DEST_PATH_IMAGE146
Dividing the operation condition into F sub-operation condition segments, and defining an expression as follows:
Figure 663521DEST_PATH_IMAGE151
and randomly selecting K sub-working condition segments from the F sub-working condition segments, and taking the K sub-working condition segments as a tentative central working condition of the K target working conditions, wherein the K target working conditions can be determined according to actual experience of the project and can also be set according to the project requirements. Within a reasonable range, the larger the K is, the more the working condition types are, and the larger the calculation amount is. For example, F is 10000, K is 10, 10000 sub-operating condition segments are respectively marked as N1, N2, …, N999, and N10000, 10 sub-operating condition segments are randomly selected from the 10000 sub-operating condition segments, for example, N2, N9, N81, N350, N450, N600, N750, N850, N950, and N999 are selected, N2 is taken as a central operating condition of a first target operating condition, N9 is taken as a central operating condition of a second target operating condition, and so on. In general, the greater the difference between F and K, the more obvious the advantage of the solution of the present embodiment will be, and the present embodiment is suitable for durable data processing with F being ten thousand.
And recording other working conditions except the selected K central working conditions in the F sub-working condition segments as non-central working conditions (F-K non-central working conditions), and determining the dispersion distance between the non-central working conditions and each central working condition according to each non-central working condition.
Record any off-center operating conditions as
Figure 760790DEST_PATH_IMAGE152
Will renThe operating conditions of the center are recorded
Figure 452672DEST_PATH_IMAGE153
The corresponding dispersion distance is recorded as
Figure 609984DEST_PATH_IMAGE154
The expression for solving the dispersion distance is as follows:
Figure 403627DEST_PATH_IMAGE155
wherein:
Figure 39008DEST_PATH_IMAGE156
-off-centre segments representing market user data matrices
Figure 850975DEST_PATH_IMAGE157
Central operating mode of user data matrix
Figure 179188DEST_PATH_IMAGE158
The dispersion distance between;
Figure 194549DEST_PATH_IMAGE159
——
Figure 633620DEST_PATH_IMAGE157
a corresponding covariance matrix;
Figure 175460DEST_PATH_IMAGE160
——
Figure 805068DEST_PATH_IMAGE158
a corresponding covariance matrix;
Figure 432358DEST_PATH_IMAGE161
、
Figure 284908DEST_PATH_IMAGE162
-matrix
Figure 337046DEST_PATH_IMAGE159
And a matrix
Figure 7062DEST_PATH_IMAGE160
A corresponding determinant;
Figure 997015DEST_PATH_IMAGE163
——
Figure 777889DEST_PATH_IMAGE157
a corresponding mean vector;
Figure 418955DEST_PATH_IMAGE164
——
Figure 994292DEST_PATH_IMAGE158
corresponding mean vectors;
Figure 596175DEST_PATH_IMAGE165
——
Figure 56106DEST_PATH_IMAGE157
the inverse of the corresponding covariance matrix.
And classifying the non-central working conditions according to the dispersion distance between each non-central working condition and each central working condition, and dividing the non-central working conditions into data sets corresponding to each central working condition. If the dispersion distance between a certain non-center working condition and a certain center working condition in each center working condition is the minimum, classifying the non-center working condition into the center working condition with the minimum dispersion distance.
And completing a round of classification according to the rule.
And according to the data set corresponding to each central working condition, re-determining the mean vector and the covariance matrix corresponding to each data set according to the provided calculation mode of the mean vector and the covariance matrix, and taking the re-determined mean vector and covariance matrix as the mean vector and the covariance matrix of the central working condition corresponding to the data set.
And repeating the steps 3-6 according to the updated mean vector and covariance matrix, determining the dispersion distance between the non-central working condition and each central working condition aiming at each non-central working condition (F-K non-central working conditions), namely determining the mean vector and covariance matrix corresponding to the non-central working condition and the dispersion distance between the mean vector and covariance matrix updated by each central working condition, classifying the non-central working conditions according to the dispersion distances until the variation amplitude of the mean vector and covariance matrix in each obtained data set accords with a preset condition (namely is smaller than a preset amplitude threshold), and then terminating the operation of the clustering algorithm.
Each finally obtained data set (particularly K-type data sets) represents K target working conditions, the data sets can be used as typical working conditions to be input into a whole vehicle endurance test and a bench endurance test, and when the vehicle is subsequently subjected to endurance test verification, data corresponding to the K target working conditions can be input into the test, so that the actual use working conditions of users are considered in the endurance test, and the comprehensiveness and the verification accuracy of the vehicle endurance test are improved.
The embodiment provides a method for processing automobile endurance load data of market users, which comprises the steps of firstly defining key endurance load signals of new energy automobile market user data (in the embodiment, the selection and the basis of the endurance load signals are described in detail by taking the motor driving system assembly of a new energy automobile as an example), then defining sub-working condition segments of the market user endurance load signals (in the proposal, the algorithm for compressing and cutting the market user endurance load data into sub-working conditions is described in detail by taking the motor driving system assembly of the new energy automobile as an example), finally, carrying out clustering operation on the sub-working condition segments of the market user endurance load data, specifically, taking the motor driving system assembly of the new energy automobile as an example, describing in detail a mean vector and a covariance matrix of a probability density function for solving the market user endurance load data based on a multi-step iteration method, calculating dispersion distances from different sub-working conditions to a center working condition based on the mean vector and the covariance matrix, and performing multi-step iteration based on the dispersion distance so as to cluster to generate a market user endurance load data sub-working condition segment set, wherein the obtained sub-working condition segment set can be used for guiding the formulation of the finished automobile and bench endurance test specifications of the automobile.
The embodiment directly analyzes the data based on the automobile user endurance load in the TBOX, further summarizes the sub-working condition set of the data of the automobile endurance load, and can be applied to guiding the formulation of the whole automobile and bench endurance test specifications. The embodiment strongly associates the durability test verification of the automobile with the use working condition of an automobile user, so that the optimal balance of the whole automobile cost and the whole automobile safety and reliability is realized on the design source. The scheme provided by the embodiment has strong application expansion and can be applied to determination of the sub-working conditions of other subsystems except the motor driving system assembly of the new energy automobile.
Based on the same inventive concept, the present embodiment provides a durable loading data processing device of a market user's automobile as shown in fig. 2, the device comprising:
a key endurance load signal data acquisition module 21, configured to acquire key endurance load signal data of a target system assembly of an automobile;
the sub-working condition segment dividing module 22 is configured to divide each kind of data in the key durable load signal data into F sub-working condition segments according to the time sequence from the last parking vehicle speed time, the maximum peak vehicle speed time, and the time to the next parking time, where F is a positive integer;
and the clustering module 23 is configured to perform clustering operation on the F sub-working condition segments to obtain K types of target working conditions, and the K types of target working conditions are used for performing an endurance test on the vehicle.
Further, the critical endurance load signal data acquisition module 21 is configured to:
when the target system assembly is the motor driving system assembly, key durable load signal data of the motor driving system assembly are obtained, wherein the key durable load signal data comprise driving motor torque signal data, driving motor rotating speed signal data, motor water temperature signal data and differential signal data.
Further, the critical endurance load signal data obtaining module 21 is specifically configured to:
acquiring left wheel speed signal data and right wheel speed signal data of an automobile;
and determining differential signal data according to the difference value of the left wheel speed signal data and the right wheel speed signal data.
Further, the apparatus further comprises:
the monitoring signal data acquisition module is used for acquiring monitoring signal data of the automobile from an automobile networking system of the automobile after acquiring key durable load signal data of a target system assembly of the automobile and before dividing various data in the key durable load signal data into F sub-working condition fragments respectively, wherein the monitoring signal data comprises automobile speed signal data, steering wheel signal data, brake pedal signal data and wheel speed signal data;
the abnormal data identification module is used for identifying whether abnormal data exist in the key durable load signal data according to the change trend of the monitoring signal data;
and the abnormal data correction module is used for correcting the abnormal data when the abnormal data exists in the key durable load signal data and updating the key durable load signal data depending on the corrected abnormal data.
Further, the apparatus further comprises:
the key point data identification module is used for identifying key point data of the key durable load signal data according to preset key point data screening conditions before dividing various types of data in the key durable load signal data into F sub-working condition segments respectively after the key durable load signal data is updated depending on the corrected abnormal data;
the system comprises a preset threshold value acquisition module of a heavy signal data channel, a data acquisition module and a data acquisition module, wherein the preset threshold value acquisition module is used for acquiring the preset threshold value of the heavy signal data channel of a target system assembly;
and the compression module is used for removing the signal data smaller than the preset threshold of the critical durable load signal data channel from the critical durable load signal data on the premise of reserving all the critical point data in the critical durable load signal data to obtain the compressed critical durable load signal data.
Further, the clustering module 23 is specifically configured to perform:
step 1, determining a mean vector and a covariance matrix of each sub-working condition segment in F sub-working condition segments;
step 2, randomly selecting K sub-working condition segments from the F sub-working condition segments as the central working condition of the K-type target working condition;
step 3, determining the dispersion distance between each non-central working condition and each central working condition according to the mean vector and the covariance matrix of each non-central working condition fragment in the F sub-working condition fragments and the mean vector and the covariance matrix of each central working condition in the K central working conditions;
step 4, dividing each non-central working condition into data sets of K types of target working conditions according to the dispersion distance between each non-central working condition and each central working condition to obtain K data sets;
step 5, aiming at each data set, connecting the sub-working condition segments in the data set into new data segments according to the time sequence, determining the mean vector and covariance matrix of the new data segments, and taking the mean vector and covariance matrix of the new data segments as the mean vector and covariance matrix after the central working condition corresponding to the data set is updated;
and 6, repeating the steps 3-5 aiming at the updated mean vector and covariance matrix of each central working condition until the variation amplitude of the mean vector and covariance matrix of each data set in the K data sets is smaller than a preset amplitude threshold value.
Based on the same inventive concept, the present embodiment provides an electronic device as shown in fig. 3, including:
a processor 31;
a memory 32 for storing instructions executable by the processor 31;
wherein the processor 31 is configured to execute to implement a market user's automobile endurance load data processing method as provided above.
Based on the same inventive concept, the present embodiment provides a non-transitory computer-readable storage medium, when instructions in the storage medium are executed by a processor 31 of an electronic device, so that the electronic device can perform a method of implementing the automobile endurance load data processing of one market user as provided above.
Since the electronic device described in this embodiment is an electronic device used for implementing the method for processing information in this embodiment, a person skilled in the art can understand the specific implementation manner of the electronic device of this embodiment and various variations thereof based on the method for processing information described in this embodiment, and therefore, how to implement the method in this embodiment by the electronic device is not described in detail here. Electronic devices used by those skilled in the art to implement the method for processing information in the embodiments of the present application are all within the scope of the present application.
As will be appreciated by one skilled in the art, embodiments of the present invention may be provided as a method, system, or computer program product. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
While preferred embodiments of the present invention have been described, additional variations and modifications in those embodiments may occur to those skilled in the art once they learn of the basic inventive concepts. Therefore, it is intended that the appended claims be interpreted as including the preferred embodiment and all changes and modifications that fall within the scope of the invention.
It will be apparent to those skilled in the art that various changes and modifications may be made in the present invention without departing from the spirit and scope of the invention. Thus, if such modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include such modifications and variations.

Claims (10)

1. A method for processing automobile endurance load data of market users, the method comprising:
acquiring key durable load signal data of a target system assembly of an automobile;
dividing various types of data in the key durable load signal data into F sub-working condition segments respectively according to the time sequence of the last parking vehicle speed, the maximum peak value of the vehicle speed and the time sequence of the next parking time, wherein F is a positive integer;
and clustering the F sub-working condition fragments to obtain K types of target working conditions, wherein the K types of target working conditions are used for carrying out an endurance test on the automobile.
2. The method of claim 1, wherein said obtaining key endurance load signal data for a target system assembly of an automobile when the target system assembly is a motor drive system assembly comprises:
obtaining key durable load signal data of the motor driving system assembly, wherein the key durable load signal data comprises driving motor torque signal data, driving motor rotating speed signal data, motor water temperature signal data and differential signal data.
3. The method of claim 2, wherein said obtaining differential signal data for said electric motor drive system assembly comprises:
acquiring left wheel speed signal data and right wheel speed signal data of an automobile;
and determining the differential signal data according to the difference value of the left wheel speed signal data and the right wheel speed signal data.
4. The method of claim 1, wherein after obtaining the key endurance load signal data for the target system assembly of the automobile, before dividing each category of data in the key endurance load signal data into F sub-operating condition segments, respectively, the method further comprises:
acquiring monitoring signal data of an automobile from an internet of vehicles system of the automobile, wherein the monitoring signal data comprises speed signal data, steering wheel signal data, brake pedal signal data and wheel speed signal data;
identifying whether abnormal data exist in the key durable load signal data according to the variation trend of the monitoring signal data;
and when abnormal data exists in the key durable load signal data, correcting the abnormal data, and updating the key durable load signal data according to the corrected abnormal data.
5. The method of claim 4, wherein after updating the critical endurance load signal data in dependence on the corrected anomaly data, before dividing each category of data in the critical endurance load signal data into F sub-operating condition segments, respectively, the method further comprises:
identifying key point data of the key durable load signal data according to a preset key point data screening condition;
acquiring a preset threshold value of a data channel of a weight-off signal of the target system assembly;
and on the premise of reserving all key point data in the key durable load signal data, removing the signal data smaller than the preset threshold value of the key durable load signal data channel from the key durable load signal data to obtain the compressed key durable load signal data.
6. The method of claim 1, wherein the clustering the F sub-condition segments to obtain K types of target conditions comprises:
step 1, determining a mean vector and a covariance matrix of each sub-working condition segment in the F sub-working condition segments;
step 2, randomly selecting K sub-working condition segments from the F sub-working condition segments as central working conditions of K types of target working conditions;
step 3, determining the dispersion distance between each non-central working condition and each central working condition according to the mean vector and the covariance matrix of each non-central working condition fragment in the F sub-working condition fragments and the mean vector and the covariance matrix of each central working condition in the K central working conditions;
step 4, dividing each non-central working condition into data sets of K types of target working conditions according to the dispersion distance between each non-central working condition and each central working condition to obtain K data sets;
step 5, aiming at each data set, connecting the sub-working condition segments in the data set into new data segments according to the time sequence, determining the mean vector and covariance matrix of the new data segments, and taking the mean vector and covariance matrix of the new data segments as the mean vector and covariance matrix after the central working condition corresponding to the data set is updated;
and 6, repeating the steps 3-5 aiming at the updated mean vector and covariance matrix of each central working condition until the variation amplitude of the mean vector and covariance matrix of each data set in the K data sets is smaller than a preset amplitude threshold value.
7. An automotive endurance load data processing apparatus for a market consumer, said apparatus comprising:
the key durable load signal data acquisition module is used for acquiring key durable load signal data of a target system assembly of the automobile;
the sub-working condition segment dividing module is used for dividing various types of data in the key durable load signal data into F sub-working condition segments according to the time of the last parking vehicle speed, the time of the maximum peak value of the vehicle speed and the time sequence from the next parking time, wherein F is a positive integer;
and the clustering module is used for clustering the F sub-working condition fragments to obtain K types of target working conditions, and the K types of target working conditions are used for carrying out an endurance test on the automobile.
8. The apparatus of claim 7, wherein the critical endurance load signal data acquisition module is specifically configured to:
when the target system assembly is a motor driving system assembly, obtaining key durable load signal data of the motor driving system assembly, wherein the key durable load signal data comprises driving motor torque signal data, driving motor rotating speed signal data, motor water temperature signal data and differential signal data.
9. An electronic device, comprising:
a processor;
a memory for storing the processor-executable instructions;
wherein the processor is configured to execute to implement a market user's automobile endurance load data processing method as claimed in any one of claims 1 to 6.
10. A non-transitory computer readable storage medium, wherein instructions, when executed by a processor of an electronic device, enable the electronic device to perform a method of implementing a market user's automobile endurance load data processing as claimed in any one of claims 1 to 6.
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