CN113128120A - Method for constructing typical running condition of automobile crane - Google Patents
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Abstract
The invention provides a method for constructing a typical running condition of an automobile crane, and relates to the technical field of automobile cranes. The invention relates to a method for constructing a typical running condition of an automobile crane, which comprises the following steps: collecting operation data of the automobile crane, preprocessing the operation data, and extracting a running speed-time segment; obtaining a short running speed-time segment of a normal running working condition according to the running speed-time segment, and determining characteristic parameters of the short running speed-time segment; constructing a principal component sample matrix according to the characteristic parameters, and carrying out cluster analysis on the principal component sample matrix to obtain a short driving speed-time segment sample library; and constructing the typical driving condition of the automobile crane according to the short driving speed-time fragment sample library. According to the technical scheme, the short running speed-time segment data of normal running are obtained to construct the typical running working condition of the automobile crane, and the working condition clustering and working condition constructing precision is improved.
Description
Technical Field
The invention relates to the technical field of automobile cranes, in particular to a construction method for typical running conditions of an automobile crane.
Background
The driving working condition of the automobile crane refers to a speed-time curve of the automobile crane driving under a road, and the driving working condition is mainly used for testing the performances of oil consumption, emission and the like of the automobile crane and matching a power system so as to achieve the purpose of balancing the power economy of the crane.
In recent years, a series of researches are developed aiming at the construction of the running conditions of passenger vehicles, generally, researchers use test vehicles to run on a specified test route, and then carry out short running segment cutting, clustering algorithm and short running segment combination method on collected data to obtain typical running conditions with high precision. Automobile crane and passenger car operating mode exist great difference, and the process of traveling opens and stops frequently, and the data of traveling contains normal operating mode, the regional operating mode and the operating mode of traveling of operation, is difficult to obtain the operating mode of traveling commonly used through conventional road test, and the problem that leads to includes but not limited to: the load distribution of a client engine cannot be accurately positioned, the matching of an engine program and accessories cannot be optimized in a targeted manner, and the dynamic economy is difficult to balance.
Disclosure of Invention
The invention solves the problem of how to construct the typical driving condition of the automobile crane.
In order to solve the problems, the invention provides a method for constructing a typical running condition of an automobile crane, which comprises the following steps: collecting operation data of the automobile crane, preprocessing the operation data, and extracting a running speed-time segment; obtaining a short running speed-time segment of a normal running working condition according to the running speed-time segment, and determining characteristic parameters of the short running speed-time segment; constructing a principal component sample matrix according to the characteristic parameters, and carrying out cluster analysis on the principal component sample matrix to obtain a short driving speed-time segment sample library; and constructing the typical driving condition of the automobile crane according to the short driving speed-time fragment sample library.
According to the method for constructing the typical running condition of the automobile crane, the main component sample matrix is constructed by acquiring the short running speed-time segment data of normal running, the main component sample matrix is subjected to clustering analysis, the typical running condition of the automobile crane is constructed according to the short running speed-time segment sample library obtained by clustering, and the precision of condition clustering and condition construction is improved.
Optionally, the acquiring the operation data of the mobile crane comprises: recording the operating data of the mobile crane through an onboard T-Box of the mobile crane; and acquiring the operating data through a CAN bus, and sending the operating data to a big data analysis platform.
The method for constructing the typical driving condition of the automobile crane can carry out offline processing on actual driving data of a large number of customers through the big data analysis platform, and reflects the actual working condition of the automobile crane.
Optionally, the preprocessing the operation data and extracting the travel speed-time segment includes: and eliminating abnormal data in the running data, and determining the running speed-time segment according to the running data after the abnormal data is eliminated.
According to the method for constructing the typical running condition of the automobile crane, the running data is obtained from the big data analysis platform, the abnormal data in the running data are removed, the running speed-time segment is determined according to the running data after the abnormal data are removed, the running data is preprocessed, the running speed-time segment is extracted, and the precision of condition clustering and condition construction is improved.
Optionally, the obtaining the short driving speed-time segment of the normal driving condition according to the driving speed-time segment includes: dividing the travel speed-time segment into a plurality of short travel speed-time segments according to a division point, wherein the division point is that the acceleration a in the travel speed-time segment is 0m/s2An idle state with a speed v of 0 m/s; determining a segment duration t for each of the short driving speed-time segments1And low speed ratio TvDeleting the segment duration t1Less than a preset time T or the low speed ratio TvAnd obtaining the short running speed-time segment of the normal running working condition by the short running speed-time segment which is larger than the preset ratio T.
According to the method for constructing the typical running condition of the automobile crane, the running speed-time segment is divided into a plurality of short running speed-time segments through the idle state, the short running segment of the running condition in the operation area is deleted, the short running segment of the normal running condition is obtained, and the precision of condition clustering and condition construction is improved.
Optionally, the determining the characteristic parameters of the short driving speed-time segment includes: and calculating characteristic parameters of the short driving speed-time segment, and determining the characteristic parameters, wherein the characteristic parameters comprise descriptive parameters, statistical parameters and oil consumption parameters.
According to the method for constructing the typical driving working condition of the automobile crane, the constructed working condition can accurately reflect the actual driving characteristic of the automobile crane by setting the characteristic parameters including descriptive parameters, statistical parameters and oil consumption parameters, the construction of the typical driving road spectrum of the automobile crane is realized, and the typical driving working condition with high accuracy can be given.
Optionally, the constructing a principal component sample matrix according to the feature parameters includes: constructing a feature matrix according to the feature parameters; and processing the feature matrix, and constructing the principal component sample matrix according to the processed feature matrix.
The method for constructing the typical running condition of the automobile crane comprises the steps of constructing a characteristic matrix according to characteristic parameters, processing the characteristic matrix, constructing a principal component sample matrix according to the processed characteristic matrix, and completing principal component analysis.
Optionally, the constructing the principal component sample matrix according to the processed feature matrix includes: determining a sample covariance matrix according to the processed feature matrix, and determining a principal component according to the sample covariance matrix; calculating the variance ratio of the principal component, and selecting the characteristics of the principal component sample according to the variance ratio; and extracting principal components with the feature quantity equivalent to the feature quantity of the principal component sample to form the principal component sample matrix.
The method for constructing the typical driving condition of the automobile crane comprises the steps of determining a sample covariance matrix according to a processed feature matrix, determining principal components according to the sample covariance matrix, calculating the variance ratio of the principal components, selecting principal component sample features according to the variance ratio, extracting principal components with the same feature quantity as the principal component sample features to form a principal component sample matrix, and completing principal component analysis.
Optionally, the performing cluster analysis on the principal component sample matrix to obtain a short driving speed-time segment sample library includes: and performing clustering analysis on the principal component sample matrix by adopting a K-means clustering method, and selecting a clustering number parameter K when the sample average contour coefficient is minimum to obtain K types of short-running-speed-time segment sample libraries.
According to the method for constructing the typical running condition of the automobile crane, the principal component sample matrix is subjected to clustering analysis through a K-means clustering method, a clustering number parameter K when the sample average profile coefficient is minimum is selected, and K types of short running speed-time segment sample libraries are obtained, so that the typical running condition with higher precision can be obtained.
Optionally, the constructing the typical driving condition of the truck crane according to the short driving speed-time segment sample library comprises: determining the running duration and the relative deviation of various running working conditions in typical working conditions according to the short running speed-time segment sample library, wherein the relative deviation refers to the deviation of the characteristic values of various short running speed-time segments and the average value of the characteristic values of various short running speed-time segment sample libraries; and sequencing the short running speed-time segments according to the relative deviation and extracting sequences, and splicing the short running speed-time segments according to the sequences to construct the typical running working condition of the automobile crane when the running duration requirement and the relative deviation requirement are met.
According to the method for constructing the typical running condition of the automobile crane, the typical running condition of the automobile crane is constructed by splicing short running speed-time segments meeting the running duration requirement and the relative deviation requirement, the short stroke is extracted according to the characteristic parameter deviation to construct the typical running condition, the characteristics of speed, acceleration, oil consumption and the like are taken into consideration, and the high-precision typical running condition is obtained.
Optionally, the relative deviation is determined by the number of characteristic parameters, the short travel speed-time segment and the sample library of short travel speed-time segments.
According to the method for constructing the typical running condition of the automobile crane, the high-precision typical running condition is obtained by setting the relative deviation to be determined by the number of characteristic parameters, the short running speed-time segment and the short running speed-time segment sample library and considering the characteristics of speed, acceleration, oil consumption and the like.
Drawings
FIG. 1 is a schematic flow chart of a method for constructing a typical driving condition of an automobile crane according to an embodiment of the invention;
FIG. 2 is a schematic diagram of the transmission of operational data according to an embodiment of the present invention;
FIG. 3 is a schematic diagram of an original speed-time curve of a single truck crane in an embodiment of the present invention;
FIG. 4 is a short travel speed-time segment schematic of an embodiment of the present invention;
FIG. 5 is a schematic diagram of principal component analysis contribution according to an embodiment of the present invention;
FIG. 6 is a diagram illustrating a clustering result according to an embodiment of the present invention;
FIG. 7 is a graphical illustration of a typical driving condition speed-time curve according to an embodiment of the present invention.
Detailed Description
The domestic passenger vehicle emission standard generally adopts a new European test cycle (NEDC) running condition and a world light vehicle test cycle (WLTC) running condition, the commercial vehicle generally adopts a heavy commercial vehicle test cycle (C-WTVC) running condition, and no special automobile crane running condition and a construction method thereof exist. The automobile crane mainly adopts a C-WTVC running working condition at present, the running working condition has large deviation with the actual running working condition of the crane, the load distribution of a client engine cannot be accurately positioned, the engine program and accessory matching cannot be optimized in a targeted manner, and the dynamic economy is difficult to balance.
In order to make the aforementioned objects, features and advantages of the present invention comprehensible, embodiments accompanied with figures are described in detail below.
As shown in fig. 1, an embodiment of the present invention provides a method for constructing a typical driving condition of an automobile crane, including: collecting operation data of the automobile crane, preprocessing the operation data, and extracting a running speed-time segment; obtaining a short running speed-time segment of a normal running working condition according to the running speed-time segment, and determining characteristic parameters of the short running speed-time segment; constructing a principal component sample matrix according to the characteristic parameters, and carrying out cluster analysis on the principal component sample matrix to obtain a short driving speed-time segment sample library; and constructing the typical driving condition of the automobile crane according to the short driving speed-time fragment sample library.
Specifically, in this embodiment, the method for constructing the typical driving condition of the truck crane includes: the running data of the automobile crane is collected, the running data is uploaded to the Internet of things cloud platform after being collected, big data are stored through the storage server, the big data analysis platform and the calculation server are utilized, and data analysis personnel and service experts can conduct offline processing on the running data, so that the running data can be reflected in the practical working condition of the automobile crane by acquiring the practical running data of a large number of clients instead of a conventional road test.
And preprocessing the running data, extracting a running speed-time segment, acquiring the running data from the cloud platform, and preprocessing the running data in a mode including removing abnormal data, thereby extracting the running speed-time segment.
And obtaining a short running speed-time segment of the normal running working condition according to the running speed-time segment, namely dividing the running speed-time segment into a plurality of short running speed-time segments, deleting the abnormal short running segment of the speed sensor and the short running segment of the running working condition of the working area, and obtaining the short running speed-time segment of the normal running working condition. According to the running characteristics of the crane, the normal running working condition, the running working condition of the operation area and the operation working condition in the running data are separated, short running speed-time segment data of normal running are obtained, and working condition clustering and working condition construction accuracy is improved.
The characteristic parameters of the short driving speed-time segment are determined, the characteristic parameters comprise descriptive parameters, statistical parameters and oil consumption parameters, and the oil consumption of the driving working condition is also the main attention characteristic of the automobile crane.
And constructing a principal component sample matrix according to the characteristic parameters, namely constructing the principal component sample matrix according to the characteristic parameters, and carrying out standardization processing on the characteristic parameters in the sample matrix so as to eliminate the interference of different characteristic parameter units and amplitudes.
And performing cluster analysis on the principal component sample matrix to obtain a short running speed-time fragment sample library, namely performing cluster analysis on the principal component sample matrix, and determining the short running speed-time fragment sample library when the clustering effect meets the requirement.
Constructing a typical driving condition of the automobile crane according to the short driving speed-time fragment sample library, wherein the typical driving condition comprises the following steps: determining the running time of various running conditions in typical conditions; calculating the relative deviation between the characteristic value of each category of short running speed-time segment and the average value of the characteristic value of each category of sample library, and selecting each category of short running speed-time segment from small to large until the running duration requirement is met; and finally, sequentially splicing the selected short driving speed-time segments to complete the construction of the typical driving working condition.
The following takes a certain model of automobile crane as an example, and introduces the construction method of the typical running condition of the automobile crane in detail.
The driving data of 50 cranes normally used by a client from 26 days at 7 months in 2020 to 6 days at 12 months in 2020 is selected, and the original speed-time curve of a single trolley on a certain day is shown in FIG. 3. And deleting data with abnormal speed and acceleration, interpolating and filling lost data, and extracting 30770 speed-time segments. As shown in connection with fig. 4, the travel speed-time segment is divided into a plurality of short travel speed-time segments.
And (3) constructing 23 short-stroke characteristic parameters, performing principal component analysis on the characteristic parameters, extracting 6 principal components and more than 80% of characteristics from the sample parameter contribution rate as shown in figure 5, and constructing a characteristic matrix.
Referring to fig. 6, performing cluster analysis on the feature matrix, wherein the samples are divided into 3 types, the cycle time of the type 1 is short, the average speed is 4km/h, the idle speed ratio (31%) is low, and the low-speed running condition of the urban area is considered; the 2 nd type cycle duration is medium, the average speed is 21km/h, the acceleration and deceleration are frequent, and the condition is regarded as the medium-low speed running condition of urban areas and suburbs; the type 3 cycle is long in duration, the average speed is 47km/h, the idle speed occupation ratio is extremely low (3%), the constant speed occupation ratio is high, and the high-speed driving working condition is considered.
The typical driving condition is constructed, fig. 7 is a spliced typical driving condition, and table 1 is a difference rate of main characteristic values of the typical driving condition and actual data, and it can be seen that the difference rate of each characteristic parameter is basically maintained within a range of 7%, which indicates that the constructed driving condition can represent the driving characteristics of an actual road vehicle.
TABLE 1 comparison of typical operating conditions with actual operating conditions
In the embodiment, the principal component sample matrix is constructed by acquiring the short running speed-time segment data of normal running, the principal component sample matrix is subjected to cluster analysis, the typical running working condition of the automobile crane is constructed according to the short running speed-time segment sample library obtained by clustering, and the working condition clustering and working condition construction precision is improved.
Optionally, the acquiring the operation data of the mobile crane comprises: recording the operating data of the mobile crane through an onboard T-Box of the mobile crane; and acquiring the operating data through a CAN bus, and sending the operating data to a big data analysis platform.
Specifically, in this embodiment, the collecting the operation data of the mobile crane includes: recording the operation data of the truck crane through the vehicle-mounted T-Box of the truck crane; the collector collects operation data through the CAN bus and sends the operation data to the big data analysis platform. That is, the mobile crane is mounted with a Telematics Box (T-Box) and can record traveling record data such as position information, time information, speed information, and trajectory information of the traveling of the target vehicle and operation information data during traveling. The collector collects the running data of the automobile crane through a Controller Area Network (CAN) bus, comprises running speed v, working time t, power takeoff state F, engine oil consumption L and the like, and sends the running data to the big data analysis platform after transmitting the running data to the Internet of things cloud platform through 4G/5G signals. Referring to fig. 2, the big data analysis platform and the calculation server allow the data analyst and the service expert to perform offline processing on the operation data, so that the actual working condition of the truck crane can be reflected by acquiring a large amount of actual driving data of the customer without using a conventional road test. The running data is directly obtained from the crane in actual operation through the CAN bus, the real running state of the automobile crane is obtained, and the human interference factor is avoided.
In the embodiment, the actual running data of a large number of customers is processed offline through the big data analysis platform, so that the actual working condition of the automobile crane is reflected.
Optionally, the preprocessing the operation data and extracting the travel speed-time segment includes: and eliminating abnormal data in the running data, and determining the running speed-time segment according to the running data after the abnormal data is eliminated.
Specifically, in this embodiment, preprocessing the operation data and extracting the travel speed-time segment includes: for example, the running data is obtained from the big data analysis platform, abnormal data in the running data is removed, and the running speed-time segment is determined according to the running data from which the abnormal data is removed, namely the data obtaining and the data screening are included.
Data acquisition: outputting 180-day running data of 50 cranes from the cloud platform of the Internet of things, and extracting a running speed v and a working time t when a power take-off state F is equal to 0; and obtaining the running condition of the crane.
And (3) screening data: obtaining travel speed-time segment acceleration
Rejecting acceleration a>4m/s2Velocity v>120km/h data (according to the actual engineering, the crane acceleration can not exceed 4m/s, the running speed does not exceed 120km/h, and the actual data has a few abnormal data due to sensor failure, which is not beneficial to data analysis, so that the abnormal values are deleted), and linear interpolation filling is carried out on the missing values of the data time, namely:
wherein v isiAt a certain deletion speed, vi+1，vi-1The speed t at the time after the missing data and the speed t at the time before the missing datai+1，ti-1The time of the next moment and the time of the previous moment of the missing data are respectively.
In the embodiment, the running data is obtained from the big data analysis platform, the abnormal data in the running data is removed, the running speed-time segment is determined according to the running data from which the abnormal data is removed, the running data is preprocessed, the running speed-time segment is extracted, and the precision of working condition clustering and working condition construction is improved.
Optionally, the obtaining the short driving speed-time segment of the normal driving condition according to the driving speed-time segment includes: dividing the travel speed-time segment into a plurality of short travel speed-time segments according to a division point, wherein the division point is that the acceleration a in the travel speed-time segment is 0m/s2An idle state with a speed v of 0 m/s; determining a segment duration t for each of the short driving speed-time segments1And low speed ratio TvDeleting the segment duration t1Less than a preset time T or the low speed ratio TvAnd obtaining the short running speed-time segment of the normal running working condition by the short running speed-time segment which is larger than the preset ratio T.
Specifically, in the present embodiment, obtaining the short travel speed-time section of the normal travel condition from the travel speed-time section includes:
adding the speed a to 0m/s in the travel speed-time segment2The state where the speed v is 0m/s is regarded as the idle state, and the total travel speed-time segment is divided into a plurality of short travel speed-time segments with the idle state as a division point.
Calculating the segment duration t of each short driving segment1And low speed ratio Tv(v<The ratio of the duration of 3km/h to the total duration), for example, the preset time T is 60s, and the preset ratio T is 50% (where T representing the preset time and T representing the preset ratio are the same as the previous operating duration T and the following typical operating duration T, and are not directly related thereto), if T is1<60s or Tv>50%, the work area travel segment is regarded as a work area travel segment, and the short travel segment is deleted.
Short driving speed-time segments of normal driving conditions are obtained.
In the embodiment, the travel speed-time segment is divided into a plurality of short travel speed-time segments through the idle state, the short travel segment of the travel working condition in the working area is deleted, the short travel segment of the normal travel working condition is obtained, and the precision of working condition clustering and working condition construction is improved.
Optionally, the determining the characteristic parameters of the short driving speed-time segment includes: and calculating characteristic parameters of the short driving speed-time segment, and determining the characteristic parameters, wherein the characteristic parameters comprise descriptive parameters, statistical parameters and oil consumption parameters.
Specifically, in the present embodiment, determining the characteristic parameters of the short travel speed-time segment includes: and calculating characteristic parameters of the short driving speed-time segment, and determining the characteristic parameters, wherein the characteristic parameters comprise descriptive parameters, statistical parameters and oil consumption parameters.
The descriptive parameters are 8, including:
duration of operation t, average speed
Maximum velocity vmaxMax (v); standard deviation of speed
Average positive acceleration
Average negative acceleration
Maximum positive acceleration amax+Max (a); maximum negative acceleration amax-＝min(a)。
Where k is the total data volume in the short travel speed-time segment, vi、aiThe vehicle speed and acceleration at time i, k1、k2The data amounts are respectively that the acceleration is greater than 0 and the acceleration is less than 0.
The statistical parameters are 14, and the specific parameters are as follows: the speeds are respectively in the interval [0,0 ]]、(0,10]、(10,20]、(20,30]、(30,40]、(40,50]、(50,60]、(60,70]、(70,80]、(80,120]km/h ratio, respectively by V1,V2,V3,V4,V5,V6,V7,V8,V9,V10Represents; and:
idle time length ratio T1: the speed v is 0, and the running state with the acceleration a being 0 accounts for the proportion of the total data;
constant speed time length ratio T2: velocity v>0, the running state with the acceleration a being 0 accounts for the total data proportion;
deceleration duration ratio T3: velocity v>0, acceleration a<The driving state of 0 accounts for the total data proportion;
acceleration duration ratio T4: velocity v>0, acceleration a>The running state of 0 accounts for the total data ratio.
The oil consumption parameters are 1, including the oil consumption of the engine in hundred kilometers, and the calculation formula is as follows:
wherein L isendFor the end of the short driving speed-time segment, L0The oil consumption is initiated for this short driving speed-time segment.
In the construction process of the working condition, the deviation of 8 characteristics such as average speed, average acceleration, average deceleration, idle speed duration ratio, acceleration duration ratio, deceleration duration ratio, constant speed duration ratio, hundred kilometers of oil consumption and the like is considered, so that the constructed working condition can accurately reflect the actual running characteristics of the automobile crane.
In the embodiment, the characteristic parameters comprise descriptive parameters, statistical parameters and oil consumption parameters, so that the constructed working condition can accurately reflect the actual running characteristic of the automobile crane, the construction of a typical running road spectrum of the automobile crane is realized, and the typical running working condition with high accuracy can be provided.
Optionally, the constructing a principal component sample matrix according to the feature parameters includes: constructing a feature matrix according to the feature parameters; and processing the feature matrix, and constructing the principal component sample matrix according to the processed feature matrix.
Specifically, in this embodiment, constructing the principal component sample matrix according to the feature parameters includes:
constructing a characteristic matrix according to the characteristic parameters: with xijThe j characteristic parameter of the ith short stroke is represented, and the characteristic matrix is represented as:
wherein m is the number of characteristic parameters, and n is the number of short driving speed-time segments.
And (3) normalizing the feature matrix:
in order to eliminate the interference of different characteristic parameter units and amplitudes, the data is normalized by the following formula:
wherein, x'ijIs standardizedThe jth characteristic parameter of the ith short stroke,
is the sample mean, σ, of the jth characteristic parameterjIs the sample standard deviation of the jth characteristic parameter.
Constructing a principal component sample matrix according to the feature matrix after the standardization treatment: calculating to obtain a sample covariance matrix
Where the covariance s of each samplejkCalculated by the following formula:
wherein s isjkRepresenting the sample covariance of the jth and kth features. The eigenvalues λ and eigenvectors e of the covariance matrix S are combined in pairs as (λ)j,ej) (j ═ 1, …, m), sorting in descending order of magnitude of feature values, obtaining new features after transformation:
Yj＝ej1X1+ej2X2+...+ejmXm j＝1，...，m
wherein, YjIs the jth feature obtained after transformation, i.e. principal component; xjIs a vector formed by j-th characteristic parameters before transformation.
Calculating the ratio of each principal variance:
wherein, PjIn is the variance ratio, λ, of the jth principal componentjSelecting principal component sample characteristics with variance ratio over 90% and setting the characteristic quantity as p, extracting the first p principal components Y1,Y2,…,YpForm a new sample matrix Y ═ Y1,Y2,…,Yp]And completing principal component analysis.
In this embodiment, a feature matrix is constructed according to the feature parameters, the feature matrix is processed, and a principal component sample matrix is constructed according to the processed feature matrix to complete principal component analysis.
Optionally, the constructing the principal component sample matrix according to the processed feature matrix includes: determining a sample covariance matrix according to the processed feature matrix, and determining a principal component according to the sample covariance matrix; calculating the variance ratio of the principal component, and selecting the characteristics of the principal component sample according to the variance ratio; and extracting principal components with the feature quantity equivalent to the feature quantity of the principal component sample to form the principal component sample matrix.
Specifically, in this embodiment, constructing the principal component sample matrix according to the processed feature matrix includes: determining a sample covariance matrix according to the processed feature matrix, and determining a principal component according to the sample covariance matrix; calculating the variance ratio of the principal component, and selecting the characteristics of the principal component sample according to the variance ratio; and extracting principal components with the characteristic quantity equal to the characteristic quantity of the principal component sample to form a principal component sample matrix. Namely, a sample covariance matrix S is determined according to the feature matrix after normalization processing, and the feature value lambda and the feature vector e of the covariance matrix S are combined in pairs as (lambda)j,ej) And (j is 1, …, m), arranging in descending order according to the size of the eigenvalue, obtaining the new transformed characteristic as the principal component, selecting principal components with the characteristic quantity p equal to the principal component sample characteristic through the variance ratio to form a new sample matrix Y as the principal component sample matrix, and completing the principal component analysis.
In this embodiment, a sample covariance matrix is determined according to the processed feature matrix, a principal component is determined according to the sample covariance matrix, a variance ratio of the principal component is calculated, a principal component sample feature is selected according to the variance ratio, the principal component with the same number of features as the principal component sample feature is extracted to form a principal component sample matrix, and principal component analysis is completed.
Optionally, the performing cluster analysis on the principal component sample matrix to obtain a short driving speed-time segment sample library includes: and performing clustering analysis on the principal component sample matrix by adopting a K-means clustering method, and selecting a clustering number parameter K when the sample average contour coefficient is minimum to obtain K types of short-running-speed-time segment sample libraries.
Specifically, in this embodiment, performing cluster analysis on the principal component sample matrix to obtain the short travel speed-time segment sample library includes:
performing clustering analysis on the principal component sample matrix by adopting a K-means clustering method, selecting a clustering number parameter K when the sample average profile coefficient is minimum, and obtaining a K-type short driving speed-time segment sample library, wherein the profile coefficient calculation formula of each sample point is as follows:
wherein, a is the average distance between the current sample point and other samples in the cluster where the current sample point is located, and b is the average distance between the current sample point and other samples in the cluster.
In this embodiment, the principal component sample matrix is subjected to clustering analysis by a K-means clustering method, a clustering number parameter K when the sample average profile coefficient is minimum is selected, and K types of short-running-speed-time segment sample libraries are obtained, so that a typical running condition with higher precision can be obtained.
Optionally, the constructing the typical driving condition of the truck crane according to the short driving speed-time segment sample library comprises: determining the running duration and the relative deviation of various running working conditions in typical working conditions according to the short running speed-time segment sample library, wherein the relative deviation refers to the deviation of the characteristic values of various short running speed-time segments and the average value of the characteristic values of various short running speed-time segment sample libraries; and sequencing the short running speed-time segments according to the relative deviation and extracting sequences, and splicing the short running speed-time segments according to the sequences to construct the typical running working condition of the automobile crane when the running duration requirement and the relative deviation requirement are met.
Specifically, in the present embodiment, constructing the typical driving condition of the truck crane according to the short driving speed-time segment sample library includes:
determining the running duration and the relative deviation of various running conditions in typical conditions according to the short running speed-time segment sample library, wherein the relative deviation refers to the deviation of the characteristic values of various short running speed-time segments and the average value of the characteristic values of various short running speed-time segment sample libraries, and determining the running duration according to the following formula:
tk＝pk*T
wherein, tkFor the occupation duration, p, of class k strokes in typical operating conditionskThe time proportion of the kth sample bank in the total time length is shown, and T is the drawn typical working condition time length;
calculating the relative deviation between the characteristic value of each category of short running speed-time segment and the average value of the characteristic values of each category of sample library according to the following formula, and sorting from small to large;
wherein N is the number of characteristic parameters, CkjlStatistical values for the jth sample of the kth class with respect to the l characteristic parameter, ZklThe statistical value of the kth characteristic parameter of the kth type sample database; b iskjThe average relative deviation of the jth class sample in the kth class sample database is shown. CkjlAnd ZklThe characteristics in (1) comprise average speed, average acceleration, average deceleration, idle speed duration ratio, acceleration duration ratio, deceleration duration ratio, constant speed duration ratio and hundred kilometers of oil consumption.
And sequencing various short running speed-time segments according to the relative deviation and extracting sequences, wherein the starting speed and the ending speed of the short running speed-time segment are both 0, so that the later selected short running speed-time segment can be seamlessly spliced with the previous segment, and when the running duration requirement and the relative deviation requirement are met, the short running speed-time segments are spliced according to the sequences to construct the typical running working condition of the automobile crane.
In the embodiment, the typical running condition of the automobile crane is constructed by splicing short running speed-time segments meeting the running duration requirement and the relative deviation requirement, the short stroke is extracted according to the characteristic parameter deviation to construct the typical running condition, the characteristics of speed, acceleration, oil consumption and the like are considered, and the high-precision typical running condition is obtained.
Optionally, the relative deviation is determined by the number of characteristic parameters, the short travel speed-time segment and the sample library of short travel speed-time segments.
Specifically, in this embodiment, the relative deviation is determined by the number of the characteristic parameters, the short driving speed-time segment, and the short driving speed-time segment sample library, that is, the relative deviation is determined by the number N of the characteristic parameters, the statistical value of the jth sample of the kth class with respect to the ith characteristic parameter, and the statistical value of the kth class sample library with respect to the ith characteristic parameter, and the characteristics of speed, acceleration, oil consumption, and the like are taken into consideration, so that a high-precision typical driving condition is obtained.
In the embodiment, the high-precision typical driving condition is obtained by setting the relative deviation to be determined by the number of characteristic parameters, the short driving speed-time segment and the short driving speed-time segment sample library and considering the characteristics of speed, acceleration, oil consumption and the like.
Although the present disclosure has been described above, the scope of the present disclosure is not limited thereto. Various changes and modifications may be effected therein by one of ordinary skill in the pertinent art without departing from the spirit and scope of the present disclosure, and these changes and modifications are intended to be within the scope of the present disclosure.
Claims (10)
1. A method for constructing a typical driving condition of an automobile crane is characterized by comprising the following steps:
collecting operation data of the automobile crane, preprocessing the operation data, and extracting a running speed-time segment;
obtaining a short running speed-time segment of a normal running working condition according to the running speed-time segment, and determining characteristic parameters of the short running speed-time segment;
constructing a principal component sample matrix according to the characteristic parameters, and carrying out cluster analysis on the principal component sample matrix to obtain a short driving speed-time segment sample library;
and constructing the typical driving condition of the automobile crane according to the short driving speed-time fragment sample library.
2. The method for constructing the typical driving condition of the truck crane according to claim 1, wherein the collecting the operation data of the truck crane comprises:
recording the operating data of the mobile crane through an onboard T-Box of the mobile crane;
and acquiring the operating data through a CAN bus, and sending the operating data to a big data analysis platform.
3. The method for constructing the typical driving condition of the truck crane according to claim 1, wherein the preprocessing the operation data and extracting the driving speed-time segment comprises:
and eliminating abnormal data in the running data, and determining the running speed-time segment according to the running data after the abnormal data is eliminated.
4. The method for constructing the typical driving condition of the truck crane according to claim 1, wherein the obtaining the short driving speed-time segment of the normal driving condition according to the driving speed-time segment comprises:
dividing the travel speed-time segment into a plurality of short travel speed-time segments according to a division point, wherein the division point is that the acceleration a in the travel speed-time segment is 0m/s2An idle state with a speed v of 0 m/s;
determining a segment duration t for each of the short driving speed-time segments1And low speed ratio TvDeleting the segment duration t1Less than a preset time T or the low speed ratio TvThe short running speed greater than a preset ratio T-a time segment, obtaining a short driving speed-time segment of the normal driving regime.
5. The method for constructing typical driving conditions of truck cranes according to claim 1, wherein the determining the characteristic parameters of the short driving speed-time segment comprises:
and calculating characteristic parameters of the short driving speed-time segment, and determining the characteristic parameters, wherein the characteristic parameters comprise descriptive parameters, statistical parameters and oil consumption parameters.
6. The method for constructing the typical driving condition of the automobile crane according to claim 1, wherein constructing the principal component sample matrix according to the characteristic parameters comprises:
constructing a feature matrix according to the feature parameters;
and processing the feature matrix, and constructing the principal component sample matrix according to the processed feature matrix.
7. The method for constructing the typical driving condition of the truck crane according to claim 6, wherein the constructing the principal component sample matrix according to the processed feature matrix comprises:
determining a sample covariance matrix according to the processed feature matrix, and determining a principal component according to the sample covariance matrix;
calculating the variance ratio of the principal component, and selecting the characteristics of the principal component sample according to the variance ratio;
and extracting principal components with the feature quantity equivalent to the feature quantity of the principal component sample to form the principal component sample matrix.
8. The method for constructing the typical driving condition of the truck crane according to claim 1, wherein the step of performing cluster analysis on the principal component sample matrix to obtain the short driving speed-time segment sample library comprises the following steps:
and performing clustering analysis on the principal component sample matrix by adopting a K-means clustering method, and selecting a clustering number parameter K when the sample average contour coefficient is minimum to obtain K types of short-running-speed-time segment sample libraries.
9. The method for constructing the typical driving condition of the truck crane according to claim 8, wherein the constructing the typical driving condition of the truck crane according to the short driving speed-time segment sample library comprises:
determining the running duration and the relative deviation of various running working conditions in typical working conditions according to the short running speed-time segment sample library, wherein the relative deviation refers to the deviation of the characteristic values of various short running speed-time segments and the average value of the characteristic values of various short running speed-time segment sample libraries;
and sequencing the short running speed-time segments according to the relative deviation and extracting sequences, and splicing the short running speed-time segments according to the sequences to construct the typical running working condition of the automobile crane when the running duration requirement and the relative deviation requirement are met.
10. Method for constructing a typical driving situation of a truck crane according to claim 9, characterized in that the relative deviation is determined by the number of characteristic parameters, the short driving speed-time segment and the sample library of short driving speed-time segments.
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