CN111339638A - Automobile driving condition construction method based on existing data - Google Patents

Automobile driving condition construction method based on existing data Download PDF

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CN111339638A
CN111339638A CN202010080677.6A CN202010080677A CN111339638A CN 111339638 A CN111339638 A CN 111339638A CN 202010080677 A CN202010080677 A CN 202010080677A CN 111339638 A CN111339638 A CN 111339638A
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陈龙
杨艺
刘昌宁
沈钰杰
杨晓峰
刘雁玲
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Abstract

The invention discloses a method for constructing a running condition of an automobile based on existing data, which comprises the following steps: collecting the existing driving data of different automobiles in different time periods in the same region; preprocessing the collected data; carrying out interval division on the data set, and extracting a kinematic segment; calculating the association degree between each characteristic parameter and the most important parameter in the data set; clustering the data sets; and constructing a running condition curve of the automobile. The method constructs the automobile driving condition curve based on the available data, does not need to specially acquire related data, reduces the cost, is easy to construct, and accelerates the test of the road driving conditions in various regions of China.

Description

Automobile driving condition construction method based on existing data
Technical Field
The invention relates to the field of traffic, in particular to a method for constructing a running condition of an automobile based on existing data.
Background
The automobile driving condition curve is the basis for reflecting the road driving characteristics of automobiles and detecting the pollutant emission/fuel consumption of the automobiles and the limit value standard, is also the main standard for the development and evaluation of new automobile type technology and the measurement of traffic control risk, and is the common core technology of the automobile industry. At present, developed countries of automobiles in Europe, America, Japan and the like adopt standards suitable for respective automobile running conditions to carry out calibration optimization of vehicle performance and energy consumption/emission certification.
The certification of the energy consumption/emission of automobile products by adopting European NEDC running conditions in China effectively promotes the development of automobile technology in the early period of the century. In recent years, with the rapid increase of automobile reserves, road traffic conditions in China are greatly changed, and governments, enterprises and people gradually find that the deviation between the actual oil consumption and the rule certification result is larger and larger when the automobiles optimally calibrated by taking the NEDC working condition as the reference. Meanwhile, in the practical use process of europe for many years, a plurality of defects in the NEDC working condition are gradually discovered, and the world light vehicle test cycle (WLTC) is adopted, and the main test characteristics of the WLTC are more different from the driving working condition of the vehicle in China. On the other hand, the region of China is wide, and the development degree, the climate condition and the traffic condition of each city are different, so that the automobile driving condition characteristics of each city are obviously different. Therefore, it is very urgent to formulate a test condition reflecting the road driving condition in each region of our country.
The current research on the aspect of the running condition of the automobile needs data acquisition or investigation before the running condition is constructed, so that enough research samples are obtained, the workload and the working difficulty of constructing the running condition of the automobile are increased invisibly, and the research period is prolonged. If the automobile running working condition of a certain city can be obtained from the existing automobile running data (such as public data of the national statistical bureau), the workload can be greatly reduced, and the test of the road running working condition of each region in China can be further accelerated.
Disclosure of Invention
Aiming at the defects in the prior art, the invention provides a method for constructing the automobile driving condition based on the existing data, which is characterized in that after the existing data of a certain area are collected, the data are preprocessed, abnormal data are filled and removed, and kinematic segments and automobile motion characteristic parameters are reasonably extracted, so that the aim of rapidly drawing the automobile driving condition curve of the area is fulfilled. The method is convenient and fast, and is beneficial to accelerating the drawing work of the road driving condition curves in various regions.
The present invention achieves the above-described object by the following technical means.
A method for constructing the running condition of an automobile based on the existing data comprises the following steps:
step 1: collecting the existing driving data of different automobiles in different time periods in the same region;
the driving data in the step1 comprise M characteristic parameters, wherein M is a positive integer and is more than or equal to 3, and the numerical value of each characteristic parameter is marked as MtxyI.e. MtxyAnd the value of the x characteristic parameter of the y vehicle in t time is represented, wherein y is a vehicle number parameter, x is a number parameter of the characteristic parameters, y and x are positive integers, y is more than or equal to 2, t is a time metering parameter, and the sampling frequency is 1 s.
Step 2: preprocessing the collected data;
the preprocessing in the step2 comprises data calibration, numerical filtering, definition of missing data meaning, data filling, data elimination and data set acquisition for analysis
And 2, the data calibration in the step2 adopts a least square method, namely a number is selected, so that the square difference between other data and the number is minimum, and zero calibration and direction calibration are carried out on the characteristic parameters of the overall numerical drift.
The meaning of data loss defined in the step2 is to judge whether the automobile loses part of characteristic parameters because of passing through a tunnel or passing nearby a high-rise building.
The numerical filtering in the step2 comprises filtering out upper and lower limits of the automobile acceleration exceeding or falling below a normal car; the duration of the automobile in the idle state exceeds 180s, the automobile speed exceeds 120km/h, and the rotating speed of the automobile is lower than 700 r/min;
the data removed in the step2 are data of the automobile passing through a tunnel and other areas with GPS signal loss during the driving process and data of abnormal flameout state of the automobile.
And step 3: carrying out interval division on the data set, and extracting a kinematic segment;
the data set division in the step3 comprises the following steps:
and judging the automobile driving work, namely an acceleration working condition, a deceleration working condition and an idle working condition, coding the acceleration working condition, the deceleration working condition and the idle working condition, reading the codes of the data set, and extracting a speed interval from the idle state to the next idle state of the automobile into a kinematic segment.
And 4, step 4: calculating the association degree between each characteristic parameter and the most important parameter in the data set;
the association degree calculation step in the step4 is as follows:
step 1: carrying out non-dimensionalization on the data in the data set, wherein a non-dimensionalization formula is as follows:
Figure RE-GDA0002469617310000021
wherein, M'txyDimensionless value of x characteristic parameter, M, representing y vehicle at time txyThe optimal value of each characteristic parameter is obtained;
step 2: selecting a reference sequence, and selecting the optimal numerical value of the most important parameter in all the characteristic parameters by an analytic hierarchy process to form a reference sequence M'x；
M′x＝{M′iI is more than or equal to 1 and less than or equal to n, n is more than or equal to 1 and less than or equal to x, and i is a positive integer, M'iThe optimal value of the ith most important parameter in y vehicles is represented, and n represents the number of final parameters;
let the most important parameter correspond to λ in the analytic hierarchy processiThen constitute the reference sequence M'xHas a central value F of ∑ lambdai·M′i；
Step 2: calculating the absolute difference Delta between other characteristic parameters and the central value Fmj：
Δmj＝Mmj-F, m ═ 1,2,. times, x-n; j is 1, 2.; wherein M ismjRepresents the jth value of the mth other characteristic parameter,j is a positive integer greater than 1;
on the basis, according to the formula: deltamax＝max(Δmj)，Δmin＝min(Δmj) The maximum difference Delta can be obtainedmaxAnd minimum difference Δmin；
Step 3: according to the formula
Figure RE-GDA0002469617310000031
Calculating ξ the correlation between the jth value of the kth other characteristic parameter and the most important parametermjWhere p is a resolution factor for attenuating ΔmaxToo large to distort the associated coefficients. The coefficient is artificially introduced to improve the significance of the difference between the correlation coefficients, and 0 < rho < 1;
step 4: calculating the association degree E between the mth other characteristic parameter and the most important parameterm：
Figure RE-GDA0002469617310000032
α thereinjThe weighting coefficient of the jth numerical value of the mth other characteristic parameter can be obtained by improving the adaptive step-size fish-swarm algorithm.
And 5: clustering the data sets;
step 6: and constructing a running condition curve of the automobile.
The automobile driving condition curve in the step 6 is formed by splicing the kinematics segments which are obtained by clustering in the step5, are close to the clustering center and have proper time length into an automobile driving condition curve of 1200-1300 seconds; when calculating the distance, the distance between each characteristic parameter and each cluster center needs to be multiplied by a weight value, wherein the weight value of the most important parameter is lambdaiThe weighted value of other characteristic parameters is the degree of association E between the other characteristic parameters and the most important parameterm。
The invention has the beneficial effects that:
1. the method constructs the automobile driving condition curve based on the available data, does not need to specially acquire related data, reduces the cost, is easy to construct, and accelerates the test of the road driving conditions in various regions of China.
2. The calculation of the correlation degree between each characteristic parameter and the most important parameter ensures that the method is still effective when some parameters are missing, can obtain the automobile driving condition curve, and has stronger containment on the characteristic parameters.
3. The step length of the common fish swarm algorithm is fixed, so that the searching capability of the algorithm is poor in the whole or local area, and the self-adaptive step length fish swarm algorithm with the self-adaptive step length can keep a larger whole searching range in the early stage of the algorithm and can adjust the visual field in the later stage so that the algorithm has better local searching capability, thereby ensuring the accuracy and the convergence of the algorithm.
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FIG. 1 is a simple flow chart of a method for constructing a driving condition of an automobile based on existing data
FIG. 2 is a finally obtained curve of the driving condition of the automobile
Detailed Description
The invention will be further described with reference to the following figures and specific examples, but the scope of the invention is not limited thereto.
As shown in fig. 1, a method for constructing a driving condition of an automobile based on existing data mainly includes the following 6 steps.
Step 1: collecting the existing driving data of different automobiles in different time periods in the same region;
the driving data comprise m characteristic parameters, wherein m is a positive integer and is more than or equal to 3;
collecting driving data of y automobiles in different time periods in the same region, wherein the driving data comprises time and GPS (global positioning system) speed, and the unit is km/h; the acceleration of the X axis is the acceleration of gravity g; acceleration of the Y axis in the unit of gravity g; z-axis acceleration in units of gravitational acceleration g; longitude; latitude; the unit of the engine speed is r/min; percent torsion; instantaneous fuel consumption in liters per hundred kilometers; the opening degree of an accelerator pedal is an angle; an air-fuel ratio; percent engine load; air intake flow of engine in m3H, among the 14 characteristic parameters, the X-axis acceleration refers to the acceleration of the driving direction of the automobileThe Y-axis acceleration refers to the acceleration in the direction perpendicular to the driving direction of the automobile, and the Z-axis acceleration refers to the vertical acceleration of the automobile; the method comprises the steps that 13 characteristic parameters except time are numbered in sequence from 1 to 13, namely the GPS vehicle speed is the 1 st characteristic parameter, the X-axis acceleration is the 2 nd characteristic parameter, the Y-axis acceleration is the 3 rd characteristic parameter, the Z-axis acceleration is the 4 th characteristic parameter, the longitude is the 5 th characteristic parameter, the latitude is the 6 th characteristic parameter, the engine speed is the 7 th characteristic parameter, the torsion percentage is the 8 th characteristic parameter, the instantaneous hundred kilometer fuel consumption is the 9 th characteristic parameter, the accelerator pedal opening is the 10 th characteristic parameter, the air-fuel ratio is the 11 th characteristic parameter, the engine load percentage is the 12 th characteristic parameter, and the engine intake air flow is the 13 th characteristic parameter; the value of each characteristic parameter is denoted MtxyI.e. MtxyAnd the value of the x characteristic parameter of the y vehicle in t time is represented, wherein y is a vehicle number parameter, x is a number parameter of the characteristic parameters, y and x are positive integers, y is more than or equal to 2, t is a time metering parameter, and the sampling frequency is 1 s.
Step 2: preprocessing collected data, wherein the preprocessing comprises data calibration, numerical filtering, defining the meaning of lost data, filling data, removing data and obtaining a data set for analysis;
the data calibration adopts a least square method, namely a number is selected, so that the square difference between other data and the number is minimum, and zero calibration and direction calibration are carried out on characteristic parameters of overall numerical value drift;
wherein, the numerical filtering comprises filtering out upper and lower limits of the automobile acceleration exceeding or being lower than the normal automobile, and the constraint conditions are as follows:
Figure RE-GDA0002469617310000051
k is a time metering length parameter, and is not less than 1 and not more than 7 and is an integer under the constraint condition;
the numerical filter also comprises data of the duration time of the automobile in an idle state exceeding 180s, data of the automobile speed exceeding 120km/h and data of the automobile engine speed lower than 700r/min, wherein the constraint conditions are respectively as follows:
M(t+k)1yless than 10, and under the constraint condition, k is more than or equal to 1 and less than or equal to 180 and is an integer; mt1y＜120；Mt7y＜700；
The meaning of data loss defined in the step2 is that whether the automobile loses part of characteristic parameters because of passing through a tunnel or passing nearby a high-rise building is judged, and if M is judgedt1y、Mt5y、Mt6y、Mt7y、Mt9y、Mt10yIf more than two values are not 0 at the time t, the data lost at the time t can be defined as the positive driving constant data, and the average value of the values at the time t-1 and the time t +1 can be adopted for filling;
the data elimination in the step2 is that the collected data are analyzed, so that the following abnormal data can be found in the driving process of the automobile, and the data are eliminated;
a. when the automobile passes through a GPS signal loss area such as a tunnel and the like in the driving process, and when the data meets the constraint condition A, the data is rejected: a ═ BUC, where B, C are all sub-constraints of constraint a;
Figure RE-GDA0002469617310000052
b. the abnormal flameout state of the automobile has the following constraint conditions:
Figure RE-GDA0002469617310000053
and step 3: carrying out interval division on the data set, and extracting a kinematic segment;
the dividing of the data set in the step3 comprises:
according to Mt2yJudging the running condition of the automobile when Mt2yWhen the pressure is higher than 0, the pressure is in an acceleration working condition; when M ist2yWhen the speed is less than 0, the speed is reduced; when M ist2yWhen the working speed is 0, the working speed is constant; the rest data is the idle working condition;
coding the working condition data according to the rules of the table 1;
TABLE 1 data encoding rules
Figure RE-GDA0002469617310000054
Figure RE-GDA0002469617310000061
Wherein extracting the kinematic fragment comprises:
and reading the codes of the data set, and extracting a vehicle speed interval from the start of the idle state of the vehicle to the start of the next idle state as a kinematic segment to provide guidance for subsequent clustering.
And 4, step 4: calculating the association degree between each characteristic parameter and the most important parameter in the data set, and the specific steps are as follows:
step 1: carrying out non-dimensionalization on the data in the data set, wherein a non-dimensionalization formula is as follows:
Figure RE-GDA0002469617310000062
wherein, M'txyDimensionless value of x characteristic parameter, M, representing y vehicle at time txyThe optimal value of each characteristic parameter is obtained;
step 2: selecting a reference sequence, and selecting the optimal numerical value of the most important parameter in all the characteristic parameters by an analytic hierarchy process to form a reference sequence M'xy；
M′x＝{M′iI is more than or equal to 1 and less than or equal to n, n is more than or equal to 1 and less than or equal to x, i is a positive integer, Mi' represents the optimal value of the ith most important parameter in y vehicles, and n represents the number of final parameters;
let the most important parameter correspond to λ in the analytic hierarchy processiThen constitute the reference sequence M'xHas a central value F of ∑ lambdai·M′i；
Step 3: calculating the absolute difference Delta between other characteristic parameters and the central value Fmj：
Δmj＝Mmj-F, m ═ 1,2,. times, x-n; j is 1, 2.; wherein M ismjRepresenting mth other characteristic parameterThe jth value, j being a positive integer greater than 1;
on the basis, according to the formula: deltamax＝max(Δmj)，Δmin＝min(Δmj) The maximum difference Delta can be obtainedmaxAnd minimum difference Δmin；
Step 4: according to the formula
Figure RE-GDA0002469617310000063
Calculating ξ the correlation between the jth value of the kth other characteristic parameter and the most important parametermjWhere p is a resolution factor for attenuating ΔmaxToo large to distort the associated coefficients. The coefficient is artificially introduced to improve the significance of the difference between the correlation coefficients, and 0 < rho < 1;
step 5: calculating the association degree E between the mth other characteristic parameter and the most important parameterm：
Figure RE-GDA0002469617310000064
α thereinjThe weighting coefficient of the jth numerical value of the mth other characteristic parameter can be obtained by improving the adaptive step-size fish-swarm algorithm.
The calculation steps for improving the adaptive step-size fish swarm algorithm are as follows:
(1) fish shoal initialization
Each artificial fish in the fish school is a group of real numbers randomly generated in a given range, the size of the fish school is set to be N, and the parameter to be optimized is αjThe value range is 0 < αj< 1, an initial fish population of 1 row and N columns is generated, each column representing the parameter α for an artificial fishj；
(2) Foraging behavior
Suppose the current state of the artificial fish school is XpRandomly selecting a state X within the sensing rangeqX represents the state position of the artificial fish individual, and in solving the maximum value problem, if the food concentration of the position of the p-th artificial fish is less than the food concentration Y of the position of the q-th artificial fishp<YqWherein Y represents an artificial fish baitThe food concentration at the previous position, then further in this direction:
Figure RE-GDA0002469617310000071
wherein XnextRepresenting the position of the fish school going forward, rand () representing a random number within a value range, and Step representing the maximum Step length of the movement of the fish school. Otherwise, the random state X is reselectedq， Xnext＝Xp+rand()·Step。
(3) Cluster behavior
Suppose the current state X of the artificial fishpExploration of satisfying X in the current fieldq-Xp||<Number of partners n of VisualfAnd a central position XcWherein Visual represents the perceived distance of the artificial fish, provided that
Figure RE-GDA0002469617310000072
And delta represents the crowding degree, which indicates that more food is placed at the central position and is not too crowded in the current field, the foraging action is carried out again towards the central position.
(4) Rear-end collision behavior
Suppose the current state X of the artificial fishpExploration of satisfying X in the current fieldq-Xp||<Number of partners n of VisualfIn these partners YpIs the largest partner XpIf it satisfies
Figure RE-GDA0002469617310000073
Then indicate XqIn a state of higher food concentration and less crowded surrounding environment, toward XpFurther, the foraging action is repeatedly performed, vice versa.
(5) Random behavior
A state is randomly selected within the field of view and moved in that direction, which may be considered as a default behavior of the foraging process, namely:
Xpnext＝Xp+rand()·Visual
(6) adaptive step size adjustment
Figure RE-GDA0002469617310000074
Wherein a represents an adaptive step size adjustment coefficient, exp represents an expectation, s is an integer greater than 1, T is a current iteration number, TmaxThe maximum number of iterations is indicated.
Using the correlation degree E between part of characteristic parameters and the most important parameters of 3 automobile data which are disclosed currently in Fujian area and normally run in urban areamAs shown in table 2 below:
TABLE 2 partial association of characteristic parameters
Figure RE-GDA0002469617310000081
And 5: clustering the data sets;
according to the kinematics segment obtained after analysis in the step3, the kinematics segment which probably has the characteristic of a cluster can be obtained, the clustering effect exists in the interval of some parameters, and the data in the rest intervals are sparse and cannot be accurately identified.
The data set mainly focuses on four types of results, and the result of obtaining the cluster center of the automobile driving condition is shown in the following table 3:
TABLE 3 Cluster center results
Figure RE-GDA0002469617310000082
Step 6: and constructing a running condition curve of the automobile.
According to the clustering result, the kinematics segments which are close to the clustering center and have proper time length can be found out in each class according to the four types of automobile driving conditions, and are spliced into the automobile driving condition curve of 1200-1300 seconds. When calculating the distance, the distance between each characteristic parameter and each cluster center needs to be multiplied by a weight value, wherein the weight value of the most important parameter is lambdaiThe weighted value of other characteristic parameters is the sum of the weighted value and the maximum valueDegree of correlation E between important parametersm。
The finally constructed running condition curve of the automobile is shown in FIG. 2.
In the description herein, references to the description of the term "one embodiment," "some embodiments," "an illustrative embodiment," "an example," "a specific example," or "some examples" or the like mean that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the invention. In this specification, the schematic representations of the terms used above do not necessarily refer to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.
While embodiments of the invention have been shown and described, it will be understood by those of ordinary skill in the art that: various changes, modifications, substitutions and alterations can be made to the embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the claims and their equivalents.

Claims (9)

1. A method for constructing the running condition of an automobile based on the existing data is characterized by comprising the following steps:
step 1: collecting the existing driving data of different automobiles in different time periods in the same region;
step 2: preprocessing the collected data;
and step 3: carrying out interval division on the data set, and extracting a kinematic segment;
and 4, step 4: calculating the association degree between each characteristic parameter and the most important parameter in the data set;
and 5: clustering the data sets;
step 6: and constructing a running condition curve of the automobile.
2. The method for constructing the driving condition of the automobile based on the existing data as claimed in claim 1, wherein the step1 is executedThe driving data comprises M characteristic parameters, wherein M is a positive integer and is more than or equal to 3, and the numerical value of each characteristic parameter is marked as MtxyI.e. MtxyAnd the value of the x characteristic parameter of the y vehicle in t time is represented, wherein y is a vehicle number parameter, x is a number parameter of the characteristic parameters, y and x are positive integers, y is more than or equal to 2, t is a time metering parameter, and the sampling frequency is 1 s.
3. The method for constructing the driving condition of the automobile based on the existing data as claimed in claim 1, wherein the preprocessing in the step2 comprises data calibration, numerical filtering, defining the meaning of lost data, filling data, removing data and obtaining a data set for analysis.
4. The method for constructing the driving condition of the automobile based on the existing data as claimed in claim 2, wherein the data calibration in the step2 adopts a least square method, namely, a number is selected, so that the square difference between other data and the number is minimum, and zero calibration and direction calibration are performed on the characteristic parameters of the overall numerical drift.
5. The method for constructing the driving condition of the vehicle based on the existing data as claimed in claim 2, wherein the step2 defines that the data is lost as determining whether the vehicle loses part of the characteristic parameters by passing through a tunnel or passing by the vicinity of a high-rise building.
6. The method for constructing the driving condition of the automobile based on the existing data as claimed in claim 2, wherein the numerical filtering in the step2 includes filtering out upper and lower limits of the acceleration of the automobile exceeding or falling below a normal car; the duration of the automobile in the idle state exceeds 180s, the automobile speed exceeds 120km/h, and the rotating speed of the automobile is lower than 700 r/min;
the data removed in the step2 are data of the automobile passing through a tunnel and other areas with GPS signal loss and abnormal flameout state of the automobile in the driving process.
7. The method for constructing the driving condition of the automobile based on the existing data as claimed in claim 1, wherein the step3 of dividing the data set comprises the following steps:
and judging the automobile driving work, namely an acceleration working condition, a deceleration working condition and an idle working condition, coding the acceleration working condition, the deceleration working condition and the idle working condition, reading the codes of the data set, and extracting a speed interval from the idle state to the next idle state of the automobile into a kinematic segment.
8. The method for constructing the driving condition of the automobile based on the existing data according to claim 1, wherein the step4 of calculating the degree of association is as follows:
step 1: carrying out non-dimensionalization on the data in the data set, wherein a non-dimensionalization formula is as follows:
Figure RE-FDA0002469617300000021
wherein, M'txyDimensionless value of x characteristic parameter, M, representing y vehicle at time txyThe optimal value of each characteristic parameter is obtained;
step 2: selecting a reference sequence, and selecting the optimal numerical value of the most important parameter in all the characteristic parameters by an analytic hierarchy process to form a reference sequence M'x；
M′x＝{M′iI is more than or equal to 1 and less than or equal to n, n is more than or equal to 1 and less than or equal to x, and i is a positive integer, M'iThe optimal value of the ith most important parameter in y vehicles is represented, and n represents the number of final parameters;
let the most important parameter correspond to λ in the analytic hierarchy processiThen constitute the reference sequence M'xHas a central value F of ∑ lambdai·M′i；
Step 3: calculating the absolute difference Delta between other characteristic parameters and the central value Fmj：
Δmj＝Mmj-F, m ═ 1,2,. times, x-n; j is 1, 2.; wherein M ismjJ is a value greater than1 is a positive integer;
on the basis, according to the formula: deltamax＝max(Δmj)，Δmin＝min(Δmj) The maximum difference Delta can be obtainedmaxAnd minimum difference Δmin；
Step 4: according to the formula
Figure RE-FDA0002469617300000022
Calculating ξ the correlation between the jth value of the kth other characteristic parameter and the most important parametermjWhere p is a resolution factor for attenuating ΔmaxToo large to distort the associated coefficients. The coefficient is artificially introduced to improve the significance of the difference between the correlation coefficients, and 0 < rho < 1;
step 5: calculating the association degree E between the mth other characteristic parameter and the most important parameterm：
Figure RE-FDA0002469617300000023
α thereinjThe weighting coefficient of the jth numerical value of the mth other characteristic parameter can be obtained by improving the adaptive step-size fish-swarm algorithm.
9. The method for constructing the automobile driving condition based on the existing data as claimed in claim 1, wherein the automobile driving condition curve in the step 6 is formed by splicing the kinematics segments which are obtained by clustering in the step5, are close to the clustering center and have proper time length into an automobile driving condition curve of 1200-1300 seconds; when calculating the distance, the distance between each characteristic parameter and each cluster center needs to be multiplied by a weight value, wherein the weight value of the most important parameter is lambdaiThe weighted value of other characteristic parameters is the degree of association E between the other characteristic parameters and the most important parameterm。
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