CN111339638B - Automobile driving condition construction method based on existing data - Google Patents
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Abstract
The invention discloses a method for constructing an automobile driving condition based on existing data, which comprises the following steps: collecting the existing driving data of different automobiles in different time periods of the same area; preprocessing the collected data; dividing intervals of the data set, and extracting kinematic segments; calculating the association degree between each characteristic parameter and the most important parameter in the data set; clustering the data set; and constructing an automobile driving condition curve. The method constructs the automobile driving condition curve based on the existing available data, does not need to specially collect related data, reduces the cost, is easy to construct, and accelerates the test of the road driving conditions in various areas in China.
Description
Technical Field
The invention relates to the field of traffic, in particular to an automobile driving condition construction method based on existing data.
Background
The automobile running condition curve is a basis for reflecting the running characteristics of an automobile road, detecting the emission amount/fuel consumption amount of automobile pollutants and the limit value standard, is also a main reference for development and evaluation of new automobile type technologies and determination of traffic control risks, and is a common core technology of the automobile industry. At present, developed countries of Europe, america, the sun and the like all adopt standards suitable for respective running working conditions of automobiles to perform vehicle performance calibration optimization and energy consumption/emission authentication.
The energy consumption/emission authentication of the automobile products by adopting the NEDC running working condition in Europe in China effectively promotes the development of automobile technology in the early stage of the century. In recent years, with the rapid increase of the quantity of the reserved automobiles, the road traffic conditions of China are greatly changed, governments, enterprises and people gradually find out the automobiles which are optimally calibrated by taking NEDC working conditions as the reference, and the deviation between the actual oil consumption and the legal authentication result is larger. Meanwhile, in the actual use process of europe for many years, a plurality of defects in NEDC working conditions are gradually discovered, and in turn, a world light vehicle test cycle (WLTC) is adopted, and the main test characteristics of the WLTC are more different from the running working conditions of automobiles in China. On the other hand, the areas of China are wide, and the development degree, the climate conditions and the traffic conditions of each city are different, so that the running condition characteristics of the automobiles in each city are obviously different. Therefore, the establishment of test conditions reflecting the road driving conditions in various areas of China is urgent.
At present, data acquisition or investigation is required before the running working condition is built, so that enough research samples are obtained, the workload and the working difficulty for building the running working condition of the automobile are increased intangibly, and the research period is prolonged. If the running condition of the automobile in a certain city can be obtained from the existing running data of the automobile (such as the public data of the national statistical bureau, etc.), the workload can be greatly reduced, and the test of the running condition of the roads in various areas of China can be further accelerated.
Disclosure of Invention
Aiming at the defects in the prior art, the invention provides an automobile driving condition construction method 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 a kinematic segment and automobile movement characteristic parameters are reasonably extracted, so that the aim of rapidly drawing an automobile driving condition curve of the area is fulfilled. The method is convenient and quick, and is beneficial to accelerating the work of drawing the road running condition curves in all areas.
The present invention achieves the above technical object by the following means.
A method for constructing the running condition of an automobile based on the existing data comprises the following steps:
step1: collecting the existing driving data of different automobiles in different time periods of the same area;
The driving data in the step 1 comprises M characteristic parameters, wherein M is a positive integer, M is more than or equal to 3, the numerical value of each characteristic parameter is denoted as M txy, namely M txy represents the value of the x characteristic parameter of the y-th vehicle in t time, y is the number parameter of the vehicle, x is the number parameter of the characteristic parameters, y and x are both positive integers, y is more than or equal to 2, t is a time metering parameter, and the sampling frequency is 1s.
Step 2: preprocessing the collected data;
The preprocessing in the step 2 comprises data calibration, numerical filtering, defining the meaning of lost data, filling data, removing data, and obtaining a data set for analysis
And 2, carrying out zero calibration and direction calibration on the characteristic parameters of the integral numerical drift by adopting a least square method, namely selecting one number to minimize the square difference between other data and the number.
The definition of lost data in the step 2 means that whether the automobile loses part of the characteristic parameters due to passing through a tunnel or passing through the vicinity of a high-rise building is judged.
The numerical filtering in the step 2 comprises filtering out that the acceleration of the automobile exceeds or is lower than the upper limit and the lower limit of a normal car; the duration time of the automobile in the idle state exceeds 180s, the speed of the automobile exceeds 120km/h, and the rotating speed of the automobile is lower than 700 r/min;
and 2, eliminating data in the step, namely, the data of the automobile passing through a GPS signal loss area such as a tunnel and the like and the abnormal flameout state of the automobile in the driving process.
Step 3: dividing intervals of the data set, and extracting kinematic segments;
the partitioning of the data set in step 3 includes:
Judging the running tools of the automobile, namely an acceleration working condition, a deceleration working condition and an idle working condition, coding the running tools, reading codes of a data set, and extracting a speed interval from the idle state to the next idle state of the automobile as a kinematic segment.
Step 4: calculating the association degree between each characteristic parameter and the most important parameter in the data set;
The association degree calculating step in the step 4 is as follows:
step1: the method comprises the steps of carrying out dimensionless treatment on data in a data set, wherein a dimensionless formula is as follows:
Wherein M' txy represents the dimensionless value of the x-th characteristic parameter of the y-th vehicle at t time, and M xy is the optimal value of each characteristic parameter;
step2: selecting a reference sequence, and selecting the optimal value of the most important parameter in all the characteristic parameters by using an analytic hierarchy process to form a reference sequence M' x;
M 'x＝{M′i }, i 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, M' i represents the optimal value of the ith most important parameter in y vehicles, and n represents the number of final parameters;
Let the parameter corresponding to the most important parameter in the analytic hierarchy process be λ i, then the central value F that constitutes the reference sequence M' x is: f= Σλ i·M′i;
step2: calculating absolute differences delta mj between other characteristic parameters and the central value F:
Δ mj＝Mmj -F, m=1, 2,..; j=1, 2,; wherein M mj represents the jth numerical value of the mth other characteristic parameter, j being a positive integer greater than 1;
On the basis, the following formula is adopted: Δ max＝max(Δmj),Δmin＝min(Δmj), the maximum difference Δ max and the minimum difference Δ min can be obtained;
Step3: according to the formula And calculating the association degree xi mj between the j number value of the k other characteristic parameters and the most important parameter, wherein rho is a resolution coefficient and is used for weakening the influence of the excessive delta max on the distortion of the association coefficient. The artificial introduction of the coefficient is to improve the difference significance between the correlation coefficients, wherein rho is more than 0 and less than 1;
Step4: calculating the association degree E m between the m other characteristic parameters and the most important parameters:
Wherein alpha j is the weighting coefficient of the j-th value of the m-th other characteristic parameter, and can be obtained by improving the adaptive step-size fish swarm algorithm.
Step 5: clustering the data set;
step 6: and constructing an automobile driving condition curve.
The automobile running condition curves in the step 6 are spliced into 1200-1300 seconds of automobile running condition curves by kinematic segments which are obtained by clustering in the step 5, are closer to the clustering center and have proper time length; 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 lambda i, and the weight values of other characteristic parameters are the association degree E m between the most important parameters and the most important parameter.
The invention has the beneficial effects that:
1. The method constructs the automobile driving condition curve based on the existing available data, does not need to specially collect related data, reduces the cost, is easy to construct, and accelerates the test of the road driving conditions in various areas in China.
2. The method is still effective when some parameters are missing due to the calculation of the association degree between the characteristic parameters and the most important parameters, and the method can obtain an automobile driving working condition curve and has stronger inclusion on the characteristic parameters.
3. The step length of the common shoal of fish algorithm is fixed, so that the searching capability of the algorithm is easily poor in the global or local, the adaptive step length shoal of fish algorithm with the adaptive step length can keep a larger global searching range in the early stage of the algorithm, and the visual field can be adjusted in the later stage so that the algorithm has better local searching capability, and the accuracy and convergence of the algorithm are ensured.
Drawings
FIG. 1 is a simplified flow chart of a method for constructing driving conditions of an automobile based on existing data
FIG. 2 is a graph showing the final driving condition of the automobile
Detailed Description
The invention will be further described with reference to the drawings and the specific embodiments, but the scope of the invention is not limited thereto.
As shown in FIG. 1, the method for constructing the running condition of the automobile based on the existing data mainly comprises the following 6 steps.
Step1: collecting the existing driving data of different automobiles in different time periods of the same area;
the driving data comprise m characteristic parameters, wherein m is a positive integer and is more than or equal to 3;
According to the collected running data of y vehicles in different time periods of the same region, the running data comprise time and GPS speed, and the unit is km/h; the X-axis acceleration is the gravity acceleration g; y-axis acceleration, wherein the unit is gravity acceleration g; z-axis acceleration, namely gravity acceleration g; longitude; latitude; the engine speed is r/min; percent twist; instantaneous hundred kilometers of oil consumption in liters; the opening degree of the accelerator pedal is given in the unit of angle; an air-fuel ratio; percentage of engine load; the unit of the air inlet flow of the engine is m 3/h, the X-axis acceleration in the 14 characteristic parameters refers to the acceleration in the running direction of the automobile, the Y-axis acceleration refers to the acceleration in the direction perpendicular to the running direction of the automobile, and the Z-axis acceleration is perpendicular to the vertical acceleration of the automobile; sequentially marking 13 characteristic parameters except time from 1 to 13, namely GPS speed is a1 st characteristic parameter, X-axis acceleration is a2 nd characteristic parameter, Y-axis acceleration is a 3 rd characteristic parameter, Z-axis acceleration is a 4th characteristic parameter, longitude is a 5th characteristic parameter, latitude is a 6 th characteristic parameter, engine speed is a 7 th characteristic parameter, torsion percentage is an 8 th characteristic parameter, instantaneous hundred kilometer oil consumption is a 9 th characteristic parameter, accelerator pedal opening is a 10 th characteristic parameter, air-fuel ratio is an 11 th characteristic parameter, engine load percentage is a 12 th characteristic parameter, and engine intake flow is a 13 th characteristic parameter; the numerical value of each characteristic parameter is recorded as M txy, namely M txy represents the value of the x characteristic parameter of the y vehicle in t time, wherein y is the number parameter of the vehicle, x is the number parameter of the characteristic parameter, y and x are positive integers, y is more than or equal to 2, t is the time metering parameter, and the sampling frequency is 1s.
Step 2: preprocessing the collected data, wherein the preprocessing comprises data calibration, numerical filtering, defining the meaning of lost data, filling the data, and removing the data to obtain a data set for analysis;
The data calibration adopts a least square method, namely, one 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 integral numerical drift;
The numerical filtering comprises filtering out that the acceleration of the automobile exceeds or falls below the upper limit and the lower limit of a normal car, and the constraint conditions are as follows:
k is a time measurement length parameter, and under the constraint condition, k is more than or equal to 1 and less than or equal to 7 and is an integer;
The numerical filtering also comprises data of the automobile in an idle state with duration exceeding 180s, data of the automobile with speed exceeding 120km/h and data of the automobile with the rotating speed lower than 700r/min, wherein the constraint conditions are as follows:
M (t+k)1y is less than 10, under the constraint condition, k is more than or equal to 1 and less than or equal to 180 and is an integer; m t1y＜120;Mt7y < 700;
The definition of lost data in the step 2 means that whether the automobile loses part of characteristic parameters because the automobile passes through a tunnel or passes near a high-rise building is judged, if more than two numerical values in M t1y、Mt5y、Mt6y、Mt7y、Mt9y、Mt10y are not 0 at the time t, the lost data at the time t can be defined as normal running data, and the average value of the numerical values at the time t-1 and the time t+1 can be adopted for filling;
The step 2 of removing data is to analyze the collected data to find out the abnormal data of the automobile in the driving process, and the data should be removed;
a. When the automobile passes through a GPS signal loss area such as a tunnel and the like in the driving process, and the data meet the constraint condition A, the data are removed: a=buc, wherein B, C are both sub-constraints of constraint a;
b. the constraint conditions of the abnormal flameout state of the automobile are as follows:
Step 3: dividing intervals of the data set, and extracting kinematic segments;
The partitioning of the data set in step3 includes:
Judging the running condition of the automobile according to M t2y, and when M t2y is more than 0, judging the running condition as an acceleration condition; when M t2y is less than 0, the speed is reduced; when M t2y =0, the constant-speed working condition is adopted; the rest data is the idle working condition;
Encoding the working condition data according to the rule of the table 1;
table 1 data encoding rules
Wherein extracting the kinematic segment comprises:
And reading codes of the data set, and extracting a vehicle speed interval from the start of the idle state of the automobile to the start of the next idle state as a kinematic segment to provide guidance for subsequent clustering.
Step 4: the association degree between each characteristic parameter and the most important parameter in the data set is calculated, and the specific steps are as follows:
step1: the method comprises the steps of carrying out dimensionless treatment on data in a data set, wherein a dimensionless formula is as follows:
Wherein M' txy represents the dimensionless value of the x-th characteristic parameter of the y-th vehicle at t time, and M xy is the optimal value of each characteristic parameter;
Step2: selecting a reference sequence, and selecting the optimal value of the most important parameter in all the characteristic parameters by using an analytic hierarchy process to form a reference sequence M' xy;
m 'x＝{M′i }, i 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, M i' represents the optimal value of the ith most important parameter in y vehicles, and n represents the number of final parameters;
Let the parameter corresponding to the most important parameter in the analytic hierarchy process be λ i, then the central value F that constitutes the reference sequence M' x is: f= Σλ i·M′i;
Step3: calculating absolute differences delta mj between other characteristic parameters and the central value F:
Δ mj＝Mmj -F, m=1, 2,..; j=1, 2,; wherein M mj represents the jth numerical value of the mth other characteristic parameter, j being a positive integer greater than 1;
On the basis, the following formula is adopted: Δ max＝max(Δmj),Δmin＝min(Δmj), the maximum difference Δ max and the minimum difference Δ min can be obtained;
Step4: according to the formula And calculating the association degree xi mj between the j number value of the k other characteristic parameters and the most important parameter, wherein rho is a resolution coefficient and is used for weakening the influence of the excessive delta max on the distortion of the association coefficient. The artificial introduction of the coefficient is to improve the difference significance between the correlation coefficients, wherein rho is more than 0 and less than 1;
Step5: calculating the association degree E m between the m other characteristic parameters and the most important parameters:
Wherein alpha j is the weighting coefficient of the j-th value of the m-th other characteristic parameter, and can be obtained by improving the adaptive step-size fish swarm algorithm.
The calculation steps of the improved self-adaptive step size fish swarm algorithm are as follows:
(1) Shoal initialization
Each artificial fish in the fish swarm is a group of real numbers randomly generated within a given range, the size of the fish swarm is set to be N, the parameter to be optimized is alpha j, the value range is 0 < alpha j < 1, an initial fish swarm of 1 row and N columns is generated, and each column value represents the parameter alpha j of one artificial fish;
(2) Foraging behavior
Assuming that the current state of the artificial fish swarm is X p, randomly selecting a state X q in a perception range, wherein X represents the state position of an artificial fish individual, and in solving the maximum problem, if the food concentration of the position of the p-th artificial fish is smaller than the food concentration Y p<Yq of the position of the q-th artificial fish, wherein Y represents the food concentration of the current position of the artificial fish, further advancing to the direction:
Where X next denotes a further forward position of the fish school, rand () denotes a random number within a range of values, and Step denotes the maximum Step size of the fish school movement. Otherwise, the random state X q,Xnext＝Xp +rand (). Step is reselected.
(3) Clustering behavior
Assuming the current state of the artificial fish X p, exploring the number of partners n f and the central position X c that satisfy the Visual in the current field of X q-Xp, where Visual represents the perceived distance of the artificial fish, provided thatDelta represents the degree of crowding, which means that in the current field there is more food in the center and less crowding, further forward in the direction of the center, and vice versa, foraging is performed again.
(4) Rear-end collision behavior
Assuming the current state of the artificial fish X p, exploring the number of partners n f meeting the Visual requirement of X q-Xp in the current field, wherein Y p is the largest partner X p, and if the number meets the Visual requirementThe state of X q is indicated to have a higher food concentration and the surrounding environment is less crowded, and then further in the direction of X p, and vice versa, the foraging action is repeatedly performed.
(5) Random behavior
Randomly selecting a state within the field of view, moving in that direction, this process can be considered as a default behavior of the foraging process, namely:
Xp|next＝Xp+rand()·Visual
(6) Adaptive step size adjustment
Where a represents the adaptive step size adjustment coefficient, exp represents the desire, s is an integer greater than 1, T is the current number of iterations, and T max represents the maximum number of iterations.
The correlation degree E m between partial characteristic parameters and the most important parameters of 3 vehicles which are currently disclosed in a certain area of Fujian and normally run in urban areas is shown in the following table 2:
TABLE 2 correlation of partial characteristic parameters
Step 5: clustering the data set;
according to the kinematic segment obtained after analysis in the step 3, the kinematic segment can be obtained to have the characteristic of clusters, the aggregation effect exists in the intervals of certain parameters, and the data in the other intervals are sparse and cannot be accurately identified.
The data set is mainly concentrated on four types of results, and the clustering center results of the automobile driving conditions are shown in the following table 3:
TABLE 3 clustering center results
Step 6: and constructing an automobile driving condition curve.
According to the clustering result, according to the four clustered automobile driving conditions, a kinematic segment which is relatively close to the clustering center and has a proper time length can be found out from each type, and the kinematic segment is spliced 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 lambda i, and the weight values of other characteristic parameters are the association degree E m between the most important parameter and the most important parameter.
The final constructed and obtained running condition curve of the automobile is shown in figure 2.
In the description of the present specification, reference to the terms "one embodiment," "some embodiments," "illustrative embodiments," "examples," "specific examples," or "some examples," etc., means 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, schematic representations of the above terms do not necessarily refer to the same embodiments or examples. 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 present invention have been shown and described, it will be understood by those of ordinary skill in the art that: many changes, modifications, substitutions and variations may be made to the embodiments without departing from the spirit and principles of the invention, the scope of which is defined by the claims and their equivalents.
Claims (8)
1. The method for constructing the running condition of the automobile based on the existing data is characterized by comprising the following steps of:
step1: collecting the existing driving data of different automobiles in different time periods of the same area;
step 2: preprocessing the collected data;
Step 3: dividing intervals of the data set, and extracting kinematic segments;
step 4: calculating the association degree between each characteristic parameter and the most important parameter in the data set;
The association degree calculating step in the step 4 is as follows:
step1: the method comprises the steps of carrying out dimensionless treatment on data in a data set, wherein a dimensionless formula is as follows:
Wherein M txy represents the value of the x characteristic parameter of the y vehicle at t time, M' txy represents the dimensionless value of the x characteristic parameter of the y vehicle at t time, and M xy is the optimal value of each characteristic parameter;
step2: selecting a reference sequence, and selecting the optimal value of the most important parameter in all the characteristic parameters by using an analytic hierarchy process to form a reference sequence M' x;
M 'x＝{M′i }, i 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, M' i represents the optimal value of the ith most important parameter in y vehicles, and n represents the number of final parameters;
Let the parameter corresponding to the most important parameter in the analytic hierarchy process be λ i, then the central value F that constitutes the reference sequence M' x is: f= Σλ i·M′i;
Step3: calculating absolute differences delta mj between other characteristic parameters and the central value F:
Δ mj＝Mmj -F, m=1, 2,..; j=1, 2,; wherein M mj represents the jth numerical value of the mth other characteristic parameter, j being a positive integer greater than 1;
on the basis, the following formula is adopted: Δ max＝max(Δmj),Δmin＝min(Δmj), the maximum difference Δ max and the minimum difference Δ min are obtained;
Step4: according to the formula Calculating a correlation degree xi mj between the jth numerical value of the kth other characteristic parameters and the most important parameters, wherein rho is a resolution coefficient used for weakening the influence of the distortion of the correlation coefficient caused by overlarge delta max, and artificially introducing the coefficient to improve the difference significance between the correlation coefficients, wherein rho is more than 0 and less than 1;
Step5: calculating the association degree E m between the m other characteristic parameters and the most important parameters:
Wherein alpha j is the weighting coefficient of the jth numerical value of the mth other characteristic parameters, and is obtained by improving the adaptive step-length fish swarm algorithm;
the calculation steps of the improved self-adaptive step size fish swarm algorithm are as follows:
(1) Shoal initialization
Each artificial fish in the fish swarm is a group of real numbers randomly generated within a given range, the size of the fish swarm is set to be N, the parameter to be optimized is alpha j, the value range is 0 < alpha j < 1, an initial fish swarm of 1 row and N columns is generated, and each column value represents the parameter alpha j of one artificial fish;
(2) Foraging behavior
Assuming that the current state of the artificial fish swarm is X p, randomly selecting a state X q in a perception range, wherein X represents the state position of an artificial fish individual, and in solving the maximum problem, if the food concentration of the position of the p-th artificial fish is smaller than the food concentration Y p<Yq of the position of the q-th artificial fish, wherein Y represents the food concentration of the current position of the artificial fish, further going to the direction of the state X q:
Wherein X next represents a further forward position of the fish swarm, rand () represents a random number within a value range, step represents a maximum Step size of the fish swarm movement, and otherwise, the random state X q,Xnext＝Xp +rand (). Step is reselected;
(3) Clustering behavior
Assuming the current state of artificial fish X p, explore the number of partners n f in the current field that satisfy ||x q-Xp || < Visual, and exploring a center position X c in the current field that satisfies the expression of expression X q-Xp expression < Visual, wherein Visual represents a perceived distance of the artificial fish, provided thatDelta represents the crowding degree, which indicates that in the current field, more food exists in the central position and the crowding is less, and the food is further in front of the central position, otherwise, the foraging behavior is performed again;
(4) Rear-end collision behavior
Assuming the current state of the artificial fish X p, exploring the number of partners n f meeting the Visual requirement of X q-Xp in the current field, wherein Y p is the largest partner X p, and if the number meets the Visual requirementThen the state of X q is indicated to have higher food concentration and the surrounding environment is less crowded, then the direction of X p is further followed, otherwise, foraging is repeatedly performed;
(5) Random behavior
Randomly selecting a state within the field of view, moving towards the selected state, the process being considered as a default behaviour of the foraging process, namely:
Xp|next＝Xp+rand()·Visual
(6) Adaptive step size adjustment
Wherein a represents an adaptive step size adjustment coefficient, exp represents an expectation, s is an integer greater than 1, T is the current iteration number, and T max represents the maximum iteration number;
Step 5: clustering the data set;
step 6: and constructing an automobile driving condition curve.
2. The method for constructing the running condition of the automobile based on the existing data according to claim 1, wherein the running data in the step 1 comprises M characteristic parameters, wherein M is a positive integer, M is greater than or equal to 3, the numerical value of each characteristic parameter is denoted as M txy, namely M txy represents the value of the x characteristic parameter of the y-th automobile in t time, wherein y is an automobile number parameter, x is a number parameter of the characteristic parameters, y and x are both positive integers, y is greater than or equal to 2, t is a time metering parameter, and the sampling frequency is 1s.
3. The method for constructing the running condition of the automobile based on the existing data according to claim 1, wherein the preprocessing in the step 2 comprises data calibration, numerical filtering, defining the meaning of lost data, filling the data, and eliminating the data to obtain a data set for analysis.
4. The method for constructing the running condition of the automobile based on the existing data according to claim 3, wherein the data calibration in the step 2 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 integral numerical drift.
5. A method for constructing a driving condition of an automobile based on existing data according to claim 3, wherein the definition of lost data in the step 2 means that it is determined whether the automobile loses part of the characteristic parameters by passing through a tunnel or passing through the vicinity of a high-rise building.
6. The method for constructing the running condition of the automobile based on the existing data according to claim 3, wherein the numerical filtering in the step 2 comprises filtering out the acceleration of the automobile exceeding or falling below the upper limit and the lower limit of a normal car; the duration time of the automobile in the idle state exceeds 180s, the speed of the automobile exceeds 120km/h, and the rotating speed of the automobile is lower than 700 r/min;
And the data removed in the step 2 are data of the automobile passing through a tunnel GPS signal loss area and an 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 according to claim 1, wherein the dividing of the data set in the step 3 includes:
Judging the running tools of the automobile, namely an acceleration working condition, a deceleration working condition and an idle working condition, coding the running tools, reading codes of a data set, and extracting a speed interval from the idle state to the next idle state of the automobile as a kinematic segment.
8. The method for constructing the running condition of the automobile based on the existing data according to claim 1, wherein the running condition curve of the automobile in the step 6 is a running condition curve of the automobile which is formed by splicing kinematic segments obtained by clustering in the step 5 into 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 lambda i, and the weight values of other characteristic parameters are the association degree E m between the most important parameters and the most important parameter.
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