CN111832225A - Method for constructing driving condition of automobile - Google Patents

Method for constructing driving condition of automobile Download PDF

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CN111832225A
CN111832225A CN202010644339.0A CN202010644339A CN111832225A CN 111832225 A CN111832225 A CN 111832225A CN 202010644339 A CN202010644339 A CN 202010644339A CN 111832225 A CN111832225 A CN 111832225A
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CN111832225B (en
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白明泽
邓川
覃春园
葛丝雨
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Guangzhou Dayu Chuangfu Technology Co ltd
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Abstract

The invention relates to the field of construction of automobile working condition data, in particular to a method for constructing an automobile driving working condition, which comprises the following steps: acquiring original GPS data of automobile driving, and preprocessing; dividing the preprocessed data into kinematic fragments; performing characteristic calculation on the kinematic segment to obtain characteristic parameters of the kinematic segment; dividing the kinematic fragments into four fragment libraries by adopting K-Means clustering; constructing a training data set; inputting the training data set into a model for training to obtain a trained long-short term memory neural network model; predicting by using the trained long and short term memory neural network model to obtain time-speed prediction curves corresponding to the four segment libraries respectively; and combining the curves of the four speed sections into a working condition curve. The method effectively identifies the implicit characteristics in the automobile driving data in the special region through the LSTM network, thereby constructing the automobile driving condition curve according with the driving characteristics.

Description

Method for constructing driving condition of automobile
Technical Field
The invention relates to the field of automobile working condition data construction, in particular to a method for constructing automobile running working conditions.
Background
The Driving Cycle (also called as vehicle test Cycle) is an important and common basic technology in the automobile industry, is a basis of a vehicle energy consumption/emission test method and a limit standard, and is also a main reference for calibrating and optimizing various performance indexes of an automobile, and is used for describing a speed-time curve of automobile Driving, the total time is generally within 1800 seconds, but no limit standard exists, the kinematic characteristics of automobile road Driving can be reflected. 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.
In the beginning of the century, China directly adopts the European NEDC running working condition to authenticate the energy consumption/emission of automobile products, but the two most main working condition characteristics of the idle speed time ratio and the average speed of the working condition are greatly different from the actual automobile running working condition of China. As the most basic basis for vehicle development and evaluation, the method is more and more important for developing deep research and formulating test conditions reflecting actual road running conditions in China. Meanwhile, the region of China is wide, and the development degree, climatic conditions and traffic conditions of each city are different, so that the driving condition characteristics of the automobiles of each city are obviously different. Therefore, the construction and research of city automobile driving conditions based on the city own automobile driving data are more and more urgent, and the constructed automobile driving conditions are expected to be matched with the driving conditions of the city automobiles as much as possible, ideally representing the driving conditions of the city automobiles, so that the construction of the automobile driving conditions of each city according to different road conditions of each city is necessary.
The current traditional construction methods involve the use of fuzzy clustering analysis, markov, etc. The fuzzy clustering analysis method basically searches representative segments in a kinematic segment library to combine to form a working condition curve close to the actual condition, and whether the working condition curve is representative or not depends on the search of the optimal segment. Furthermore, none of these methods is able to efficiently estimate and extract all the implicit information in the data, resulting in the selection of only one generic segment that can represent all the routes. The Markov method is that the final road running working condition is obtained according to the influence of the current data on the future data through the state transition probability, although the representative working condition with the specified duration can be randomly generated, the result depends on the accuracy of the state transition probability, and the information quantity of the previous data which can be reserved in the new state is less, so that the generalization of the new prediction data on the future situation is lower than that of the previous data.
Disclosure of Invention
In order to solve the above problems, the present invention provides a method for constructing a driving condition of an automobile. The method deeply learns the implicit characteristics in the driving data according to the collected driving data which accord with a certain special region and area, so as to extract effective information, predict the working condition curve of a given interval in a segmented manner and finally construct a complete automobile driving working condition curve which accords with the region characteristics.
A method for constructing a driving condition of an automobile comprises the following steps:
acquiring original GPS data of automobile driving, and preprocessing the original GPS data of the automobile driving;
dividing the preprocessed data into kinematic fragments by a short-stroke dividing method;
performing feature calculation on the kinematic segment to obtain feature parameters of the kinematic segment, and filtering irrelevant features by adopting a principal component analysis method to obtain effective feature parameters;
dividing the kinematic fragments into four fragment libraries by adopting K-Means clustering, wherein the four fragment libraries are respectively as follows: a low-speed interval fragment library, a medium-speed interval fragment library, a high-number interval fragment library and an extremely high-speed interval fragment library;
constructing a training data set: splicing all the kinematic segments in each segment library to obtain four long segments, and taking the four long segments as a training data set;
inputting the training data set into a long-short term memory neural network model for training to obtain a trained long-short term memory neural network model;
predicting by using the trained long and short term memory neural network model to obtain time-speed prediction curves corresponding to the four segment libraries respectively, wherein the specific process comprises the following steps: inputting the last sample data of the training data set as a first input element into the trained long-short term memory neural network model, and outputting a first prediction sequence; deleting the first input element, taking the first predicted value as a second input element, and inputting the model to obtain a second prediction sequence; by analogy, a prediction sequence of one fragment library is finally obtained, and time-speed prediction curves corresponding to the four fragment libraries are obtained;
after time-speed prediction curves corresponding to the four segment libraries are obtained, determining the time occupied by the four segment libraries in the final working condition synthesis according to the time proportion occupied by the four segment libraries in the whole kinematic segment, and combining the curves of the four speed segments into a working condition curve;
and sending the working condition curve to a control device, and evaluating the vehicle exhaust emission and evaluating the environmental protection grade by the control device according to the working condition curve.
Further, the preprocessing of the raw GPS data of the vehicle driving includes:
the method comprises the steps of performing traversal search on original GPS data of automobile driving from the beginning, searching for a first intermittent point, and dividing the original GPS data into different driving segments from the first intermittent point, wherein the first intermittent point is an area with a time interval larger than 55 seconds in the original GPS data of the automobile driving;
judging whether a second time breakpoint exists in the obtained driving segment, if so, fitting a series of new speed data points by adopting an improved polynomial fitting method according to speed data before and after the second time breakpoint to supplement the second time breakpoint in the driving segment, wherein the second time breakpoint is a region with a time interval of more than 2 seconds and less than or equal to 55 seconds in original GPS data of automobile driving;
after the data fitting and supplementing are completed, calculating the acceleration of each driving segment at each time point, and removing the driving segments with abnormal acceleration from the data according to an acceleration abnormal screening rule;
for abnormal data of long-term idling for more than 180 seconds, sliding the time and the vehicle speed of each segment by using a sliding window with the size of 180 seconds, wherein the sliding step length is 1s, and in the window sliding process, if all data in the window are idling data, screening out the first data of the window; when the tail of the window slides to the tail of the driving segment, if the data in the window is idle speed data, all the data in the window are screened out, and the data are screened out on all the driving segments in the same way to obtain the preprocessed data.
Further, the method for dividing the preprocessed data into the kinematic segments by adopting the short-stroke dividing method comprises the following steps: firstly, judging whether the running time of each running section is more than 20s, and if the running time of each running section is less than 20s, rejecting the running section; if the distance is greater than 20s, searching a kinematic segment from the driving segment according to a search rule of the kinematic segment, wherein the search rule of the kinematic segment comprises the following steps:
(1) searching a first GPS vehicle speed point of 0 downwards from the starting time of the driving segment, namely an idling starting point, and recording the position of the idling starting point if the idling starting point is found; then, continuously finding a point, namely a middle point, of which the first GPS vehicle speed is not 0 downwards, and recording the position of the middle point;
(2) calculating the time difference from the intermediate point to the idling starting point, if the time difference is greater than 20s, moving the position of the idling starting point downwards for 20s, and then judging the time difference from the intermediate point to the idling starting point until the time difference is less than 20 s; searching a next point with the GPS vehicle speed of 0, namely an idle speed terminal of the kinematic segment, and recording the position of the idle speed terminal;
(3) screening the kinematics segment according to a kinematics segment screening rule, and if the kinematics segment screening rule is met, extracting the kinematics segment from the driving segment according to the recorded positions of the idling starting point and the idling ending point;
the kinematic fragment screening rule comprises:
(1) the duration of the kinematic segment is not less than 20 seconds, namely the time from one idle starting point to the next idle starting point is at least 20 seconds;
(2) the kinematic segment comprises at least one acceleration state and one deceleration state, so that at least one acceleration of the vehicle is required to be greater than 0.1m/s2And deceleration less than-0.1 m/s2A continuous fragment of (a);
(3) the idle time of the kinematic segment does not exceed 20 seconds.
Further, the characteristic parameters of the kinematic segment include a time characteristic parameter, a speed characteristic parameter and an acceleration characteristic parameter, wherein the time characteristic parameter includes: running time t(s), constant speed time ti(s) idle time tc(s), acceleration time ta(s), deceleration time td(s); the speed characteristic parameters comprise: average velocity vm(km/h), average speed of travel vmr(km/h), maximum speed vmax(km/h), velocity standard deviation vstd(km/h); the acceleration characteristic parameters comprise: average acceleration ama(m/s2) Average deceleration amd(m/s2) Acceleration standard deviation astd(m/s2) Constant velocity time ratio Pc(%), ratio of idle time Pi(%), acceleration time ratio Pa(%), deceleration time ratio Pd(％)。
Further, the step of dividing the kinematic fragments into four fragment libraries by adopting K-Means clustering specifically comprises the following steps: firstly, randomly selecting 4 kinematic segments from all the kinematic segments as an initial clustering center; then, the cluster assignment operation is carried out: calculating Euclidean distances from each kinematic segment to 4 initial clustering centers respectively, classifying according to the Euclidean distances from the kinematic segments to the initial clustering centers, and assigning each kinematic segment to the initial clustering center with the nearest Euclidean distance to form 4 clusters; and after 4 clusters are obtained, recalculating the clustering center of each cluster, and executing the step S42 until the composition of the kinematic segment of each cluster is not changed any more, and finally obtaining four segment libraries of the kinematic segment.
Further, the calculation method of the euclidean distance from the kinematic segment to the cluster center includes:
Figure BDA0002572572590000051
wherein d isijIs the Euclidean distance, x 'of the ith kinematic fragment to the clustering center j'imIs the m-th characteristic feature of the i-th kinematic segment,
Figure BDA0002572572590000052
is the mth characteristic element of the cluster center j.
Further, the structure of the long-short term memory neural network model comprises: an input layer, an LSTM layer, a full link layer, and an output layer.
Further, the training data set needs to be preprocessed before being input into the model, and the preprocessing of the training data set includes: discarding the time dimension of the long segment and keeping the speed dimension of the long segment; setting a sliding window, wherein the length of the window is the size of a time step, sliding the window backwards from an initial position on a long segment, the sliding step length is 1 second each time, taking a speed-time sequence segment of an area covered by the window at the current moment as a speed-time sequence of the current moment, and taking an initial speed value of the area covered by the window at the next moment as a label of the area covered by the window at the current moment; by analogy, the four long segments are respectively preprocessed to obtain the preprocessed D of the four long segmentsL,DM,DH,DEHA training data set.
The invention has the beneficial effects that:
the invention provides a convenient and rapid method for automobile driving conditions, which can effectively identify hidden characteristics in automobile driving data of special regions through a long-term and short-term memory neural LSTM network model, thereby constructing an automobile driving condition curve according with the driving characteristics.
Drawings
The invention will be described in further detail below with reference to the drawings and the detailed description, which are provided for the purpose of illustrating preferred embodiments only and are not to be construed as limiting the invention.
FIG. 1 is a flowchart of a method for constructing a driving condition of an automobile according to an embodiment of the present invention;
FIG. 2 is a schematic diagram of a network structure of an LSTM prediction model;
FIG. 3 is a flow chart of data preprocessing;
FIG. 4 is a comparison of pre-data processing and post-data processing;
FIG. 5 is a schematic diagram of two short-stroke dividing methods;
FIG. 6 is a flow chart of a kinematic fragment screening;
FIG. 7 is a schematic diagram of an input data generation strategy for the LSTM model;
FIG. 8 is a schematic of the results of the kinematics segment training and prediction for the intermediate velocity segment;
FIG. 9 is a graphical illustration of the results of kinematics segment training and prediction for the high velocity segment;
FIG. 10 is a graph of results of a sample set-up of a vehicle driving profile.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
As shown in FIG. 1, a method for constructing a driving condition of an automobile includes, but is not limited to, the following steps:
the method comprises the steps of obtaining original GPS data of automobile driving, wherein the original GPS data of the automobile driving comprises acquisition time, GPS speed and GPS acceleration, and the original GPS data of the automobile driving often comprises bad data values due to various external factors and vehicle self reasons, and the reliability and accuracy of the original data are directly related to the effectiveness of a subsequent construction working condition, so that the original GPS data of the automobile driving is preprocessed, a data screening principle is established according to the bad data types in the original data to screen the original data, invalid data in the original GPS data of the automobile driving is deleted, effective and reasonable data are reserved, the analysis of the subsequent data is facilitated, and the quality of the working condition construction is improved by optimizing characteristic parameters.
In one embodiment, the bad data types in the raw data and the data filtering rules include:
(1) fragment screening for time-discrete cases: the situation that the GPS signal is lost when the automobile runs due to the conditions of high-rise building coverage or tunnel passing and the like can cause the situation that the time in the original GPS data of the automobile running is discontinuous. In order to ensure the validity of the original data on the prediction result, the time break needs to be preprocessed. The processing mode comprises the following steps: searching a time discontinuity, if no missing value exists, indicating that the time is continuous, and not processing; if the missing value exists, indicating that the time is discontinuous, and if the discontinuous time is less than or equal to 55 seconds, performing data completion; if the interruption time is more than 55 seconds, no processing is carried out, the data with the interruption time more than 55 seconds does not cause great influence on the whole data, and meanwhile, the idle time ratio is also ensured.
(2) Acceleration anomaly screening rules: generally, the acceleration time of a normal automobile from 0 to 100km/h is more than 7 seconds, and the maximum acceleration of the automobile is 3.968m/s2(ii) a Meanwhile, the maximum deceleration of the emergency brake of the automobile is 7.5-8m/s2. In the original data of the automobile running process, if the acceleration does not meet the range of the maximum acceleration and/or the range of the maximum deceleration, the data is considered to be data with abnormal acceleration and deceleration, because the abnormal data has larger influence on the prediction of the whole running condition, the whole kinematic segment of the abnormal acceleration and deceleration segment and a connected idling segment are removed, and the kinematic segments before and after the abnormal segment are connected after the removal.
(3) Maximum idle time duration rule: the original GPS data of the running automobile can have the situations of long-term parking or long-term traffic jam such as people who do not stop or stop, but the data acquisition equipment is not closed, and the like, under the situations, the automobile stops running, but the data acquisition equipment is not closed, at the moment, the data equipment still works, and the data section of which the GPS speed is kept 0 for a long time is acquired, so that the data section does not accord with the actual running situation of the road, and is processed according to the idling situation; in addition, there are cases where the vehicle runs at a low speed intermittently during running, i.e., the maximum vehicle speed is less than 10km/h, and such data belongs to the spike data, and these data points are usually modified to the idling point. And the data segment with the excessively long idle time does not meet the requirements of the kinematic segment, the data segment with the excessively long idle time is deleted to reduce the influence of the data on the working condition curve, the data with the idle time exceeding 180 seconds is considered as the excessively long idle time, the idle data point within 180 seconds is reserved as the idle time segment, and the rest part is deleted.
In one embodiment, the order of pre-processing includes, but is not limited to:
as shown in fig. 3, a traversal search is first performed on three raw data files collected by the same vehicle in different time periods. All data in one file are divided into different driving segments from the beginning according to the first break point (the area with the time interval larger than 55 seconds in the original GPS data of the driving of the automobile).
And then, aiming at all the obtained driving segments, judging whether second time break points exist in all the obtained driving segments (if the time interval in the original GPS data of the automobile driving is more than 2 seconds and less than or equal to 55 seconds, the interval region is regarded as the second time break point), supplementing the second time break points in the driving segments by adopting an improved polynomial fitting method, wherein the supplemented length is the length of time difference, fitting a series of new speed data points according to the speed data before and after the second time break points, filling the new speed data points at the second time break points, and supplementing the second time break points in the driving segments, wherein the vehicle speed of the GPS in the actual situation is not less than 0, so that the negative numbers in the data points fitted by the polynomial function are all replaced by 0.
After the data fitting and supplementing are completed, the acceleration of each driving segment at each time point is calculated, the driving segment with abnormal acceleration and an idling segment connected with the abnormal segment are removed from the data according to an acceleration abnormal screening rule, and the motion segments before and after the abnormal segment are connected after the abnormal segment is removed.
And for abnormal data of long-term idling for more than 180 seconds, sliding is carried out on the time and the vehicle speed of each segment by using a sliding window with the size of 180 seconds, and the step length of the sliding is 1 s. In the sliding process of the window, if all data in the window are idle speed data, screening out the first piece of data of the window; when the tail of the window slides to the tail of the driving segment, if the data in the window at the moment are idle speed data, the data in the window are all screened out. And in this way, screening out data of all the driving segments to obtain the preprocessed data.
And (3) performing kinematic fragment division on the preprocessed data by adopting a short-stroke division method.
As shown in fig. 5, the kinematic segment includes an idle time period and a driving time period, and the time length M of each kinematic segment does not exceed 600s, and wherein the idle time period does not exceed 20s at most, and the portion exceeding the idle time is deleted. Each kinematic segment begins at zero speed and ends at zero speed, and the interval between the beginning and the ending segment may include an idle segment.
In an alternative embodiment, in the kinematic segment, the idle time period and the driving time period may be intercepted by the following method: the kinematic segment begins with a speed of zero and ends with a speed of zero, the idle period preceding the driving period.
In an alternative embodiment, in the kinematic segment, the idle time period and the driving time period may be intercepted by the method two: the kinematic segment begins with a speed of zero and ends with a speed of zero, the idle period following the travel period.
In one embodiment, the kinematic fragment screening rules are first established:
(1) the duration of the kinematic segment is not less than 20 seconds, i.e. the time from one idle start to the next is at least 20 seconds.
(2) The kinematic segment comprises at least one acceleration state and one deceleration state, so that at least one acceleration of the vehicle is required to be greater than 0.1m/s2And deceleration less than-0.1 m/s2A continuous fragment of (a).
(3) The idle time of the kinematic segment does not exceed 20 seconds.
As shown in fig. 6, according to the principle of kinematics segment screening, finding out a kinematics segment from the pre-processed driving segment data by using a short-stroke segmentation method specifically includes: firstly, judging whether the running time of each running section is more than 20s, and if the running time of each running section is less than 20s, rejecting the running section; if the time is more than 20s, searching the kinematic segment from the driving segment according to the searching rule of the kinematic segment.
In one embodiment, the search rule of the kinematic segment includes:
(1) an idle starting point (a point at which the GPS vehicle speed is 0) is searched downwards from the starting time of the driving segment, and if the idle starting point is found, the position of the idle starting point is recorded. Continuously finding a point with the speed of the first GPS not being 0 downwards, recording the point as a middle point, recording the position of the middle point and judging the time difference from the middle point to the idling starting point;
(2) and if the time difference is greater than 20s, moving the position of the idle speed starting point downwards by 20s, judging the time difference from the middle point to the idle speed starting point until the time difference is less than 20s, searching a next idle speed point, wherein the next idle speed point is the idle speed end point (the point of the GPS vehicle speed is 0) of the kinematic segment, and if the next idle speed point is found, recording the position of the idle speed end point.
(3) And screening the kinematic segment according to a kinematic segment screening rule, and if the kinematic segment screening rule is met, extracting the kinematic segment from the driving segment according to the recorded positions of the idling starting point and the idling ending point.
And performing characteristic calculation on the kinematics segment according to the characteristic parameter calculation formula of the kinematics segment to obtain the characteristic parameter of the kinematics segment. The characteristic parameters of the kinematics segment include 16 characteristic parameters, which can be classified into three types, namely time characteristic parameters, speed characteristic parameters and acceleration characteristic parameters, wherein the time characteristic parameters include: running time t(s), constant speed time ti(s) idle time tc(s), acceleration time ta(s), deceleration time td(s); the speed characteristic parameters comprise: average velocity vm(km/h), average lineSpeed v of travelmr(km/h), maximum speed vmax(km/h), velocity standard deviation vstd(km/h); the acceleration characteristic parameters comprise: average acceleration ama(m/s2) Average deceleration amd(m/s2) Acceleration standard deviation astd(m/s2) Constant velocity time ratio Pc(%), ratio of idle time Pi(%), acceleration time ratio Pa(%), deceleration time ratio Pd(％)。
In one embodiment, the feature parameter calculation formula of the kinematic segment includes:
(1) operating time t(s): since the sampling frequency is 1Hz, the running time is calculated as follows:
t＝n
wherein n is the number of the collected driving data.
(2) Idle time ti：tiThe number of data in the kinematic segment is 0.
(3) Acceleration time ta：taAcceleration is greater than 0.1m/s2The total number of points.
(4) Deceleration time td：tdAcceleration is less than-0.1 m/s2The total number of points.
(5) Time t of uniform velocitycThe calculation method is as follows: t is tc＝t-ti-ta-td
(6) Average velocity vmThe calculation method is as follows:
Figure BDA0002572572590000101
in the formula (6), q is the total number of data points of one kinematic segment, vpRepresenting the velocity value of the data point p.
(7) Average running speed vmrThe calculation method is as follows:
Figure BDA0002572572590000102
where q is the total number of data points for one kinematic segment.
(8) Maximum velocity vmaxThe calculation method is as follows:
vmax＝max{vp,p＝1,2,3...q}
(9) standard deviation of velocity vstdThe calculation method is as follows:
Figure BDA0002572572590000103
(10) average acceleration amaThe calculation method is as follows:
Figure BDA0002572572590000104
(11) average deceleration amdThe calculation method is as follows:
Figure BDA0002572572590000111
wherein, apIndicating the acceleration value corresponding to the data point p measured by the GPS.
(12) Standard deviation of acceleration astdThe calculation method is as follows:
Figure BDA0002572572590000112
(13) idle time ratio PiThe calculation method is as follows: pi＝ti/t
(14) Uniform time ratio PcThe calculation method is as follows: pc＝tc/t
(15) Acceleration time ratio PaThe calculation method is as follows: pa＝ta/t
(16) Deceleration time ratio PdThe calculation method is as follows: pd＝td/t
Further, in a preferred embodiment, after obtaining the features of the above-mentioned kinematic segment, the principal component analysis method is used to filter the irrelevant features, so as to obtain the feature parameters with larger relationship.
Since the filtering of the irrelevant features by the principal component analysis method is not an innovative part of the invention and is not a key point of the invention, the description of the specification does not give excessive details, and the specific process of filtering the irrelevant features by the principal component analysis method can refer to the corresponding principal component analysis step in the research of the urban road automobile driving condition construction method based on the K-means clustering analysis in the prior art.
According to the characteristics of the kinematic fragments, dividing the kinematic fragments into four fragment libraries by adopting K-Means clustering, wherein the four fragment libraries are respectively as follows: a low-speed interval fragment library L, a medium-speed interval fragment library M, a high-number interval fragment library H and an extremely high-speed interval fragment library EH. The highest speed of the kinematics segment in the low-speed interval segment library is not more than 60km/h, the highest speed of the kinematics segment in the medium-speed interval segment library is not more than 80km/h, the highest speed of the kinematics segment in the high-speed interval segment library is not more than 100km/h, and the highest speed of the kinematics segment in the ultra-high speed interval segment library is not more than 130 km/h.
In one embodiment, a K-Means clustering algorithm is adopted to perform the division of the kinematic segments, and the euclidean distance is used as a distance measurement standard of the category distance, which specifically includes:
(1) firstly, randomly selecting 4 kinematic segments from all the kinematic segments as an initial clustering center;
(2) performing cluster assignment operation: calculating Euclidean distances from each kinematic segment to 4 initial clustering centers respectively, classifying according to the Euclidean distances from the kinematic segments to the initial clustering centers, and assigning each kinematic segment to the initial clustering center with the nearest Euclidean distance to form 4 clusters;
(3) and (3) after 4 clusters are obtained, recalculating a new clustering center of each cluster, and executing the step (2) until the composition of the kinematic segments of each cluster is not changed any more, and finally obtaining four segment libraries of the kinematic segments.
The calculation mode of the new cluster center in each cluster comprises the following steps:
Ci＝mean(x1,x2,x3,…,xn)
wherein, CiRepresenting the new cluster center in cluster i,x is the kinematic segment assigned to cluster i, n is the number of kinematic segments belonging to cluster i, and mean is the averaging operation.
Further, in one embodiment, in the K-means clustering algorithm, the criterion function for measuring the clustering quality is the sum of squares of errors, and the smaller the sum of squares of errors, the higher the clustering quality is. The criterion function of the K-means clustering algorithm is as follows:
Figure BDA0002572572590000121
wherein SSE represents Sum of Squared Errors (SSE), k represents k clustering centers, that is, the final clustering result has k classes, where k is 4, x is a data point to be clustered, and C isiTo cluster center, dist is the Euclidean metric.
Further, in one embodiment, the K-means clustering algorithm uses euclidean metric to compute the similarity, and the euclidean distance between the kinematic segment and the cluster center is:
Figure BDA0002572572590000122
wherein d isijIs the Euclidean distance, x 'of the ith kinematic fragment to the clustering center j'imIs the m-th characteristic feature of the i-th kinematic segment,
Figure BDA0002572572590000123
is the mth characteristic element of the cluster center j.
It should be noted that, in addition to calculating the similarity degree by using the euclidean metric, the similarity degree may alternatively be calculated by using other methods in the present specification, for example: the euclidean distance, the minkowski distance, the chebyshev distance, the block distance, etc. may be any achievable measure of similarity in the prior art.
Further, in an embodiment, if the minimum euclidean distances from the kinematic segment to the centers of the various types are equal and the similarity degree cannot be determined, the similarity criterion is used for determining, and the formula of the similarity criterion is as follows:
Figure BDA0002572572590000131
wherein,
Figure BDA0002572572590000132
representing the velocity-time vector of any kinematic segment i that needs to be subjected to a closeness decision,
Figure BDA0002572572590000133
is the velocity-time vector of the cluster center m,
Figure BDA0002572572590000134
representing the proximity value of the kinematic segment i to the cluster center m.
Calculating the proximity values of the kinematic segments and all the clustering centers according to the formula of the proximity criterion, finding the maximum value from the obtained proximity values, and dividing the kinematic segments into the clustering centers corresponding to the maximum values of the proximity values, namely the clustering centers corresponding to the maximum values of the proximity values are the categories to which the kinematic segments belong.
And respectively splicing all the kinematic segments in each segment library to obtain four long segments, and taking the four long segments as a training data set.
The training data set construction method comprises the following steps: for each fragment library, all the kinematic fragments S in each fragment library are spliced to obtain a speed-time sequence Si (namely a long fragment), and for four fragment libraries, S is obtained correspondinglyL，SM，SHAnd SEHFour long segments.
Inputting the training data set into a long-short term memory neural network model (LSTM) for training, continuously learning the change characteristics of the speed-time sequences contained in different types of segment libraries in the training process of the long-short term memory neural network model (LSTM), and training four corresponding time-speed sequence prediction models to obtain the trained long-short term memory neural network model. It should be noted that the time-velocity sequence prediction model and the long-short term memory neural network model in the present specification are the same model.
The long-short term memory neural network model can complete the reconstruction of time sequences according to the space-time correlation of segment libraries of different classes, and the time correlation characteristics are identified and strengthened by means of model training, so that different classes of kinematics segments with representative characteristics are obtained through prediction. Because the network structure of the long-short term memory neural network model is determined, vectors with specific dimensionality need to be input, and the data length in the training data set is different and does not meet the condition, the training data set needs to be preprocessed before being input into the model, and the training data set is processed into supervised learning data which can be used by a time-speed sequence prediction model.
Preprocessing the training data set includes:
1. discarding the time dimension of the long segment and keeping the speed dimension of the long segment;
2. setting a sliding window, wherein the length of the window is the size of a time step, sliding the window backwards from an initial position on a long segment, the sliding step length is 1 second each time, taking a speed-time sequence segment of an area covered by the window at the current moment as a speed-time sequence of the current moment, and taking an initial speed value of the area covered by the window at the next moment as a label of the area covered by the window at the current moment;
specifically, the sliding window W has a length n, and slides over a speed-time series S (a long segment) having a length L, at t0Time of day start, t0The coverage area of the time sliding window is
Figure BDA0002572572590000146
Taking the velocity-time sequence segment of the coverage area as t0Time-of-day speed-time sequence s0Get tn+1Velocity value of time of day
Figure BDA0002572572590000141
As s0Is marked with a labelForm t0Data segment of a time of day
Figure BDA0002572572590000142
By analogy, the sliding window continues to slide backwards, generating t1Data segment of a time of day
Figure BDA0002572572590000143
Until t is generatedL-nData segment of a time of day
Figure BDA0002572572590000147
Obtaining L-n data segments in total, and constructing a training data set with long segments preprocessed according to the L-n data segments, wherein the training data set is D ═ DmL0 < m < L-n }. Wherein L represents the data length of the long segment S, dmDenotes the mth data segment, s1Represents t1The speed-time sequence of the time of day,
Figure BDA0002572572590000144
represents tn+2The speed value of the moment in time,
Figure BDA0002572572590000145
represents tLThe velocity value of the moment.
3. By analogy, the four long segments are respectively preprocessed to obtain the preprocessed D of the four long segmentsL,DM,DH,DEHA training data set.
The invention utilizes the LSTM recurrent neural network as a prediction core to generate an automobile driving condition curve and construct a time-speed sequence prediction model. The time-velocity sequence prediction model is shown in figure 2 and comprises an input layer, an LSTM layer, a fully-connected layer and an output layer, wherein the input layer is followed by the LSTM layer, and then is connected to the output layer through two fully-connected layers. The input data has potential periodicity and is beneficial to training because the input in the input layer of the model is a kinematics fragment library of four categories after K-Means clustering, and the LSTM layer internally comprises 3 hidden layers and uses tanh as an activation function for learning potential time sequence relation and abstract characteristics in an input sequence; the full connection layer reduces the output dimension of the LSTM layer and maps the abstract features learned by the LSTM layer, and the output dimension of the full connection layer is 1; and finally, outputting a result of the predicted speed data by the output layer, and sequentially splicing the predicted results to finally obtain an automobile running condition curve.
After preprocessing the input training data set, preprocessing the training data set DL,DM,DH,DEHInputting the long-short term memory neural network model to train a time-speed sequence prediction model, wherein the training of the time-speed sequence prediction model comprises the following steps: for the LSTM layer part, the input dimension of the first layer hidden layer is (1, n), the input dimension of the second layer hidden layer is (1,6), the input dimension of the third layer hidden layer is (1,8), and the flow direction of the input data in the LSTM layer is: the processed intermediate output is obtained through the LSTM gate structure, and then the intermediate output is further processed through the tanh activation function as the output result of the layer. And finally, inputting the output result of the LSTM layer into a full-connection layer, wherein the input dimension of the full-connection layer is 4, the output dimension of the full-connection layer is 1, and the output is the predicted value of the speed point next to the current input vector. The number of training iteration rounds of the model is 100, and the training process is divided into a forward propagation process and an error back propagation process: forward propagation Using inputs of a time-velocity sequence prediction model as velocity vector data D in a preprocessed training set XiAnd i ═ L, M, H, EH) is used as input, the weights of all layers are inactivated at random with a probability of 0.2 through the LSTM layer and the fully connected layer, output is obtained, mean square error is used as a loss function to obtain error, error back propagation is carried out, Adam function is used as an optimization function of each layer to update the weight of each layer, the learning rate of the optimization function is set to be 0.001, the batch processing size of network training is set to be 1, and four segment libraries D in the training set X are respectively subjected to one-time matchingL,DM,DH,DEHTraining all the input data to obtain four corresponding time-speed sequence prediction models ML,MM,MH,MEHThe internal structure of the four models is the same, but trainedThe data sets of (a) are different, so that the obtained internal weights are different, and correspond to segments capable of predicting four parts, namely low, medium, high and extremely high in the working condition of the automobile.
Preferably, the basic gate structure in the LSTM employed is:
forget the door: f. oft＝σ(Wf×[ht-1,xt]+bf)
An input gate: i.e. it＝σ(Wi×[ht-1,xt]+bi)
An output gate: ot＝σ(Wo×[ht-1,xt]+bo)
ht＝ot×tanh(ct)
Figure BDA0002572572590000161
Figure BDA0002572572590000162
Wherein, the symbol
Figure BDA0002572572590000163
Representing the multiplication of elements at corresponding positions of two vectors; the notation "σ" denotes the sigmoid function, which is used as the activation function; c represents the state of the cell and c represents the state of the cell,
Figure BDA0002572572590000164
representing the currently input cell state, xtFor cell input, htFor the cell output, W, b are the weight and offset of each gate in the algorithm, respectively. The method specifically comprises the following steps: f. oftIs a forgetting vector that determines which information was forgotten from the state of the cell at the previous time, WfTo forget the weight in the door, ht-1For the output of the last gate structure, xtIs an input at time t, bfIs a bias in the forgetting gate; i.e. itIs an input gate vector, determines the information held in the cell state, WiIs the weight in the input gate, biFor deflection in the input gatePlacing; otIs the output vector of the output gate, WoIs the weight in the output gate, boIs the offset in the output gate or gates,
Figure BDA00025725725900001610
is an update value of the cell unit state, WcIs a weight in the cell unit network, bcIs a bias in the cell unit network, ct-1Is the cell state at the last time (t-1).
And predicting by using the trained long and short term memory neural network model to obtain time-speed prediction curves corresponding to the four segment libraries respectively.
And for a certain segment library, predicting the speed at a certain time in the future according to the kinematic data in the segment library to obtain the automobile working condition driving curve.
The prediction process comprises the following steps: model M obtained as described aboveiI ═ L, M, H, EH) to correspond to training data D in training set XiLast data in (2)
Figure BDA00025725725900001611
As input, t is predictedL+1Velocity value of time of day
Figure BDA0002572572590000165
Thereby further comprising
Figure BDA0002572572590000166
Predict tL+2Velocity value of time of day
Figure BDA0002572572590000167
Knowing the predicted tL+MVelocity value of
Figure BDA0002572572590000168
Get
Figure BDA0002572572590000169
The speed value is taken as a time-speed prediction curve P extracted from the interval and capable of representing all speed change curve characteristics of the intervali. In this way, finally, the time-speed prediction curves P corresponding to the low-speed interval fragment library L, the medium-speed interval fragment library M, the high-number interval fragment library H and the extremely high-speed interval fragment library EH are obtainedL，PM，PH，PEH。
After the time-speed prediction curves corresponding to the four segment libraries are obtained, the time of the four segment libraries in the final working condition synthesis is determined according to the time proportion of the four segment libraries in the whole kinematic segment, and the time-speed prediction curves P of the four speed segments are obtainedL，PM，PH，PEHyAnd sequentially splicing the working condition curves to obtain a final automobile running working condition curve P.
According to the technical scheme provided by the method for constructing the automobile running condition curve, the speed change is complex in the running process of the automobile, and the time-speed curve generated by the running of the automobile can contain all the information of the running of the automobile: acceleration, maximum deceleration, average acceleration, deceleration, average speed and idle speed intervals, and has the conditions of no strict rule, uncertainty, instability, nonlinearity in practice and space and large data volume. The long-short term memory (LSTM) cyclic neural network is complex in structure, output information of each time is related to input, memory content of a cell unit and a last output result, and meanwhile, the LSTM model makes up the problems of gradient dispersion and gradient explosion, insufficient long-term memory capability and the like of a typical machine learning model such as a cyclic neural network (RNN) and other structures, so that the cyclic neural network can really and effectively utilize long-distance time sequence information compared with a traditional fitting method, an automobile working condition curve is just typical time sequence data, and ideal results can be obtained by utilizing the LSTM model to predict the working condition curve. In the LSTM model, the new cell state and the previous state are an accumulation process, nonlinear fitting can be carried out on the driving kinematics curve segment of the automobile, the time sequence of input data is effectively considered, and time sequence coding and decoding are achieved.
In order to make the present specification clearer and more complete, the following further description is made with specific data.
In one embodiment, original data collected by vehicle-mounted GPS equipment in ' mathematic modeling competition of Chinese researchers ' D ' in 2019 are selected for analysis, the data are collected by actual road running of a light automobile in a certain city, the sampling frequency is 1Hz, and the data comprise the collection time (seconds) and the GPS speed.
The method comprises the steps of preprocessing original data collected by vehicle-mounted GPS equipment, eliminating bad data values and invalid data, and reserving valid data.
The raw data collected by the onboard GPS device consists of 3 files. Corresponding processing is performed according to the preprocessing method of the original data to obtain a final preprocessing result, which is shown in the following table 1: the processed moving picture recording number of the file 1 is 186255, the processed moving picture recording number of the file 2 is 149032, and the processed moving picture recording number of the file 3 is 170808.
TABLE 1 number of records before and after preprocessing of each file data
Figure BDA0002572572590000181
In order to better show the preprocessing result, a part of processed data segments in the file 2 according to 3 screening principles is selected to be compared with original data segments, as shown in fig. 4, the upper 3 figures are the original data segments, the 3 original data segments respectively have the problems of data loss, abnormal acceleration/deceleration and abnormal segments with overlong idle time, and the lower 3 figures are the results of preprocessing the original data abnormal segments with data loss, abnormal acceleration/deceleration and overlong idle time, so that it can be obviously seen that the speed-time curve of the preprocessed automobile running is closer to the working condition curve of the actual automobile.
Obtaining a kinematic fragment by dividing the preprocessed data through a short stroke, and establishing a screening rule of the kinematic fragment:
(1) the duration of the kinematic segment is not less than 20 seconds, i.e. the time from one idle start to the next is at least 20 seconds.
(2) The kinematic segment comprises at least one acceleration state and one deceleration state, so that at least one acceleration of the vehicle is required to be greater than 0.1m/s2And deceleration less than-0.1 m/s2A continuous fragment of (a).
(3) The idle time of the kinematic segment does not exceed 20 seconds.
Applying the short-stroke division method, as shown in fig. 6, finding out the kinematic segment from the pre-processed driving segment data by using the short-stroke division method
And performing characteristic calculation on the kinematics segment according to the characteristic parameter calculation formula of the kinematics segment to obtain the characteristic parameter of the kinematics segment.
The calculated values of the characteristic parameters of the partial kinematic segment are shown in table 3 below, for example.
TABLE 3 kinematic fragment eigenvalues
Figure BDA0002572572590000182
Figure BDA0002572572590000191
And dividing the kinematic fragments into four fragment libraries by adopting K-Means clustering according to the characteristics of the kinematic fragments. Through the characteristic matrix clustering of the segments, the original 386 kinematic segments are divided into four segment libraries, namely a low-speed interval segment library L, a medium-speed interval segment library M, a high-speed interval segment library H and an ultrahigh-speed interval segment library EH. In order to further analyze the vehicle running characteristics represented by the kinematic segments in each class of speed interval segment library, the characteristic parameters related to speed and acceleration are selected from the 16 characteristic parameters to correspondingly calculate the four classes, so as to obtain the comprehensive characteristic values of all the kinematic segments in each class of segment library, which is shown in table 4.
As can be seen from Table 4, the segment library M (first type) of the medium-speed interval comprises 203 kinematic segments, the average speed is 12.15km/h, and the maximum speed does not exceed 106.74 km/h; the low-speed interval fragment library L (the second type) comprises 39 kinematic fragments, the average speed is 6.02km/h, and the maximum speed does not exceed 149.88 km/h; the extremely high speed interval fragment library EH (third type) comprises 10 kinematic fragments, the average speed is 37.54km/h, and the maximum speed does not exceed 76.60 km/h; the high-speed interval fragment library H (fourth type) comprises 134 kinematic fragments, the average speed is 23.88km/H, and the maximum speed does not exceed 139.61 km/H.
TABLE 4 values of the comprehensive characteristic parameters of the various classes of fragment libraries
Characteristic parameter First kind Second class Class III Class IV
Total number of fragments 203 39 10 134
vm 12.15 6.02 37.54 23.88
vmax 106.74 149.88 76.60 139.61
vstd 9.657 14.786 14.027 14.672
ama 0.5970 0.4356 0.3387 0.4856
amd -0.7155 -0.5479 -0.4549 -0.6179
astd 0.6023 0.5509 0.4236 0.5810
And respectively splicing all the kinematic segments in each segment library to obtain four long segments, and taking the four long segments as a training data set.
The input dimensions of a typical LSTM model include: sample (sample), time step (timestep), feature of the previous network (feature), input of LSTM must be "sequence", which is unfamiliar with our requirements: the speed at the next time is predicted by inputting a speed sequence.
(2) Preprocessing of the training data set: since the obtained long segment is a time-velocity sequence, and the time dimension in the long segment is discarded, the actual dimension of the long segment after discarding the time dimension is (s,1), and s is the length of the segment, but when performing model training, data needs to be processed into supervised learning data that can be used by the model.
The treatment method comprises the following steps: setting a sliding window for the speed sequence of the long segment, wherein the length of the window is the size of a time step, sliding the window from the initial speed backwards, the sliding step length is 1 second each time, taking the next value of the window as the processing value of the current window, and taking the next value as the segment x covered in the windowtLabel h oftAnd by analogy, obtaining an input data set 5 with the length of s-time, wherein the input data generation strategy of the long-short term memory neural network model is shown in FIG. 7.
(3) After preprocessing the training data set, inputting the preprocessed training data set into a long-short term memory neural network model (LSTM) for training:
the data set X is input into a model for training, and the used hyper-parameters are as follows: the batch processing size (batch-size) is equal to 5, the time step is equal to 300 seconds, and the specific structure of the model is as follows: and one LSTM layer internally comprising 3 hidden layers, using tanh as an activation function, using a fully-connected layer to process the output of all the LSTM layers, wherein the output dimension of the fully-connected layer is 1, and predicting the speed of the next moment (second) according to the sequence speed data of the current input model.
(4) Prediction of fragments using the model:
the input of the model is a time period sequence with the corresponding length of len, and a corresponding speed sequence Y is predictedlenInputting the last sample data of the training data set as a seed to generate a first predicted value, deleting the first input element, inputting the first predicted value as the next element, outputting a second predicted value by the model, repeating the steps to obtain all predicted sequences Y, predicting the four segment libraries respectively to obtain time-speed prediction curves corresponding to the four segment libraries respectively,and then, splicing the time-speed prediction curves corresponding to the four segment libraries respectively to obtain the whole complete working condition data, wherein table 5 provides the loss function related data in the model training process.
TABLE 5 loss function correlation data for model training procedure
Figure BDA0002572572590000211
Using the trained long and short term memory neural network model for prediction to finally obtain time-speed prediction curves corresponding to four kinematics segment libraries, wherein fig. 8 is a schematic diagram of the training and prediction results of the kinematics segment in the middle speed segment, a symbol "+" data point part (a "True" curve in fig. 8) is training data, a solid curve (a "Train" curve in fig. 8) is a prediction result on a retraining data set after model training, and shows that the coincidence degree of the prediction result and the training data is high, a symbol "+" data point part (a "previous" curve in fig. 8) is a measured working condition curve, fig. 9 is a schematic diagram of the training and prediction results of the kinematics segment in the high speed segment, wherein the symbol "+" data point part is training data, and the solid curve is the prediction result on the retraining data set after model training, and shows that the coincidence degree of the prediction result and the training data is high, the working condition curve measured by the sign ". DELTA.data point part can be seen from FIGS. 8-9 that the predicted result overlap ratio of the LSTM model is better, and a more accurate result can be obtained when the method is used for constructing the working condition curve.
After the time-speed prediction curves corresponding to the four kinematics segment libraries are obtained, the time occupied by each type of segment in the final working condition synthesis is determined according to the time proportion occupied by each type in the whole kinematics segment, and the curves of the four speed segments are combined into a working condition curve according to the prediction result obtained by the training of the LSTM model, as shown in figure 10.
And after the working condition curve is obtained, the working condition curve is sent to the control equipment, the control equipment controls the tested vehicle to carry out simulated driving according to the working condition curve, so that data such as oil consumption data, tail gas emission and the like of the tested vehicle in the driving process are obtained, and vehicle emission evaluation and environmental protection grade evaluation are carried out according to the obtained data such as the oil consumption data, the tail gas emission and the like.
As used herein, the singular forms "a", "an", "the" and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise. It will be further understood that the terms "comprises," "comprising," "includes" and/or "including," when used in this specification, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof. It should be understood that the term "and/or" as used herein includes any and all combinations of one or more of the associated listed items.
It should be noted that, as one of ordinary skill in the art would understand, all or part of the processes of the above method embodiments may be implemented by a computer program to instruct related hardware, where the computer program may be stored in a computer readable storage medium, and when executed, the computer program may include the processes of the above method embodiments. The storage medium may be a magnetic disk, an optical disk, a Read-0nly Memory (ROM), a Random Access Memory (RAM), or the like.
The foregoing is directed to embodiments of the present invention and it will be appreciated by those skilled in the art that changes may be made in these embodiments without departing from the principles and spirit of the invention, the scope of which is defined in the appended claims and their equivalents.

Claims (8)

1. A method for constructing the driving condition of an automobile is characterized by comprising the following steps:
acquiring original GPS data of automobile driving, and preprocessing the original GPS data of the automobile driving;
dividing the preprocessed data into kinematic fragments by a short-stroke dividing method;
performing feature calculation on the kinematic segment to obtain feature parameters of the kinematic segment, and filtering irrelevant features by adopting a principal component analysis method to obtain effective feature parameters;
dividing the kinematic fragments into four fragment libraries by adopting K-Means clustering, wherein the four fragment libraries are respectively as follows: a low-speed interval fragment library, a medium-speed interval fragment library, a high-number interval fragment library and an extremely high-speed interval fragment library;
constructing a training data set: splicing all the kinematic segments in each segment library to obtain four long segments, and taking the four long segments as a training data set;
inputting the training data set into a long-short term memory neural network model for training to obtain a trained long-short term memory neural network model;
predicting by using the trained long and short term memory neural network model to obtain time-speed prediction curves corresponding to the four segment libraries respectively, wherein the specific process comprises the following steps: inputting the last sample data of the training data set as a first input element into the trained long-short term memory neural network model, and outputting a first prediction sequence; deleting the first input element, taking the first predicted value as a second input element, and inputting the model to obtain a second prediction sequence; by analogy, a prediction sequence of one fragment library is finally obtained, and time-speed prediction curves corresponding to the four fragment libraries are obtained;
after time-speed prediction curves corresponding to the four segment libraries are obtained, determining the time occupied by the four segment libraries in the final working condition synthesis according to the time proportion occupied by the four segment libraries in the whole kinematic segment, and combining the curves of the four speed segments into a working condition curve;
and sending the working condition curve to a control device, and evaluating the vehicle exhaust emission and evaluating the environmental protection grade by the control device according to the working condition curve.
2. The method for constructing the driving condition of the automobile according to claim 1, wherein the preprocessing of the raw GPS data of the driving of the automobile comprises:
traversing and searching original GPS data of automobile driving from the beginning, searching a first time break point, and dividing the original GPS data into different driving segments from the first time break point;
judging whether a second time breakpoint exists in the obtained driving segment, if so, fitting a series of new speed data points by adopting an improved polynomial fitting method according to speed data before and after the second time breakpoint, and supplementing the second time breakpoint in the driving segment;
after the data fitting and supplementing are completed, calculating the acceleration of each driving segment at each time point, and removing the driving segments with abnormal acceleration from the data according to an acceleration abnormal screening rule;
for abnormal data of long-term idling for more than 180 seconds, sliding the time and the vehicle speed of each segment by using a sliding window with the size of 180 seconds, wherein the sliding step length is 1s, and in the window sliding process, if all data in the window are idling data, screening out the first data of the window; when the tail of the window slides to the tail of the driving segment, if the data in the window is idle speed data, all the data in the window are screened out, and the data are screened out on all the driving segments in the same way to obtain the preprocessed data.
3. The method for constructing the driving condition of the automobile according to claim 1, wherein the step of performing the kinematic segmentation on the preprocessed data by using the short-stroke segmentation method comprises the following steps: firstly, judging whether the running time of each running section is more than 20s, and if the running time of each running section is less than 20s, rejecting the running section; if the time is more than 20s, searching the kinematic segment from the driving segment according to the searching rule of the kinematic segment,
the search rule of the kinematic segment includes:
(1) searching a first GPS vehicle speed point of 0 downwards from the starting time of the driving segment, namely an idling starting point, and recording the position of the idling starting point if the idling starting point is found; then, continuously finding a point, namely a middle point, of which the first GPS vehicle speed is not 0 downwards, and recording the position of the middle point;
(2) calculating the time difference from the intermediate point to the idling starting point, if the time difference is greater than 20s, moving the position of the idling starting point downwards for 20s, and then judging the time difference from the intermediate point to the idling starting point until the time difference is less than 20 s; searching a next point with the GPS vehicle speed of 0, namely an idle speed terminal of the kinematic segment, and recording the position of the idle speed terminal;
(3) screening the kinematics segment according to a kinematics segment screening rule, and if the kinematics segment screening rule is met, extracting the kinematics segment from the driving segment according to the recorded positions of the idling starting point and the idling ending point;
the kinematic fragment screening rule comprises:
(1) the duration of the kinematic segment is not less than 20 seconds, namely the time from one idle starting point to the next idle starting point is at least 20 seconds;
(2) the kinematic segment comprises at least one acceleration state and one deceleration state, so that at least one acceleration of the vehicle is required to be greater than 0.1m/s2And deceleration less than-0.1 m/s2A continuous fragment of (a);
(3) the idle time of the kinematic segment does not exceed 20 seconds.
4. The method for constructing the driving condition of the automobile according to claim 1, wherein the characteristic parameters of the kinematic segment comprise a time characteristic parameter, a speed characteristic parameter and an acceleration characteristic parameter, wherein the time characteristic parameter comprises: running time t(s), constant speed time ti(s) idle time tc(s), acceleration time ta(s), deceleration time td(s); the speed characteristic parameters comprise: average velocity vm(km/h), average speed of travel vmr(km/h), maximum speed vmax(km/h), velocity standard deviation vstd(km/h); the acceleration characteristic parameters comprise: average acceleration ama(m/s2) Average deceleration amd(m/s2) Acceleration standard deviation astd(m/s2) Constant velocity time ratio Pc(%), ratio of idle time Pi(%), acceleration time ratio Pa(%), deceleration time ratio Pd(％)。
5. The method for constructing the driving conditions of the automobile according to claim 1, wherein the step of dividing the kinematic segments into four segment libraries by adopting K-Means clustering specifically comprises the following steps:
s41, firstly, randomly selecting 4 kinematic segments from all the kinematic segments as an initial clustering center;
s42, performing cluster assignment operation: calculating Euclidean distances from each kinematic segment to 4 initial clustering centers respectively, classifying according to the Euclidean distances from the kinematic segments to the initial clustering centers, and assigning each kinematic segment to the initial clustering center with the nearest Euclidean distance to form 4 clusters;
s43, after 4 clusters are obtained, the clustering center of each cluster is recalculated, and the step S42 is executed until the composition of the kinematic fragments of each cluster does not change any more, and finally four fragment libraries of the kinematic fragments are obtained.
6. The method for constructing the driving condition of the automobile according to claim 5, wherein the Euclidean distance from the kinematic segment to the cluster center is calculated in a manner of:
Figure FDA0002572572580000031
wherein d isijIs the Euclidean distance, x 'of the ith kinematic fragment to the clustering center j'imIs the m-th characteristic feature of the i-th kinematic segment,
Figure FDA0002572572580000041
is the mth characteristic element of the cluster center j.
7. The method for constructing the driving condition of the automobile according to claim 1, wherein the structure of the long-short term memory neural network model comprises: an input layer, an LSTM layer, a full link layer, and an output layer.
8. The method for constructing the driving condition of the automobile according to claim 1, wherein the training data set needs to be preprocessed before being input into the model, and the preprocessing of the training data set comprises:
discarding the time dimension of the long segment and keeping the speed dimension of the long segment;
setting a sliding window, wherein the length of the window is the size of a time step, sliding the window backwards from an initial position on a long segment, the sliding step length is 1 second each time, taking a speed-time sequence segment of an area covered by the window at the current moment as a speed-time sequence of the current moment, and taking an initial speed value of the area covered by the window at the next moment as a label of the area covered by the window at the current moment;
by analogy, the four long segments are respectively preprocessed to obtain the preprocessed D of the four long segmentsL,DM,DH,DEHA training data set.
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