CN110738754A - Vehicle kinematic segment extraction method, vehicle working condition analysis method and corresponding device - Google Patents

Vehicle kinematic segment extraction method, vehicle working condition analysis method and corresponding device Download PDF

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CN110738754A
CN110738754A CN201911006306.7A CN201911006306A CN110738754A CN 110738754 A CN110738754 A CN 110738754A CN 201911006306 A CN201911006306 A CN 201911006306A CN 110738754 A CN110738754 A CN 110738754A
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CN110738754B (en
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杨世春
张正杰
刘健
冯松
陈飞
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Beihang University
Beijing University of Aeronautics and Astronautics
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Abstract

The invention discloses a vehicle kinematics segment extraction method which includes the steps of firstly, obtaining parameters capable of describing vehicle kinematics characteristics in a complete kinematics segment of a vehicle, secondly, performing principal component analysis on the obtained parameters to obtain parameters capable of describing macroscopic characteristics of the complete kinematics segment, and finally, calculating to obtain parameters capable of describing transient characteristics of the complete kinematics segment, mainly emphasizing the macroscopic description of the kinematics segment by the parameters obtained through the principal component analysis, wherein on the basis, two parameters of an acceleration and deceleration mileage ratio and a segment power spectral density are introduced as transient characteristic descriptions, the acceleration and deceleration mileage ratio represents the ratio of mileage driven by vehicle acceleration and deceleration in kinematics segments to the total mileage of the segment, namely the occurrence probability of an acceleration and deceleration process, the segment power spectral density is power in unit frequency, and can represent the intensity of vehicle acceleration and deceleration in kinematics segments, and the method can realize accurate and effective extraction.

Description

Vehicle kinematic segment extraction method, vehicle working condition analysis method and corresponding device
Technical Field
The invention relates to the technical field of vehicle working condition establishment and analysis, in particular to an vehicle kinematic fragment extraction method, a vehicle working condition analysis method and a corresponding device.
Background
The effective establishment and analysis of the vehicle kinematics segment have important significance for scientific management and control and reasonable prediction of vehicle energy consumption in the development process at the present stage, and can also provide a solution for solving the problem of mileage anxiety of the new energy vehicle. In the future, when the automobile industry enters the era of intelligent networking, the accurate kinematics segment extraction method can provide a solid foundation for path planning and decision schemes.
The traditional vehicle kinematics segment extraction method is limited to the simple extraction of information such as speed, acceleration, mileage, time and the like of a vehicle in the past periods, the parameter extracted in aspects may not truly and perfectly reflect the driving characteristics of the vehicle in the past time, the concept of periods of time is not the analysis of numerical characteristics but general expressions, the information extracted in aspects is only the simple expansion of a database, the automatic acquisition and analysis of data cannot be realized after the data amount is increased, and the method has great limitation.
Therefore, how to provide vehicle kinematic segment extraction methods capable of achieving accurate and effective extraction becomes a technical problem to be solved urgently by those skilled in the art.
Disclosure of Invention
The invention aims to provide vehicle kinematic fragment extraction methods, vehicle working condition analysis methods and corresponding devices.
The invention provides an vehicle kinematic fragment extraction method, which comprises the following steps:
acquiring parameters capable of describing vehicle kinematic characteristics in a complete kinematic segment of a vehicle, wherein the parameters at least comprise: driving distance s and average driving speed vavgMaximum traveling speed vmaxLength of operation tallAnd an acceleration α, the complete kinematic segment being an interval segment in which the vehicle speed is zero two adjacent times;
and performing principal component analysis on the obtained parameters to obtain parameters capable of describing the macroscopic features of the complete kinematic segment, wherein the parameters comprise the following steps: driving distance s and average driving speed vavgMaximum traveling speed vmaxAnd a running time period tall；
Calculating parameters capable of describing transient characteristics of the complete kinematic segment: the acceleration-deceleration mileage ratio W and the segment power spectral density S are as follows:
Figure BDA0002242877340000021
Figure BDA0002242877340000022
wherein the content of the first and second substances,n is the number of accelerations added to the complete kinematic segment, TiThe duration of the ith instant acceleration and deceleration process, s is the mileage of the complete kinematic segment, α is TiAcceleration over a period of time-function, Ff(ω) is a function of acceleration obtained by Fourier transform, and f represents frequency.
Preferably, the method for obtaining parameters that can describe the kinematic characteristics of the vehicle in a complete kinematic segment of the vehicle comprises:
directly acquiring original parameters of a vehicle in the running process through a sensor, wherein the sensor at least comprises a wheel speed sensor, a timing sensor and an acceleration sensor;
and calculating the original parameters to obtain parameters capable of describing the vehicle kinematic characteristics.
Preferably, the parameters capable of describing the vehicle kinematic characteristics in the acquired complete kinematic segment of the vehicle further include: idle time tillAcceleration time taccTime of deceleration tdecMaximum acceleration amaxMinimum acceleration aminAverage acceleration a of deceleration sectionavgAnd the average acceleration a of the acceleration sectionavg+。
The method for extracting the vehicle kinematic segment provided by the invention has the following technical effects:
on the basis that the parameter obtained by principal component analysis mainly focuses on the macroscopic description of the kinematics segments, two parameters of an acceleration and deceleration mileage ratio and a segment power spectrum density are introduced as transient characteristic descriptions, the acceleration and deceleration mileage ratio represents the ratio of the mileage driven by a vehicle in kinematics segments during acceleration and deceleration to the total mileage of the segments, namely the occurrence probability of the acceleration and deceleration process, the segment power spectrum density is the power in unit frequency, the intensity of acceleration and deceleration of the vehicle in kinematics segments can be represented, and the method can realize accurate and effective extraction.
The invention also provides vehicle working condition analysis methods, which comprise:
the vehicle kinematics segment extraction method is adopted to respectively extract a plurality of complete kinematics segments,extracting the following six parameters in each complete kinematic segment that can describe the macro features and transient features of the corresponding kinematic segment: driving distance s and average driving speed vavgMaximum traveling speed vmaxAnd a running time period tallAcceleration-deceleration mileage ratio W and segment power spectral density S;
selecting a typical complete kinematic segment from a plurality of complete kinematic segments, wherein the selection process comprises the following steps:
calculating a correlation coefficient η XY of the complete kinematics segment according to the following formula, wherein the complete kinematics segment with the correlation coefficient more than or equal to 50% is a typical complete kinematics segment,
Figure BDA0002242877340000023
wherein, X is parameter data which can describe macro features and transient features of each complete kinematic segment, Y is average parameter data of the complete kinematic segment as a whole, Cov (X, Y) is covariance of X and Y, and D (X) and D (Y) are variances of X and Y respectively;
the method comprises the following steps of training parameters which can describe macro features and transient features of corresponding kinematic segments by adopting typical complete kinematic segments through a neural network, and establishing identification from the parameters to vehicle appearance duration, driving scenes and driving styles, wherein:
based on the length of time t of movementallAnd a given duration t1、t2Wherein, t1＜t2Duration of motion t of a complete kinematic segmentall≤t1Then, it is the movement duration t of the complete kinematics segment for the short tripall＞t1And t isall≤t2If the motion duration is the middle-time trip, the motion duration t of the complete kinematics segmentall＞t2Then long trip, based on the average traveling speed vavgAnd a given velocity v1、v2Wherein v is1＜v2，vavg≤v1If the driving scene is congested, vavg＞v1And v isavg≤v2If the driving scene is normal, vavg＞v2The driving scene is relaxed and is based on the acceleration-deceleration mileage ratio W, the segment power spectral density S and the given W1、W2、S1、S2Wherein W is1＜W2、S1＜S2，W≤W1And S is less than or equal to S1The driving style of the driver is economic, and W is less than or equal to W2And W > W1And S is less than or equal to S1The driving style of the driver is , W > W2And S is less than or equal to S1The driving style of the driver is dynamic, W is less than or equal to W1And S is less than or equal to S2And S > S1The driving style of the driver is economic, and W is less than or equal to W2And W > W1And S is less than or equal to S2And S > S1The driving style of the driver is , W > W2And S is less than or equal to S2And S > S1The driving style of the driver is dynamic, W is less than or equal to W1And S > S2The driving style of the driver is economic, and W is less than or equal to W2And W > W1And S > S2The driving style of the driver is dynamic, W is more than W2And S > S2If the driver is in the power type, the driving style of the driver is changed into the power type;
and acquiring parameters capable of describing vehicle kinematic characteristics in real time, and inputting the parameters into the trained neural network to analyze the vehicle working condition.
The invention is also based on the vehicle working condition analysis method of the extraction method, so the same technical effect is achieved, step is further carried out, a mapping relation of ' characteristic parameters ' -vehicle-road-person ' information is established through a neural network algorithm, reliable identification from the established parameters to the vehicle occurrence time, the driving scene and the driving style is completed, the parameters collected in real time are analyzed, and therefore the identified vehicle travel time, the scene and the driving style information can be provided for a decision control system of the vehicle to use.
Preferably, the neural network is an LVQ neural network.
Preferably, t is1Is 15min, t2Is 1h, v1Is 2m/s, v2Is 5 m/s.
The invention also provides vehicle kinematics segment extraction systems, comprising:
a parameter acquisition module that acquires parameters of a complete kinematic segment of a vehicle that can describe kinematic characteristics of the vehicle, the parameters including at least: driving distance s and average driving speed vavgMaximum traveling speed vmaxLength of operation tallAnd an acceleration α, the complete kinematic segment being an interval segment in which the vehicle speed is zero two adjacent times;
the principal component analysis module is used for carrying out principal component analysis on the obtained parameters to obtain parameters capable of describing the macroscopic features of the complete kinematic segment, and comprises the following steps: driving distance s and average driving speed vavgMaximum traveling speed vmaxAnd a running time period tall；
The transient characteristic parameter calculation module is used for calculating parameters capable of describing transient characteristics of the complete kinematic segment: the acceleration-deceleration mileage ratio W and the segment power spectral density S are as follows:
Figure BDA0002242877340000031
Figure BDA0002242877340000032
wherein N is the number of deceleration times in the complete kinematic segment, TiThe duration of instantaneous acceleration and deceleration processes, s is the mileage of the complete kinematic segment, α is TiAcceleration over a period of time-function, Ff(ω) is a function of acceleration obtained by Fourier transform, and f represents frequency.
Preferably, the parameter obtaining module includes:
the sensor submodule at least comprises a wheel speed sensor, a timing sensor and an acceleration sensor and is used for acquiring original parameters in the running process of the vehicle;
and the calculation submodule is used for calculating the original parameters to obtain parameters capable of describing the vehicle kinematic characteristics.
The invention also provides vehicle working condition analysis systems, which comprise the vehicle kinematic fragment extraction system and further comprise:
the typical complete kinematics segment acquisition module selects a typical complete kinematics segment from a plurality of complete kinematics segments, and the selection process is as follows:
the correlation coefficient η for the complete kinematic segment is calculated as followsXYThe complete kinematics segment with the correlation coefficient more than or equal to 50 percent is a typical complete kinematics segment,
wherein, X is parameter data which can describe macro features and transient features of each complete kinematic segment, Y is average parameter data of the complete kinematic segment as a whole, Cov (X, Y) is covariance of X and Y, and D (X) and D (Y) are variances of X and Y respectively;
the neural network training module is used for carrying out neural network training on parameters which are described by macro features and transient features of corresponding kinematics segments and are typical complete kinematics segments, and establishing the recognition of the parameters to the occurrence time of the vehicle, the driving scene and the driving style, wherein:
based on the length of time t of movementallAnd a given duration t1、t2Wherein, t1＜t2Duration of motion t of a complete kinematic segmentall≤t1Then, it is the movement duration t of the complete kinematics segment for the short tripall＞t1And t isall≤t2If the motion duration is the middle-time trip, the motion duration t of the complete kinematics segmentall＞t2Then long trip, based on the average traveling speed vavgAnd a given velocity v1、v2Wherein v is1＜v2，vavg≤v1If the driving scene is congested, vavg＞v1And v isavg≤v2If the driving scene is normal, vavg＞v2The driving scene is relaxed, andfor acceleration-deceleration mileage ratio W, segment power spectral density S and given value W1、W2、S1、S2Wherein W is1＜W2、S1＜S2，W≤W1And S is less than or equal to S1The driving style of the driver is economic, and W is less than or equal to W2And W > W1And S is less than or equal to S1The driving style of the driver is , W > W2And S is less than or equal to S1The driving style of the driver is dynamic, W is less than or equal to W1And S is less than or equal to S2And S > S1The driving style of the driver is economic, and W is less than or equal to W2And W > W1And S is less than or equal to S2And S > S1The driving style of the driver is , W > W2And S is less than or equal to S2And S > S1The driving style of the driver is dynamic, W is less than or equal to W1And S > S2The driving style of the driver is economic, and W is less than or equal to W2And W > W1And S > S2The driving style of the driver is dynamic, W is more than W2And S > S2If the driver is in the power type, the driving style of the driver is changed into the power type;
and the analysis module acquires parameters capable of describing vehicle kinematic characteristics in real time and inputs the parameters into the trained neural network to analyze the vehicle working condition.
The present invention also provides electronic devices, comprising:
a memory for storing a computer program;
a processor for implementing the steps of the vehicle kinematics segment extraction method or the vehicle behavior analysis method as described above when executing the computer program.
The invention further provides computer-readable storage media, wherein the computer-readable storage media stores computer programs, and the computer programs, when executed by a processor, implement the steps of the vehicle kinematics segment extraction method or the vehicle condition analysis method described above.
The invention provides vehicle kinematic segment extraction systems, vehicle working condition analysis systems, electronic equipment and computer readable storage media, which all have the technical effects.
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In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings needed to be used in the description of the embodiments or the prior art will be briefly introduced below, it is obvious that the drawings in the following description are only embodiments of the present application, and for those skilled in the art, other drawings can be obtained according to the provided drawings without creative efforts.
FIG. 1 is a flowchart of an embodiment of vehicle kinematics segment extraction methods provided by the present invention;
FIG. 2 is a flowchart of an embodiment of an vehicle condition analysis method provided by the present invention;
FIG. 3 is a schematic structural diagram of an embodiment of vehicle kinematic fragment extraction systems provided by the invention;
fig. 4 is a schematic structural diagram of an embodiment of vehicle condition analysis systems provided by the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be described clearly and completely with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only partial embodiments of of the present invention, rather than all embodiments.
Referring to fig. 1 to 4, fig. 1 is a flowchart of an embodiment of vehicle kinematic fragment extraction methods provided by the present invention, fig. 2 is a flowchart of an embodiment of vehicle condition analysis methods provided by the present invention, fig. 3 is a schematic structural diagram of an embodiment of vehicle kinematic fragment extraction systems provided by the present invention, and fig. 4 is a schematic structural diagram of an embodiment of vehicle condition analysis systems provided by the present invention.
With reference to fig. 1, the invention discloses vehicle kinematic segment extraction methods, in embodiments, the method is as follows:
firstly, parameters capable of describing vehicle kinematic characteristics in a complete kinematic segment of a vehicle are obtained, and the parameters at least comprise: driving distance s and average driving speed vavgMaximum traveling speed vmaxLength of operation tallAnd an acceleration α, the complete kinematic segment being an interval segment in which the vehicle speed is zero two adjacent times;
in this embodiment, how to obtain the parameters that can describe the vehicle kinematic features in the complete kinematic segment of the vehicle is not limited, and for example, the corresponding parameters may be obtained by setting sensors.
In the invention, the complete kinematics segments are interval segments with zero vehicle speed in two adjacent times, compared with the prior art, the complete kinematics segments can completely comprise working conditions of acceleration, deceleration, uniform speed and the like, and the motion characteristics can be better described.
Secondly, performing principal component analysis on the obtained parameters to obtain parameters capable of describing the macroscopic features of the complete kinematic segment, wherein the parameters are as follows: driving distance s and average driving speed vavgMaximum traveling speed vmaxAnd a running time period tall；
The principal component analysis method is common analysis methods, which are applied to a plurality of technical fields such as biotechnology field by . in the prior art, it is mentioned that too many characteristic parameters cause large calculation amount, the describing capability of the parameters is different, and the calculation amount is easy to waste, too few characteristic parameters cannot visually describe the actual kinematics characteristics, incomplete expression causes data shortage to the subsequent energy consumption analysis, in order to eliminate the correlation among the variables and finally obtain a plurality of comprehensive variables to reflect most information of the researched problem, the principal component analysis method is adopted to summarize and reflect the essence of things and simplify the subsequent calculation, concretely, the principal component analysis is carried out on 12 characteristic parameters of 30000 pieces of data and single piece of data to obtain the contribution rate of each parameter, and finally the driving mileage s and the average driving speed v are selectedavgMaximum traveling speed vmaxAnd a running time period tallThese 4 characteristic parameters serve as parameters for principal component analysis.
And finally, calculating parameters capable of describing transient characteristics of the complete kinematic segment: the acceleration-deceleration mileage ratio W and the segment power spectral density S are as follows:
Figure BDA0002242877340000062
wherein N is the number of deceleration times in the complete kinematic segment, TiThe duration of the ith instant acceleration and deceleration process, s is the mileage of the complete kinematic segment, α is TiAcceleration in the time period is a function of time, Ff (omega) is a function obtained by Fourier transformation of the acceleration, and f represents frequency.
In the invention, the parameter obtained by principal component analysis mainly focuses on the macroscopic description of the kinematic segments, on the basis, two parameters of an acceleration-deceleration mileage ratio and a segment power spectral density are introduced as transient characteristic descriptions, the acceleration-deceleration mileage ratio represents the ratio of the mileage of a vehicle in kinematic segments during acceleration-deceleration running to the total mileage of the segments, namely the occurrence probability of an acceleration-deceleration process, the segment power spectral density is the power in unit frequency, the intensity of acceleration and deceleration of the vehicle in kinematic segments can be represented, and the method can realize accurate and effective extraction.
In another embodiments, a method for obtaining parameters describing kinematic characteristics of a vehicle in a complete kinematic segment of the vehicle includes:
directly acquiring original parameters of a vehicle in the running process through a sensor, wherein the sensor at least comprises a wheel speed sensor, a timing sensor and an acceleration sensor;
and calculating the original parameters to obtain parameters capable of describing the vehicle kinematic characteristics.
Specifically, the wheel speed sensor can directly acquire the running speed v, and the timing sensor can directly acquire the running time tallAcceleration sensor can directly collectWhen the acceleration is reached, step , the mileage is obtained according to the following formulaThe average traveling speed can be calculated as followsFor maximum driving speed vmaxAnd the signal can be directly acquired by a wheel speed sensor.
Obviously, the method is not limited to this, and for example, a GPS sensor may be provided to directly collect the mileage.
, the parameters of the obtained complete kinematics section of the vehicle capable of describing the kinematics characteristics further include idle time tillAcceleration time taccTime of deceleration tdecMaximum acceleration amaxMinimum acceleration aminAverage acceleration a of deceleration sectionavgAnd the average acceleration a of the acceleration sectionavg+。
With reference to fig. 2, the present invention further provides vehicle condition analysis methods, and embodiments include:
firstly, the vehicle kinematic segment extraction method is adopted to respectively extract a plurality of complete kinematic segments, and the following six parameters which can describe the macro features and the transient features of the corresponding kinematic segments in each complete kinematic segment are extracted: driving distance s and average driving speed vavgMaximum traveling speed vmaxAnd a running time period tallAcceleration-deceleration mileage ratio W and segment power spectral density S;
secondly, selecting a typical complete kinematic segment from a plurality of complete kinematic segments, wherein the selection process is as follows:
the correlation coefficient η for the complete kinematic segment is calculated as followsXYThe complete kinematics segment with the correlation coefficient more than or equal to 50 percent is a typical complete kinematics segment,
Figure BDA0002242877340000065
wherein, X is parameter data which can describe macro features and transient features of each complete kinematic segment, Y is average parameter data of the complete kinematic segment as a whole, Cov (X, Y) is covariance of X and Y, and D (X) and D (Y) are variances of X and Y respectively;
furthermore, the parameters of the typical complete kinematics segment, which can describe the macro features and the transient features of the corresponding kinematics segment, are adopted to carry out neural network training, and the identification from the parameters to the occurrence time of the vehicle, the driving scene and the driving style is established, wherein:
based on the length of time t of movementallAnd a given duration t1、t2Wherein, t1＜t2Duration of motion t of a complete kinematic segmentall≤t1Then, it is the movement duration t of the complete kinematics segment for the short tripall＞t1And t isall≤t2If the motion duration is the middle-time trip, the motion duration t of the complete kinematics segmentall＞t2Then long trip, based on the average traveling speed vavgAnd a given velocity v1、v2Wherein v is1＜v2，vavg≤v1If the driving scene is congested, vavg＞v1And v isavg≤v2If the driving scene is normal, vavg＞v2The driving scene is relaxed and is based on the acceleration-deceleration mileage ratio W, the segment power spectral density S and the given W1、W2、S1、S2Wherein W is1＜W2、S1＜S2，W≤W1And S is less than or equal to S1The driving style of the driver is economic, and W is less than or equal to W2And W > W1And S is less than or equal to S1The driving style of the driver is , W > W2And S is less than or equal to S1The driving style of the driver is dynamic, W is less than or equal to W1And S is less than or equal to S2And S > S1The driving style of the driver is economic, and W is less than or equal to W2And W > W1And S is less than or equal to S2And S > S1The driving style of the driver is , W > W2And S is less than or equal to S2And S > S1The driving style of the driver is dynamic, W is less than or equal to W1And S > S2The driving style of the driver is economic, and W is less than or equal to W2And W > W1And S > S2The driving style of the driver is dynamic, W is more than W2And S > S2If the driver is in the power type, the driving style of the driver is changed into the power type;
and finally, acquiring parameters capable of describing vehicle kinematic characteristics in real time, and inputting the parameters into the trained neural network to analyze the vehicle working condition.
Preferably, the neural network is an LVQ neural network.
Specifically, the LVQ neural network is divided into an offline training part and an online recognition part. The online identification part is used for acquiring parameters capable of describing vehicle kinematic characteristics in real time and inputting the parameters into the trained neural network to analyze the vehicle working condition. The off-line training part extracts 6 kinematic characteristic parameters by using the selected typical kinematic segment data, and then trains the LVQ neural network by using the parameters; the online identification part extracts characteristic parameters of vehicle running data collected in real time, the characteristic parameters are used as an input layer of the LVQ neural network identification model, the working condition type of the segment is further output, real-time identification and updating are carried out, and an identification period can be selected according to the real-time data processing capacity and the length of the collected segment. The trained LVQ neural network can realize on-line recognition of vehicle running data, can judge the vehicle travel time, the scene where the vehicle is located and the driving style information, and can be used by a decision control system of the vehicle.
The vehicle working condition analysis method based on the extraction method provided by the invention has the same technical effects as above, and the mapping relation between the characteristic parameters and the vehicle-road-person information is established through a neural network algorithm in step , so that the reliable identification from the established parameters to the vehicle occurrence time, the driving scene and the driving style is completed, the parameters collected in real time are analyzed, and therefore, the identified vehicle travel time, the scene and the driving style information can be provided for a decision control system of the vehicle to use.
In another cases, t1Is 15min, t2The reaction time is 1 hour,v1is 2m/s, v2The value is not limited to , and may be selected according to specific situations, such as congestion conditions between cities, and thus the value may be different.
With reference to fig. 3, the present invention provides vehicle kinematics fragment extraction systems comprising:
a parameter acquisition module that acquires parameters of a complete kinematic segment of a vehicle that can describe kinematic characteristics of the vehicle, the parameters including at least: driving distance s and average driving speed vavgMaximum traveling speed vmaxLength of operation tallAnd an acceleration α, the complete kinematic segment being an interval segment in which the vehicle speed is zero two adjacent times;
the principal component analysis module is used for carrying out principal component analysis on the obtained parameters to obtain parameters capable of describing the macroscopic features of the complete kinematic segment, and comprises the following steps: driving distance s and average driving speed vavgMaximum traveling speed vmaxAnd a running time period tall；
The transient characteristic parameter calculation module is used for calculating parameters capable of describing transient characteristics of the complete kinematic segment: the acceleration-deceleration mileage ratio W and the segment power spectral density S are as follows:
Figure BDA0002242877340000081
wherein N is the number of deceleration times in the complete kinematic segment, TiThe duration of instantaneous acceleration and deceleration processes, s is the mileage of the complete kinematic segment, α is TiAcceleration over a period of time-function, Ff(ω) is a function of acceleration obtained by Fourier transform, and f represents frequency.
, the parameter obtaining module includes:
the sensor submodule at least comprises a wheel speed sensor, a timing sensor and an acceleration sensor and is used for acquiring original parameters in the running process of the vehicle;
and the calculation submodule is used for calculating the original parameters to obtain parameters capable of describing the vehicle kinematic characteristics.
Specifically, the wheel speed sensor can directly acquire the running speed v, and the timing sensor can directly acquire the running time tallThe acceleration sensor can directly acquire the acceleration, and the driving mileage is obtained according to the following formula in step
Figure BDA0002242877340000083
The average traveling speed can be calculated as followsFor maximum driving speed vmaxAnd the signal can be directly acquired by a wheel speed sensor.
With reference to fig. 4, the present invention further provides kinds of vehicle condition analysis systems, including the above vehicle kinematic segment extraction system, further including:
the typical complete kinematics segment acquisition module selects a typical complete kinematics segment from a plurality of complete kinematics segments, and the selection process is as follows:
the correlation coefficient η for the complete kinematic segment is calculated as followsXYThe complete kinematics segment with the correlation coefficient more than or equal to 50 percent is a typical complete kinematics segment,
Figure BDA0002242877340000085
wherein, X is parameter data which can describe macro features and transient features of each complete kinematic segment, Y is average parameter data of the complete kinematic segment as a whole, Cov (X, Y) is covariance of X and Y, and D (X) and D (Y) are variances of X and Y respectively;
the neural network training module is used for carrying out neural network training on parameters which are described by macro features and transient features of corresponding kinematics segments and are typical complete kinematics segments, and establishing the recognition of the parameters to the occurrence time of the vehicle, the driving scene and the driving style, wherein:
based on the length of time t of movementallAnd a given duration t1、t2Wherein, t1＜t2Duration of motion t of a complete kinematic segmentall≤t1Then, it is the movement duration t of the complete kinematics segment for the short tripall＞t1And t isall≤t2If the motion duration is the middle-time trip, the motion duration t of the complete kinematics segmentall＞t2Then long trip, based on the average traveling speed vavgAnd a given velocity v1、v2Wherein v is1＜v2，vavg≤v1If the driving scene is congested, vavg＞v1And v isavg≤v2If the driving scene is normal, vavg＞v2The driving scene is relaxed and is based on the acceleration-deceleration mileage ratio W, the segment power spectral density S and the given W1、W2、S1、S2Wherein W is1＜W2、S1＜S2，W≤W1And S is less than or equal to S1The driving style of the driver is economic, and W is less than or equal to W2And W > W1And S is less than or equal to S1The driving style of the driver is , W > W2And S is less than or equal to S1The driving style of the driver is dynamic, W is less than or equal to W1And S is less than or equal to S2And S is more than S1, the driving style of the driver is economical, W is less than or equal to W2And W > W1And S is less than or equal to S2And S > S1The driving style of the driver is , W > W2And S is less than or equal to S2And S > S1The driving style of the driver is dynamic, W is less than or equal to W1And S > S2The driving style of the driver is economic, and W is less than or equal to W2And W > W1And S > S2The driving style of the driver is dynamic, W is more than W2And S > S2If the driver is in the power type, the driving style of the driver is changed into the power type;
and the analysis module acquires parameters capable of describing vehicle kinematic characteristics in real time and inputs the parameters into the trained neural network to analyze the vehicle working condition.
The present invention also provides electronic devices, comprising:
a memory for storing a computer program;
a processor for implementing the steps of the vehicle kinematics segment extraction method or the vehicle behavior analysis method as described above when executing the computer program.
, the present invention further provides computer readable storage media having stored thereon a computer program that, when executed by a processor, implements the steps of the vehicle kinematics segment extraction method or the vehicle behaviour analysis method as described above.
Various modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other embodiments without departing from the spirit or scope of the application.

Claims (10)

  1. The vehicle kinematic fragment extraction method is characterized by comprising the following steps:
    acquiring parameters capable of describing vehicle kinematic characteristics in a complete kinematic segment of a vehicle, wherein the parameters at least comprise: driving distance s and average driving speed vavgMaximum traveling speed vmaxLength of operation tallAnd an acceleration α, the complete kinematic segment being an interval segment in which the vehicle speed is zero two adjacent times;
    and performing principal component analysis on the obtained parameters to obtain parameters capable of describing the macroscopic features of the complete kinematic segment, wherein the parameters comprise the following steps: driving distance s and average driving speed vavgMaximum traveling speed vmaxAnd a running time period tall；
    Calculating parameters capable of describing transient characteristics of the complete kinematic segment: the acceleration-deceleration mileage ratio W and the segment power spectral density S are as follows:
    Figure FDA0002242877330000011
    Figure FDA0002242877330000012
    wherein N is the number of deceleration times in the complete kinematic segment, TiThe duration of the ith instant acceleration and deceleration process, s is the mileage of the complete kinematic segment, α is TiAcceleration over a period of time-function, Ff(ω) is a function of acceleration obtained by Fourier transform, and f represents frequency.
  2. 2. The vehicle kinematic segment extraction method according to claim 1, characterized in that the method of obtaining parameters that can describe vehicle kinematic features in a complete kinematic segment of a vehicle comprises:
    directly acquiring original parameters of a vehicle in the running process through a sensor, wherein the sensor at least comprises a wheel speed sensor, a timing sensor and an acceleration sensor;
    and calculating the original parameters to obtain parameters capable of describing the vehicle kinematic characteristics.
  3. 3. The vehicle kinematic segment extraction method according to claim 1, wherein the parameters that can describe the vehicle kinematic features in the acquired complete kinematic segment of the vehicle further comprise: idle time tillAcceleration time taccTime of deceleration tdecMaximum acceleration amaxMinimum acceleration aminAverage acceleration a of deceleration sectionavg-And average acceleration a of acceleration sectionavg+。
  4. The method for analyzing the working condition of the vehicle is characterized by comprising the following steps:
    the vehicle kinematics segment extraction method of any of claims 1-3 is adopted to respectively extract a plurality of complete kinematics segments, and six parameters which can describe macro features and transient features of the corresponding kinematics segment in each complete kinematics segment are extracted, namely driving mileage s and average driving speed vavgMaximum traveling speed vmaxAnd a running time period tallAcceleration-deceleration mileage ratio W and segment power spectral density S;
    selecting a typical complete kinematic segment from a plurality of complete kinematic segments, wherein the selection process comprises the following steps:
    the correlation coefficient η for the complete kinematic segment is calculated as followsXYThe complete kinematics segment with the correlation coefficient more than or equal to 50 percent is a typical complete kinematics segment,
    Figure FDA0002242877330000013
    wherein, X is parameter data which can describe macro features and transient features of each complete kinematic segment, Y is average parameter data of the complete kinematic segment as a whole, Cov (X, Y) is covariance of X and Y, and D (X) and D (Y) are variances of X and Y respectively;
    the method comprises the following steps of training parameters which can describe macro features and transient features of corresponding kinematic segments by adopting typical complete kinematic segments through a neural network, and establishing identification from the parameters to vehicle appearance duration, driving scenes and driving styles, wherein:
    based on the length of time t of movementallAnd a given duration t1、t2Wherein, t1＜t2Duration of motion t of a complete kinematic segmentall≤t1Then, it is the movement duration t of the complete kinematics segment for the short tripall＞t1And t isall≤t2If the motion duration is the middle-time trip, the motion duration t of the complete kinematics segmentall＞t2Then long trip, based on the average traveling speed vavgAnd a given velocity v1、v2Wherein v is1＜v2，vavg≤v1If the driving scene is congested, vavg＞v1And v isavg≤v2If the driving scene is normal, vavg＞v2The driving scene is relaxed and is based on the acceleration-deceleration mileage ratio W, the segment power spectral density S and the given W1、W2、S1、S2Wherein W is1＜W2、S1＜S2，W≤W1And S is less than or equal to S1The driving style of the driver is economic, and W is less than or equal to W2And W > W1And S is less than or equal to S1The driving style of the driver is , W > W2And S is less than or equal to S1The driving style of the driver is dynamic, W is less than or equal to W1And S is less than or equal to S2And S > S1The driving style of the driver is economic, and W is less than or equal to W2And W > W1And S is less than or equal to S2And S > S1The driving style of the driver is , W > W2And S is less than or equal to S2And S > S1The driving style of the driver is dynamic, W is less than or equal to W1And S > S2The driving style of the driver is economic, and W is less than or equal to W2And W > W1And S > S2The driving style of the driver is dynamic, W is more than W2And S > S2If the driver is in the power type, the driving style of the driver is changed into the power type;
    and acquiring parameters capable of describing vehicle kinematic characteristics in real time, and inputting the parameters into the trained neural network to analyze the vehicle working condition.
  5. 5. The vehicle condition analysis method according to claim 4, wherein the neural network employs an LVQ neural network.
  6. 6. The vehicle condition analysis method according to claim 4, characterized in that t1Is 15min, t2Is 1h, v1Is 2m/s, v2Is 5 m/s.
  7. The extraction system of vehicle kinematic fragments of 7, , comprising:
    a parameter acquisition module that acquires parameters of a complete kinematic segment of a vehicle that can describe kinematic characteristics of the vehicle, the parameters including at least: driving distance s and average driving speed vavgMaximum traveling speed vmaxLength of operation tallAnd an acceleration α, the complete kinematic segment being an interval segment in which the vehicle speed is zero two adjacent times;
    the principal component analysis module is used for carrying out principal component analysis on the obtained parameters to obtain parameters capable of describing the macroscopic features of the complete kinematic segment, and comprises the following steps: driving distance s and average driving speed vavgMaximum traveling speed vmaxAnd a running time period tall；
    The transient characteristic parameter calculation module is used for calculating parameters capable of describing transient characteristics of the complete kinematic segment: the acceleration-deceleration mileage ratio W and the segment power spectral density S are as follows:
    Figure FDA0002242877330000021
    Figure FDA0002242877330000022
    wherein N is the number of deceleration times in the complete kinematic segment, TiThe duration of instantaneous acceleration and deceleration processes, s is the mileage of the complete kinematic segment, α is TiAcceleration over a period of time-function, Ff(ω) is a function of acceleration obtained by Fourier transform, and f represents frequency.
  8. 8. The vehicle kinematic fragment extraction system of claim 7, wherein the parameter acquisition module comprises:
    the sensor submodule at least comprises a wheel speed sensor, a timing sensor and an acceleration sensor and is used for acquiring original parameters in the running process of the vehicle;
    and the calculation submodule is used for calculating the original parameters to obtain parameters capable of describing the vehicle kinematic characteristics.
  9. The vehicle behavior analysis system of claim 9 or , comprising the vehicle kinematics segment extraction system of claim 7 or 8, further comprising:
    the typical complete kinematics segment acquisition module selects a typical complete kinematics segment from a plurality of complete kinematics segments, and the selection process is as follows:
    the correlation coefficient η for the complete kinematic segment is calculated as followsXYThe complete kinematics segment with the correlation coefficient more than or equal to 50 percent is a typical complete kinematics segment,
    Figure FDA0002242877330000031
    wherein, X is parameter data which can describe macro features and transient features of each complete kinematic segment, Y is average parameter data of the complete kinematic segment as a whole, Cov (X, Y) is covariance of X and Y, and D (X) and D (Y) are variances of X and Y respectively;
    the neural network training module is used for carrying out neural network training on parameters which are described by macro features and transient features of corresponding kinematics segments and are typical complete kinematics segments, and establishing the recognition of the parameters to the occurrence time of the vehicle, the driving scene and the driving style, wherein:
    based on the length of time t of movementallAnd a given duration t1、t2Wherein, t1＜t2Duration of motion t of a complete kinematic segmentall≤t1Then, it is the movement duration t of the complete kinematics segment for the short tripall＞t1And t isall≤t2If the motion duration is the middle-time trip, the motion duration t of the complete kinematics segmentall＞t2Then long trip, based on the average traveling speed vavgAnd a given velocity v1、v2Wherein v is1＜v2，vavg≤v1If so, the driving scene is a congestion,vavg＞v1and v isavg≤v2If the driving scene is normal, vavg＞v2The driving scene is relaxed and is based on the acceleration-deceleration mileage ratio W, the segment power spectral density S and the given W1、W2、S1、S2Wherein W is1＜W2、S1＜S2，W≤W1And S is less than or equal to S1The driving style of the driver is economic, and W is less than or equal to W2And W > W1And S is less than or equal to S1The driving style of the driver is , W > W2And S is less than or equal to S1The driving style of the driver is dynamic, W is less than or equal to W1And S is less than or equal to S2And S > S1The driving style of the driver is economic, and W is less than or equal to W2And W > W1And S is less than or equal to S2And S > S1The driving style of the driver is , W > W2And S is less than or equal to S2And S > S1The driving style of the driver is dynamic, W is less than or equal to W1And S > S2The driving style of the driver is economic, and W is less than or equal to W2And W > W1And S > S2The driving style of the driver is dynamic, W is more than W2And S > S2If the driver is in the power type, the driving style of the driver is changed into the power type;
    and the analysis module acquires parameters capable of describing vehicle kinematic characteristics in real time and inputs the parameters into the trained neural network to analyze the vehicle working condition.
  10. 10, electronic device, comprising:
    a memory for storing a computer program;
    a processor for implementing the steps of the method as claimed in any of claims 1-6 as claimed in claim when executing said computer program.
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