CN109872075A - A kind of appraisal procedure and system of the driving behavior of oil consumption correlation - Google Patents
A kind of appraisal procedure and system of the driving behavior of oil consumption correlation Download PDFInfo
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Abstract
The present invention provides the appraisal procedure and system of a kind of oil consumption correlation driving behavior, this method are as follows: obtains driving behavior data corresponding to each section of default mileage of vehicle to be assessed, wherein the total kilometres of vehicle to be assessed are divided into n sections of default mileages in advance.Based on driving behavior data, the corresponding driving behavior index of difference driving behavior in each default mileage is calculated.Using driving behavior index as the input value of preset oil consumption Rating Model, the oil consumption correlation driving behavior evaluation total score of vehicle to be assessed is calculated.In the present solution, the corresponding relationship being in advance based between different driving behaviors and oil consumption establishes oil consumption Rating Model.By the driving behavior data for extracting vehicle to be assessed, based on the corresponding driving behavior index of the different driving behaviors of driving behavior data calculating, total score is evaluated into the oil consumption correlation driving behavior that vehicle to be assessed is calculated as the input value of oil consumption Rating Model in driving behavior index, the objective degree and accuracy of vehicle oil consumption management can be improved.
Description
Technical field
The present invention relates to vehicle data processing technology fields, and in particular to a kind of appraisal procedure of oil consumption correlation driving behavior
And system.
Background technique
Transportation trade relatively conventional at present is vehicle transport, during using vehicle transport, to transport vehicle
The oil consumption management of team becomes one of the Important Problems of each carrier concern.
During Automobile Transportation, artificial experience is generally relied on for the oil consumption management of transport column and is managed, mainly
By manually studying the influence of transport distance and haulage time to oil consumption.But on the one hand, due to the influence of artificial subjectivity, because
This is low to transport column progress oil consumption management objective degree by artificial experience.On the other hand, only pass through research transport distance and fortune
Influence of the defeated time to oil consumption is not studied influence of the driving behavior to oil consumption of driver in the process of moving, is caused to fortune
It is low that defeated fleet carries out assessment accuracy when oil consumption assessment.
Therefore, the problems such as existing low low with accuracy there are objective degree to the mode of transport column progress oil consumption management.
Summary of the invention
In view of this, the embodiment of the present invention provides the appraisal procedure and system of a kind of oil consumption correlation driving behavior, to solve
The problems such as existing low low with accuracy there are objective degree to the mode of transport column progress oil consumption management.
To achieve the above object, the embodiment of the present invention provides the following technical solutions:
First aspect of the embodiment of the present invention discloses a kind of appraisal procedure of oil consumption correlation driving behavior, the method packet
It includes:
Obtain driving behavior data corresponding to each section of default mileage of vehicle to be assessed, wherein in advance will it is described to
The total kilometres of assessment vehicle are divided into the n sections of default mileages；
Based on the driving behavior data, the corresponding driving row of the difference driving behavior in each default mileage is calculated
For index；
Using the driving behavior index as the input value of preset oil consumption Rating Model, the vehicle to be assessed is calculated
Oil consumption correlation driving behavior evaluate total score, wherein the oil consumption Rating Model is by the driving behavior sample based on sample vehicle
This index and oil consumption related coefficient construct to obtain.
Optionally, the oil consumption Rating Model by based on sample vehicle driving behavior sample index and oil consumption related coefficient
Building obtains, comprising:
Obtain the traveling sample data of the sample vehicle；
The data prediction traveling sample data that obtains that treated is carried out to the traveling sample data；
Determine multiple driving behaviors；
The total kilometres of the sample vehicle are divided into the m sections of default mileages, and calculates the sample vehicle and exists
Oil consumption in each default mileage；
For each default mileage, traveling sample data that treated based on described in calculates each driving behavior pair
The driving behavior sample index answered；
For each default mileage, it is based on the driving behavior sample index and oil consumption, calculates and goes with the driving
For the corresponding oil consumption related coefficient of sample index；
Using the driving behavior sample index and oil consumption related coefficient as the weight factor of Lasso regression model, to institute
It states Lasso regression model progress Lasso regression training and obtains oil consumption scoring submodel；
In conjunction with oil consumption scoring submodel and preset averaged submodel, the oil consumption Rating Model is obtained.
Optionally, the process of oil consumption of the calculating sample vehicle in each default mileage includes:
It is based onCalculate oil consumption Y of the sample vehicle in each default mileage, wherein t is institute
It states and presets time required for mileage, F described in sample vehicle drivingRFor the transient fuel consumption rate of the sample vehicle.
Optionally, described to be based on the driving behavior sample index and oil consumption, it calculates and the driving behavior sample index
Corresponding oil consumption related coefficient, comprising:
It is based onIt is corresponding to calculate each driving behavior sample index
Oil consumption related coefficient Ai, wherein N is the total number of driving behavior sample index, and Y is the sample vehicle in described preset
Oil consumption in journey, XiFor driving behavior sample index.
Optionally, described using the driving behavior index as the input value of preset oil consumption Rating Model, it is calculated
Total score is evaluated in the oil consumption correlation driving behavior of the vehicle to be assessed, comprising:
Using the driving behavior index as the input value of oil consumption scoring submodel, obtain in each default mileage
Oil consumption correlation driving behavior scoring；
N oil consumption correlation driving behavior is scored the input value as averaged submodel, be calculated described in
Total score is evaluated in the oil consumption correlation driving behavior for assessing vehicle；
Wherein, the oil consumption Rating Model is made of oil consumption scoring submodel and the averaged submodel.
Optionally, the input value that n oil consumption correlation driving behavior is scored as the averaged submodel,
The oil consumption correlation driving behavior evaluation total score of the vehicle to be assessed is calculated, comprising:
The n oil consumption correlation driving behavior is scored and is used as the averaged submodelInput
The oil consumption correlation driving behavior evaluation total score G of the vehicle to be assessed is calculated in valuei, wherein j is the default mileage
Serial number, YpjFor the corresponding oil consumption correlation driving behavior scoring of j-th of default mileage.
Second aspect of the embodiment of the present invention discloses a kind of assessment system of oil consumption correlation driving behavior, the system packet
It includes:
Acquiring unit, for obtaining driving behavior data corresponding to each section of default mileage of vehicle to be assessed, wherein
The total kilometres of the vehicle to be assessed are divided into the n sections of default mileages in advance；
Computing unit calculates the different driving rows in each default mileage for being based on the driving behavior data
For corresponding driving behavior index；
Score unit, for calculating using the driving behavior index as the input value of preset oil consumption Rating Model
Total score is evaluated in oil consumption correlation driving behavior to the vehicle to be assessed, wherein the oil consumption Rating Model is by being based on sample vehicle
Driving behavior sample index and oil consumption related coefficient construct to obtain.
Optionally, the scoring unit includes:
Module is obtained, for obtaining the traveling sample data of the sample vehicle；
Processing module, for obtaining to traveling sample data progress data prediction, treated travels sample number
According to；
Determining module, for determining multiple driving behaviors；
Oil consumption computing module for the total kilometres of the sample vehicle to be divided into the m sections of default mileages, and is counted
Calculate oil consumption of the sample vehicle in each default mileage；
First computing module, for being directed to each default mileage, treated based on described in travels sample data, meter
Calculate the corresponding driving behavior sample index of each driving behavior；
Second computing module, for being based on the driving behavior sample index and oil consumption for each default mileage,
Calculate oil consumption related coefficient corresponding with the driving behavior sample index；
Training module, for using the driving behavior sample index and oil consumption related coefficient as Lasso regression model
Weight factor carries out Lasso regression training to the Lasso regression model and obtains oil consumption scoring submodel；
Module is constructed, for obtaining described in conjunction with oil consumption scoring submodel and preset averaged submodel
Oil consumption Rating Model.
Optionally, the scoring unit includes:
First grading module, for obtaining every using the driving behavior index as the input value of oil consumption scoring submodel
Oil consumption correlation driving behavior scoring in the one default mileage；
Second grading module, the input for scoring n oil consumption correlation driving behavior as averaged submodel
The oil consumption correlation driving behavior evaluation total score of the vehicle to be assessed is calculated in value；
Wherein, the oil consumption Rating Model is made of oil consumption scoring submodel and the averaged submodel.
Optionally, second grading module, being specifically used for will be described in n oil consumption correlation driving behavior scoring conduct
Averaged submodelInput value, be calculated the vehicle to be assessed oil consumption correlation driving behavior evaluation
Total score Gi, wherein j is the serial number of the default mileage, YpjIt is commented for the corresponding oil consumption correlation driving behavior of j-th of default mileage
Point.
Appraisal procedure and system based on a kind of oil consumption correlation driving behavior that the embodiments of the present invention provide, this method
Are as follows: obtain driving behavior data corresponding to each section of default mileage of vehicle to be assessed, wherein in advance by vehicle to be assessed
Total kilometres are divided into n sections of default mileages.Based on driving behavior data, different driving behaviors pair in each default mileage are calculated
The driving behavior index answered.Using driving behavior index as the input value of preset oil consumption Rating Model, it is calculated to be assessed
Total score is evaluated in the oil consumption correlation driving behavior of vehicle.In the present solution, being in advance based on pair between different driving behaviors and oil consumption
It should be related to and establish oil consumption Rating Model.By extracting the driving behavior data of vehicle to be assessed, calculated based on driving behavior data
The corresponding driving behavior index of different driving behaviors, is calculated driving behavior index as the input value of oil consumption Rating Model
Total score is evaluated in the oil consumption correlation driving behavior of vehicle to be assessed, can improve the objective degree and accuracy of vehicle oil consumption management.
Detailed description of the invention
In order to more clearly explain the embodiment of the invention or the technical proposal in the existing technology, to embodiment or will show below
There is attached drawing needed in technical description to be briefly described, it should be apparent that, the accompanying drawings in the following description is only this
The embodiment of invention for those of ordinary skill in the art without creative efforts, can also basis
The attached drawing of offer obtains other attached drawings.
Fig. 1 is a kind of appraisal procedure flow chart of oil consumption correlation driving behavior provided in an embodiment of the present invention；
Fig. 2 is the flow chart provided in an embodiment of the present invention for establishing oil consumption Rating Model；
Fig. 3 is a kind of structural block diagram of the assessment system of oil consumption correlation driving behavior provided in an embodiment of the present invention；
Fig. 4 is a kind of structural block diagram of the assessment system of oil consumption correlation driving behavior provided in an embodiment of the present invention；
Fig. 5 is a kind of structural block diagram of the assessment system of oil consumption correlation driving behavior provided in an embodiment of the present invention.
Specific embodiment
Following will be combined with the drawings in the embodiments of the present invention, and technical solution in the embodiment of the present invention carries out clear, complete
Site preparation description, it is clear that described embodiments are only a part of the embodiments of the present invention, instead of all the embodiments.It is based on
Embodiment in the present invention, it is obtained by those of ordinary skill in the art without making creative efforts every other
Embodiment shall fall within the protection scope of the present invention.
In this application, the terms "include", "comprise" or any other variant thereof is intended to cover non-exclusive inclusion,
So that the process, method, article or equipment for including a series of elements not only includes those elements, but also including not having
The other element being expressly recited, or further include for elements inherent to such a process, method, article, or device.Do not having
There is the element limited in the case where more limiting by sentence "including a ...", it is not excluded that in the mistake including the element
There is also other identical elements in journey, method, article or equipment.
It can be seen from background technology that, artificial experience is generally relied on for the oil consumption management of transport column at present and is managed, it is main
It will be by manually studying the influence of transport distance and haulage time to oil consumption.But it studies due to the influence of artificial subjectivity and not
Influence of the driving behavior of driver to oil consumption in the process of moving, the existing mode for carrying out oil consumption management to transport column exist
The problems such as objective degree is low low with accuracy.
Therefore, the embodiment of the present invention provides the appraisal procedure and system of a kind of oil consumption correlation driving behavior, is in advance based on not
Oil consumption Rating Model is established with the corresponding relationship between driving behavior and oil consumption.Calculate the different driving behaviors pair of vehicle to be assessed
The driving behavior index answered, the oil of vehicle to be assessed is calculated using driving behavior index as the input value of oil consumption Rating Model
It consumes related driving behavior and evaluates total score, to improve the objective degree and accuracy of vehicle oil consumption management.
With reference to Fig. 1, a kind of appraisal procedure flow chart of oil consumption correlation driving behavior provided in an embodiment of the present invention is shown,
It the described method comprises the following steps:
Step S101: driving behavior data corresponding to each section of default mileage of vehicle to be assessed are obtained.
During implementing step S101, the vehicle of the vehicle to be assessed is obtained from car networking data storage backstage
Running data, the vehicle operation data are acquired by vehicle dress remote information processor (Telematics BOX, T-BOX).Institute
State total kilometres and the driving behavior data that vehicle operation data includes at least the vehicle to be assessed.
The total kilometres of the vehicle to be assessed are divided into the n sections of default mileages, for example assume that default mileage is
100 kilometers, the total kilometres of vehicle to be assessed are 10000 kilometers, then divide equally the total kilometres of the vehicle to be assessed
For 100 sections of default mileages.
It should be noted that if the aliquant default mileage of the total kilometres of the vehicle to be assessed, then n only takes
Integer part ignores complementing part.Such as: assuming that default mileage is 100 kilometers, the total kilometres of vehicle to be assessed are
10090 kilometers, the total kilometres are equal to more than 100 90 divided by the default mileage, then n is equal to 100, ignore the remainder
90。
It should be noted that needing after the vehicle operation data for obtaining the vehicle to be assessed to the vehicle row
It sails data to be pre-processed, pretreatment mode includes at least: outlier processing and missing values processing.
Pretreated mode is carried out to the vehicle operation data to see detailed description below.
Outlier processing: the abnormal data in the vehicle operation data is deleted.It should be noted that acquiring and transmitting
When vehicle operation data, vehicle operation data error of transmission will lead to there are many reason.But for occupy-place or indicate particular meaning,
It is possible that exceptional value in vehicle operation data after acquisition, and exceptional value for the present embodiments relate to data processing
Without in all senses, it is therefore desirable to delete exceptional value.Such as it is by the way of screening that speed in the vehicle operation data is different
Often, the row of neutral signal exceptional value screens out.
Missing values processing: if occurring the blank signal of any data type in the vehicle operation data, it is based on the sky
The preceding signal value and latter signal value of white signal carry out interpolation filling to the blank signal.It should be noted that in advance really
The fixed vehicle driving signal for needing to shift to an earlier date, such as vehicle identification number (Vehicle, IdentificationNumber, VIN),
Time signal, milimeter number (odometer, ODO), speed signal, engine rotational speed signal, neutral signal, gas pedal aperture letter
Number and brake pedal opening amount signal etc..Assuming that partial blank value occurs in speed signal, the preceding signal value based on blank value is with after
One signal value carries out interpolation filling to the blank value.
Step S102: being based on the driving behavior data, calculates the different driving behaviors pair in each default mileage
The driving behavior index answered.
During implementing step S102, based on the feature of different driving behaviors, the vehicle to be assessed is determined
Driving behavior, the type of driving behavior includes: that neutral position sliding, non-neutral position sliding, excess revolutions, idling be too long and parking Hong throttle etc.
Driving behavior.Based on the corresponding driving behavior data of the driving behavior, calculate different in each default mileage
The corresponding driving behavior index of driving behavior.It should be noted that the driving behavior data are as follows: the vehicle to be assessed is expert at
During sailing, driving data when being driven using different driving behaviors, such as time of neutral position sliding, non-neutral position sliding when
Between, the revolving speed of excess revolutions and the time of idling etc..
Preferably to illustrate the driving behavior for how determining vehicle to be assessed, and the different driving behaviors of explanation
Corresponding driving behavior index.Following driving behaviors for illustrating how to determine vehicle to be assessed by example A1-A5, pass through
The corresponding driving behavior index of the different driving behaviors of example B1-B5 illustration.
Example A1-A5:
A1, driving behavior: neutral position sliding.Determine condition: the initial velocity of vehicle is greater than preset value, fast in the process of moving
Degree is greater than 0, and gas pedal aperture and brake pedal aperture are all 0, neutral signal 1.
A2, driving behavior: non-neutral position sliding.Determine condition: the initial velocity of vehicle is greater than preset value, in the process of moving
Speed is greater than 0, and gas pedal aperture and brake pedal aperture are all 0, neutral signal 0.
A3, driving behavior: excess revolutions.Determine condition: vehicle motor revolving speed is greater than preset value and continues preset time or more.
A4, driving behavior: idling is too long.Determine condition: the vehicle idling time is more than threshold value.
A5, driving behavior: parking Hong throttle.Determine condition: the speed of vehicle is 0, and gas pedal aperture is greater than 0.
Example B1-B5:
B1, driving behavior: neutral position sliding.Driving behavior index: neutral position sliding time accounting X11, neutral position sliding time average
X12, neutral position sliding time variance X13, neutral position sliding is apart from accounting X14, neutral position sliding is apart from mean value X15, neutral position sliding distance variance
X16。
B2, driving behavior: non-neutral position sliding.Driving behavior index: non-neutral gear coasting time accounting X21, non-neutral position sliding when
Between mean value X22, non-neutral gear coasting time variance X23, non-neutral gear coasting distance accounting X24, non-neutral gear coasting distance mean value X25, non-empty
Keep off coasting distance variance X26。
B3, driving behavior: excess revolutions.Driving behavior index: excess revolutions mean speed mean value X31, excess revolutions mean speed variance X32、
Excess revolutions time accounting X33, excess revolutions time average X34, excess revolutions time variance X35。
B4, driving behavior: idling is too long.Driving behavior index: idling crosses long-time accounting X41, the too long time average of idling
X42, the too long time variance X of idling43, idling cross for a long time and X44。
B5, driving behavior: parking Hong throttle.Driving behavior index: parking Hong throttle is averaged gas pedal X51, parking Hong oil
Door maximum throttle pedal X52, parking Hong throttle when gas pedal variance X53, parking Hong throttle time X54, parking Hong the throttle time
Accounting X55, parking Hong throttle number X56。
It should be noted that content shown in above-mentioned example A1-A5 and example B1-B5 is only used for illustrating.
Step S103: using the driving behavior index as the input value of preset oil consumption Rating Model, institute is calculated
State the oil consumption correlation driving behavior evaluation total score of vehicle to be assessed.
During implementing step S103, the driving behavior sample data based on sample vehicle is preselected, calculates institute
Driving behavior sample index and the oil consumption related coefficient of sample vehicle are stated, and according to the driving behavior sample index and oil consumption phase
Relationship number constructs to obtain the oil consumption Rating Model.Wherein.The oil consumption Rating Model is by oil consumption scoring submodel and seeks putting down
Mean value submodel is constituted.
During total score is evaluated in the oil consumption correlation driving behavior for specifically calculating the vehicle to be assessed, it is based on above-mentioned step
The total kilometres by the vehicle to be assessed being related in rapid S101 are divided into the n sections of default mileages, will be each described
Input value of the corresponding driving behavior index as oil consumption scoring submodel in default mileage, obtain it is each it is described preset in
N oil consumption correlation driving behavior scoring is always obtained in oil consumption correlation driving behavior scoring in journey.N oil consumption correlation is driven
The oil consumption correlation driving behavior of the vehicle to be assessed is calculated in input value of the behavior scoring as averaged submodel
Evaluate total score.Wherein, shown in the averaged submodel such as formula (1), in the formula (1), GiIt is described to be evaluated
Estimate the oil consumption correlation driving behavior evaluation total score of vehicle, j is the serial number of the default mileage, YpjIt is corresponding for j-th of default mileage
Oil consumption correlation driving behavior scoring.
It should be noted that the total kilometres of the vehicle to be assessed are divided into the n sections of default fare registers, to described
Each section of default mileage in n sections of default mileages is ranked up according to tandem, and with the sequence of serial number 1 to n.
In embodiments of the present invention, the corresponding relationship being in advance based between different driving behaviors and oil consumption establishes oil consumption scoring
Model.By extracting the driving behavior data of vehicle to be assessed, it is corresponding that different driving behaviors are calculated based on driving behavior data
The oil consumption phase of vehicle to be assessed is calculated using driving behavior index as the input value of oil consumption Rating Model for driving behavior index
It closes driving behavior and evaluates total score, the objective degree and accuracy of vehicle oil consumption management can be improved.
The building process for the oil consumption Rating Model that above-mentioned steps S103 is related to shows the embodiment of the present invention with reference to Fig. 2
The flow chart for establishing oil consumption Rating Model provided, comprising the following steps:
Step S201: the traveling sample data of the sample vehicle is obtained.
During implementing step S201, sample vehicle is pre-selected, and obtains the traveling of the sample vehicle
Sample data.
Step S202: the data prediction traveling sample data that obtains that treated is carried out to the traveling sample data.
During implementing step S202, to the treatment process for travelling sample data referring to aforementioned present invention
The open corresponding content of step S101 of implementation example figure 1.
Step S203: multiple driving behaviors are determined.
During implementing step S203, the type of the driving behavior is referring to embodiments of the present invention Fig. 1
The open corresponding content of step S102, this is no longer going to repeat them.
Step S204: the total kilometres of the sample vehicle are divided into the m sections of default mileages, and calculate the sample
Oil consumption of this vehicle in each default mileage.
During implementing step S204, the sample vehicle is calculated each described default based on formula (2)
Oil consumption Y in mileage.In the formula (2), t is that time required for mileage, F are preset described in the sample vehicle drivingR
For the transient fuel consumption rate of the sample vehicle.
Step S205: being directed to each default mileage, and traveling sample data that treated based on described in, calculating is each driven
Sail the corresponding driving behavior sample index of behavior.
During implementing step S205, the particular content of driving behavior sample index is referring to aforementioned present invention reality
The open corresponding content of step S102 of example diagram 1 is applied, this is no longer going to repeat them.
Step S206: being directed to each default mileage, is based on the driving behavior sample index and oil consumption, calculating and institute
State the corresponding oil consumption related coefficient of driving behavior sample index.
During implementing step S207, it is corresponding that each driving behavior sample index is calculated based on formula (3)
Oil consumption related coefficient Ai。
In the formula (3), N is the total number of driving behavior sample index, and Y is the sample vehicle described default
Oil consumption in mileage, XiFor driving behavior sample index.
The corresponding oil consumption related coefficient of driving behavior sample index how is calculated more preferably to illustrate, in conjunction with above-mentioned Fig. 1
27 driving behavior indexs in disclosed example B1-B5, are exemplified below:
By the neutral position sliding time accounting X11It is substituted into above-mentioned formula (3) with Y, neutral position sliding time accounting is calculated
With the related coefficient A of oil consumption11.By the neutral position sliding time average X12It is substituted into above-mentioned formula (3) with Y, obtains neutral position sliding
The related coefficient A of time average and oil consumption12。
Similarly, driving behavior can be calculated by other driving behavior sample index and oil consumption being substituted into above-mentioned formula (3)
The corresponding oil consumption related coefficient of sample index.Related coefficient is bigger, illustrates that influence of the driving behavior to oil consumption is bigger.
Step S207: using the driving behavior sample index and oil consumption related coefficient as the weight of Lasso regression model
The factor carries out Lasso regression training to the Lasso regression model and obtains oil consumption scoring submodel.
During implementing step S207, by above-mentioned steps S206 the driving behavior sample index and oil
Weight factor of the related coefficient as Lasso regression model is consumed, Lasso regression training is carried out to the Lasso regression model and is obtained
To oil consumption scoring submodel.
It should be noted that the variable characterized by the driving behavior sample index, oil consumption correlation driving behavior scoring are
Outcome variable constructs the oil consumption scoring submodel.Shown in the loss function such as formula (4) that the Lasso is returned.
In the formula (4), X is sample vector, and Y is output variable, θ is coefficient vector, n is sample dimension, a is normal
Number system number, | | θ | |1For L1Norm.It should be noted that a is adjusted according to the actual situation by technical staff.
It should be noted that Lasso recurrence is to obtain each driving behavior sample index and oil consumption based on above-mentioned formula (4)
Respective function relationship.
Step S208: in conjunction with oil consumption scoring submodel and preset averaged submodel, the oil consumption is obtained
Rating Model.
During implementing step S208, using oil consumption scoring submodel as the first of oil consumption Rating Model
Straton model, using the averaged submodel as the second straton model of the oil consumption Rating Model, building obtains institute
State oil consumption Rating Model.Shown in the averaged submodel such as above-mentioned formula (1).
Implement in example in the present invention, by the traveling sample data and oil consumption related coefficient of sample vehicle, constructs oil consumption
Score submodel.Oil consumption scoring submodel and preset averaged submodel are combined and obtain oil consumption Rating Model.It will drive
It is total to sail the oil consumption correlation driving behavior evaluation that vehicle to be assessed is calculated as the input value of oil consumption Rating Model in behavioral indicator
Point, the objective degree and accuracy of vehicle oil consumption management can be improved.
A kind of appraisal procedure of the related driving behavior of oil consumption provided to the embodiments of the present invention is corresponding, with reference to Fig. 3,
The embodiment of the invention also provides a kind of structural block diagram of the assessment system of oil consumption correlation driving behavior, the system comprises:
Acquiring unit 301, for obtaining driving behavior data corresponding to each section of default mileage of vehicle to be assessed,
In, the total kilometres of the vehicle to be assessed are divided into the n sections of default mileages in advance.
In the concrete realization, it obtains the detailed process of driving behavior data and pretreated mistake is carried out to driving behavior data
Journey, the corresponding content of step S101 disclosed referring specifically to embodiments of the present invention Fig. 1.
Computing unit 302 calculates different in each default mileage drive for being based on the driving behavior data
The corresponding driving behavior index of behavior.In the concrete realization, the detailed process of driving behavior index is obtained referring to aforementioned present invention
The open corresponding content of step S102 of implementation example figure 1.
Score unit 303, for calculating using the driving behavior index as the input value of preset oil consumption Rating Model
Obtain the oil consumption correlation driving behavior evaluation total score of the vehicle to be assessed, wherein the oil consumption Rating Model is by being based on sample
The driving behavior sample index of vehicle and oil consumption related coefficient construct to obtain.
In embodiments of the present invention, the corresponding relationship being in advance based between different driving behaviors and oil consumption establishes oil consumption scoring
Model.By extracting the driving behavior data of vehicle to be assessed, it is corresponding that different driving behaviors are calculated based on driving behavior data
The oil consumption phase of vehicle to be assessed is calculated using driving behavior index as the input value of oil consumption Rating Model for driving behavior index
It closes driving behavior and evaluates total score, the objective degree and accuracy of vehicle oil consumption management can be improved.
With reference to Fig. 4, a kind of structure of the assessment system of oil consumption correlation driving behavior provided in an embodiment of the present invention is shown
Block diagram, the scoring unit 303 include: to obtain module 3031, processing module 3032, determining module 3033, oil consumption computing module
3034, the first computing module 3035, the second computing module 3036, training module 3037 and building module 3038.
Module 3031 is obtained, for obtaining the traveling sample data of the sample vehicle.
Processing module 3032, for obtaining to traveling sample data progress data prediction, treated travels sample
Data.
Determining module 3033, for determining multiple driving behaviors.
Oil consumption computing module 3034, for the total kilometres of the sample vehicle to be divided into the m sections of default mileages,
And calculate oil consumption of the sample vehicle in each default mileage.
Preferably, the oil consumption computing module 3034 is specifically used for existing based on above-mentioned formula (2) calculating sample vehicle
Oil consumption in each default mileage.
First computing module 3035, for being directed to each default mileage, treated based on described in travels sample number
According to calculating the corresponding driving behavior sample index of each driving behavior.
Second computing module 3036, for being directed to each default mileage, based on the driving behavior sample index and
Oil consumption calculates oil consumption related coefficient corresponding with the driving behavior sample index.
Preferably, second computing module 3036 is specifically used for calculating each driving behavior sample based on above-mentioned formula (3)
The corresponding oil consumption related coefficient of this index.
Training module 3037, for returning mould using the driving behavior sample index and oil consumption related coefficient as Lasso
The weight factor of type carries out Lasso regression training to the Lasso regression model and obtains oil consumption scoring submodel.
Module 3038 is constructed, for obtaining in conjunction with oil consumption scoring submodel and preset averaged submodel
The oil consumption Rating Model.
Implement in example in the present invention, by the traveling sample data and oil consumption related coefficient of sample vehicle, constructs oil consumption
Score submodel.Oil consumption scoring submodel and preset averaged submodel are combined and obtain oil consumption Rating Model.It will drive
It is total to sail the oil consumption correlation driving behavior evaluation that vehicle to be assessed is calculated as the input value of oil consumption Rating Model in behavioral indicator
Point, the objective degree and accuracy of vehicle oil consumption management can be improved.
With reference to Fig. 5, a kind of structure of the assessment system of oil consumption correlation driving behavior provided in an embodiment of the present invention is shown
Block diagram, the scoring unit 303 include:
First grading module 3039, for obtaining using the driving behavior index as the input value of oil consumption scoring submodel
To the oil consumption correlation driving behavior scoring in each default mileage.
Second grading module 3010, for scoring n oil consumption correlation driving behavior as averaged submodel
The oil consumption correlation driving behavior evaluation total score of the vehicle to be assessed is calculated in input value.
Wherein, the oil consumption Rating Model is made of oil consumption scoring submodel and the averaged submodel.
Preferably, second grading module 3010 is specifically used for conduct that the n oil consumption correlation driving behavior is scored
The oil consumption correlation driving behavior evaluation total score G of the vehicle to be assessed is calculated in the input value of above-mentioned formula (1)i。
In conclusion the embodiment of the present invention provides the appraisal procedure and system of a kind of oil consumption correlation driving behavior, this method
Are as follows: obtain driving behavior data corresponding to each section of default mileage of vehicle to be assessed, wherein in advance by vehicle to be assessed
Total kilometres are divided into n sections of default mileages.Based on driving behavior data, different driving behaviors pair in each default mileage are calculated
The driving behavior index answered.Using driving behavior index as the input value of preset oil consumption Rating Model, it is calculated to be assessed
Total score is evaluated in the oil consumption correlation driving behavior of vehicle.In the present solution, being in advance based on pair between different driving behaviors and oil consumption
It should be related to and establish oil consumption Rating Model.By extracting the driving behavior data of vehicle to be assessed, calculated based on driving behavior data
The corresponding driving behavior index of different driving behaviors, is calculated driving behavior index as the input value of oil consumption Rating Model
Total score is evaluated in the oil consumption correlation driving behavior of vehicle to be assessed, can improve the objective degree and accuracy of vehicle oil consumption management.
All the embodiments in this specification are described in a progressive manner, same and similar portion between each embodiment
Dividing may refer to each other, and each embodiment focuses on the differences from other embodiments.Especially for system or
For system embodiment, since it is substantially similar to the method embodiment, so describing fairly simple, related place is referring to method
The part of embodiment illustrates.System and system embodiment described above is only schematical, wherein the conduct
The unit of separate part description may or may not be physically separated, component shown as a unit can be or
Person may not be physical unit, it can and it is in one place, or may be distributed over multiple network units.It can root
According to actual need that some or all of the modules therein is selected to achieve the purpose of the solution of this embodiment.Ordinary skill
Personnel can understand and implement without creative efforts.
Professional further appreciates that, unit described in conjunction with the examples disclosed in the embodiments of the present disclosure
And algorithm steps, can be realized with electronic hardware, computer software, or a combination of the two, in order to clearly demonstrate hardware and
The interchangeability of software generally describes each exemplary composition and step according to function in the above description.These
Function is implemented in hardware or software actually, the specific application and design constraint depending on technical solution.Profession
Technical staff can use different methods to achieve the described function each specific application, but this realization is not answered
Think beyond the scope of this invention.
The foregoing description of the disclosed embodiments enables those skilled in the art to implement or use the present invention.
Various modifications to these embodiments will be readily apparent to those skilled in the art, as defined herein
General Principle can be realized in other embodiments without departing from the spirit or scope of the present invention.Therefore, of the invention
It is not intended to be limited to the embodiments shown herein, and is to fit to and the principles and novel features disclosed herein phase one
The widest scope of cause.
Claims (10)
1. a kind of appraisal procedure of oil consumption correlation driving behavior, which is characterized in that the described method includes:
Obtain driving behavior data corresponding to each section of default mileage of vehicle to be assessed, wherein in advance will be described to be assessed
The total kilometres of vehicle are divided into the n sections of default mileages；
Based on the driving behavior data, calculates the corresponding driving behavior of difference driving behavior in each default mileage and refer to
Mark；
Using the driving behavior index as the input value of preset oil consumption Rating Model, the vehicle to be assessed is calculated
Total score is evaluated in oil consumption correlation driving behavior, wherein the oil consumption Rating Model is referred to by the driving behavior sample based on sample vehicle
Mark and oil consumption related coefficient construct to obtain.
2. the method according to claim 1, wherein the oil consumption Rating Model is by the driving based on sample vehicle
Behavior sample index and oil consumption related coefficient construct to obtain, comprising:
Obtain the traveling sample data of the sample vehicle；
The data prediction traveling sample data that obtains that treated is carried out to the traveling sample data；
Determine multiple driving behaviors；
The total kilometres of the sample vehicle are divided into the m sections of default mileages, and calculate the sample vehicle each
Oil consumption in the default mileage；
For each default mileage, it is corresponding to calculate each driving behavior for traveling sample data that treated based on described in
Driving behavior sample index；
For each default mileage, it is based on the driving behavior sample index and oil consumption, is calculated and the driving behavior sample
The corresponding oil consumption related coefficient of this index；
Using the driving behavior sample index and oil consumption related coefficient as the weight factor of Lasso regression model, to described
Lasso regression model carries out Lasso regression training and obtains oil consumption scoring submodel；
In conjunction with oil consumption scoring submodel and preset averaged submodel, the oil consumption Rating Model is obtained.
3. according to the method described in claim 2, it is characterized in that, it is described calculate the sample vehicle it is each it is described it is default in
The process of oil consumption in journey includes:
It is based onCalculate oil consumption Y of the sample vehicle in each default mileage, wherein t is the sample
Time required for mileage, F are preset described in vehicle drivingRFor the transient fuel consumption rate of the sample vehicle.
4. according to the method described in claim 2, it is characterized in that, it is described be based on the driving behavior sample index and oil consumption,
Calculate oil consumption related coefficient corresponding with the driving behavior sample index, comprising:
It is based onCalculate the corresponding oil of each driving behavior sample index
Consume related coefficient Ai, wherein N is the total number of driving behavior sample index, and Y is the sample vehicle in the default mileage
Oil consumption, XiFor driving behavior sample index.
5. method according to any of claims 1-4, which is characterized in that it is described using the driving behavior index as
The oil consumption correlation driving behavior evaluation total score of the vehicle to be assessed is calculated in the input value of preset oil consumption Rating Model,
Include:
Using the driving behavior index as the input value of oil consumption scoring submodel, the oil consumption in each default mileage is obtained
Related driving behavior scoring；
Input value by n oil consumption correlation driving behavior scoring as averaged submodel, is calculated described to be assessed
Total score is evaluated in the oil consumption correlation driving behavior of vehicle；
Wherein, the oil consumption Rating Model is made of oil consumption scoring submodel and the averaged submodel.
6. according to the method described in claim 5, it is characterized in that, described regard n oil consumption correlation driving behavior scoring as institute
The oil consumption correlation driving behavior evaluation total score of the vehicle to be assessed is calculated in the input value for stating averaged submodel,
Include:
The n oil consumption correlation driving behavior is scored and is used as the averaged submodelInput value, meter
It calculates and obtains the oil consumption correlation driving behavior evaluation total score G of the vehicle to be assessedi, wherein j is the serial number of the default mileage,
YpjFor the corresponding oil consumption correlation driving behavior scoring of j-th of default mileage.
7. a kind of assessment system of oil consumption correlation driving behavior, which is characterized in that the system comprises:
Acquiring unit, for obtaining driving behavior data corresponding to each section of default mileage of vehicle to be assessed, wherein in advance
The total kilometres of the vehicle to be assessed are divided into the n sections of default mileages；
Computing unit calculates the different driving behaviors pair in each default mileage for being based on the driving behavior data
The driving behavior index answered；
Score unit, for institute to be calculated using the driving behavior index as the input value of preset oil consumption Rating Model
State the oil consumption correlation driving behavior evaluation total score of vehicle to be assessed, wherein the oil consumption Rating Model is by based on sample vehicle
Driving behavior sample index and oil consumption related coefficient construct to obtain.
8. system according to claim 7, which is characterized in that the scoring unit includes:
Module is obtained, for obtaining the traveling sample data of the sample vehicle；
Processing module, for obtaining to traveling sample data progress data prediction, treated travels sample data；
Determining module, for determining multiple driving behaviors；
Oil consumption computing module for the total kilometres of the sample vehicle to be divided into the m sections of default mileages, and calculates institute
State oil consumption of the sample vehicle in each default mileage；
First computing module, for being directed to each default mileage, traveling sample data that treated based on described in is calculated every
The corresponding driving behavior sample index of a driving behavior；
Second computing module is based on the driving behavior sample index and oil consumption, calculates for being directed to each default mileage
Oil consumption related coefficient corresponding with the driving behavior sample index；
Training module, for using the driving behavior sample index and oil consumption related coefficient as the weight of Lasso regression model
The factor carries out Lasso regression training to the Lasso regression model and obtains oil consumption scoring submodel；
Module is constructed, for obtaining the oil consumption in conjunction with oil consumption scoring submodel and preset averaged submodel
Rating Model.
9. system according to claim 7 or 8, which is characterized in that the scoring unit includes:
First grading module, for obtaining each institute using the driving behavior index as the input value of oil consumption scoring submodel
State the oil consumption correlation driving behavior scoring in default mileage；
Second grading module, the input value for scoring n oil consumption correlation driving behavior as averaged submodel, meter
It calculates and obtains the oil consumption correlation driving behavior evaluation total score of the vehicle to be assessed；
Wherein, the oil consumption Rating Model is made of oil consumption scoring submodel and the averaged submodel.
10. system according to claim 9, which is characterized in that second grading module is specifically used for the n
Oil consumption correlation driving behavior scoring is used as the averaged submodelInput value, be calculated described to be evaluated
Estimate the oil consumption correlation driving behavior evaluation total score G of vehiclei, wherein j is the serial number of the default mileage, YpjIn being preset for j-th
The corresponding oil consumption correlation driving behavior scoring of journey.
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