CN106128099A - Driver's recognition methods and device - Google Patents
Driver's recognition methods and device Download PDFInfo
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- CN106128099A CN106128099A CN201610513833.7A CN201610513833A CN106128099A CN 106128099 A CN106128099 A CN 106128099A CN 201610513833 A CN201610513833 A CN 201610513833A CN 106128099 A CN106128099 A CN 106128099A
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Abstract
The present invention provides a kind of driver's recognition methods and device, and wherein method includes: obtaining the driving data of Current vehicle, driving data includes: the running section data of vehicle driving data, driver status data and Current vehicle；The driving data of Current vehicle is carried out quantification treatment, obtains the driving data of presets；The driving data of presets is inputted to the recurrence disaggregated model being pre-created, judge according to the output result returning disaggregated model whether the driver of Current vehicle is to preset driver, when the driver of Current vehicle is for presetting driver, provide personalized service based on driving habits data corresponding to driving data and default driver for default driver.
Description
Technical field
The present invention relates to communication technical field, particularly relate to a kind of driver's recognition methods and device.
Background technology
Along with communication, development of Mobile Internet technology universal, and the height of intelligent mobile terminal covers, car machine market of networking
Flourish, occur in that many personalized, vehicle-mounted services of customization.As a example by digital map navigation, except providing roading, boat
Line, the conventional service such as address administration, real-time traffic condition, also provide for circuit collection, message based on user search history recommendation
Deng service.
But, the offer of above-mentioned various services, depend on vehicle data or the driving data etc. of the collections such as OBD terminal, base
It is analyzed in these data, service can only be provided for vehicle.And the driver driving vehicle can have multiple, OBD terminal
Etc. being difficult to differentiate the current driver identity driving vehicle, accordingly, it is difficult to provide the personalized service for driver, such as,
For the tired monitoring of specific driver or assisting automobile driver etc..
Summary of the invention
The present invention provides a kind of driver's recognition methods and device, is used for solving to be difficult in prior art provide for driving
The problem of the personalized service of member.
The first aspect of the invention is to provide a kind of driver's recognition methods, including:
Obtain Current vehicle driving data, described driving data includes: vehicle driving data, driver status data with
And the running section data of described Current vehicle；
The driving data of described Current vehicle is carried out quantification treatment, obtains the driving data of presets；
The driving data of described presets is inputted to the recurrence disaggregated model being pre-created, according to returning classification mould
The output result of type judges whether the driver of Current vehicle is to preset driver；
If the driver of Current vehicle is for presetting driver, then based on described driving data and described default driver couple
The driving habits data answered provide service for described default driver.
Further, the described driving data by described presets inputs to the recurrence disaggregated model being pre-created it
Before, also include:
Number driven by the first sample driving data and described first sample that obtain each driver driving Current vehicle
According to corresponding model theory output valve；
Described first sample driving data is carried out quantification treatment, obtains the first sample driving data of presets；
First sample driving data output of described presets is returned in disaggregated model to initial, returns according to initial
The output result of disaggregated model and model theory output valve corresponding to described first sample driving data are to initially returning classification
The regression coefficient of model is adjusted, and obtains described recurrence disaggregated model.
Further, described by described presets first sample driving data export to initially returning disaggregated model
In, according to the model theory output valve that the initial output result returning disaggregated model and described first sample driving data are corresponding
The initial regression coefficient returning disaggregated model is adjusted, after obtaining described recurrence disaggregated model, also includes:
Number driven by the detection sample driving data and the described detection sample that obtain each driver driving Current vehicle
According to corresponding model theory output valve；
Described detection sample driving data is carried out quantification treatment, obtains the detection sample driving data of presets；
The detection sample driving data of described presets is exported to described recurrence disaggregated model, it is judged that described recurrence
Whether the output result of the disaggregated model model theory output valve corresponding with described detection sample driving data mates；
If the ratio of the detection sample driving data that the output result of correspondence is mated with corresponding model theory output valve is big
In equal to pre-set ratio, the regression coefficient of described recurrence disaggregated model is not adjusted.
Further, described by described presets first sample driving data export to initially returning disaggregated model
In, according to the model theory output valve that the initial output result returning disaggregated model and described first sample driving data are corresponding
The initial regression coefficient returning disaggregated model is adjusted, after obtaining described recurrence disaggregated model, also includes:
If the ratio of the detection sample driving data that the output result of correspondence is mated with corresponding model theory output valve is little
In pre-set ratio, then the second sample driving data and described second sample that obtain each driver driving Current vehicle are driven
Sail the model theory output valve that data are corresponding, based on described second sample driving data and described second sample driving data pair
The regression coefficient of described recurrence disaggregated model is adjusted by the model theory output valve answered, until the output result of correspondence is with right
The ratio of the detection sample driving data of the model theory output valve coupling answered is more than or equal to till pre-set ratio.
Further, the formula of described recurrence disaggregated model is,
Wherein, z=w0HB+w1ST+w2SB+w3RC+w4SD+w5RL+w6FD+w7AS+w8FKM；
Wherein, σ (z) is the output result returning disaggregated model, and HB, ST, SB, RC, SD, RL, FD, AS and FKM are default
Parameters in the driving data of form.
Further, described vehicle driving data include: acceleration, speed, steering wheel angle, average speed per hour and oil consumption；
Driver status data include: seatbelt wearing state and fatigue state；
Running section data include: speed limit data and traffic lights data.
In the present invention, it is provided that a kind of driver's recognition methods, by obtaining the driving data of Current vehicle, driving data bag
Include: the running section data of vehicle driving data, driver status data and Current vehicle；Driving data to Current vehicle
Carry out quantification treatment, obtain the driving data of presets；The driving data of presets is inputted to the recurrence being pre-created
In disaggregated model, judge according to the output result returning disaggregated model whether the driver of Current vehicle is to preset driver,
When the driver of Current vehicle is for presetting driver, the driving habits data corresponding based on driving data and default driver are
Default driver provides personalized service.
The second aspect of the invention is to provide a kind of driver identification device, including:
First acquisition module, for obtaining the driving data of Current vehicle, described driving data includes: vehicle driving number
According to, driver status data and the running section data of described Current vehicle；
First processing module, for the driving data of described Current vehicle is carried out quantification treatment, obtains presets
Driving data；
Input module, for the driving data of described presets is inputted to the recurrence disaggregated model being pre-created,
Judge according to the output result returning disaggregated model whether the driver of Current vehicle is to preset driver；
There is provided module, for when the driver of Current vehicle is for presetting driver, based on described driving data and institute
State driving habits data corresponding to default driver and provide service for described default driver.
Further, described device also includes:
Second acquisition module, for inputting the driving data of described presets to being pre-created at described input module
Recurrence disaggregated model in before, obtain the first sample driving data and described the of each driver driving Current vehicle
The model theory output valve that one sample driving data is corresponding；
Second processing module, for described first sample driving data is carried out quantification treatment, obtains the of presets
One sample driving data；
Adjusting module, for exporting the first sample driving data of described presets to initially returning disaggregated model
In, according to the model theory output valve that the initial output result returning disaggregated model and described first sample driving data are corresponding
The initial regression coefficient returning disaggregated model is adjusted, obtains described recurrence disaggregated model.
Further, described device also includes:
3rd acquisition module, at described adjusting module according to the output result of initial recurrence disaggregated model and described
The initial regression coefficient returning disaggregated model is adjusted by the model theory output valve that the first sample driving data is corresponding, obtains
After described recurrence disaggregated model, obtain the detection sample driving data of each driver driving Current vehicle and described inspection
The model theory output valve that this driving data of test sample is corresponding；
3rd processing module, for described detection sample driving data is carried out quantification treatment, obtains the inspection of presets
This driving data of test sample；
Judge module, for exporting the detection sample driving data of described presets to described recurrence disaggregated model
In, it is judged that the output result of the described recurrence disaggregated model model theory output valve corresponding with described detection sample driving data is
No coupling；
Operation module, drives for the detection sample mated with corresponding model theory output valve in corresponding output result
When the ratio of data is more than or equal to pre-set ratio, the regression coefficient of described recurrence disaggregated model is not adjusted.
Further, the formula of described recurrence disaggregated model is,
Wherein, z=w0HB+w1ST+w2SB+w3RC+w4SD+w5RL+w6FD+w7AS+w8FKM；
Wherein, σ (z) is the output result returning disaggregated model, and HB, ST, SB, RC, SD, RL, FD, AS and FKM are default
Parameters in the driving data of form.
In the present invention, it is provided that a kind of driver identification device, by obtaining the driving data of Current vehicle, driving data bag
Include: the running section data of vehicle driving data, driver status data and Current vehicle；Driving data to Current vehicle
Carry out quantification treatment, obtain the driving data of presets；The driving data of presets is inputted to the recurrence being pre-created
In disaggregated model, judge according to the output result returning disaggregated model whether the driver of Current vehicle is to preset driver,
When the driver of Current vehicle is for presetting driver, the driving habits data corresponding based on driving data and default driver are
Default driver provides personalized service.
Accompanying drawing explanation
The flow chart of one embodiment of driver's recognition methods that Fig. 1 provides for the present invention；
The flow chart of driver's another embodiment of recognition methods that Fig. 2 provides for the present invention；
The structural representation of one embodiment of driver identification device that Fig. 3 provides for the present invention；
The structural representation of another embodiment of driver identification device that Fig. 4 provides for the present invention；
The structural representation of another embodiment of driver identification device that Fig. 5 provides for the present invention.
Detailed description of the invention
For making the purpose of the embodiment of the present invention, technical scheme and advantage clearer, below in conjunction with the embodiment of the present invention
In accompanying drawing, the technical scheme in the embodiment of the present invention is clearly and completely described, it is clear that described embodiment is
The a part of embodiment of the present invention rather than whole embodiments.Based on the embodiment in the present invention, those of ordinary skill in the art
The every other embodiment obtained under not making creative work premise, broadly falls into the scope of protection of the invention.
The flow chart of one embodiment of driver's recognition methods that Fig. 1 provides for the present invention, as it is shown in figure 1, include:
101, obtain Current vehicle driving data, driving data includes: vehicle driving data, driver status data with
And the running section data of Current vehicle.
The executive agent of driver's recognition methods that the present invention provides is driver identification device, and driver identification device has
Body can be car-mounted terminal or the onboard servers etc. being connected with car-mounted terminal, and driver identification device can also be for being arranged on
Software etc. on car-mounted terminal or onboard servers.
Wherein, the mode of the driving data obtaining Current vehicle at least can have three kinds: vehicle intelligent terminal OBD, driving
The vehicular applications installed on the mobile terminal of member or onboard operations system.Vehicle driving data are specifically as follows acceleration, speed
Degree, steering wheel angle, average speed per hour and oil consumption etc.；Driver status data may include that seatbelt wearing state and tired shape
State etc.；Running section data may include that speed limit data and traffic lights data etc..
Further, vehicle driving data can also include: lane line signal, indicator signal, throttle signal, clutch
Signal, shift signal and gyro data etc..In the above parameter any one or be combined with each other and can embody driving
The following driving behavior of member: bring to a halt, take a sudden turn, seatbelt wearing state, the most rapidly rob lamp, whether drive over the speed limit, whether
Pressure lanes, whether fatigue driving, average speed per hour and fuel consumption per hundred kilometers etc..
102, the driving data of Current vehicle is carried out quantification treatment, obtain the driving data of presets.
Concrete, the driving data of Current vehicle is carried out quantification treatment, the driving data of the presets obtained is concrete
Can be: hundred kilometers bring to a halt number, hundred kilometers zig zag number, seatbelt wearing situation, rapidly rob modulation frequency, hundred kilometers hypervelocity row
Sail rate, hundred kilometers of diatoms of delaying unloading travel number, hundred kilometers of fatigue driving numbers, hundred kilometers of average speed per hours and fuel consumption per hundred kilometers etc..
103, the driving data of presets is inputted to the recurrence disaggregated model being pre-created, according to returning classification mould
The output result of type judges whether the driver of Current vehicle is to preset driver.
Wherein, the formula returning disaggregated model can be,
Wherein, z=w0HB+w1ST+w2SB+w3RC+w4SD+w5RL+w6FD+w7AS+w8FKM；
Wherein, σ (z) is the output result returning disaggregated model, and HB, ST, SB, RC, SD, RL, FD, AS and FKM are default
Parameters in the driving data of form.
Corresponding, HB is hundred kilometers of numbers of bringing to a halt；ST is hundred kilometers of zig zag numbers；SB is seatbelt wearing situation；RC is
Rapidly rob modulation frequency；SD is hundred kilometers of rates of driving over the speed limit；RL is that hundred kilometers of diatoms of delaying unloading travel number；FD is hundred kilometers of fatigue drivings
Number；AS is hundred kilometers of average speed per hours；FKM is fuel consumption per hundred kilometers.W0, W1, W2, W3, W4, W5, W6, W7, W8 are followed successively by hundred kilometers
Bring to a halt number, hundred kilometers of zig zag numbers, seatbelt wearing situations, rapidly rob modulation frequency, hundred kilometers of rates of driving over the speed limit, hundred kilometers of pressures
Lane line travels number, hundred kilometers of fatigue driving numbers, hundred kilometers of average speed per hours and the regression coefficient of fuel consumption per hundred kilometers.
Concrete, after the driving data of presets is inputted to the recurrence disaggregated model being pre-created, can obtain
To scope output valve between 0-1, when output valve is more than or equal to 0.5, represent that the driver of Current vehicle is car owner；
When output valve is less than 0.5, represent that the driver of Current vehicle is non-car owner.
If the driver of 104 Current vehicle is for presetting driver, then corresponding based on driving data and default driver
Driving habits data provide service for default driver.
Wherein, default driver can be car owner or non-car owner.
In the present embodiment, it is provided that a kind of driver's recognition methods, by obtaining the driving data of Current vehicle, driving data
Including: the running section data of vehicle driving data, driver status data and Current vehicle；Driving number to Current vehicle
According to carrying out quantification treatment, obtain the driving data of presets；The driving data of presets is inputted to returning of being pre-created
Return in disaggregated model, judge according to the output result returning disaggregated model whether the driver of Current vehicle is to preset driver,
When the driver of Current vehicle is for presetting driver, based on the driving habits data that driving data and default driver are corresponding
Personalized service is provided for default driver.
Fig. 2 has the flow chart of an embodiment for driver's recognition methods that the present invention provides, as in figure 2 it is shown, in Fig. 1 institute
On the basis of showing embodiment, before step 103, it is also possible to including:
105, number driven by the first sample driving data and the first sample that obtain each driver driving Current vehicle
According to corresponding model theory output valve.
Wherein, the first sample driving data be specifically as follows current time for the previous period in drive Current vehicle each
The history driving data of driver.The length of this period can be set as required.
106, the first sample driving data is carried out quantification treatment, obtain the first sample driving data of presets.
Wherein, quantification treatment to the first sample driving data is referred to the place of the driving data to Current vehicle herein
Reason mode, is the most no longer described in detail.
107, the first sample driving data output of presets is returned in disaggregated model to initial, return according to initial
The output result of disaggregated model and model theory output valve corresponding to the first sample driving data are to initially returning disaggregated model
Regression coefficient be adjusted, obtain return disaggregated model.
Further, after step 107, it is also possible to including: obtain the detection sample of each driver driving Current vehicle
This driving data and detection model theory output valve corresponding to sample driving data；Detection sample driving data is quantified
Process, obtain the detection sample driving data of presets；The detection sample driving data of presets is exported to recurrence point
In class model, it is judged that return the output result of disaggregated model and whether detect model theory output valve corresponding to sample driving data
Coupling；If the ratio of the detection sample driving data that the output result of correspondence is mated with corresponding model theory output valve is more than
In pre-set ratio, the regression coefficient returning disaggregated model is not adjusted.
In addition, it is necessary to illustrate, if the inspection that the output result of correspondence is mated with corresponding model theory output valve
The ratio of this driving data of test sample is less than pre-set ratio, then the second sample obtaining each driver driving Current vehicle is driven
Data and model theory output valve corresponding to the second sample driving data, based on the second sample driving data and the second sample
The regression coefficient returning disaggregated model is adjusted by the model theory output valve that driving data is corresponding, until the output knot of correspondence
Till fruit is more than or equal to pre-set ratio with the ratio of the detection sample driving data of corresponding model theory output valve coupling.
In the present embodiment, it is provided that a kind of driver's recognition methods, by obtaining the driving data of Current vehicle, driving data
Including: the running section data of vehicle driving data, driver status data and Current vehicle；Driving number to Current vehicle
According to carrying out quantification treatment, obtain the driving data of presets；Obtain first sample of each driver driving Current vehicle
Driving data and model theory output valve corresponding to the first sample driving data；The of each driver based on Current vehicle
One sample driving data and model theory output valve corresponding to the first sample driving data return in disaggregated model initial
Each regression coefficient is adjusted, and obtains returning disaggregated model；The driving data of presets is inputted to returning of being pre-created
Return in disaggregated model, judge according to the output result returning disaggregated model whether the driver of Current vehicle is to preset driver,
When the driver of Current vehicle is for presetting driver, based on the driving habits data that driving data and default driver are corresponding
Personalized service is provided for default driver.
One of ordinary skill in the art will appreciate that: all or part of step realizing above-mentioned each method embodiment can be led to
The hardware crossing programmed instruction relevant completes.Aforesaid program can be stored in a computer read/write memory medium.This journey
Sequence upon execution, performs to include the step of above-mentioned each method embodiment；And aforesaid storage medium includes: ROM, RAM, magnetic disc or
The various media that can store program code such as person's CD.
The structural representation of one embodiment of driver identification device that Fig. 3 provides for the present invention, as it is shown on figure 3, include:
First acquisition module 31, for obtaining the driving data of Current vehicle, driving data includes: vehicle driving data,
The running section data of driver status data and Current vehicle；
First processing module 32, for the driving data of Current vehicle is carried out quantification treatment, obtains driving of presets
Sail data；
Input module 33, for the driving data of presets is inputted to the recurrence disaggregated model being pre-created, root
Judge according to the output result returning disaggregated model whether the driver of Current vehicle is to preset driver；
There is provided module 34, for Current vehicle driver for preset driver time, based on driving data and preset
Driving habits data corresponding to driver provide service for default driver.
The present invention provide driver identification device be specifically as follows car-mounted terminal or be connected with car-mounted terminal vehicle-mounted
Servers etc., driver identification device can also be for be arranged on the software etc. in car-mounted terminal or onboard servers.
Wherein, the mode of the driving data obtaining Current vehicle at least can have three kinds: vehicle intelligent terminal OBD, driving
The vehicular applications installed on the mobile terminal of member or onboard operations system.Vehicle driving data are specifically as follows acceleration, speed
Degree, steering wheel angle, average speed per hour and oil consumption etc.；Driver status data may include that seatbelt wearing state and tired shape
State etc.；Running section data may include that speed limit data and traffic lights data etc..
Further, vehicle driving data can also include: lane line signal, indicator signal, throttle signal, clutch
Signal, shift signal and gyro data etc..In the above parameter any one or be combined with each other and can embody driving
The following driving behavior of member: bring to a halt, take a sudden turn, seatbelt wearing state, the most rapidly rob lamp, whether drive over the speed limit, whether
Pressure lanes, whether fatigue driving, average speed per hour and fuel consumption per hundred kilometers etc..
Concrete, the driving data of Current vehicle is carried out quantification treatment, the driving data of the presets obtained is concrete
Can be: hundred kilometers bring to a halt number, hundred kilometers zig zag number, seatbelt wearing situation, rapidly rob modulation frequency, hundred kilometers hypervelocity row
Sail rate, hundred kilometers of diatoms of delaying unloading travel number, hundred kilometers of fatigue driving numbers, hundred kilometers of average speed per hours and fuel consumption per hundred kilometers etc..
Further, the formula returning disaggregated model can be,
Wherein, z=w0HB+w1ST+w2SB+w3RC+w4SD+w5RL+w6FD+w7AS+w8FKM；
Wherein, σ (z) is the output result returning disaggregated model, and HB, ST, SB, RC, SD, RL, FD, AS and FKM are default
Parameters in the driving data of form.
Corresponding, HB is hundred kilometers of numbers of bringing to a halt；ST is hundred kilometers of zig zag numbers；SB is seatbelt wearing situation；RC is
Rapidly rob modulation frequency；SD is hundred kilometers of rates of driving over the speed limit；RL is that hundred kilometers of diatoms of delaying unloading travel number；FD is hundred kilometers of fatigue drivings
Number；AS is hundred kilometers of average speed per hours；FKM is fuel consumption per hundred kilometers.W0, W1, W2, W3, W4, W5, W6, W7, W8 are followed successively by hundred kilometers
Bring to a halt number, hundred kilometers of zig zag numbers, seatbelt wearing situations, rapidly rob modulation frequency, hundred kilometers of rates of driving over the speed limit, hundred kilometers of pressures
Lane line travels number, hundred kilometers of fatigue driving numbers, hundred kilometers of average speed per hours and the regression coefficient of fuel consumption per hundred kilometers.
Concrete, after the driving data of presets is inputted to the recurrence disaggregated model being pre-created, can obtain
To scope output valve between 0-1, when output valve is more than or equal to 0.5, represent that the driver of Current vehicle is car owner；
When output valve is less than 0.5, represent that the driver of Current vehicle is non-car owner.
Further, the structural representation of another embodiment of driver identification device that Fig. 4 provides for the present invention, such as figure
Shown in 4, on the basis of embodiment illustrated in fig. 3, described driver identification device also includes:
Second acquisition module 35, for inputting the driving data of presets to the recurrence being pre-created at input module
Before in disaggregated model, the first sample driving data and the first sample that obtain each driver driving Current vehicle are driven
The model theory output valve that data are corresponding；
Second processing module 36, for the first sample driving data is carried out quantification treatment, obtains the first of presets
Sample driving data；
Adjusting module 37, for the first sample driving data output of presets is returned in disaggregated model to initial,
According to model theory output valve corresponding to the initial output result returning disaggregated model and the first sample driving data to initially
The regression coefficient returning disaggregated model is adjusted, and obtains returning disaggregated model.
Further, the structural representation of another embodiment of driver identification device that Fig. 5 provides for the present invention, such as figure
Shown in 5, on the basis of embodiment illustrated in fig. 4, described driver identification device also includes:
3rd acquisition module 38, is used at adjusting module according to the initial output result returning disaggregated model and the first sample
The initial regression coefficient returning disaggregated model is adjusted by the model theory output valve that this driving data is corresponding, obtains returning and divides
After class model, obtain detection sample driving data and the detection sample driving data of each driver driving Current vehicle
Corresponding model theory output valve；
3rd processing module 39, for detection sample driving data is carried out quantification treatment, obtains the detection of presets
Sample driving data；
Judge module 40, for exporting the detection sample driving data of presets to returning in disaggregated model, it is judged that
Return the output result of disaggregated model and detect whether model theory output valve corresponding to sample driving data mates；
Operation module 41, drives for the detection sample mated with corresponding model theory output valve in corresponding output result
When sailing the ratio of data more than or equal to pre-set ratio, the regression coefficient returning disaggregated model is not adjusted.
In addition, it is necessary to illustrate, if the inspection that the output result of correspondence is mated with corresponding model theory output valve
The ratio of this driving data of test sample is less than pre-set ratio, then the second sample obtaining each driver driving Current vehicle is driven
Data and model theory output valve corresponding to the second sample driving data, based on the second sample driving data and the second sample
The regression coefficient returning disaggregated model is adjusted by the model theory output valve that driving data is corresponding, until the output knot of correspondence
Till fruit is more than or equal to pre-set ratio with the ratio of the detection sample driving data of corresponding model theory output valve coupling.
In the present embodiment, it is provided that a kind of driver identification device, by obtaining the driving data of Current vehicle, driving data
Including: the running section data of vehicle driving data, driver status data and Current vehicle；Driving number to Current vehicle
According to carrying out quantification treatment, obtain the driving data of presets；The driving data of presets is inputted to returning of being pre-created
Return in disaggregated model, judge according to the output result returning disaggregated model whether the driver of Current vehicle is to preset driver,
When the driver of Current vehicle is for presetting driver, based on the driving habits data that driving data and default driver are corresponding
Personalized service is provided for default driver.
Last it is noted that various embodiments above is only in order to illustrate technical scheme, it is not intended to limit；To the greatest extent
The present invention has been described in detail by pipe with reference to foregoing embodiments, it will be understood by those within the art that: it depends on
So the technical scheme described in foregoing embodiments can be modified, or the most some or all of technical characteristic is entered
Row equivalent；And these amendments or replacement, do not make the essence of appropriate technical solution depart from various embodiments of the present invention technology
The scope of scheme.
Claims (10)
1. driver's recognition methods, it is characterised in that including:
Obtaining the driving data of Current vehicle, described driving data includes: vehicle driving data, driver status data and institute
State the running section data of Current vehicle；
The driving data of described Current vehicle is carried out quantification treatment, obtains the driving data of presets；
The driving data of described presets is inputted to the recurrence disaggregated model being pre-created, according to returning disaggregated model
Output result judges whether the driver of Current vehicle is to preset driver；
If the driver of Current vehicle is for presetting driver, then corresponding based on described driving data and described default driver
Driving habits data provide service for described default driver.
Method the most according to claim 1, it is characterised in that the described driving data by described presets inputs in advance
Before in the recurrence disaggregated model first created, also include:
Obtain the first sample driving data of each driver driving Current vehicle and described first sample driving data pair
The model theory output valve answered；
Described first sample driving data is carried out quantification treatment, obtains the first sample driving data of presets；
First sample driving data output of described presets is returned in disaggregated model, according to initially returning classification to initial
The output result of model and model theory output valve corresponding to described first sample driving data are to initially returning disaggregated model
Regression coefficient be adjusted, obtain described recurrence disaggregated model.
Method the most according to claim 2, it is characterised in that described by the first sample driving data of described presets
Export and return in disaggregated model to initial, drive number according to the initial output result returning disaggregated model and described first sample
According to corresponding model theory output valve, the initial regression coefficient returning disaggregated model is adjusted, obtains described recurrence classification mould
After type, also include:
Obtain the detection sample driving data of each driver driving Current vehicle and described detection sample driving data pair
The model theory output valve answered；
Described detection sample driving data is carried out quantification treatment, obtains the detection sample driving data of presets；
The detection sample driving data of described presets is exported to described recurrence disaggregated model, it is judged that described recurrence is classified
Whether the output result of the model model theory output valve corresponding with described detection sample driving data mates；
If the ratio of the detection sample driving data that the output result of correspondence is mated with corresponding model theory output valve is more than
In pre-set ratio, the regression coefficient of described recurrence disaggregated model is not adjusted.
Method the most according to claim 3, it is characterised in that described by the first sample driving data of described presets
Export and return in disaggregated model to initial, drive number according to the initial output result returning disaggregated model and described first sample
According to corresponding model theory output valve, the initial regression coefficient returning disaggregated model is adjusted, obtains described recurrence classification mould
After type, also include:
If the ratio of the detection sample driving data that the output result of correspondence is mated with corresponding model theory output valve is less than pre-
If ratio, then number driven by the second sample driving data and described second sample that obtain each driver driving Current vehicle
According to corresponding model theory output valve, corresponding based on described second sample driving data and described second sample driving data
The regression coefficient of described recurrence disaggregated model is adjusted by model theory output valve, until the output result of correspondence is with corresponding
The ratio of the detection sample driving data of model theory output valve coupling is more than or equal to till pre-set ratio.
Method the most according to claim 1, it is characterised in that the formula of described recurrence disaggregated model is,
Wherein, z=w0HB+w1ST+w2SB+w3RC+w4SD+w5RL+w6FD+w7AS+w8FKM；
Wherein, σ (z) is the output result returning disaggregated model, and HB, ST, SB, RC, SD, RL, FD, AS and FKM are presets
Driving data in parameters.
Method the most according to claim 1, it is characterised in that described vehicle driving data include: acceleration, speed, side
To dish corner, average speed per hour and oil consumption；
Driver status data include: seatbelt wearing state and fatigue state；
Running section data include: speed limit data and traffic lights data.
7. a driver identification device, it is characterised in that including:
First acquisition module, for obtaining the driving data of Current vehicle, described driving data includes: vehicle driving data, drive
The running section data of the person's of sailing status data and described Current vehicle；
First processing module, for the driving data of described Current vehicle is carried out quantification treatment, obtains the driving of presets
Data；
Input module, for the driving data of described presets is inputted to the recurrence disaggregated model being pre-created, according to
The output result returning disaggregated model judges whether the driver of Current vehicle is to preset driver；
There is provided module, for when the driver of Current vehicle is for presetting driver, based on described driving data and described pre-
If driving habits data corresponding to driver provide service for described default driver.
Device the most according to claim 7, it is characterised in that also include:
Second acquisition module, for inputting the driving data of described presets to returning of being pre-created at described input module
Before returning in disaggregated model, obtain the first sample driving data of each driver driving Current vehicle and described first sample
The model theory output valve that this driving data is corresponding；
Second processing module, for described first sample driving data is carried out quantification treatment, obtains the first sample of presets
This driving data；
Adjusting module, for the first sample driving data output of described presets is returned in disaggregated model to initial, root
According to model theory output valve corresponding to the initial output result returning disaggregated model and described first sample driving data to just
The regression coefficient beginning to return disaggregated model is adjusted, and obtains described recurrence disaggregated model.
Device the most according to claim 8, it is characterised in that also include:
3rd acquisition module, is used at described adjusting module according to the initial output result and described first returning disaggregated model
The initial regression coefficient returning disaggregated model is adjusted by the model theory output valve that sample driving data is corresponding, obtains described
After returning disaggregated model, obtain the detection sample driving data of each driver driving Current vehicle and described detection sample
The model theory output valve that this driving data is corresponding；
3rd processing module, for described detection sample driving data is carried out quantification treatment, obtains the detection sample of presets
This driving data；
Judge module, for being exported to described recurrence disaggregated model by the detection sample driving data of described presets, sentences
Whether the output result of the disconnected described recurrence disaggregated model model theory output valve corresponding with described detection sample driving data be
Join；
Operation module, for the detection sample driving data mated with corresponding model theory output valve in corresponding output result
Ratio more than or equal to pre-set ratio time, the regression coefficient of described recurrence disaggregated model is not adjusted.
Device the most according to claim 7, it is characterised in that the formula of described recurrence disaggregated model is,
Wherein, z=w0HB+w1ST+w2SB+w3RC+w4SD+w5RL+w6FD+w7AS+w8FKM；
Wherein, σ (z) is the output result returning disaggregated model, and HB, ST, SB, RC, SD, RL, FD, AS and FKM are presets
Driving data in parameters.
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