CN106915354A - A kind of mobile unit and identification method for recognizing human pilot - Google Patents

A kind of mobile unit and identification method for recognizing human pilot Download PDF

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Publication number
CN106915354A
CN106915354A CN201710085385.XA CN201710085385A CN106915354A CN 106915354 A CN106915354 A CN 106915354A CN 201710085385 A CN201710085385 A CN 201710085385A CN 106915354 A CN106915354 A CN 106915354A
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vehicle
data
human pilot
unit
turning
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CN106915354B (en
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高文
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Dalian No Chain Information Technology Co Ltd
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Dalian No Chain Information Technology Co Ltd
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    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
    • B60W40/00—Estimation or calculation of non-directly measurable driving parameters for road vehicle drive control systems not related to the control of a particular sub unit, e.g. by using mathematical models
    • B60W40/08—Estimation or calculation of non-directly measurable driving parameters for road vehicle drive control systems not related to the control of a particular sub unit, e.g. by using mathematical models related to drivers or passengers
    • B60W40/09—Driving style or behaviour
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
    • B60W40/00—Estimation or calculation of non-directly measurable driving parameters for road vehicle drive control systems not related to the control of a particular sub unit, e.g. by using mathematical models
    • B60W40/08—Estimation or calculation of non-directly measurable driving parameters for road vehicle drive control systems not related to the control of a particular sub unit, e.g. by using mathematical models related to drivers or passengers
    • B60W2040/0809—Driver authorisation; Driver identical check
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
    • B60W2510/00—Input parameters relating to a particular sub-units
    • B60W2510/06—Combustion engines, Gas turbines
    • B60W2510/0638—Engine speed
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
    • B60W2520/00—Input parameters relating to overall vehicle dynamics
    • B60W2520/10—Longitudinal speed
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
    • B60W2540/00—Input parameters relating to occupants
    • B60W2540/10—Accelerator pedal position
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
    • B60W2540/00—Input parameters relating to occupants
    • B60W2540/12—Brake pedal position
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
    • B60W2540/00—Input parameters relating to occupants
    • B60W2540/16—Ratio selector position
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
    • B60W2540/00—Input parameters relating to occupants
    • B60W2540/18—Steering angle

Abstract

The present invention is, on a kind of mobile unit and identification method for recognizing human pilot, to belong to vehicle intelligent equipment field.Technical scheme is as follows：Including vehicle bus data decryptor reading unit, MCU computing units, local storage unit, network communication unit and data characteristic storage and analysis cloud platform, described vehicle bus data decryptor reading unit one end is attached with vehicle, the other end is connected with the MCU computing units, the MCU computing units are connected with local storage unit and network communication unit respectively, and the network communication unit stores and analyzes with the data characteristics cloud platform and is connected.Beneficial effect is：The present invention carries out data parsing after mobile unit accesses bus, and using certain computational methods obtain driving characteristics after reach cloud platform, in addition the continuous self study renewal of big data and matching optimization that cloud platform is preserved are combined, the purpose of different human pilots is accurately distinguished to reach, so as to more intelligently and efficiently to vehicle and driver be managed.

Description

A kind of mobile unit and identification method for recognizing human pilot
Technical field
The present invention relates to a kind of car-mounted terminal monitoring device, more particularly to a kind of mobile unit for recognizing human pilot and distinguish Verifying method.
Background technology
There is the terminal monitoring equipment of various access vehicle bus on current market, this kind equipment can obtain people Vehicle real-time running data checks Vehicular behavior and failure situation.But the also no one kind of in the market can be by total Knot analyzes driver to the related data of vehicle operating so as to reach the equipment for distinguishing human pilot, and such hardware device will It is widely used in the fields, the present invention such as vehicle management, settlement of insurance claim, disposal of breaking rules and regulations, automatic Pilot, automobile leasing, shared trip Device and method can by extract the driving characteristics and application class algorithm of human pilot obtain distinguish human pilot mesh 's.
The content of the invention
For more intelligence and efficiently vehicle and driver be managed, the present invention provides a kind of identification human pilot Mobile unit and identification method, the apparatus and method can by analysis and summary driver to the related data of vehicle operating from And the purpose for distinguishing human pilot is reached, so as to more intelligently and efficiently to vehicle and driver be managed, Ke Yiguang It is general to be applied to the fields such as vehicle management, settlement of insurance claim, disposal of breaking rules and regulations, automatic Pilot, automobile leasing, shared trip.The technology Scheme is as follows：
It is a kind of recognize human pilot mobile unit, including vehicle bus data decryptor reading unit, MCU computing units, Local storage unit, network communication unit and data characteristic storage and analysis cloud platform, the vehicle bus data decryptor read Unit one end is attached with vehicle, and the other end is connected with the MCU computing units, the MCU computing units respectively with locally Memory cell and network communication unit are connected, and the network communication unit stores and analyze cloud platform company with the data characteristics Connect.
Further, the vehicle bus data decryptor reading unit connects with vehicle bus or vehicle standard diagnosis interface Connect.
Further, the vehicle bus data decryptor reading unit is connected by CAN or Ethernet with vehicle.
Present invention additionally comprises a kind of method for recognizing human pilot, using the mobile unit of above-mentioned identification human pilot, Perform following steps：
S1, vehicle bus data decryptor reading unit obtain the speed of vehicle, engine in real time by accessing automobile bus Rotating speed, throttle, brake, gear and steering wheel angle information；
S2, using medium filtering, filter noise data；
Whether the data in S3, real-time judge newest some seconds have direction information, if not turning to message, return to S1, If turning to message, into S4；
S4,5 dimensional characteristics vector is extracted, 5 dimensional feature vector includes super turning most value, surpassing the percentage for turning and accounting for whole cycle Than, return mean location, lack and turn most value, scarce turn the percentage for accounting for whole cycle；
S5, data characteristics storage and analysis cloud platform produce grader using ANN algorithm, and the characteristic vector that equipment is uploaded is led to Cross and compared with property data base, judge the identity of human pilot.
Further, characteristic vector step is extracted in S4 as follows：
P1, the axle center coordinate that (Xr, Yr) and (Xf, Yf) is respectively vehicle rear axle and front axle is divided into inertial coodinate system OXY, Φ is the yaw angle of car body, and Φ f are front wheel slip angle, and Vr is vehicle rear axle central speed, and Vf is automobile front-axle central speed, and L is Vehicle wheelbase, R is rear-axle steering radius, and P is that vehicle rotates the center of circle, and M is vehicle rear axle axle center, and N is front axle axle center；
P2, drawn using following equatioies rear axle travel axle center (Xr, Yr) place speed：
Vr=Xr ' cos (Φ)+Yr ' sin (Φ),
Xr and Yr are coordinates, and Xr ' and Yr ' is speed of the trailing wheel under relative coordinate system；
P3, the kinematical constraint according to the antero posterior axis of automobile
Xf ' sin (Φ+Φ f)-Yf ' cos (Φ+Φ f)=0,
Xr ' sin (Φ)-Yr ' cos (Φ)=0；
Push away：
Xr '=Vrcos (Φ),
Yr '=Vrsin (Φ)；
P4, obtained according to the geometrical relationship of front and back wheel：
Xf=Xr+Lcos (Φ),
Yf=Yf+Lsin (Φ)；
And then derive that the yaw velocity of automobile is：
W=Vr/L*tan (Φ f)；
P5, turning radius R and front wheel slip angle Φ f is obtained according to yaw velocity W and vehicle velocity V r
R=Vr/W,
Φ f=arctan (L/R)；
P6, the kinematics model for obtaining vehicle：
Φ '=tan (Φ f)/L*Vr；
P7, according to yaw angle formula, five features of turn are extracted using following formula,
T be from yaw velocity be 0 to yaw velocity it is maximum again to 0 time period, trIt is the time point in 0-T,
Feature1 is that the super most value, feature2 of turning is to surpass to turn the percentage for accounting for whole cycle, feature3 to return Value position, feature4 are that the scarce most value, feature5 of turning is to lack the percentage for turning and accounting for whole cycle.
Further, judged using statistics according to the result drawn in S5, i.e., is asked for multiple result the mathematics phase Hope, draw final result.
Further, the grader is obtained by big data learning method, and the method is to extract many people using early stage Multiple characteristic values, data training is carried out using the BP neural network in artificial neural network, so as to draw one classification Device.
Further, the BP neural network point three-layer network, input layer is 5 nodes, and intermediate layer is 30 nodes, defeated Go out layer for 2 nodes.
The beneficial effects of the invention are as follows：
The mobile unit and identification method of identification human pilot of the present invention utilize automobile self-sensor device data, lead to Crossing after mobile unit accesses bus carries out data parsing acquirement driving characteristics, and applies certain computational methods, extracts 5 dimensional characteristics Vector, combines the continuous self study renewal of big data and matching optimization that high in the clouds preserves in addition, and different driving are accurately distinguished to reach The purpose of personnel, so as to more intelligently and efficiently to vehicle and driver be managed, can be widely applied to vehicle management, The fields such as settlement of insurance claim, disposal of breaking rules and regulations, automatic Pilot, automobile leasing, shared trip.
Brief description of the drawings
Fig. 1 is present device composition schematic diagram；
Fig. 2 is workflow diagram of the present invention；
Fig. 3 is motor turning motion model figure of the present invention；
Specific embodiment
Embodiment 1：
It is a kind of recognize human pilot mobile unit, including vehicle bus data decryptor reading unit, MCU computing units, Local storage unit, network communication unit and data characteristic storage and analysis cloud platform, the vehicle bus data decryptor read Unit one end is attached with vehicle, and the other end is connected with the MCU computing units, the MCU computing units respectively with locally Memory cell and network communication unit are connected, and the network communication unit stores and analyze cloud platform company with the data characteristics Connect.
Further, the vehicle bus data decryptor reading unit and vehicle bus or vehicle standard diagnosis interface (OBD) connect.
Further, the vehicle bus data decryptor reading unit is connected by CAN or Ethernet with vehicle.
Present invention additionally comprises a kind of method for recognizing human pilot, using the mobile unit of above-mentioned identification human pilot, Perform following steps：
S1, vehicle bus data decryptor reading unit obtain the speed of vehicle, engine in real time by accessing automobile bus Rotating speed, throttle, brake, gear and steering wheel angle information；
S2, using medium filtering, filter noise data；
Whether the data in newest 10 seconds of S3, real-time judge have direction information, if not turning to message, return to S1, such as Fruit has steering message, into S4；
S4,5 dimensional characteristics vector is extracted, 5 dimensional feature vector includes super turning most value, surpassing the percentage for turning and accounting for whole cycle Than, return mean location, lack and turn most value, scarce turn the percentage for accounting for whole cycle；
S5, data characteristics storage and analysis cloud platform produce grader using ANN algorithm, and the characteristic vector that equipment is uploaded is led to Cross and compared with property data base, judge the identity of human pilot.
Further, characteristic vector step is extracted in S4 as follows：
P1, as shown in Figure 3, divides into (Xr, Yr) and (Xf, Yf) and is respectively vehicle rear axle and preceding in inertial coodinate system OXY The axle center coordinate of axle, Φ is the yaw angle (course angle) of car body, and Φ f are front wheel slip angle, and Vr is vehicle rear axle central speed, and Vf is Automobile front-axle central speed, L is vehicle wheelbase, and R is rear-axle steering radius, and P is that vehicle rotates the center of circle, and M is vehicle rear axle axle center, N is front axle axle center；
P2, drawn using following equatioies rear axle travel axle center (Xr, Yr) place speed：
Vr=Xr ' cos (Φ)+Yr ' sin (Φ),
Xr and Yr are coordinates, relative to regard distance as, and Xr ' and Yr ' is speed of the trailing wheel under relative coordinate system；
P3, the kinematical constraint according to the antero posterior axis of automobile
Xf ' sin (Φ+Φ f)-Yf ' cos (Φ+Φ f)=0,
Xr ' sin (Φ)-Yr ' cos (Φ)=0；
Push away：
Xr '=Vrcos (Φ),
Yr '=Vrsin (Φ)；
P4, obtained according to the geometrical relationship of front and back wheel：
Xf=Xr+Lcos (Φ),
Yf=Yf+Lsin (Φ)；
And then derive that the yaw velocity of automobile is：
W=Vr/L*tan (Φ f)；
P5, turning radius R and front wheel slip angle Φ f is obtained according to yaw velocity W and vehicle velocity V r
R=Vr/W,
Φ f=arctan (L/R)；
P6, the kinematics model for obtaining vehicle：
Φ '=tan (Φ f)/L*Vr；
P7, according to yaw angle formula, five features of turn are extracted using following formula,
T is turn duration, i.e., from yaw velocity be 0 to yaw velocity it is maximum again to 0 time period, trIt is 0- Time point in T,
Feature1 is that the super most value, feature2 of turning is to surpass to turn the percentage for accounting for whole cycle, feature3 to return Value position, feature4 are that the scarce most value, feature5 of turning is to lack the percentage for turning and accounting for whole cycle.
Further, judged using statistics according to the result drawn in S5, i.e., is asked for multiple result the mathematics phase Hope, draw final result.
Further, the grader, is obtained by big data learning method, and the method is to extract many people using early stage Multiple characteristic values, data training is carried out using the BP neural network in artificial neural network (ANN), so as to show that this is one Grader.
Further, the BP neural network point three-layer network, input layer is 5 nodes, and intermediate layer is 30 nodes, defeated Go out layer for 2 nodes.
Embodiment 2：
As a kind of single embodiment or the supplement to embodiment 1, the explanation of nouns in S1：
Speed:Refer to automobile true velocity (unit km/h), obtained from vehicle bus；
Rotating speed:Refer to rotating speed of automobile engine (unit rad/min), obtained from vehicle bus；
Throttle:Refer to half point ratio (dimensionless) that accelerator pedal of automobile is stepped on, scope 0%~100%, 0% represents do not have Step on the gas, 100% expression throttle is floored, and is equally also obtained from vehicle bus；
Brake:Refer to half point ratio (dimensionless) that brake-pedal of automobile is stepped on, scope 0%~100%, 0% represents do not have Brake, 100% expression brake is floored, and is equally also obtained from vehicle bus.
Feature extraction, i.e., quantify the characteristic value for everyone driving, it is necessary to consider everyone to control errors by algorithm Mode.In daily driving, the influence due to environmental factor to changes in vehicle speed feature is to account for most of, so can not Extraction as absolute feature.Actual conditions, in the case of vehicle turning, when environment road is the same, everyone drives Maximum difference is exactly the mode that control is turned, and somebody can at the uniform velocity turn round, and somebody understands early stage and turns big curved, and the later stage turns small curved again Etc..Therefore the present invention is main based on turning feature, is aided with speed, rotating speed, throttle, brake, gear feature.According to above The yaw angle formula of release, five features (Primary Stage Data normalization) for extracting turn are respectively：Super turning most is worth, and super turning accounts for The percentage of whole cycle, returns mean location, and scarce turning most is worth, and lacks the percentage for turning and accounting for whole cycle.(five features are also five Individual dimension)
The training of data, equipment early stage is extracted multiple characteristic value of many people and is transmitted to data characteristics and stores and analyze Cloud platform, cloud platform application artificial neural network (ANN) carries out data training, so as to draw a grader.Obtaining afterwards During the characteristic value that equipment is uploaded, it is possible to carry out distinguishing different drivers using this grader.
Present invention application ANN one of which networks are called BP neural network, and it is a kind of multilayer inversely propagated by error Feedforward neural network.Here I will divide three-layer network input layer to be 5 nodes (because a feature is five dimensions) intermediate layer It is 30 nodes, output layer is two nodes (two classification)).
The mode of big data, refers to that platform is being trained BP neural network after data accumulation to certain degree, certainly It is main to correct study, so that the accuracy rate that platform distinguishes driver is stepped up.
The above, the only present invention preferably specific embodiment, but protection scope of the present invention is not limited thereto, Any one skilled in the art in the technical scope of present disclosure, technology according to the present invention scheme and its Inventive concept is subject to equivalent or change, should all be included within the scope of the present invention.

Claims (8)

1. it is a kind of recognize human pilot mobile unit, it is characterised in that including vehicle bus data decryptor reading unit, MCU Computing unit, local storage unit, network communication unit and data characteristic storage and analysis cloud platform, the vehicle bus data Monitor reading unit one end to be attached with vehicle, the other end is connected with the MCU computing units, the MCU computing units point It is not connected with local storage unit and network communication unit, the network communication unit stores and analyze cloud with the data characteristics Platform is connected.
2. the mobile unit of human pilot is recognized as claimed in claim 1, it is characterised in that the vehicle bus data decryptor Reading unit is connected with vehicle bus or vehicle standard diagnosis interface.
3. the mobile unit of human pilot is recognized as claimed in claim 1, it is characterised in that the vehicle bus data decryptor Reading unit is connected by CAN or Ethernet with vehicle.
4. it is a kind of recognize human pilot method, it is characterised in that usage right requirement any one of 1-3 described in identification driver The mobile unit of member, performs following steps：
S1, vehicle bus data decryptor reading unit obtained in real time by accessing automobile bus the speed of vehicle, engine speed, Throttle, brake, gear and steering wheel angle information；
S2, using medium filtering, filter noise data；
Whether the data in S3, real-time judge newest some seconds have direction information, if not turning to message, return to S1, if There is steering message, into S4；
S4, extract 5 dimensional characteristics vector, 5 dimensional feature vector include it is super turn most value, surpass turn the percentage for accounting for whole cycle, Mean location is returned, is lacked and is turned most value, lacks the percentage for turning and accounting for whole cycle；
S5, data characteristics storage and analysis cloud platform using ANN algorithm produce grader, equipment upload characteristic vector by with Property data base is compared, and judges the identity of human pilot.
5. the method for recognizing human pilot as claimed in claim 4, it is characterised in that extract characteristic vector step such as in S4 Under：
P1, the axle center coordinate that (Xr, Yr) and (Xf, Yf) is respectively vehicle rear axle and front axle is divided into inertial coodinate system OXY, Φ is The yaw angle of car body, Φ f are front wheel slip angle, and Vr is vehicle rear axle central speed, and Vf is automobile front-axle central speed, and L is vehicle Wheelbase, R is rear-axle steering radius, and P is that vehicle rotates the center of circle, and M is vehicle rear axle axle center, and N is front axle axle center；
P2, drawn using following equatioies rear axle travel axle center (Xr, Yr) place speed：
Vr=Xr ' cos (Φ)+Yr ' sin (Φ),
Xr and Yr are coordinates, and Xr ' and Yr ' is speed of the trailing wheel under relative coordinate system；
P3, the kinematical constraint according to the antero posterior axis of automobile
Xf ' sin (Φ+Φ f)-Yf ' cos (Φ+Φ f)=0,
Xr ' sin (Φ)-Yr ' cos (Φ)=0；
Push away：
Xr '=Vrcos (Φ),
Yr '=Vrsin (Φ)；
P4, obtained according to the geometrical relationship of front and back wheel：
Xf=Xr+Lcos (Φ),
Yf=Yf+Lsin (Φ)；
And then derive that the yaw velocity of automobile is：
W=Vr/L*tan (Φ f)；
P5, turning radius R and front wheel slip angle Φ f is obtained according to yaw velocity W and vehicle velocity V r
R=Vr/W,
Φ f=arctan (L/R)；
P6, the kinematics model for obtaining vehicle：
Φ '=tan (Φ f)/L*Vr；
P7, according to yaw angle formula, five features of turn are extracted using following formula,
g ( t r ) = ∫ 0 t r w d t - ∫ 0 T w d t T * t r t r ∈ [ 0 , T ] ,
T be from yaw velocity be 0 to yaw velocity it is maximum again to 0 time period, trIt is the time point in 0-T,
g ( t r ) ′ ′ = 0 g ( t r ) ′ > 0 ⇒ t 1 = t r f e a t u r e 1 = g ( t 1 ) f e a t u r e 2 = t 1 T ,
g ( t r ) = 0 ⇒ t 2 = t r f e a t u r e 3 = t 2 T ,
g ( t r ) &prime; &prime; = 0 g ( t r ) &prime; < 0 &DoubleRightArrow; t 3 = t r f e a t u r e 4 = g ( t 3 ) f e a t u r e 5 = t 3 T ,
Feature1 is that the super most value, feature2 of turning is to surpass to turn the percentage for accounting for whole cycle, feature3 to return average position Put, feature4 is that the scarce most value, feature5 of turning is to lack the percentage for turning and accounting for whole cycle.
6. the method for recognizing human pilot as claimed in claim 4, it is characterised in that according to the result drawn in S5 using system Count to be judged, i.e., mathematic expectaion is asked for multiple result, draw final result.
7. the method for recognizing human pilot as claimed in claim 4, it is characterised in that the grader is by big data study side Method is obtained, and the method is that multiple characteristic values of many people are extracted using early stage, using the BP neural network in artificial neural network Data training is carried out, so as to draw one grader.
8. the method for recognizing human pilot as claimed in claim 7, it is characterised in that the BP neural network point three-layer network Network, input layer is 5 nodes, and intermediate layer is 30 nodes, and output layer is 2 nodes.
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