CN106980911A - Driving methods of risk assessment and device based on the static factor - Google Patents
Driving methods of risk assessment and device based on the static factor Download PDFInfo
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- CN106980911A CN106980911A CN201710217268.4A CN201710217268A CN106980911A CN 106980911 A CN106980911 A CN 106980911A CN 201710217268 A CN201710217268 A CN 201710217268A CN 106980911 A CN106980911 A CN 106980911A
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- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q40/00—Finance; Insurance; Tax strategies; Processing of corporate or income taxes
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Abstract
The embodiments of the invention provide a kind of driving methods of risk assessment and device based on the static factor, belong to risk profile field.Wherein, methods described includes：The first car static data is obtained, wherein, the first car static data includes first car owner's personal characteristics data, the first vehicle physical supplemental characteristic and first is in danger data；The first car static data is input to the risk forecast model obtained in advance and carries out risk profile；Assessment result is obtained according to the first the car static data and the risk forecast model.The assessment result accuracy is high, on the one hand can be recognized by data output for major insurance companies and screen driving risk, so as to reduce its combined ratio, realize doulbe-sides' victory；On the other hand technical support and data supporting can also be provided for vehicle insurance Industry Innovation sex differernceization price, is that the good car owner of safe driving wins fairer and more reasonable business risk premium calculation principle.
Description
Technical field
The present invention relates to risk profile field, in particular to a kind of driving risk assessment side based on the static factor
Method and device.
Background technology
As UBI car networkings insure the rise with big data, the driving in the urgent need to a kind of method of science to driver
Risk is estimated, the assessment result can as test vehicle car owner driving risk data support, remind and supervise car
It is main to improve driving custom, so as to improve the awareness of safety of car owner, or insurance company formulates different grades of premium and provided
Foundation, the at present insurance at home and abroad on UBI is assessed and simply distributes risks and assumptions after appropriate weight, do simple fitting and
Computing, but the assessment result that this method is obtained, it is impossible to accurately reflect the situation of actual danger, can not be insurance
Company formulates different premiums for different clients and provides more accurate foundation.
The content of the invention
In view of this, the purpose of the embodiment of the present invention is to provide a kind of driving methods of risk assessment based on the static factor
And device, to improve above mentioned problem.
In a first aspect, the embodiments of the invention provide a kind of driving methods of risk assessment based on the static factor, the side
Method includes：The first car static data is obtained, wherein, the first car static data includes first car owner's personal characteristics number
It is in danger data according to, the first vehicle physical supplemental characteristic and first；The first car static data is input to what is obtained in advance
Risk forecast model carries out risk profile；Obtained according to the first the car static data and the risk forecast model and assess knot
Really.
Second aspect, the embodiments of the invention provide a kind of driving risk assessment device based on the static factor, the dress
Put including：First acquisition module, for obtaining the first car static data, wherein, the first car static data includes the
One car owner's personal characteristics data, the first vehicle physical supplemental characteristic and first are in danger data；Risk profile module, for by described in
The first car static data is input to the risk forecast model obtained in advance and carries out risk profile；Assessment result acquisition module, is used
According to the first car static data and the risk forecast model acquisition assessment result.
The beneficial effect of the embodiment of the present invention is：
The embodiment of the present invention provides a kind of driving methods of risk assessment and device based on the static factor, passes through the of acquisition
One people's car static data, and the first car static data inputs to the risk forecast model that obtains in advance to carry out risk pre-
Survey, assessment result can be obtained further according to the first the car static data and the risk forecast model, the assessment result is accurate
Property it is high, on the one hand can be recognized by data output for major insurance companies and screen driving risk, so as to reduce its synthesis
Loss ratio, realizes doulbe-sides' victory；On the other hand technical support and data branch can also be provided for vehicle insurance Industry Innovation sex differernceization price
Support, is that the good car owner of safe driving wins fairer and more reasonable business risk premium calculation principle.
Other features and advantages of the present invention will be illustrated in subsequent specification, also, partly be become from specification
It is clear that or by implementing understanding of the embodiment of the present invention.The purpose of the present invention and other advantages can be by saying for being write
Specifically noted structure is realized and obtained in bright book, claims and accompanying drawing.
Brief description of the drawings
In order to illustrate the technical solution of the embodiments of the present invention more clearly, below will be attached to what is used required in embodiment
Figure is briefly described, it will be appreciated that the following drawings illustrate only certain embodiments of the present invention, therefore is not construed as pair
The restriction of scope, for those of ordinary skill in the art, on the premise of not paying creative work, can also be according to this
A little accompanying drawings obtain other related accompanying drawings.
Fig. 1 shows a kind of structured flowchart for the electronic equipment that can be applied in the embodiment of the present application；
A kind of flow for driving methods of risk assessment based on the static factor that Fig. 2 provides for first embodiment of the invention
Figure；
A kind of flow for driving methods of risk assessment based on the static factor that Fig. 3 provides for second embodiment of the invention
Figure；
A kind of structural frames for driving risk assessment device based on the static factor that Fig. 4 provides for third embodiment of the invention
Figure；
A kind of structural frames for driving risk assessment device based on the static factor that Fig. 5 provides for fourth embodiment of the invention
Figure.
Embodiment
Below in conjunction with accompanying drawing in the embodiment of the present invention, the technical scheme in the embodiment of the present invention is carried out clear, complete
Ground is described, it is clear that described embodiment is only a part of embodiment of the invention, rather than whole embodiments.Generally exist
The component of the embodiment of the present invention described and illustrated in accompanying drawing can be arranged and designed with a variety of configurations herein.Cause
This, the detailed description of the embodiments of the invention to providing in the accompanying drawings is not intended to limit claimed invention below
Scope, but it is merely representative of the selected embodiment of the present invention.Based on embodiments of the invention, those skilled in the art are not doing
The every other embodiment obtained on the premise of going out creative work, belongs to the scope of protection of the invention.
It should be noted that：Similar label and letter represents similar terms in following accompanying drawing, therefore, once a certain Xiang Yi
It is defined in individual accompanying drawing, then it further need not be defined and explained in subsequent accompanying drawing.Meanwhile, the present invention's
In description, term " first ", " second " etc. are only used for distinguishing description, and it is not intended that indicating or implying relative importance.
Fig. 1 is refer to, Fig. 1 shows a kind of structured flowchart for the electronic equipment 100 that can be applied in the embodiment of the present application.
Electronic equipment 100 can include the driving risk assessment device based on the static factor, memory 101, storage control 102, place
Manage device 103, Peripheral Interface 104, input-output unit 105, audio unit 106, display unit 107.
The memory 101, storage control 102, processor 103, Peripheral Interface 104, input-output unit 105, sound
Frequency unit 106, each element of display unit 107 are directly or indirectly electrically connected with each other, to realize the transmission or friendship of data
Mutually.It is electrically connected with for example, these elements can be realized by one or more communication bus or signal wire each other.It is described to be based on
The driving risk assessment device of the static factor, which includes at least one, to be stored in institute in the form of software or firmware (firmware)
State in memory 101 or be solidificated in the operating system (operating of the driving risk assessment device based on the static factor
System, OS) in software function module.The processor 103 is used to perform the executable module stored in memory 101,
Software function module or computer program that such as described driving risk assessment device based on the static factor includes.
Wherein, memory 101 may be, but not limited to, random access memory (Random Access Memory,
RAM), read-only storage (Read Only Memory, ROM), programmable read only memory (Programmable Read-Only
Memory, PROM), erasable read-only memory (Erasable Programmable Read-Only Memory, EPROM),
Electricallyerasable ROM (EEROM) (Electric Erasable Programmable Read-Only Memory, EEPROM) etc..
Wherein, memory 101 is used for storage program, and the processor 103 performs described program after execute instruction is received, foregoing
The method performed by server that the stream process that any embodiment of the embodiment of the present invention is disclosed is defined can apply to processor 103
In, or realized by processor 103.
Processor 103 is probably a kind of IC chip, the disposal ability with signal.Above-mentioned processor 103 can
To be general processor, including central processing unit (Central Processing Unit, abbreviation CPU), network processing unit
(Network Processor, abbreviation NP) etc.；Can also be digital signal processor (DSP), application specific integrated circuit (ASIC),
It is ready-made programmable gate array (FPGA) or other PLDs, discrete gate or transistor logic, discrete hard
Part component.It can realize or perform disclosed each method, step and the logic diagram in the embodiment of the present invention.General processor
Can be microprocessor or the processor 103 can also be any conventional processor etc..
Various input/output devices are coupled to processor 103 and memory 101 by the Peripheral Interface 104.At some
In embodiment, Peripheral Interface 104, processor 103 and storage control 102 can be realized in one single chip.Other one
In a little examples, they can be realized by independent chip respectively.
Input-output unit 105 is used to be supplied to user input data to realize user and the server (or local terminal)
Interaction.The input-output unit 105 may be, but not limited to, mouse and keyboard etc..
Audio unit 106 provides a user COBBAIF, and it may include one or more microphones, one or more raises
Sound device and voicefrequency circuit.
Display unit 107 provides an interactive interface (such as user's operation circle between the electronic equipment 100 and user
Face) or for display image data give user reference.In the present embodiment, the display unit 107 can be liquid crystal display
Or touch control display.If touch control display, it can be support single-point and the capacitance type touch control screen or resistance of multi-point touch operation
Formula touch screen etc..Single-point and multi-point touch operation is supported to refer to that touch control display can sense on the touch control display one
Or at multiple positions simultaneously produce touch control operation, and by the touch control operation that this is sensed transfer to processor 103 carry out calculate and
Processing.
Various input/output devices are coupled to processor 103 and memory 101 by the Peripheral Interface 104.At some
In embodiment, Peripheral Interface 104, processor 103 and storage control 102 can be realized in one single chip.Other one
In a little examples, they can be realized by independent chip respectively.
Input-output unit 105 is used to be supplied to user input data to realize interacting for user and processing terminal.It is described defeated
Enter output unit 105 may be, but not limited to, mouse and keyboard etc..
It is appreciated that the structure shown in Fig. 1 is only signal, the electronic equipment 100 may also include more more than shown in Fig. 1
Either less component or with the configuration different from shown in Fig. 1.Each component shown in Fig. 1 can use hardware, software
Or its combination is realized.
First embodiment
It refer to Fig. 2, a kind of driving risk assessment side based on the static factor that Fig. 2 provides for first embodiment of the invention
The flow chart of method, methods described specifically includes following steps：
Step S110：The first car static data is obtained, wherein, the first car static data includes the first car owner
People's characteristic, the first vehicle physical supplemental characteristic and first are in danger data.
The first car static data is obtained first, and the first car static data includes first car owner's personal characteristics number
Be in danger data according to, the first vehicle physical supplemental characteristic and first, these data all represent car owner or vehicle it is related it is static because
Son.Wherein, first car owner's personal characteristics data can be including break in traffic rules and regulations situation of the car owner in preset time, the year of car owner
The data such as age, the sex of car owner, the credit record of car owner, the marital status of car owner, for example, break in traffic rules and regulations situation is acquisition data
The first five months in number of times violating the regulations 3 times, the age of car owner is 38, and sex is that man is expressed as numeral 1, the credit record of car owner and
Marital status can all be represented with corresponding numeral or quantized value, for example, bad credit record is expressed as 0, good credit record
It is expressed as 1；It is married to be expressed as 1, it is unmarried to be expressed as 0.
First vehicle physical supplemental characteristic can include car fare, car age, vehicle brand, the wheelbase of vehicle, the maximum of vehicle
Speed, rotating speed, drive form of car weight and vehicle etc., for example, car fare 100,000,3 years car ages, vehicle brand is masses, axle for vehicle
Away from for 2450mm, max. speed is 175KM/H, and rotating speed 800r/min, 1.2 tons of car weight, the drive form of vehicle can be quantified as
Preposition forerunner is expressed as 1, and other are expressed as 0；First be in danger data for be in danger number of times of the car owner in preset time, compensate the amount of money
Deng for example, be in danger number of times of the car owner in a upper insurance year is 3, as long as the compensation amount of money wherein once is more than 500
Define first and be in danger data for 1, it can be understood as the probability that is in danger is 1, or, car owner's being in danger in a upper insurance year
Number of times is 3, and each number of times that is in danger that the compensation amount of money is less than or equal to 500 and car owner is in a upper insurance year is 0, i.e.,
Definable first is in danger data for 0, it can be understood as the probability that is in danger also is 0, because after being changed according to business's fare in 2016, the previous year
The situation of being in danger the premium of car owner's next year can be caused to sharp rise, therefore expense change rear car owner for some small amounts Claims Resolution be more likely to
Danger is not quoted, is settled a dispute by the parties concerned themselves, so in modeling process, changes rear car owner to fully demonstrate expense and quotes dangerous this to insurance company
One change, Claims Resolution data have done above-mentioned optimization processing before especially changing to expense.
The acquisition of first car owner's personal characteristics data and the first vehicle physical supplemental characteristic can be by mobile phone car treasured APP
The relevant physical parameter data for the vehicle that the car owner that records drives on data that car owner uploads and the APP, it first is in danger number
According to can be obtained from insurance company.
It should be noted that car owner's one car of correspondence, that is, the first car static data obtained refers to one obtained
Car owner's personal characteristics data of individual car owner and the data of being in danger of the car owner, and the vehicle physical supplemental characteristic that the car owner drives.
Also, it is separate without correlation between the first car static data.
Step S120：The first car static data is input to the risk forecast model progress risk obtained in advance pre-
Survey.
The first car static data obtained in above-mentioned steps is input in the risk forecast model obtained in advance and carried out
Risk profile.The risk forecast model can be obtained according to the first car static data of input., should as a kind of embodiment
Risk forecast model can be expressed asWherein, factor beta0,β1,
β2...βnIt can be calculated by statistical software, wherein, statistical software can be using SAS, SPSS, S-plus etc..
Step S130：Assessment result is obtained according to the first the car static data and the risk forecast model.
The first car static data is inputted into risk forecast model and carries out risk profile, then can obtain assessment knot
Really, i.e. the probability that is in danger of the car owner, the then driving risk that can be carried out according to assessment result to car owner is analyzed, to remind and
Car owner is supervised to improve driving custom, so that the awareness of safety of car owner is improved, in addition, can be on the one hand each by the assessment result
Big insurance company recognizes and screens driving risk, so as to reduce its combined ratio, realizes profit；On the other hand can also be car
Dangerous Industry Innovation sex differernceization price provides technical support and data supporting, be safe driving good car owner win it is more fair
Rational premium is preferential.
Certainly, in actual application, may be limited due to condition can't get whole the first cars static state
Data, so in order to carry out risk profile, the risk forecast model for obtaining obtaining in assessment result, the present embodiment may also be used for
It is estimated for single risk factors, for example, for first car owner's personal characteristics data, the first vehicle physical parameter number
It is estimated according to, first at least one of data data of being in danger；For example, 30 years old age of car owner, sex man are expressed as 1,
Car fare 100,000,4 years car ages, vehicle wheelbase be 2450mm, max. speed be 175KM/H, rotating speed 800r/min, 1.2 tons of car weight this
One or more data inputs in a little data enter risk forecast model, can finally draw the assessment of the probability that is in danger, the i.e. car owner
As a result, risk profile is carried out by the risk forecast model, the assessment result accuracy finally obtained is higher, can effectively improve
Car owner's drive safety, also formulates different grades of premium for different clients for insurance company and provides strong foundation.
A kind of driving methods of risk assessment based on the static factor that first embodiment of the invention is provided, passes through the of acquisition
One people's car static data, and the first car static data inputs to the risk forecast model that obtains in advance to carry out risk pre-
Survey, assessment result can be obtained further according to the first the car static data and the risk forecast model, the assessment result is accurate
Property it is high, on the one hand can be recognized by data output for major insurance companies and screen driving risk, so as to reduce its synthesis
Loss ratio, realizes doulbe-sides' victory；On the other hand technical support and data branch can also be provided for vehicle insurance Industry Innovation sex differernceization price
Support, is that the good car owner of safe driving wins fairer and more reasonable business risk premium calculation principle.
Second embodiment
It refer to Fig. 3, a kind of driving risk assessment side based on the static factor that Fig. 3 provides for second embodiment of the invention
The flow chart of method, this method specifically includes following steps：
Step S210：Multiple second people car static datas are obtained, wherein, each second people car static data includes the
Two car owner's personal characteristics data, the second vehicle physical supplemental characteristic and second are in danger data.
In order to build risk forecast model, the accuracy of assessment result is improved, then needs to obtain multiple second people cars static state
Data, each second people car static data include second car owner's personal characteristics data, the second vehicle physical supplemental characteristic and
Second is in danger data, wherein, second car owner's personal characteristics data can include the car owner break in traffic rules and regulations feelings in preset time
The data such as condition, the age of car owner, the sex of car owner, the credit record of car owner, the marital status of car owner, the second vehicle physical parameter
Data include the driving shape of car fare, car age, vehicle brand, the wheelbase of vehicle, the max speed of vehicle, rotating speed, car weight and vehicle
Formula etc., second be in danger data for be in danger number of times of the car owner in preset time, compensate amount of money etc..
The acquisition of second car owner's personal characteristics data and the second vehicle physical supplemental characteristic can be by mobile phone car treasured APP
The relevant physical parameter data for the vehicle that the data and the APP that car owner uploads are recorded, its second be in danger data can be public from insurance
Department obtains.
It should be noted that what is obtained in car owner's one car of correspondence, the present embodiment is the personal characteristics of multiple car owners
The vehicle physical supplemental characteristic of data and many cars.Also, it is separate without correlation between the second people car static data.
This step no longer can excessively be repeated herein with specific reference to the description in step S110.
Step S220：Logistic regression function is obtained according to the multiple second people car static data.
If the vector representation that the multiple second people car static data is constituted is x=(x1,x2,x3...xn), Y is
Dangerous probability, what Y=1 was represented is absolutely to be in danger, and what Y=0 was represented is to be in danger probability for 0, then the expression formula of conditional probability can be with
P (Y=1 | x)=p is expressed as, then the expression formula of logistic regression function can be expressed as
Wherein, g (x)=β0+β1x1+β2x2+…+βnxn。
Step S230：Based on the logistic regression function acquisition probability function.
Obtaining the expression formula of logistic regression function：Afterwards, becauseSo can acquisition probability function expression
Formula, the expression formula of the probability function is expressed as
Step S240：Risk forecast model is obtained based on the probability function.
Because probability function expression formula isThe probability for being as in danger and not being in danger
The ratio between, then it can drawAgain because g (x)=β0+β1x1+β2x2+…+βnxn, then by statistical software,
G (x)=β can be calculated0+β1x1+β2x2+…+βnxnIn factor beta0,β1,β2...βn, it is possible to release risk profile mould
The expression formula of type can be expressed as
Step S250：The first car static data is obtained, wherein, the first car static data includes the first car owner
People's characteristic, the first vehicle physical supplemental characteristic and first are in danger data.
If desired risk profile is carried out to some car owner, then needs first to obtain the first car static data of the car owner, it has
Body implementation method can refer to step S110, succinct for description, no longer excessively repeat herein.
Step S260：The first car static data is input to the risk forecast model being pre-created and carries out risk
Prediction.
The first car static data of acquisition is input to the risk forecast model obtained in above-mentioned steps S240Carry out risk profile.Its concrete methods of realizing can refer to step
S120, it is succinct for description, no longer excessively repeat herein.
Step S270：Assessment result is obtained according to the first the car static data and the risk forecast model.
The first car static data of acquisition is inputted to the risk forecast model, i.e., by the first car static data band
EnterIn formula, so as to calculate the value for Probability p of being in danger.Its
Concrete methods of realizing can refer to step S130, succinct for description, no longer excessively repeat herein.
It should be noted that in the present embodiment, during the logistic regression function of acquisition, being asked using statistical software
Take g (x)=β0+β1x1+β2x2+…+βnxnIn factor beta0,β1,β2...βnWhen, the statistical software can be by second people's car of input
N data in static data, that is, the second people's car static data inputted are rejected with the incoherent data of the probability that is in danger,
It is exactly, when some single data, such as car age, to be input to when being detected in risk forecast model, the probability that is in danger drawn is big
When default confidence level, then just illustrate that the car age variable is uncorrelated to the probability that is in danger, then reject the car age variable, so that
Merely entered when creating risk forecast model and probability correlation of being in danger data variable, i.e., last second people's car static state by after rejecting
Remaining m data in data is inputted to the risk forecast model, so as to improve by commenting that the risk forecast model is obtained
Estimate the accuracy of result.
In addition, on the one hand can be recognized by the assessment result for major insurance companies and screen driving risk, so as to drop
Its low combined ratio, realizes profit；On the other hand technical support can also be provided for vehicle insurance Industry Innovation sex differernceization price
And data supporting, it is that to win fairer and more reasonable premium preferential by the good car owner of safe driving.
For the implementation procedure of the above method, base of the present invention is described in detail with a specific embodiment below
In the driving methods of risk assessment of the static factor.
Statistics merging is carried out to nearly 5,000,000 data using statistical software SAS softwares, including 8 the second vehicle things
Supplemental characteristic is managed, 1 second car owner's personal characteristics data, 1 second data of being in danger carry out these data the system of logistic regression
Meter modeling.Driving null hypothesis (H0) inside logistic regression function represents given second people's car static data variables with being in danger
Probability is uncorrelated.So as p value (Pr>When ChiSq) being less than confidence level (0.05), refuse null hypothesis；And put when p value is more than
During letter level, do not refuse null hypothesis.
When the corresponding p value of some variable is noticeably greater than 0.05 in the output result of statistical software, does not refuse to drive and examine
Assuming that (H0), i.e., do not refuse second given people's car static data variables and (be considered and go out with the incoherent hypothesis of the probability that is in danger
Dangerous probability is uncorrelated), therefore reject these inapparent variables before next step is carried out.The result exported from statistical software is come
See, it is car weight (p=0.685), wheelbase (p=0.634) that the factor screened out is returned for the first time.
Reject after above-mentioned data, repeat above-mentioned logistic regression step, obtain somewhere vehicle static with reference to second people's car
The model of data is：
G (x)=β0+β1x1+β2x2+…βnxn, wherein, factor beta can be obtained0=-1.5121, β1=-0.0226, β2=
0.1418, β3=0.00944, β4=0.0184.
For example, 38 years old car owner, drives the Mazda 6 that 3 years new preposition forerunner's the max speed is 220 kilometers, substitute into public
Formula obtains g (x)=- 0.0971, then the probability that is in danger of the user is about 0.48, is in danger rate 0.4 higher than sample mean.
A kind of driving methods of risk assessment based on the static factor that second embodiment of the invention is provided, it is multiple by obtaining
Second people's car static data, logistic regression function is obtained further according to the plurality of second people car static data, is then based on described patrol
Probability function can be obtained by collecting regression function, obtain risk forecast model based on probability function, then obtain the first car static number
According to, by the first car static data input to the risk forecast model carry out risk profile so that it is higher to obtain accuracy
Assessment result, on the one hand can be recognized by data output for major insurance companies and screen driving risk, so as to reduce it
Combined ratio, realizes doulbe-sides' victory；On the other hand technical support sum can also be provided for vehicle insurance Industry Innovation sex differernceization price
It is that the good car owner of safe driving wins fairer and more reasonable business risk premium calculation principle according to support.
3rd embodiment
Fig. 4 is refer to, Fig. 4 provides a kind of driving risk assessment device based on the static factor for third embodiment of the invention
200 structured flowchart, described device is specifically included：
First acquisition module 210, for obtaining the first car static data, wherein, the first car static packet
Include first car owner's personal characteristics data, the first vehicle physical supplemental characteristic and first be in danger data.
Risk profile module 220, for the first car static data to be input into the risk profile mould obtained in advance
Type carries out risk profile.
Assessment result acquisition module 230, for being obtained according to the first the car static data and the risk forecast model
Take assessment result.
Fourth embodiment
It refer to Fig. 5, a kind of driving risk assessment dress based on the static factor that Fig. 5 provides for fourth embodiment of the invention
Put 300 structured flowchart, described device specifically includes：
Second acquisition module 310, for obtaining multiple second people car static datas, wherein, each second people Che Jing
State data include second car owner's personal characteristics Data Data, the second vehicle physical supplemental characteristic and second are in danger data.
Logistic regression function acquisition module 320, for obtaining logistic regression according to the multiple second people car static data
Function.
Probability function acquisition module 330, for based on the logistic regression function acquisition probability function.
Risk forecast model acquisition module 340, for obtaining risk forecast model based on the probability function.
First acquisition module 350, for obtaining the first car static data, wherein, the first car static packet
Include first car owner's personal characteristics data, the first vehicle physical supplemental characteristic and first be in danger data.
Risk profile module 360, for the first car static data to be input into the risk profile mould obtained in advance
Type carries out risk profile.
Assessment result acquisition module 370, for being obtained according to the first the car static data and the risk forecast model
Take assessment result.
Wherein, the logistic regression function is
Wherein, x=(x1,x2,x3...xn) vector that is made up of the multiple second people car static data, Y is is in danger
Probability, g (x)=β0+β1x1+β2x2+…+βnxn。
The logistic regression function acquisition module 330 includes：First computing submodule, for based onProbability function is obtained, wherein, the probability function is
The risk forecast model acquisition module 340 includes：Second computing submodule, for based onRisk forecast model is obtained, wherein, the risk forecast model is
It is apparent to those skilled in the art that, for convenience and simplicity of description, the device of foregoing description
Specific work process, may be referred to the corresponding process in preceding method, no longer excessively repeat herein.
In summary, the embodiment of the present invention provides a kind of driving methods of risk assessment and device based on the static factor, leads to
The first car static data obtained is crossed, and the first car static data is inputted to the risk forecast model obtained in advance
Risk profile is carried out, assessment result can be obtained further according to the first the car static data and the risk forecast model, this is commented
Estimate result accuracy high, on the one hand can recognize for major insurance companies and screen driving risk, compensated so as to reduce it and integrate
Rate, realizes profit；On the other hand technical support and data supporting can also be provided for vehicle insurance Industry Innovation sex differernceization price, is
It is preferential that the good car owner of safe driving wins fairer and more reasonable premium.
In several embodiments provided herein, it should be understood that disclosed apparatus and method, it can also pass through
Other modes are realized.Device embodiment described above is only schematical, for example, flow chart and block diagram in accompanying drawing
Show according to the device of multiple embodiments of the present invention, the architectural framework in the cards of method and computer program product,
Function and operation.At this point, each square frame in flow chart or block diagram can represent the one of a module, program segment or code
Part a, part for the module, program segment or code is used to realize holding for defined logic function comprising one or more
Row instruction.It should also be noted that in some implementations as replacement, the function of being marked in square frame can also with different from
The order marked in accompanying drawing occurs.For example, two continuous square frames can essentially be performed substantially in parallel, they are sometimes
It can perform in the opposite order, this is depending on involved function.It is also noted that every in block diagram and/or flow chart
The combination of individual square frame and block diagram and/or the square frame in flow chart, can use the special base for performing defined function or action
Realize, or can be realized with the combination of specialized hardware and computer instruction in the system of hardware.
In addition, each functional module in each embodiment of the invention can integrate to form an independent portion
Point or modules individualism, can also two or more modules be integrated to form an independent part.
If the function is realized using in the form of software function module and is used as independent production marketing or in use, can be with
It is stored in a computer read/write memory medium.Understood based on such, technical scheme is substantially in other words
The part contributed to prior art or the part of the technical scheme can be embodied in the form of software product, the meter
Calculation machine software product is stored in a storage medium, including some instructions are to cause a computer equipment (can be individual
People's computer, server, or network equipment etc.) perform all or part of step of each of the invention embodiment methods described.
And foregoing storage medium includes：USB flash disk, mobile hard disk, read-only storage (ROM, Read-Only Memory), arbitrary access are deposited
Reservoir (RAM, Random Access Memory), magnetic disc or CD etc. are various can be with the medium of store program codes.Need
Illustrate, herein, such as first and second or the like relational terms be used merely to by an entity or operation with
Another entity or operation make a distinction, and not necessarily require or imply between these entities or operation there is any this reality
The relation or order on border.Moreover, term " comprising ", "comprising" or its any other variant are intended to the bag of nonexcludability
Contain, so that process, method, article or equipment including a series of key elements are not only including those key elements, but also including
Other key elements being not expressly set out, or also include for this process, method, article or the intrinsic key element of equipment.
In the absence of more restrictions, the key element limited by sentence "including a ...", it is not excluded that including the key element
Process, method, article or equipment in also there is other identical element.
The preferred embodiments of the present invention are the foregoing is only, are not intended to limit the invention, for the skill of this area
For art personnel, the present invention can have various modifications and variations.Within the spirit and principles of the invention, that is made any repaiies
Change, equivalent substitution, improvement etc., should be included in the scope of the protection.It should be noted that：Similar label and letter exists
Similar terms is represented in following accompanying drawing, therefore, once being defined in a certain Xiang Yi accompanying drawing, is then not required in subsequent accompanying drawing
It is further defined and explained.
The foregoing is only a specific embodiment of the invention, but protection scope of the present invention is not limited thereto, any
Those familiar with the art the invention discloses technical scope in, change or replacement can be readily occurred in, should all be contained
Cover within protection scope of the present invention.Therefore, protection scope of the present invention described should be defined by scope of the claims.
It should be noted that herein, such as first and second or the like relational terms are used merely to a reality
Body or operation make a distinction with another entity or operation, and not necessarily require or imply these entities or deposited between operating
In any this actual relation or order.Moreover, term " comprising ", "comprising" or its any other variant are intended to
Nonexcludability is included, so that process, method, article or equipment including a series of key elements not only will including those
Element, but also other key elements including being not expressly set out, or also include being this process, method, article or equipment
Intrinsic key element.In the absence of more restrictions, the key element limited by sentence "including a ...", it is not excluded that
Also there is other identical element in process, method, article or equipment including the key element.
Claims (10)
1. a kind of driving methods of risk assessment based on the static factor, it is characterised in that methods described includes：
The first car static data is obtained, wherein, the first car static data includes first car owner's personal characteristics data, the
One vehicle physical supplemental characteristic and first it is in danger data；
The first car static data is input to the risk forecast model obtained in advance and carries out risk profile；
Assessment result is obtained according to the first the car static data and the risk forecast model.
2. according to the method described in claim 1, it is characterised in that before the step of the acquisition the first car static data,
Including：
Multiple second people car static datas are obtained, wherein, it is personal special that each second people car static data includes the second car owner
Levy data, the second vehicle physical supplemental characteristic and second be in danger data；
Logistic regression function is obtained according to the multiple second people car static data；
Based on the logistic regression function acquisition probability function；
Risk forecast model is obtained based on the probability function.
3. method according to claim 2, it is characterised in that the logistic regression function is
Wherein, x=(x1,x2,x3...xn) vector that is made up of the multiple second people car static data, Y is be in danger probability, g
(x)=β0+β1x1+β2x2+…+βnxn。
4. method according to claim 3, it is characterised in that based on the logistic regression function acquisition probability function, bag
Include：
It is based onProbability function is obtained, wherein, the probability function is
5. method according to claim 4, it is characterised in that risk forecast model, bag are obtained based on the probability function
Include：
It is based onRisk forecast model is obtained, wherein, the risk forecast model is
6. a kind of driving risk assessment device based on the static factor, it is characterised in that described device includes：
First acquisition module, for obtaining the first car static data, wherein, the first car static data includes the first car
Main personal characteristics data, the first vehicle physical supplemental characteristic and first are in danger data；
Risk profile module, sector-style is entered for the first car static data to be input into the risk forecast model obtained in advance
Danger prediction；
Assessment result acquisition module, knot is assessed for being obtained according to the first the car static data and the risk forecast model
Really.
7. device according to claim 6, it is characterised in that described device also includes：
Second acquisition module, for obtaining multiple second people car static datas, wherein, each second people car static packet
Include second car owner's personal characteristics data, the second vehicle physical supplemental characteristic and second be in danger data；
Logistic regression function acquisition module, for obtaining logistic regression function according to the multiple second people car static data；
Probability function acquisition module, for based on the logistic regression function acquisition probability function；
Risk forecast model acquisition module, for obtaining risk forecast model based on the probability function.
8. device according to claim 7, it is characterised in that the logistic regression function is
Wherein, x=(x1,x2,x3...xn) vector that is made up of the multiple second people car static data, Y is be in danger probability, g
(x)=β0+β1x1+β2x2+…+βnxn。
9. device according to claim 8, it is characterised in that the logistic regression function acquisition module includes：
First computing submodule, for based onProbability function is obtained, wherein, it is described
Probability function is
10. device according to claim 9, it is characterised in that the risk forecast model acquisition module includes：
Second computing submodule, for based onRisk forecast model is obtained, wherein, institute
Stating risk forecast model is
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