CN106022846A - Automobile insurance pricing method, second-hand automobile pricing method and corresponding devices - Google Patents
Automobile insurance pricing method, second-hand automobile pricing method and corresponding devices Download PDFInfo
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Abstract
The invention relates to an automobile insurance pricing method. The automobile insurance pricing method includes the following steps that: the driving data and bad driving behavior data of an automobile are obtained by doing statistics, wherein the driving data and the bad driving behavior data both are multi-dimensional data; the hundred-mark scores of data of each dimension in the driving data and the bad driving behavior data are calculated, and the coefficients of variation of the data of each dimension are calculated, the weights of the data of each dimension are calculated according to the coefficients of variation and based on the analytic hierarchy process; and the driving risk score of the automobile is calculated according to the scores of the data of each dimension and the weights of the data of each dimension, so that automobile insurance pricing can be determined, and the automobile insurance pricing is more accurate and objective. The invention also relates to a second-hand automobile pricing method. According to the second-hand vehicle pricing method, multi-dimensional failure data are obtained by doing statistics, so that the quality score of an automobile can be calculated; second-hand automobile pricing is carried out according to the score; and therefore, the accuracy of the second-hand automobile pricing can be improved. The invention also relates to an automobile insurance pricing device and a second-hand automobile pricing device.
Description
Technical field
The present invention relates to big technical field of data processing, particularly relate to a kind of vehicle insurance price and used car price
Method and apparatus method and apparatus.
Background technology
Traditional vehicle insurance price and used car price are generally based on and fix a price from the car factor, from the car factor
Including static informations such as brand, car system, car age, year money, prices.Vehicle insurance price and used car price ginseng
The Pricing Factor examined is single, it is impossible to accurately reasonably carry out vehicle insurance price and used car is fixed a price.
Summary of the invention
Based on this, it is necessary to for above-mentioned problem, it is provided that one can improve vehicle insurance price and used car is fixed a price
The vehicle insurance price of accuracy and used car pricing method and device.
A kind of vehicle insurance pricing method, described method comprises the steps:
The running data of calculating vehicle and bad steering behavioral data, wherein said running data and described bad
Driving behavior data are multi-dimensional data；
Running data described in pretreatment and described bad steering behavioral data, calculate described running data and described
The hundred-mark system scoring score value of each dimension data in misconduct data, the described scoring of the most each dimension divides
It is worth the highest, drives risk the least；
Calculate the coefficient of variation of described each dimension data, and according to described coefficient of variation binding hierarchy analytic process
Calculate the weight of each dimension data；
Described scoring score value and the described weight calculation vehicle of each dimension data according to each dimension data are driven
Sailing danger score value, and determine that vehicle insurance is fixed a price according to the described vehicle drive risk score value calculated.
In one embodiment, described running data includes long data, traveling when driving mileage data, traveling
Period preference data and traveling accumulative city number；Described bad steering behavioral data includes sudden turn of events speed frequency, surpasses
Speed frequency, fatigue driving frequency, idling frequency and car owner divert one's attention to drive frequency.
In one embodiment, the described running data of mobile terminal collection vehicle is combined by car networking hardware
With bad steering behavioral data.
A kind of used car pricing method, described method comprises the steps:
The fault data of each parts of calculating vehicle, described fault data is that to carry out dimension division according to parts many
Dimension data；
Fault data described in pretreatment, calculates the hundred-mark system scoring of each dimension data in described row fault data
Score value, the failure-frequency that wherein said fault data shows is the highest, and described scoring score value is the lowest, vehicle mass
The poorest；
Calculate the coefficient of variation of described each dimension data, and according to described coefficient of variation binding hierarchy analytic process
Calculate the weight of each dimension data；
Described scoring score value according to each dimension data and the described weight calculation vehicle matter of each dimension data
Amount score value, and determine that used car is fixed a price according to the described vehicle mass score value calculated.
In one embodiment, described fault data is DTC；
Before fault data described in pretreatment, also include:
According to the car system mark that described DTC is corresponding, search the standard failure prestored under corresponding car system mark
Code；
The described DTC gathered according to the described standard fault codes identification prestored, determines that described DTC is corresponding
Vehicle part, and then determine the dimension that the DTC of collection is corresponding.
A kind of vehicle insurance pricing device, described device includes:
Data acquisition module, for running data and the bad steering behavioral data of calculating vehicle, wherein said
Running data and described running data bad steering behavioral data are multi-dimensional data；
Each dimension grading module, for running data described in pretreatment and described bad steering behavioral data, meter
Calculate the hundred-mark system scoring score value of each dimension data in described running data and described misconduct data, wherein
The described scoring score value of each dimension is the highest, drives risk the least；
Weight computation module, for calculating the coefficient of variation of described each dimension data, and according to described variation
Coefficient binding hierarchy analytic process calculates the weight of each dimension data；
Vehicle insurance pricing module, for according to the described scoring score value of each dimension data and each dimension data
Described weight calculation vehicle drive risk score value, and determine car according to the described vehicle drive risk score value calculated
Danger price.
In one embodiment, described running data includes long data, traveling when driving mileage data, traveling
Period preference data and traveling accumulative city number；Described bad steering behavioral data includes sudden turn of events speed frequency, surpasses
Speed frequency, fatigue driving frequency, idling frequency and car owner divert one's attention to drive frequency.
In one embodiment, the described running data of mobile terminal collection vehicle is combined by car networking hardware
With bad steering behavioral data.
A kind of used car pricing device, described device includes:
Data acquisition module, for the fault data of each parts of calculating vehicle, described fault data is according to portion
Part carries out the multi-dimensional data of dimension division；
Each dimension grading module, for fault data described in pretreatment, calculates in described row fault data each
The hundred-mark system scoring score value of dimension data, the failure-frequency that wherein said fault data shows is the highest, institute's commentary
Dividing score value the lowest, vehicle mass is the poorest；
Weight computation module, for calculating the coefficient of variation of described each dimension data, and according to described variation
Coefficient binding hierarchy analytic process calculates the weight of each dimension data；
Used car pricing module, for the described scoring score value according to each dimension data and each dimension data
Described weight calculation vehicle mass score value, and determine that used car is fixed according to the described vehicle mass score value calculated
Valency.
In one embodiment, described fault data is DTC；
Described device also includes:
Divide car system to search module, for the car system mark corresponding according to described DTC, search correspondence car system mark
The standard fault codes prestored under Shiing；
DTC identification module, for the described DTC gathered according to the described standard fault codes identification prestored,
Determine the vehicle part that described DTC is corresponding, and then determine the dimension that the DTC of collection is corresponding.
Above-mentioned vehicle insurance pricing method and device, by running data and the bad steering behavioral data of calculating vehicle,
Wherein said running data and described bad steering behavioral data are multi-dimensional data；Travel described in pretreatment
Data and described bad steering behavioral data, calculate in described running data and described misconduct data each
The hundred-mark system scoring score value of dimension data, the described scoring score value of the most each dimension is the highest, drives risk more
Little；Calculate the coefficient of variation of described each dimension data, and according to described coefficient of variation binding hierarchy analytic process
Calculate the weight of each dimension data；Described scoring score value according to each dimension data and each dimension data
Described weight calculation vehicle drive risk score value, and according to calculate described vehicle drive risk score value determine
Vehicle insurance is fixed a price, it is contemplated that these are dynamic and comprise multiple for vehicle operation data and bad steering behavioral data
By the multi-dimensional data of statistics is analyzed calculating, the data of the dimension factor, determine that vehicle insurance is fixed a price, make car
Danger price is the most objective.It addition, above-mentioned used car pricing method and device, by statistics various dimensions
Fault data calculates vehicle mass score value, carries out used car price according to score value, it is achieved that according to different parts
Vehicle mass is accurately marked by the fault data of (dimension), and then obtains more rationally the most second-hand
Car is fixed a price.
Accompanying drawing explanation
Fig. 1 is vehicle insurance price and the applied environment figure of used car pricing method in an embodiment；
Fig. 2 is the flow chart of vehicle insurance pricing method in an embodiment；
Fig. 3 is each dimension data that in an embodiment, running data and bad steering behavioral data include
Schematic diagram；
Fig. 4 is the flow chart of used car price in an embodiment；
Fig. 5 is the schematic diagram of each dimension data that fault data includes in an embodiment；
Fig. 6 is the structured flowchart of vehicle insurance pricing device in an embodiment；
Fig. 7 is the structured flowchart of used car pricing device in an embodiment；
Fig. 8 is the structured flowchart of used car pricing device in another embodiment.
Detailed description of the invention
In order to make the purpose of the present invention, technical scheme and advantage clearer, below in conjunction with accompanying drawing and reality
Execute example, the present invention is further elaborated.Only should be appreciated that specific embodiment described herein
Only in order to explain the present invention, it is not intended to limit the present invention.
As it is shown in figure 1, in one embodiment, it is provided that a kind of vehicle insurance price and used car pricing method
Applied environment figure.This applied environment includes car networking intelligent terminal 110, mobile terminal 120 and server
130, car networking intelligent terminal 110, mobile terminal 120 and server 130 are communicated by network.Its
In, car networking intelligent terminal 110 includes speed sensor-based system, Vehicle positioning system and onboard diagnostic system,
By car, networking intelligent terminal 110 can obtain the running data of vehicle, bad steering behavioral data and car
The data such as fault data.Mobile terminal 120 can obtain the driving data of diverting one's attention of driver, diverts one's attention to drive
Data reflection refer to driver whether having carried out when driving make a phone call, receive and dispatch short breath, see video etc. with
Drive the closely-related driving behavior of diverting one's attention of risk.Server 130 can receive car networking intelligent terminal 110 He
The data that mobile terminal 120 is collected, and the big data collected are carried out the analyzing and processing of various dimensions finally give
The driving behavior score value of vehicle and vehicle mass score value, carried out according to driving behavior score value and vehicle mass score value
Vehicle insurance price and used car are fixed a price.
As in figure 2 it is shown, in one embodiment, it is provided that a kind of vehicle insurance pricing method, the method is with application
Being illustrated in server as shown in Figure 1, the method comprises the steps:
Step S210: the running data of calculating vehicle and bad steering behavioral data, wherein said running data
It is multi-dimensional data with described running data bad steering behavioral data.
Concrete, as it is shown on figure 3, long data when the running data of vehicle includes driving mileage data, travels,
At least one in travel period preference data and traveling accumulative city number data.Bad steering behavioral data bag
Include sudden turn of events speed frequency, overdrive rate, fatigue driving frequency, idling frequency and car owner to divert one's attention to drive in frequency
At least one, wherein sudden turn of events speed frequency includes anxious accelerating frequency and anxious deceleration frequency.
According to Fig. 3, VMT Vehicle-Miles of Travel data are the distance travelled that each run of vehicle is corresponding, can pass through car
The localization function module of networking intelligent terminal obtains the gps data of vehicle in real time, the travel route of reduction vehicle,
Travel route according to vehicle obtains VMT Vehicle-Miles of Travel data.
Further, it is also possible to calculated by the longitude and latitude data of setting start time period with finish time
These period VMT Vehicle-Miles of Travel data, finally sue for peace the distance travelled of all periods of all collections
Distance travelled to vehicle.
(car is networked: be huge by information structures such as vehicle location, speed and routes can to pass through car networking hardware
The big Internet.By GPS (Global Positioning System global positioning system), RFID (Radio
Frequency Identification wireless radio-frequency), sensor, the device such as camera image process, vehicle
The collection of self environment and status information can be completed), when such as vehicle net intelligent terminal obtains the driving of vehicle
Long, and the period residing during vehicle drive, can get car according to long data when driving and driving period data
Travel period preference data, can embody vehicle travel period preference by preference.Travelled by vehicle
The setting of this dimension data of preference of period can make vehicle insurance price it is contemplated that different the driving of different periods
Sailing danger.In daylight and at night preference is the biggest, and driving risk is the biggest, and corresponding vehicle insurance price is the highest.Wherein,
The collection of vehicle travel period preference data can " my god " or " all " etc. for collecting unit.Collect one day or
The duration of person's vehicle traveling on daytime in a week and the duration of night running.
The latitude and longitude coordinates of vehicle can be obtained in real time by the localization function module of car networking intelligent terminal, according to
The latitude and longitude coordinates obtained can get the quantity in the city that vehicle adds up to arrive and and is distributed, if city is divided
Cloth is more scattered, can illustrate that car owner is being unfamiliar with road probability relatively greatly, compare concentration compared to city distribution
Situation, drives Hazard ratio in the case of city distribution is scattered bigger.
With reference to Fig. 3, sudden turn of events speed frequency is divided into again anxious acceleration to slow down with anxious.
In one embodiment, the average acceleration that can define five seconds reaches 1.8 meters/s2And the speed in five seconds
Increments ＞ 32 kilometers/hour and speed increment ＞ 50% in three seconds is defined as anxious acceleration.Five seconds averagely subtract
Speed reaches 3.5 meters/s2, speed decrement ＞ 35 kilometers/hour in five seconds, speed decrement is in three seconds
＞ 50%, it is also possible to different definition is done in different configurations and performance according to vehicle, the most specifically limits at this.
Can be according to the location data of vehicle, to positioning section corresponding to data and section Maximum speed limit, as
Really travel speed then judges this overspeed of vehicle more than section speed limit.
Drive duration to exceed the setting time and be then judged as fatigue driving.If such as car owner drives duration more than 4
Hour, it is believed that it is fatigue driving.
Can be according to Real-time Collection to Vehicle Speed, it may be judged whether vehicle motor operating occurs but travels
Rate over time is zero, if any, then it is judged as idling behavior.
Car owner drives frequency of diverting one's attention, the row unrelated with driver behavior that car owner makes
Behavior of diverting one's attention is driven for being car owner.
In one embodiment, car owner drives the behavior that behavior is car owner's operating handset of diverting one's attention, as called,
The behaviors such as short-message sending, broadcasting video.Can drive, by acquisition for mobile terminal car owner, data of diverting one's attention, specifically may be used
By obtaining the operation behavior data of mobile terminal records, and corresponding vehicle travel period searches the corresponding period
Operation behavior data to mobile terminal, the operation behavior data to mobile phone of this vehicle travel period are car
Main driving is divert one's attention data, can calculate car owner and drives, according to driving data of diverting one's attention, frequency of diverting one's attention.Concrete,
Driving data of diverting one's attention include communicating data, short message data, broadcasting video, voice data etc., wherein converse
Data include the data such as the duration of call, voice frequency；Short message data includes the frequency of short-message sending and short disappears
The data such as breath content size；Play video, voice data includes playing duration and play frequency data.
Step S220: running data described in pretreatment and described bad steering behavioral data, calculates described traveling
The hundred-mark system scoring score value of each dimension data in data and described misconduct data, the most each dimension
Described scoring score value is the highest, drives risk the least.
Concrete, running data and the bad steering behavioral data number of various dimensions to the various dimensions gathered
According to cleaning, and the data after cleaning are carried out structuring process.Wherein, cleaning data are not substantially inconsistent for rejecting
Logical data or the data not meeting logic are modified.
Calculate the hundred-mark system scoring point of each dimension data in described running data and described misconduct data
Value, the described scoring score value of the most each dimension is the highest, drives risk the least.Concrete, each number of dimensions
Hazardous act frequency according to display is the lowest, and during hundred-mark system scoring, deduction of points score value is the fewest, and the scoring score value obtained is more
Height, drives risk the least.
Concrete, when the dimension data gathered is discrete data, simple bisection method can be used to carry out percentage
System scoring, is specially the probability density function using similar exponential, the corresponding hazardous act frequency of structure
Deduction of points formula:
F (x)=α eβx
Wherein α and β is undetermined constant, controls deduction of points threshold value respectively and deduction of points yardstick successively decreases degree (i.e. curve
Steep), f (x) is deduction of points value, and x is the frequency that above-mentioned hazardous act occurs.Such as, when row of causing danger
For frequency be 1 time time, deduction of points value is f (1)=α eβ, when the frequency of the behavior of causing danger is 2 times, button
Score value is f (2)=α e2β, the like.
Wherein, data correspondence α of different dimensions and β value can be different, can be according to dissimilar hazardous act
The height of frequency does suitable adjustment.
Such as, α=9 desirable for a certain dimension data, β=0.1.
When an i.e. x=1 of hazardous act occurs, simple score 9.95 points；
When secondary hazardous act i.e. x=2 occurs, simple score 11 points, amount of increase 1.05；
When the three i.e. x=3 of subsidiary risk behavior occur, simple score 12.14 points, amount of increase 3.3；
When the four i.e. x=4 of subsidiary risk behavior occur, simple score 13.42 points, amount of increase 1.22；
When the five i.e. x=5 of subsidiary risk behavior occur, simple score 14.83 points, amount of increase 1.42；
Along with the number of times of the behavior of causing danger increases, the amount of increase of simple score (deduction of points) is also gradually increased.Also
Being exactly that hazardous act frequency is the highest, deduct points the most, deduction of points dynamics is the biggest.
If other dimension datas are relative to the hazardous act of the dimension data (α=9, β=0.1) described in citing
Occurrence frequency is high, then can suitably increase β value, if the frequency that hazardous act occurs is low, can suitably reduce β
Value.So can more deduct points, finally for the dimension data that hazardous act frequency frequency is higher
Hundred-mark system score also can be more reasonable.
When dimension data runs up to a certain amount of, when forming continuous data, normal state can be used for continuous print data
Distribution standard is marked.
When dimension data runs up to a certain amount of, it is believed that dimension data meets or close to normal distribution.Such as
The number of times of bringing to a halt of average every kilometer can use normal distribution to be analyzed it, determines this system of this driver
The meter index relative position in overall.
Particularly as follows: be first standardized dimension data processing.
Using standard score, standard score is statistically referred to as Z score, and formula is:Wherein generation
Table original value, represents overall meansigma methods (replacement of usable samples average), represents overall standard deviation (available
Sample standard deviation replaces), it may be assumed that
From the formula of Z score it can be shown that, its order of magnitude represents that original value is away from population mean
Short range degree, Z=0 i.e. represents that original value is equal to population mean；Its positive negative indication original value is greater than still
Less than population mean.
By 3 σ principles of normal distribution:
P (μ-σ < X≤μ+σ)=68.268949%；
P (μ-2 σ < X≤μ+2 σ)=95.449974%；
P (μ-3 σ < X≤μ+3 σ)=99.730020%；
Understanding, Z score, between-3 and+3, is negative value sometimes, is multidigit decimal sometimes, uses not
Meet the evaluation custom of people.Z score can be carried out linear transformation, be the most statistically referred to as T mark,
Its formula is:
T=KZ+c.
I.e. Z score is expanded k times, and move on to this center of c.
From gaussian distribution table, each 3 Z in left and right of the mean place (i.e. Z=0) of Z score divide,
Including the value of overall 99.73%, almost all contains overall all values.It is divided into 100 at hundred-mark system fullness in the epigastrium and abdomen
Point, if then making k=-7, c=80, (80 are our the mark concentration position of most people in hundred-mark system usually
Put), then Z score becomes point system that full marks are 99.73 after conversion, with 100 the most closely.
According to T mark, (taking-Z, to be because hazardous act the most, and score should be the lowest for T=7Z+80.As T < 0,
Then T takes 0；Work as T > 100, then T takes 100) i.e. can get single input parameter driving scoring.
Step S230: calculate the coefficient of variation of described each dimension data, and combine according to the described coefficient of variation
Analytic hierarchy process (AHP) calculates the weight of each dimension data.
The coefficient of variation (Coefficient of Variation) is initial data standard deviation and initial data average
Ratio.
Analytic hierarchy process (AHP) (Analytic Hierarchy Process is called for short AHP) is so-called analytic hierarchy process (AHP),
Refer to a complicated decision-making problem of multi-objective as a system, be multiple target or standard by goal decomposition
Then, and then be decomposed into some levels of multi objective (or criterion, constraint), by qualitative index fuzzy quantization
Method calculates Mode of Level Simple Sequence (flexible strategy) and total sequence, using as target (multi objective), multi-scheme optimization certainly
The system approach of plan.The weight of each dimension is can get by analytic hierarchy process (AHP).
In one embodiment, use analytic hierarchy process (AHP) when carrying out weight calculation, each dimension determined important
What degree was maximum is anxious deceleration, is secondly anxious acceleration, and the importance degree of each dimension determined is qualitative index, calculates
The weight obtained is quantitative target.
The weight using each dimension of chromatographic assays calculating lays particular stress on the experience according to people and carries out weight guiding, has
Certain limitation, in order to be that weight is more objective, adds the coefficient of variation, the coefficient of variation in the present embodiment
It is to directly utilize the information that indices is comprised, by being calculated the parameter of index, is a kind of objective tax
The method of power, uses the coefficient of variation to be optimized, after optimization according to the calculated weight of analytic hierarchy process (AHP)
The weight of each dimension more objective, accurate.
Step S240: according to described scoring score value and the described weight of each dimension data of each dimension data
Calculate vehicle drive risk score value, and determine that vehicle insurance is fixed a price according to the described vehicle drive risk score value calculated.
Concrete, after the hundred-mark system scoring score value of each dimension is multiplied by the weight of corresponding dimension, superposition is the most available
Drive risk score value, drive risk score value the biggest, drive risk the biggest.
Server prestores the corresponding relation driven between risk score value or score value interval and vehicle insurance price,
Determine, according to this corresponding relation, the vehicle insurance price that the driving risk score value of calculating is corresponding, or first determine calculating
Score value corresponding to driving risk score value interval, the corresponding relation between and vehicle insurance price interval according to score value is true
Determine vehicle insurance price.
In the present embodiment, by running data and the bad steering behavioral data of collection vehicle, wherein said row
Sail data and described running data bad steering behavioral data is multi-dimensional data；Number is travelled described in pretreatment
According to described bad steering behavioral data, calculate the coefficient of variation of each dimension；Calculate each according to analytic hierarchy process (AHP)
The weight of dimension；Weight calculation vehicle drive row according to described each dimension and the coefficient of variation and described each dimension
For score value, determine that vehicle insurance is fixed a price according to described score value, it is achieved that carry out more rationally according to multidimensional data and
Vehicle insurance price accurately.
In one embodiment, as shown in Figure 4, it is provided that a kind of used car pricing method, described method bag
Include following steps:
Step S310: the fault data of each parts of calculating vehicle, described fault data is for tie up according to parts
The multi-dimensional data that degree divides.
Concrete, as it is shown in figure 5, can be by the real time execution of the car networking each parts of intelligent terminal's collection vehicle
Status data, when detecting that running state data exceeds normal range, then it is assumed that these parts exist fault, note
Record this fault, and form fault data according to current operating conditions.
In one embodiment, owing to different car based part running state data there are differences, parts are normally transported
The scope of row is the most different, in order to the fault data carrying out different car system is collected, server is deposited in advance
Having stored up the failure-description of different car system, such as A car system electromotor exists > 7000r/min is fault, B car system is sent out
Motivation exists > 9000r/min is fault, owing to the failure-description of different car systems has been carried out combing, it is right to realize
Different car systems carry out fault and accurately judge the collection with fault data.
With reference to Fig. 5, the fault data of collection carries out division according to parts can include multiple dimension, specifically includes:
Electromotor, variator, brake system, driving, air bag etc. affect the multi-dimensional data of driving safety and air-conditioning,
Skylight, seat, vehicle-mounted, windscreen wiper etc. affect multi-dimensional data and head/taillight, the reversing automatically of driving experience
Image constant speed, adaptive learning algorithms etc. affect the multi-dimensional data of automatization's driving experience.
Step S320: fault data described in pretreatment, calculates each dimension data in described row fault data
Hundred-mark system scoring score value, the failure-frequency that wherein said fault data shows is the highest, and described scoring score value is the lowest,
Vehicle mass is the poorest.
Obtain each dimension (parts) according to the fault data gathered and send out out of order frequency, according to fault frequently
The size of rate carries out hundred-mark system scoring to each dimension, and the frequency of fault is the highest, and scoring score value is the lowest, vehicle
Quality the poorest.
Step S330: calculate the coefficient of variation of described each dimension data, and combine according to the described coefficient of variation
Analytic hierarchy process (AHP) calculates the weight of each dimension data.
In one embodiment, use analytic hierarchy process (AHP) when carrying out weight calculation, each dimension determined important
What degree was maximum is engine failure occurrence frequency, is secondly brake system failure-frequency.
Step S340: according to described scoring score value and the described weight of each dimension data of each dimension data
Calculate vehicle mass score value, and determine that used car is fixed a price according to the described vehicle mass score value calculated.
Concrete, after the hundred-mark system scoring score value of each dimension is multiplied by the weight of corresponding dimension, superposition is the most available
Vehicle mass score value, vehicle mass score value is the biggest, and vehicle mass is the best.
Server prestores the corresponding pass between vehicle mass score value or score value interval with used car price
System, determines, according to this corresponding relation, the used car price that the vehicle mass score value of calculating is corresponding, or the most true
Score value corresponding to vehicle mass score value that devise a stratagem is calculated is interval, right between and used car price interval according to score value
Should be related to and determine that used car is fixed a price.
In the present embodiment, by the fault data of all parts of collection vehicle, according to vehicle all parts
Failure-frequency and embody the weight factor of each parts significance level and used car is fixed a price, relatively with tradition
Only consider vehicle brand, car system, year money, the Static implicit method such as price carries out used car price, the present embodiment
The pricing method considering vehicle trouble data more flexibly and rationally.
In one embodiment, described fault data is DTC.Described DTC can be by car networking hardware:
OBD (On-Board Diagnostic onboard diagnostic system) gathers.Owing to the DTC of different car systems is corresponding
The failure-description of vehicle not quite identical, therefore, in order to identify DTC more accurately, need to distinguish
Car system corresponding to DTC gathered, according to the failure-description identification DTC under corresponding car system, and then determines
The parts of the vehicle that DTC is corresponding.
Concrete, used car pricing method also includes:
According to the car system mark that described DTC is corresponding, search the standard failure prestored under corresponding car system mark
Code.The described DTC gathered according to the described standard fault codes identification prestored, determines that described DTC is corresponding
Vehicle part, and then determine the dimension that the DTC of collection is corresponding.
The failure-description that DTC and DTC are corresponding is stored by server in advance by car system classification.
Identifying with car system in the DTC gathered, server is according to this car system mark location seeking scope, in correspondence
Search the DTC of coupling under car system, get the failure-description that this DTC is corresponding, and then determine appearance event
The parts of the vehicle of barrier, obtain concrete dimension data.
In the present embodiment, it is achieved that the identification to different car systems DTC, and then difference of can fixing a price exactly
The used car of car system, applied widely, fix a price objective and accurate.
In one embodiment, as shown in Figure 6, it is provided that a kind of vehicle insurance pricing device, described device includes:
Data statistics module 410, for running data and bad steering behavioral data, the Qi Zhongsuo of calculating vehicle
State running data and described running data bad steering behavioral data is multi-dimensional data.
Each dimension grading module 420, for running data described in pretreatment and described bad steering behavioral data,
Calculate the hundred-mark system scoring score value of each dimension data in described running data and described misconduct data, its
In the described scoring score value of each dimension the highest, drive risk the least.
Weight computation module 430, for calculating the coefficient of variation of described each dimension data, and according to described change
Different coefficient binding hierarchy analytic process calculates the weight of each dimension data.
Vehicle insurance pricing module 440, for the described scoring score value according to each dimension data and each dimension data
Described weight calculation vehicle drive risk score value, and according to calculate described vehicle drive risk score value determine
Vehicle insurance is fixed a price.
In one embodiment,
Described running data includes long data, travel period preference data and row when driving mileage data, traveling
Sail accumulative city number four dimension data；Described bad steering behavioral data include sudden turn of events speed frequency, overdrive rate,
Fatigue driving frequency, idling frequency and car owner divert one's attention to drive frequency.
In one embodiment, the described running data of mobile terminal collection vehicle is combined by car networking hardware
With bad steering behavioral data.
In one embodiment, as it is shown in fig. 7, a kind of used car pricing device, described device includes:
Data statistics module 510, for the fault data of each parts of calculating vehicle, described fault data be according to
Parts carry out the multi-dimensional data of dimension division.
Each dimension grading module 520, for fault data described in pretreatment, calculates in described row fault data every
The hundred-mark system scoring score value of individual dimension data, the failure-frequency that wherein said fault data shows is the highest, described
Scoring score value is the lowest, and vehicle mass is the poorest.
Weight computation module 530, for calculating the coefficient of variation of described each dimension data, and according to described change
Different coefficient binding hierarchy analytic process calculates the weight of each dimension data.
Used car pricing module 540, for the described scoring score value according to each dimension data and each number of dimensions
According to described weight calculation vehicle mass score value, and according to calculate described vehicle mass score value determine used car
Price.
In one embodiment, described fault data is DTC；
Described device also includes:
Divide car system to search module 610, for the car system mark corresponding according to described DTC, search correspondence car system
The standard fault codes prestored under Biao Shi.
DTC identification module 620, for the described fault gathered according to the described standard fault codes identification prestored
Code, determines the vehicle part that described DTC is corresponding, and then determines the dimension that the DTC of collection is corresponding.
One of ordinary skill in the art will appreciate that all or part of flow process realizing in above-described embodiment method,
Can be by computer program and complete to instruct relevant hardware, program can be stored in a computer-readable
Taking in storage medium, in the embodiment of the present invention, this program can be stored in the storage medium of computer system,
And performed by least one processor in this computer system, to realize including such as the enforcement of above-mentioned each method
The flow process of example.Wherein, storage medium can be magnetic disc, CD, read-only store-memory body (Read-Only Memory,
Or random store-memory body (Random Access Memory, RAM) etc. ROM).
Each technical characteristic of embodiment described above can combine arbitrarily, for making description succinct, the most right
The all possible combination of each technical characteristic in above-described embodiment is all described, but, if these skills
There is not contradiction in the combination of art feature, is all considered to be the scope that this specification is recorded.
Embodiment described above only have expressed the several embodiments of the present invention, and it describes more concrete and detailed,
But can not therefore be construed as limiting the scope of the patent.It should be pointed out that, for this area
For those of ordinary skill, without departing from the inventive concept of the premise, it is also possible to make some deformation and change
Entering, these broadly fall into protection scope of the present invention.Therefore, the protection domain of patent of the present invention should be with appended power
Profit requires to be as the criterion.
Claims (10)
1. a vehicle insurance pricing method, described method comprises the steps:
The running data of calculating vehicle and bad steering behavioral data, wherein said running data and described bad
Driving behavior data are multi-dimensional data；
Running data described in pretreatment and described bad steering behavioral data, calculate described running data and described
The hundred-mark system scoring score value of each dimension data in misconduct data, the described scoring of the most each dimension divides
It is worth the highest, drives risk the least；
Calculate the coefficient of variation of described each dimension data, and according to described coefficient of variation binding hierarchy analytic process
Calculate the weight of each dimension data；
Described scoring score value and the described weight calculation vehicle of each dimension data according to each dimension data are driven
Sailing danger score value, and determine that vehicle insurance is fixed a price according to the described vehicle drive risk score value calculated.
Method the most according to claim 1, it is characterised in that:
Described running data includes long data, travel period preference data and row when driving mileage data, traveling
Sail at least one in the number of accumulative city；Described bad steering behavioral data includes sudden turn of events speed frequency, hypervelocity frequency
Rate, fatigue driving frequency, idling frequency and car owner divert one's attention to drive at least one in frequency.
Method the most according to claim 1 and 2, it is characterised in that: combined by car networking hardware and move
The described running data of dynamic terminal collection vehicle and bad steering behavioral data.
4. a used car pricing method, described method comprises the steps:
The fault data of each parts of calculating vehicle, described fault data is that to carry out dimension division according to parts many
Dimension data；
Fault data described in pretreatment, calculates the hundred-mark system scoring of each dimension data in described row fault data
Score value, the failure-frequency that wherein said fault data shows is the highest, and described scoring score value is the lowest, vehicle mass
The poorest；
Calculate the coefficient of variation of described each dimension data, and according to described coefficient of variation binding hierarchy analytic process
Calculate the weight of each dimension data；
Described scoring score value according to each dimension data and the described weight calculation vehicle matter of each dimension data
Amount score value, and determine that used car is fixed a price according to the described vehicle mass score value calculated.
Method the most according to claim 4, it is characterised in that described fault data is DTC；
Before fault data described in pretreatment, also include:
According to the car system mark that described DTC is corresponding, search the standard failure prestored under corresponding car system mark
Code；
The described DTC gathered according to the described standard fault codes identification prestored, determines that described DTC is corresponding
Vehicle part, and then determine the dimension that the DTC of collection is corresponding.
6. a vehicle insurance pricing device, it is characterised in that described device includes:
Data acquisition module, for running data and the bad steering behavioral data of calculating vehicle, wherein said
Running data and described running data bad steering behavioral data are multi-dimensional data；
Each dimension grading module, for running data described in pretreatment and described bad steering behavioral data, meter
Calculate the hundred-mark system scoring score value of each dimension data in described running data and described misconduct data, wherein
The described scoring score value of each dimension is the highest, drives risk the least；
Weight computation module, for calculating the coefficient of variation of described each dimension data, and according to described variation
Coefficient binding hierarchy analytic process calculates the weight of each dimension data；
Vehicle insurance pricing module, for according to the described scoring score value of each dimension data and each dimension data
Described weight calculation vehicle drive risk score value, and determine car according to the described vehicle drive risk score value calculated
Danger price.
Device the most according to claim 1, it is characterised in that:
Described running data includes long data, travel period preference data and row when driving mileage data, traveling
Sail accumulative city number four dimension data；Described bad steering behavioral data include sudden turn of events speed frequency, overdrive rate,
Fatigue driving frequency, idling frequency and car owner divert one's attention to drive frequency.
8. according to the device described in claim 6 or 7, it is characterised in that: combined by car networking hardware and move
The described running data of dynamic terminal collection vehicle and bad steering behavioral data.
9. a used car pricing device, it is characterised in that described device includes:
Data acquisition module, for the fault data of each parts of calculating vehicle, described fault data is according to portion
Part carries out the multi-dimensional data of dimension division；
Each dimension grading module, for fault data described in pretreatment, calculates in described row fault data each
The hundred-mark system scoring score value of dimension data, the failure-frequency that wherein said fault data shows is the highest, institute's commentary
Dividing score value the lowest, vehicle mass is the poorest；
Weight computation module, for calculating the coefficient of variation of described each dimension data, and according to described variation
Coefficient binding hierarchy analytic process calculates the weight of each dimension data；
Used car pricing module, for the described scoring score value according to each dimension data and each dimension data
Described weight calculation vehicle mass score value, and determine that used car is fixed according to the described vehicle mass score value calculated
Valency.
Device the most according to claim 9, it is characterised in that described fault data is DTC；
Described device also includes:
Divide car system to search module, for the car system mark corresponding according to described DTC, search correspondence car system mark
The standard fault codes prestored under Shiing；
DTC identification module, for the described DTC gathered according to the described standard fault codes identification prestored,
Determine the vehicle part that described DTC is corresponding, and then determine the dimension that the DTC of collection is corresponding.
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| CN112258095A (en) * | 2020-12-22 | 2021-01-22 | 中国平安财产保险股份有限公司 | Standard normal distribution based scoring method, device, equipment and storage medium |
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| CN112508213A (en) * | 2020-12-25 | 2021-03-16 | 武汉理工大学 | Method and equipment for evaluating residual value of running pure electric automobile |
| CN114735008A (en) * | 2022-04-18 | 2022-07-12 | 中国第一汽车股份有限公司 | Driving behavior scoring method and device, electronic equipment and storage medium |
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