CN106056144A - System and method for establishing region division remote damage assessment of different vehicle types based on artificial intelligence - Google Patents
System and method for establishing region division remote damage assessment of different vehicle types based on artificial intelligence Download PDFInfo
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- G06F18/20—Analysing
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Abstract
The invention relates to a system and method for establishing region division remote damage assessment of different vehicle types based on artificial intelligence and belongs to the vehicle damage assessment field. The objective of the invention is to solve problems in region detection after a vehicle collision. According to the technical schemes of the invention, a region detection subsystem is adopted to judge collision regions in the vehicle collision; and the region detection subsystem learns region training data so as to generate a region model. With the system and method provided by the technical schemes of the invention adopted, region detection in the vehicle collision can be realized; and a machine learning method is used in the remote damage assessment technical field, so that the accuracy of judgment in a damage assessment process can be improved.
Description
Technical field
The invention belongs to car damage identification field, relate to one and set up the long-range setting loss in different automobile types subregion based on artificial intelligence
System and method.
Background technology
During low-speed motion (including low speed links traveling, vehicle parking etc.), take place frequently collision accident for vehicle and lead
The Claims Resolution dispute problem caused, long-range setting loss technology by multi-signal in collection vehicle driving process (as speed, acceleration,
Angular velocity, sound etc.) and analyzed with signal processing and machine learning techniques, to judge whether collision occurs and collision rift
The damage situation of vehicle.
After vehicle crashes, headend equipment is capable of detecting when the generation of collision and intercepts the signal of collision process,
Being sent to high in the clouds by wireless network, remote server extracts the eigenvalue of design in advance from the signal received, and uses machine
Learning algorithm is analyzed, and first judges the accuracy of crash data, then judges collision object and operating mode situation, to determine Collision Number
Create the damage of which kind of grade according to what part of set pair, then go out with reference to amount for which loss settled concurrent according to part injury rating calculation
Deliver to insurance company.The detection for vehicle, operating mode, target, part and region can be related to during this.
Summary of the invention
After solving vehicle collision, for the problem of region detection, the present invention proposes and sets up not based on artificial intelligence
With the long-range loss assessment system in vehicle subregion and method, to realize the region detection during setting loss.
In order to solve above-mentioned technical problem, the technical scheme that the present invention provides is characterized by: including:
Truck type choice subsystem, selects the model data corresponding to vehicle as total data set；
Data classification subsystem, reads CAE emulation data and real vehicle data, and classifies data accordingly；
Collision detection subsystem, it is judged that the most whether vehicle collides；Described collision detection subsystem pair
Collision training data carries out learning thus generates collision model；
Operating mode detection subsystem, it is judged that all work informations that collision occurs；Operating mode is instructed by described operating mode detection subsystem
White silk data carry out learning thus generate condition model；
Region detection subsystem, judges hit region during vehicle collision；Described region detection subsystem, to regional training number
According to carrying out learning thus formation zone model.
Beneficial effect: technique scheme, it is possible to achieve for the region detection of vehicle collision, at this of long-range setting loss
Technical field employs the method for machine learning, for machine learning method, during setting loss, it determines accuracy rate on
Promoted；The present invention is by selecting vehicle to import the data corresponding to this vehicle, and data classification is then in order to model is instructed
Practice and the purpose of test and the step that adds；The detection in region is the purpose that the program realizes, and is to be wanted through sequence of operations
The result obtained.
Accompanying drawing explanation
Fig. 1 is the structural schematic block diagram of system of the present invention.
Detailed description of the invention
In order to the present invention being made apparent explanation, below the technical term that the present invention relates to is made definitions:
Operating mode: all collision informations such as collision angle, direction, target, region；
Vehicle: automobile model；
Target: collision target；
Region: position of collision；
Part: auto parts；
Operating mode detects: detect all collision informations such as this car collision angle, direction, target, region；
Vehicle detects: the automobile model that detection collides with this car；
Target detection: detect this car collision target；
Region detection: detect this car position of collision；
Piece test: detect this car auto parts.
Embodiment 1:
One sets up the long-range loss assessment system in different automobile types subregion based on artificial intelligence, including:
Truck type choice subsystem, selects the model data corresponding to vehicle as total data set；
Data classification subsystem, reads CAE emulation data and real vehicle data, and classifies data accordingly；
Collision detection subsystem, it is judged that the most whether vehicle collides；Described collision detection subsystem pair
Collision training data carries out learning thus generates collision model；
Operating mode detection subsystem, it is judged that all work informations that collision occurs；Operating mode is instructed by described operating mode detection subsystem
White silk data carry out learning thus generate condition model；
Region detection subsystem, judges hit region during vehicle collision；Described region detection subsystem, to regional training number
According to carrying out learning thus formation zone model.
Described collision detection subsystem includes, collision training module, crash tests module, collision authentication module, described in touch
Hitting training module and generate collision model for learning collision training data, crash tests module is for surveying collision
Examination data bring the result detecting collision model in collision model into, and collision authentication module uses true sport car data verification collision mould
The reliability of type and accuracy rate；
Described operating mode detection subsystem includes, operating mode training module, working condition measurement module, operating mode authentication module, described work
Condition training module generates condition model for learning operating mode training data, and described working condition measurement module is for by work
Condition test data bring the result detecting condition model in model into, and operating mode authentication module uses true sport car data verification operating mode mould
The reliability of type and accuracy rate；
Described region detection subsystem includes, regional training module, domain test module, area verification module, described district
Territory training module is for carrying out learning thus formation zone model by regional training data, and domain test module is for surveying region
Examination data bring the result of detection zone domain model in model into, and area verification module uses true sport car data verification regional model
Reliability and accuracy rate.
Described collision model is set up and is used intelligence supervised learning linear regression method, Intelligent Energy model method, intelligence
Unsupervised learning FuzzyC-Means clustering algorithm, intelligence supervised learning AdaBoost method more than one；
Described condition model is set up and is used intelligence supervised learning linear regression method, Intelligent Energy model method, intelligence
Unsupervised learning FuzzyC-Means clustering algorithm, intelligence supervised learning AdaBoost method more than one；
Described regional model is set up and is used intelligence supervised learning linear regression method, Intelligent Energy model method, intelligence
Unsupervised learning FuzzyC-Means clustering algorithm, intelligence supervised learning AdaBoost method more than one；
And, described collision model, described condition model, described regional model set up the algorithm used or method is not complete
Identical algorithm or method.
As a kind of embodiment:
Described supervised learning linear regression method includes:
S1. input variable x is characterized, and the predictive value y of output is desired value；The curve table of matching is shown as y=h (x)；
S2. output y is the linear function of x, and is expressed as matrix form；
S3. introduce cost function, use gradient descent algorithm, after initializing learning parameter, repeat renewal learning parameter
Value, to obtain minimum side's more new regulation.
Described descent algorithm is: batch gradient declines and/or statistical gradient declines, supervised learning linear regression algorithm
For:
Output y is that the linear function of x is:
hθ(x)=θ0+θ1x1+θ2x2
θ i is parameter, it is assumed that x0=1, above formula is expressed as matrix form:
θ and x is column vector, certain training set given, introduces cost function, and it is defined as follows:
N represents that Characteristic Number is numbered, m representation dimension；
By initial guess initiation parameter θ, the most constantly change the value of parameter θ so that parameter J (θ) is the least, directly
To finally giving the J (θ) minimized, use gradient descent algorithm, after initiation parameter θ, repeat following renewal equation, with
The value of undated parameter θ；
J (θ) is cost function, and j is the subscript of parameter, and θ j is parameter, and α represents learning rate；
Thus, update equation to be reduced to:
As a kind of embodiment:
The general type of described Intelligent Energy model:
Wherein, WijIt is spin SiBetween coupling, the namely feature in machine learning, hiIt is outfield physically, also
I.e. bias item in machine learning；
The probabilistic model definition of the EBM probability distribution by energy function, x is characterized vector, is shown below:
Regularization coefficient Z is referred to as partition functions；
Use above-mentioned model, the method for described Intelligent Energy model:
Use following formula is expressed:
X, h are hidden unit；
The free energy of created symbol, is defined as follows:
Can also be written as:
The negative maximum likelihood gradient table of data is shown as:
For Maximum-likelihood estimation, gradient table above is shown as two parts, relates to the part in front and negative portion
Point, front and negative do not indicate that the symbol of every part in equation, but represent the impact of probability density in model, first
Dividing and increase the probability of training data (by reducing corresponding free energy), Part II reduces model and determines downward gradient；The
Two parts relate to calculate, under all configurations, the expectation of x meets by probability distribution P of model generation；
CalculateThe first step be the expectation of model sample calculating and estimating fixed qty, be used for representing negative
Part gradient be expressed as negative particle, be expressed asGradient is written as:
Element is sampled according to P(such as. we can do DSMC), by above formula, we
Random particles algorithm almost can be used to learn EBM model, and being how of uniquely lacking extracts these negative particlesSystem
Meter has many sampling approach on learning, and Markov chain Monte Carlo is particularly suitable for the most limited Boltzmann machine of model
(RBM), its a kind of special EBM.
As a kind of embodiment:
Described intelligence unsupervised learning FuzzyC-Means clustering algorithm is:
FCM n vector x i (i=1,2 ..., n) be divided into c ambiguity group, and seek the cluster centre often organized so that non-phase
Minimizing like the cost function of property index, FCM makes each data-oriented point value 0, and the degree of membership between 1 determines that it belongs to
In each degree organized, adapt with introducing fuzzy division, Subject Matrix u allowed value 0, the element between 1, add normalizing
Change regulation, the degree of membership of a data set and total equal to 1, such as following formula:
The cost function of FCM is following formula vague generalization form:
uijBetween 0, between 1；Ci is the cluster centre of ambiguity group i, and dij=| | ci-xj | | is ith cluster center and jth
Euclidean distance between individual data point；And m is a Weighted Index, m belongs to 1 to infinite；
It is constructed as follows new object function, tries to achieve the essential condition making following formula minimize value；
Input parameter derivations all to above formula, the essential condition making above formula minimize is:
With
By said two essential condition, Fuzzy C-Means Cluster Algorithm is an iterative process, runs at batch processing mode
Time, FCM the following step determines cluster centre ci and Subject Matrix U [1]:
Step 1: by value 0, the random number between 1 initializes Subject Matrix U；
Step 2: calculate c cluster centre ci, i=1 by formula (3) ..., c.
Step 3: be worth according to formula (1) given price value function, the threshold values determined if below certain, or its relative last time
The knots modification of functional value is less than certain threshold values, then algorithm stops；
Step 4: with formula (4) calculate new U matrix and, return step 2.
As a kind of embodiment:
Described intelligence supervised learning AdaBoost method includes:
S1. the weight initializing all training examples is 1/N, and wherein N is sample number；
S2. for m=1 ... M, m are the initial value of data volume M, and M is sample number；
A). training Weak Classifier ym () so that it is minimize weighted error function (weighted error function):
Wherein: X is sample, n is sample number, and tn is weighting function assigned value, and wn is the weight that sample is corresponding；
B) right of speech α of this Weak Classifier is calculated:
C) weight is updated:
Wherein: α: right of speech, t: parameter, y: Weak Classifier, x: characteristic quantity, exp: exponential function, w: weight, Z is specification
Change the factor；
Wherein:
It is standardizing factor, make all w's and be 1；
S3. last grader is obtained:
Wherein: α: right of speech, m is the initial value of data volume M, and M is sample number, y: Weak Classifier, sign: symbol letter
Number.
Embodiment 2:
One sets up the long-range damage identification method in different automobile types subregion based on artificial intelligence, comprises the following steps:
Step one. select the model data corresponding to vehicle as total data set；
Step 2. read CAE emulation data and real vehicle data, and accordingly data are classified；
Step 3. judge the most whether vehicle collides；Collision training data is learnt thus raw
Becoming collision model, described collision model is set up and is used intelligence supervised learning linear regression method；
Step 4. judge all work informations that collision occurs；Operating mode training data is learnt thus generates operating mode
Model, described condition model is set up and is used intelligence supervised learning linear regression method；
Step 5. judge hit region during vehicle collision；Regional training data are learnt thus formation zone model,
Described regional model is set up and is used intelligence supervised learning linear regression method.
Comprise the concrete steps that:
Step 3 includes:
S3.1. use collision detection subsystem that CAE collision simulation data are processed, then classify to produce collision to it
Training data and crash tests data；
S3.2. in collision training module, collision training data learnt and produces collision model, carrying out simulated crash
The effect of training data；
S3.3. crash tests data are used to carry out the result of test collisions model in crash tests module；
S3.4. use true sport car data as collision checking data and to bring collision authentication module into, verify collision mould
The accuracy of type；
Step 4 includes:
S4.1. CAE operating mode emulation data are processed by applying working condition detection subsystem, then it is carried out classification generation operating mode instruction
Practice data and working condition measurement data；
S4.2. in operating mode training module, operating mode training data learnt and produce condition model, carrying out simulated condition
The effect of training data；
S4.3. in working condition measurement module, applying working condition test data carry out the result of measurement condition model；
S4.4. use true sport car data as operating mode checking data and to bring operating mode authentication module into, verify operating mode mould
The accuracy of type；
Step 5 includes:
S5.1. use region detection subsystem that CAE simulation of domain data are processed, then classify to produce region to it
Training data and domain test data；
S5.2. in regional training module, regional training data learnt and produce regional model, carrying out simulated domain
The effect of training data；
S5.3. domain test data are used to carry out the result of test zone model in domain test module；
S5.4. use true sport car data as area validation data and to bring area verification module into, carry out validation region mould
The accuracy of type.
Described collision model is set up and is used intelligence supervised learning linear regression method, Intelligent Energy model method, intelligence
Unsupervised learning FuzzyC-Means clustering algorithm, intelligence supervised learning AdaBoost method more than one；
Described condition model is set up and is used intelligence supervised learning linear regression method, Intelligent Energy model method, intelligence
Unsupervised learning FuzzyC-Means clustering algorithm, intelligence supervised learning AdaBoost method more than one；
Described regional model is set up and is used intelligence supervised learning linear regression method, Intelligent Energy model method, intelligence
Unsupervised learning FuzzyC-Means clustering algorithm, intelligence supervised learning AdaBoost method more than one；
And, described collision model, described condition model, described regional model set up the algorithm used or method is not complete
Identical algorithm or method.
Described various algorithm or the concrete steps of method are identical with the algorithm in embodiment 1 or method.
Embodiment 3:
Supervised learning linear regression method described in embodiment 1 or 2 is supplemented by the present embodiment, uses and has supervision
The linear regression method of study, in statistics, linear regression is the least square function pair utilizing and being referred to as equation of linear regression
A kind of regression analysis that between one or more independent variables and dependent variable, relation is modeled.The target of regression problem is given D
Tie up input variable x, and each input vector x has value y of correspondence, it is desirable to for the company of new its correspondence of data prediction
Continuous desired value t.In the present system, x refers to the signal data that preprocessed module extracts, and y refers to impairment scale mark
Label/operating mode label/collision labels.
Essential Terms in above-mentioned learning process: overall data set is referred to as training set；Input variable x is characterized；Output
Predictive value y be desired value；The curve of matching, is typically expressed as y=h (x), referred to as hypothesized model.
In order to utilize supervised learning, it would be desirable to the form of determining function h.As an initial selected, we can be false
Surely output y is the linear function of x.That is:
hθ(x)=θ0+θ1x1+θ2x2
Here, for parameter, also referred to as weights (weights).It is assumed that=1.The most above-mentioned can be expressed as rectangular
Formula:
In above formula, n represents the number of input variable, and is all column vector.By formulation above, the most just have a talk about, give
After certain training set fixed, our work is how to select learning parameter θ in other words.In order to define prediction output and phase
Answering the difference between true output, next we introduce cost function (costfunction), and it is defined as follows:
Our target is, by the value of Selection parameter θ, the value making above-mentioned cost function as far as possible is minimum.Can
Carry out initiation parameter θ first passing through initial guess (initialguess), the most constantly change the value of parameter θ so that value
Parameter J (θ) is the least, until finally giving the J (θ) minimized.Particularly, it is considered to a Gradient declines
(gradientdescent) algorithm.After it initializes θ, then repeat equation below to update the value of θ.
Here, α represents learning rate (learningrate).It is noted that the least meeting of α value makes finally to converge
Very slow, i.e. gradient declines very slow, and value is too big, can make finally to converge quickly, i.e. gradient declines quickly；And it is different
Initial value α, it is possible to cause the value of parameter θ minimum for the J (θ) finally made to differ.Next we need it is of concern that
The solving of that partial derivative on the right, for our example above-mentioned, we can obtain:
So renewal equation above can be reduced to:
Above formula is exactly our known lowest mean square (Leastmeansquares, LMS) more new regulation, or also referred to as
Widrow-Hoff learning rules.By above formula it may be seen that the amplitude proportional updated is in error term (that in bracket
, it represents the difference between actual value and predictive value) value.
The relational expression only for a training set example that we obtain above, during for multiple training set example
Under, we also need to make above formula some amendment, have two kinds of methods: (1) batch gradient declines
(batchgradientdescent)；(2) statistical gradient declines (stochasticgradientdescent).The most first be given
The algorithm steps of the two.
Declining for batch gradient, its algorithm is:
Declining for statistical gradient, its algorithm is:
Under the most at first sight, do not think what difference the two has, and essence is quite different.For batch gradient descent algorithm,
It will search for training dataset really in renewal each time；And for statistical gradient descent algorithm, train for each
The single-instance of collection, it is all made renewal, i.e. can make a response at once.So generally we can select statistical gradient to decline
Algorithm.
By running above-mentioned algorithm, we can obtain finding the value of parameter θ to meet given training set.Obtain this letter
After number h, for the example being newly added, its output valve just can be done a prediction by us.A place is had to it should be noted that
The value of parameter θ likely can not converge, and simply makes what J (θ) minimized to oscillate about, but it practice, near this value θ
Require that through meeting.So this situation should be taken in program realizes when.
Embodiment 4:Have and the identical technical scheme of embodiment 1 or 2 or 3, more specifically:
Conceptual data collection in such scheme: be entirely CAE emulation data and sport car data；Be divided into three parts as follows
1. training dataset: be used to training pattern or determine model parameter (CAE emulation data and sport car data).
2. checking data set: be used to do Model Selection (modelselection), i.e. does the final optimization pass and really of model
Fixed (CAE emulation data and sport car data).
3. test data set: the Generalization Ability of the model then trained for purely test.(CAE emulates data
With sport car data).
In the present embodiment also to the filtering related to during setting loss, weighting choose, feature extraction, normalization, eigentransformation
Have been described.
1. wave filter technology: the filtering method realized includes FIR filtering, FIR Chebyshev approximation, Chebyshev's filter
Ripple, butterworth filter etc., the Filtering.m file in mastery routine realizes.Each wave filter is common wave filter,
Matlab has corresponding function to realize, and specific algorithm refers to signal processing professional book.Provide the interior of FIR filter herein
Hold and the introduction of flow process.
Limited impulse response digital filter (FIR, FiniteImpulseResponse) is the system of a kind of full zero point,
The design of FIR filter is ensureing that amplitude characteristic meets the colleague that technology requires, it is easy to accomplish strict linear phase characteristic,
So being the outstanding advantages of FIR filter according to there being stable and linear phase characteristic.Chebyshev approximation is that the ripples such as one are forced
Nearly method, it is possible to make error frequency band be uniformly distributed, to same technical specification, this filter order sending out needs shoulder to shoulder is low, right
In the wave filter of same exponent number, this approximatioss maximum error is minimum, and the key step of its design is as follows:
Step 1: the setting of filter parameter
The parameter of wave filter includes: cut-off frequecy of passband, stopband cut-off frequency, passband maximum attenuation and stopband minimum decline
Subtract；
Step 2: be arranged on passband and the amplitude-frequency response of stopband coideal
Step 3: be scheduled on the weighting on cut-off frequecy of passband and stopband cut-off frequency point
Step 4: utilize Equation for Calculating Chebyshev approximation filter coefficient
Step 5: preserve coefficient
Step 6: extraction coefficient carries out data filtering
Wherein: the guarantee signal that is disposed to of filter parameter does not haves distortion now during processing
As, the cut-off frequency of filtered signal and sample frequency need to meet Nyquist's theorem, the most after the filtering signal
Highest frequency not can exceed that the 1/2 of original signal sample frequency, otherwise arises that Lou frequency phenomenon.According to the signal in current project
The sample frequency of collection plate is mainly 50Hz and 1KHz, according to formula F as a example by 50HzCut-off< 50/2, therefore select filter cutoff
Frequency is below 25.
2. Feature Extraction Technology: feature extraction is carried out on collision alarm.Judge that the feature that collision uses includes window
Acceleration in difference between acceleration maxima and minima, window in the maximum of acceleration absolute value, window in mouthful
In average energy (in window the quadratic sum of acceleration a little divided by counting), window, the absolute value of each point slope is average
Value.
Judge the feature that part category is used include the average energy between speed, acceleration peak to peak,
Amplitude between maximum and minima/width between the two, acceleration maximum, acceleration minima, maximum place
The width of half-wave, minima place half-wave width, maximum and minima between difference, between peak to peak
Span, the meansigma methods of absolute value of each point slope, signal carry out each of the signal after Fourier transform in 0～38 frequency ranges
The amplitude of frequency component.
3. normalization technology: that causes classification task to eliminate the dimension between feature or order of magnitude difference is unfavorable
Impact, needs to be normalized characteristic so that have comparability between each eigenvalue, it is to avoid the spy that numerical value is bigger
Levy and flood the feature that numerical value is less.Original characteristic is after normalized, and each feature is in identical codomain scope.
Owing to the performance of Z-Score is more preferable, use Z-Score as method for normalizing.
4. feature transform technique: in the case of feature is more, for the dependency eliminated between feature and reduce redundancy
Feature, needs to convert feature, carrys out reflected sample information with the fewest new feature.In the less situation of experiment sample
Under (practical situation of this project) reduce too much intrinsic dimensionality, moreover it is possible to avoid sending out of over-fitting or poor fitting to a certain extent
Raw.According to actual needs, the eigentransformation the most realized is PCA.Being found through experiments, PCA divides for improving this project
Class performance there is no help, has declined, and this is that the feature owing to being used at present is less, does not has redundancy feature, therefore
PCA wouldn't be used, but be as being stepped up of subsequent characteristics, however not excluded that use the probability of PCA later.
In accompanying drawing 1, record: the Truck type choice subsystem that Truck type choice is in the present invention；Data categorization module is
Data classification subsystem in the present invention；The collision detection subsystem that collision judgment module is in the present invention；Operating mode detection mould
Block is the operating mode detection subsystem of the present invention；Vehicle detection module is the vehicle detection subsystem of the present invention；Piece test
Module i.e. piece test subsystem；Module of target detection is the target detection subsystem of the present invention, and region detection module is
The region detection subsystem of the present invention.
The above, only the invention preferably detailed description of the invention, but the protection domain of the invention is not
Being confined to this, any those familiar with the art is in the technical scope that the invention discloses, according to the present invention
The technical scheme created and inventive concept thereof in addition equivalent or change, all should contain the invention protection domain it
In.
Claims (3)
1. set up the long-range loss assessment system in different automobile types subregion based on artificial intelligence for one kind, it is characterised in that including:
Truck type choice subsystem, selects the model data corresponding to vehicle as total data set；
Data classification subsystem, reads CAE emulation data and real vehicle data, and classifies data accordingly；
Collision detection subsystem, it is judged that the most whether vehicle collides；Described collision detection subsystem is to collision
Training data carries out learning thus generates collision model；
Operating mode detection subsystem, it is judged that all work informations that collision occurs；Operating mode is trained number by described operating mode detection subsystem
Condition model is generated according to carrying out learning；
Region detection subsystem, judges hit region during vehicle collision；Regional training data are entered by described region detection subsystem
Row learns thus formation zone model.
2. set up the long-range loss assessment system in different automobile types subregion based on artificial intelligence as claimed in claim 1, it is characterised in that
Described collision detection subsystem includes, collision training module, crash tests module, collision authentication module, described collision is instructed
Practicing module and generate collision model for learning collision training data, crash tests module is for by crash tests number
According to bringing the result detecting collision model in collision model into, collision authentication module uses true sport car data verification collision model
Reliability and accuracy rate；
Described operating mode detection subsystem includes, operating mode training module, working condition measurement module, operating mode authentication module, and described operating mode is instructed
Practicing module and generate condition model for learning operating mode training data, described working condition measurement module is for surveying operating mode
Examination data bring the result detecting condition model in model into, and operating mode authentication module uses true sport car data verification condition model
Reliability and accuracy rate；
Described region detection subsystem includes, regional training module, domain test module, area verification module, and described region is instructed
Practicing module to be used for carrying out learning thus formation zone model by regional training data, domain test module is for by domain test number
According to bringing the result of detection zone domain model in model into, area verification module uses the reliable of true sport car data verification regional model
Property and accuracy rate.
3. set up the long-range loss assessment system in different automobile types subregion such as claim 1 or 2 based on artificial intelligence, it is characterised in that
Described collision model is set up and is used intelligence supervised learning linear regression method, Intelligent Energy model method, intelligence without prison
Educational inspector practise FuzzyC-Means clustering algorithm, intelligence supervised learning AdaBoost method more than one；
Described condition model is set up and is used intelligence supervised learning linear regression method, Intelligent Energy model method, intelligence without prison
Educational inspector practise FuzzyC-Means clustering algorithm, intelligence supervised learning AdaBoost method more than one；
Described regional model is set up and is used intelligence supervised learning linear regression method, Intelligent Energy model method, intelligence without prison
Educational inspector practise FuzzyC-Means clustering algorithm, intelligence supervised learning AdaBoost method more than one；
And, described collision model, described condition model, described regional model set up the algorithm used or method is not identical
Algorithm or method.
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| CN103303237A (en) * | 2013-06-21 | 2013-09-18 | 湖南大学 | Air bag detonation control method based on genetic neural network |
| CN103350670A (en) * | 2013-07-16 | 2013-10-16 | 厦门金龙联合汽车工业有限公司 | Vehicle forward collision warning method based on vehicle networking technology |
| CN103810637A (en) * | 2013-12-17 | 2014-05-21 | 深圳市般若计算机系统有限公司 | Motor vehicle insurance fraud detecting method and system |
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Application publication date: 20161026 |