CN106096624A - The long-range loss assessment system of different automobile types divided working status and method is set up based on artificial intelligence - Google Patents
The long-range loss assessment system of different automobile types divided working status and method is set up based on artificial intelligence Download PDFInfo
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Abstract
Set up the long-range loss assessment system of different automobile types divided working status and method based on artificial intelligence, belong to car damage identification field, after solving vehicle collision, for the problem of the detection of operating mode, have technical point that operating mode detects subsystem, it is judged that all work informations that collision occurs；Operating mode detection subsystem, it is judged that all work informations that collision occurs；Operating mode training data is learnt thus generates condition model by described operating mode detection subsystem, effect is: technique scheme, can realize the operating mode of vehicle collision is detected, in the method that this technical field of long-range setting loss employs machine learning, for machine learning method, during setting loss, it determines accuracy rate on promoted.
Description
Technical field
The invention belongs to car damage identification field, relate to one and set up the long-range setting loss of different automobile types divided working status 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 the detection of operating mode, the present invention proposes and sets up based on artificial intelligence
The long-range loss assessment system of different automobile types divided working status and method, to realize the operating mode 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.
Beneficial effect: technique scheme, it is possible to achieve the operating mode for vehicle collision detects, 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
To promote；The present invention is by selecting vehicle to import the data corresponding to this vehicle, and data classification is then for model training
With the purpose of test and the step that adds；The detection of operating mode is the purpose that the program realizes, and is to obtain through sequence of operations
The result arrived.
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 of different automobile types divided working status 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.
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 collision model set up use semi-supervised learning cluster hypothesis method, unsupervised learning principal component analytical method,
More than one in supervised learning linear regression method；
Described condition model set up use semi-supervised learning cluster hypothesis method, unsupervised learning principal component analytical method,
More than one in supervised learning linear regression method；
And, described collision model, described condition model set up using method not for identical method.
As a kind of embodiment:
Described semi-supervised learning cluster hypothesis method include in following methods more than one: division methods, the side of level
Method, method based on density, method based on grid, method based on model.
Described division methods, is divided into K group according to user input values K given object and (meets 2 conditions: the most each group extremely
Comprise an object less.The most each object and must be pertaining only to a group), often organizing is all a cluster, then utilizes circulation again
Object inside location technology conversion cluster, until till the objective criteria for classifying (often becoming similar function, such as distance) optimum.Allusion quotation
Type represents: K-MEANS, K-MEDOIDS.
The method of described level carries out hierachical decomposition to given object set, is divided into 2 classes: cohesion and division；Solidifying
Poly-method is bottom-up method, the most at the beginning using each object as one single bunch, then carries out according to standard
Merge, until all object mergings are one bunch or reach end condition；The method of division is also top-down method,
The most all objects are put in one bunch, then divide, until all objects all become single one bunch or
Till reaching end condition.Typical Representative: CURE, BIRCH.
Described method based on density, i.e. constantly increases the cluster obtained until neighbouring (object) density exceedes certain
Till threshold values (number of objects at least must be comprised in the number of objects in a cluster or a given radius).Typical Representative:
DBSCAN, OPTICS.
Described method based on grid, will object space subdivision be a limited number of unit to form network, institute
Cluster operation is had all to carry out in this network.Typical Representative: STING.
Described method based on model is each cluster and assumes a model, then according to model go to find to meet right
Picture, such method often itself based on an assumption that data are to generate according to potential probability distribution.Mainly there are 2 classes: system
Meter method and neural net method.Typical Representative: COBWEB, SOMS.
As a kind of embodiment:
Described unsupervised learning principal component analytical method:
Step 1. represents data set；
Step 2. spin data, each sample concentrating data rotates respectively；
Step 3. Data Dimensionality Reduction number；
Step 4. reduces approximate data；
Step 5. selects main constituent number.
Said method particularly as follows:
Step 1. represents data set:
Data set table is shown as { x(1), x(2)..., x(m), calculate matrix Σ,
The average assuming x is zero, and ∑ is the covariance matrix of x, and m is Grad；
Step 2. spin data, each sample concentrating data rotates respectively, obtains spin dataU is data variation direction, computing UTX represents and rotates to base u1,u2,...,unOn training data, n is
The number of base；
Step 3. Data Dimensionality Reduction number: dataDrop to k dimension table showChoose xrotFront k composition,
Before the most corresponding, k is according to the principal direction of change,It is xrotLast n-k element set to 0 the approximate representation of gained；
Step 4. reduces approximate data: make x=Uxrot；
Step 5. selects main constituent number: set λ1, λ2..., λnRepresent the eigenvalue of Σ so that λjFor corresponding to feature to
Amount ujEigenvalue.If that we retain front k composition, then the variance percentage retained can be calculated as:
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 gradient descent algorithm is: batch gradient declines and/or statistical gradient declines.
Further, said method particularly as follows:
Output y is that the linear function of x is:
hθ(x)=θ0+θ1x1+θ2x2
Here, θiFor parameter, n is Grad, it is assumed that x0=1, above formula is expressed as matrix form:
θ and x is column vector, and m is Grad, certain training set given, introduces cost function, and it is defined as follows:
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, θjFor parameter (asking for an interview h (x) function above to explain)；
α represents learning rate,
Thus, update equation to be reduced to:
Embodiment 2:
One sets up the long-range damage identification method of different automobile types divided working status based on artificial intelligence, including
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
Become collision model；
Step 4. judge all work informations that collision occurs；Operating mode training data is learnt thus generates operating mode
Model.
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.
Described collision model set up use semi-supervised learning cluster hypothesis method, unsupervised learning principal component analytical method,
More than one in supervised learning linear regression method；
Described condition model set up use semi-supervised learning cluster hypothesis method, unsupervised learning principal component analytical method,
More than one in supervised learning linear regression method；
And, described collision model, described condition model set up using method not for identical method.
Described various algorithm or the concrete steps of method are identical with the algorithm in embodiment 1 or method.
Embodiment 3:
There is the technical scheme identical with embodiment 1 or 2, 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 (model selection), 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, Finite Impulse Response) be a kind of full zero point be
System, the design of FIR filter is ensureing that amplitude characteristic meets the colleague that technology requires, it is easy to accomplish that strict linear phase is special
Property, so being the outstanding advantages of FIR filter according to there being stable and linear phase characteristic.Chebyshev approximation is the ripples such as one
Approximatioss, 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,
For 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 feelings of experiment sample
Under condition, (practical situation of this project) reduces too much intrinsic dimensionality, moreover it is possible to avoid over-fitting or poor fitting to a certain extent
Occur.According to actual needs, the eigentransformation the most realized is PCA.Being found through experiments, PCA is for improving this project
Classification 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, because of
This wouldn't use PCA, but is 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 of different automobile types divided working status 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.
2. set up the long-range loss assessment system of different automobile types divided working status 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.
3. set up the long-range loss assessment system of different automobile types divided working status based on artificial intelligence as claimed in claim 1, it is characterised in that
Described collision model is set up and is used semi-supervised learning cluster hypothesis method, unsupervised learning principal component analytical method, has prison
Educational inspector practises more than one in linear regression method；
Described condition model is set up and is used semi-supervised learning cluster hypothesis method, unsupervised learning principal component analytical method, has prison
Educational inspector practises more than one in linear regression method；
And, described collision model, described condition model set up using method not for identical method.
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Cited By (2)
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| WO2019165617A1 (en) * | 2018-02-28 | 2019-09-06 | 深圳市元征软件开发有限公司 | Vehicle-mounted apparatus and vehicle collision analysis method and device |
| TWI698802B (en) * | 2018-08-31 | 2020-07-11 | 香港商阿里巴巴集團服務有限公司 | Vehicle parts detection method, device and equipment |
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