CN106056453A - System and method for establishing working condition division remote damage assessment of different vehicle types based on artificial intelligence semi-supervised learning clustering hypothesis method - Google Patents
System and method for establishing working condition division remote damage assessment of different vehicle types based on artificial intelligence semi-supervised learning clustering hypothesis method Download PDFInfo
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- CN106056453A CN106056453A CN201610365678.9A CN201610365678A CN106056453A CN 106056453 A CN106056453 A CN 106056453A CN 201610365678 A CN201610365678 A CN 201610365678A CN 106056453 A CN106056453 A CN 106056453A
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Abstract
The invention relates to a system and method for establishing working condition division remote damage assessment of different vehicle types based on an artificial intelligence semi-supervised learning clustering hypothesis method and belongs to the vehicle damage assessment field. The objective of the invention is to solve problems in working condition detection after a vehicle collision. According to the technical schemes of the invention, a working condition detection subsystem is adopted to judge all working condition information in the collision; the working condition detection subsystem learns working condition training data so as to generate a working condition model, wherein the working condition model is built by adopting the intelligent semi-supervised learning clustering hypothesis method. With the system and method provided by the technical schemes of the invention adopted, working condition 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 a kind of based on the cluster hypothesis method foundation of artificial intelligence's semi-supervised learning
The long-range loss assessment system of different automobile types divided working status 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 based on artificial intelligence half prison
Superintend and direct Learning Clustering and assume that method sets up the long-range loss assessment system of different automobile types divided working status and method, to realize the operating mode during setting loss
Detection.
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, and described collision model is set up and used semi-supervised learning cluster hypothesis side
Method；
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, and described condition model is set up and used semi-supervised learning cluster hypothesis method.
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's semi-supervised learning cluster hypothesis method,
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, and described collision model is set up and used semi-supervised learning cluster hypothesis side
Method；
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, and described condition model is set up and used semi-supervised learning cluster hypothesis method.
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 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.
Embodiment 2:
One sets up the long-range damage identification method of different automobile types divided working status based on artificial intelligence's semi-supervised learning cluster hypothesis method,
Comprise 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；Described collision detection subsystem is to collision training
Data carry out learning thus generate collision model, and described collision model is set up and used semi-supervised learning cluster hypothesis method；
Step 4. judge all work informations that collision occurs；Operating mode training data is entered by described operating mode detection subsystem
Row learns thus generates condition model, and described condition model is set up and used semi-supervised learning cluster hypothesis 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.
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.
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 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 (8)
1. set up the long-range loss assessment system of different automobile types divided working status based on artificial intelligence's semi-supervised learning cluster hypothesis method, its
It is characterised 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 is to collision
Training data carries out learning thus generates collision model, and described collision model is set up and used semi-supervised learning cluster hypothesis method；
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
Generating condition model according to carrying out learning, described condition model is set up and is used semi-supervised learning cluster hypothesis method.
2. set up different automobile types divided working status based on artificial intelligence's semi-supervised learning cluster hypothesis method as claimed in claim 1 remote
Journey loss assessment system, 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. such as claim 1 or 2, based on artificial intelligence's semi-supervised learning cluster hypothesis method, to set up different automobile types divided working status long-range
Loss assessment system, it is characterised in that described semi-supervised learning cluster hypothesis method include in following methods more than one: division side
Method, the method for level, method based on density, method based on grid, method based on model.
4. set up the long-range setting loss of different automobile types divided working status such as claim 3 based on artificial intelligence's semi-supervised learning cluster hypothesis method
System, it is characterised in that
Described division methods, is divided into K group according to input value K given object, and meets 2 conditions during packet, and first, each group
Including at least an object, second, each object and must be pertaining only to a group, and often organizing is all a cluster, then utilizes and follows
Ring relocates the object inside technology conversion cluster, until objective criteria for classifying optimum；
The method of described level, carries out hierachical decomposition to given object set, is divided into 2 classes: cohesion and division；Cohesion
Method be at the beginning using each object as one single bunch, then merge, until all object mergings are one bunch
Or till reaching end condition；All objects are put in one bunch by the method for division at the beginning, then divide, until institute
Object is had all to become single one bunch or till reaching end condition；
Described method based on density, i.e. constantly increases the cluster obtained until neighbouring density exceedes certain threshold values；
Described method based on grid, will object space subdivision be a limited number of unit to form network, all poly-
Generic operation is all carried out in this network；
Described method based on model is each cluster and assumes a model, then according to model go to find to meet to picture,
Based on an assumption that data are to generate according to potential probability distribution, mainly there are 2 classes: statistical method and neutral net
Method.
5. set up the long-range damage identification method of different automobile types divided working status based on artificial intelligence's semi-supervised learning cluster hypothesis method, its
It is characterised by, 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 generates and touch
Hitting model, described collision model is set up and is used semi-supervised learning cluster hypothesis method；
Step 4. judge all work informations that collision occurs；Operating mode training data is learnt thus generates condition model,
Described condition model is set up and is used semi-supervised learning cluster hypothesis method.
6. set up different automobile types divided working status based on artificial intelligence's semi-supervised learning cluster hypothesis method as claimed in claim 5 remote
Journey damage identification method, it is characterised in that 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 training to it
Data and crash tests data；
S3.2. in collision training module, collision training data learnt and produces collision model, carrying out simulated crash training
The effect of 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 model
Accuracy；
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 training number
According to working condition measurement data；
S4.2. in operating mode training module, operating mode training data learnt and produce condition model, carrying out simulated condition training
The effect of 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 condition model
Accuracy.
7. as described in claim 5 or 6 based on artificial intelligence's semi-supervised learning cluster hypothesis method set up different automobile types the division of labor
The long-range damage identification method of condition, it is characterised in that described semi-supervised learning cluster hypothesis method include in following methods more than one:
Division methods, the method for level, method based on density, method based on grid, method based on model.
8. set up the long-range setting loss of different automobile types divided working status such as claim 7 based on artificial intelligence's semi-supervised learning cluster hypothesis method
Method, it is characterised in that
Described division methods, is divided into K group according to input value K given object, and meets 2 conditions during packet, and first, each group
Including at least an object, second, each object and must be pertaining only to a group, and often organizing is all a cluster, then utilizes and follows
Ring relocates the object inside technology conversion cluster, until objective criteria for classifying optimum；
The method of described level, carries out hierachical decomposition to given object set, is divided into 2 classes: cohesion and division；Cohesion
Method be at the beginning using each object as one single bunch, then merge, until all object mergings are one bunch
Or till reaching end condition；All objects are put in one bunch by the method for division at the beginning, then divide, until institute
Object is had all to become single one bunch or till reaching end condition；Described method based on density, i.e. constantly increases and is obtained
The cluster obtained is until neighbouring density exceedes certain threshold values；
Described method based on grid, will object space subdivision be a limited number of unit to form network, all poly-
Generic operation is all carried out in this network；
Described method based on model is each cluster and assumes a model, then according to model go to find to meet to picture,
Based on an assumption that data are to generate according to potential probability distribution, mainly there are 2 classes: statistical method and neutral net
Method.
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