CN104932359A - Vehicle remote unattended loss assessment system based on CAE technology and loss assessment method thereof - Google Patents
Vehicle remote unattended loss assessment system based on CAE technology and loss assessment method thereof Download PDFInfo
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- CN104932359A CN104932359A CN201510289368.9A CN201510289368A CN104932359A CN 104932359 A CN104932359 A CN 104932359A CN 201510289368 A CN201510289368 A CN 201510289368A CN 104932359 A CN104932359 A CN 104932359A
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- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B19/00—Programme-control systems
- G05B19/02—Programme-control systems electric
- G05B19/04—Programme control other than numerical control, i.e. in sequence controllers or logic controllers
- G05B19/042—Programme control other than numerical control, i.e. in sequence controllers or logic controllers using digital processors
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- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B2219/00—Program-control systems
- G05B2219/20—Pc systems
- G05B2219/25—Pc structure of the system
- G05B2219/25314—Modular structure, modules
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Abstract
The invention provides a vehicle remote unattended loss assessment system based on a CAE technology and a loss assessment method thereof. The vehicle remote unattended loss assessment system comprises a data acquisition module, a cloud platform computing center module, a CAE loss assessment database and an output module. The cloud platform computing center module is respectively connected with the data acquisition module, the CAE loss assessment database and the output module. Problems of the vehicle loss assessment industry that loss assessment is not timely, definition is low, cooperation is poor and transparency is low are solved by the vehicle remote unattended loss assessment system based on the CAE technology.
Description
Technical field
The invention belongs to vehicle remote setting loss field, is the unmanned loss assessment system of a kind of vehicle remote based on CAE technology and damage identification method specifically.
Background technology
For a long time, insurance company's car damage identification carries out according to following two patterns always: 1. original setting loss pattern: client vehicles because of accident classification need repair time, first car is needed to reach 4S shop, 4S shop declaration insurance company setting loss personnel, to scene, the damaged condition of vehicle is identified, assess repair cost, price determines that rear 4S shop starts maintenance, and expense is paid by insurance company subsequently simultaneously.2. video monitoring Network Based: the pattern realizing long-distance video setting loss, the method, by Network Video Surveillance, carries out remote monitoring service to settlement of insurance claim site and vehicle maintenance point in institute of insurance company administrative area.
Along with the surge of insurance vehicle, above-mentioned two kinds of setting loss patterns expose increasing problem, pay for time long, setting loss evaluation process and price is opaque, the high and intentional insurance fraud of lawless person of manpower and materials cost etc. in constant speed process as setting loss core.
Summary of the invention
In order to solve the problems referred to above that prior art exists, the invention provides the unmanned loss assessment system of a kind of vehicle remote based on CAE technology and damage identification method, solve the setting loss of car damage identification industry not in time, the problem such as sharpness is not high, concertedness is poor, transparency is low.
For achieving the above object, technical scheme of the present invention is: the unmanned loss assessment system of the vehicle remote based on CAE technology, comprises data acquisition module, cloud platform computing center module, CAE setting loss database and output module; Described cloud platform computing center module is connected with output module with data acquisition module, CAE setting loss database respectively;
Whether described data acquisition module is used for vehicle location, attitude signal in collection vehicle driving process, reads car fault diagnosis data in real time, record driving behavior and driver's biological property (fatigue driving and drunk driving);
Described cloud platform computing center module is in order to receive and to process the signal parameter of data collecting module collected; When vehicle has an accident, the various signal parameters that cloud platform computing center module gathers according to vehicle, are calculated by fuzzy neural network, match with CAE setting loss database, complete car damage identification and calculate;
Described CAE setting loss database, is used for coordinating with cloud platform computing center module, jointly completes setting loss;
Described output module generates long-range setting loss report, record collision time of origin, place, accident responsibility side, nearby maintenace point, part injury grade and maintenance price, and the client long-range setting loss report being sent to insurer and insurance company.
Described data acquisition module comprises: 3-axis acceleration sensor, three-axis gyroscope, three axle magnetometers, pressure altimeter, GPS sensor, sound transducer, car fault diagnosis device, WIFI hot spot administration module, 4G wireless communication module and biomedical sensor.
Described CAE setting loss database, comprises CAE simulation data base and setting loss assessment data storehouse.
Described CAE simulation data base, comprises the information such as peak acceleration eigenwert, maximum angular rate eigenwert, the deflection of critical area, the stress and strain of parts under the different collision accident of different automobile types.
Described setting loss assessment data storehouse, comprises the judgement of division to part injury grade and part maintenance cost.
The unmanned damage identification method of vehicle remote based on CAE technology, realize based on said system, concrete steps are as follows:
S1: vehicle ignition, native system is started working;
S2: the signal parameter in data collecting module collected vehicle travel process, this signal parameter is the angular velocity of vehicle, acceleration, position, sound, air pressure, real-time vehicle fault diagnosis and driver's biomedicine signals;
S3: cloud platform computing center module receives and processes the signal parameter of data collecting module collected, and is calculated by fuzzy neural network, by the result of calculating and CAE setting loss database matching;
S4: output module generates long-range setting loss report, record collision time of origin, place, accident responsibility side, nearby maintenace point, part injury grade and maintenance price, and the client long-range setting loss report being sent to insurer and insurance company.
Beneficial effect of the present invention is: solve the setting loss of car damage identification industry not in time, the problem such as sharpness is not high, concertedness is poor, transparency is low; Native system structure is simple, cost is low, setting loss result is accurate.
Accompanying drawing explanation
The present invention has accompanying drawing 3 width:
Fig. 1 is the structured flowchart of native system;
Fig. 2 is CAE setting loss database structure block diagram;
Fig. 3 is the workflow diagram of native system.
Embodiment
Below by embodiment, and 1-3 is further described specifically technical scheme of the present invention by reference to the accompanying drawings.
The unmanned loss assessment system of vehicle remote based on CAE technology, comprises data acquisition module, cloud platform computing center module, CAE setting loss database and output module; Described cloud platform computing center module is connected with output module with data acquisition module, CAE setting loss database respectively; Described data acquisition module is used for the signal parameter in collection vehicle driving process; Described cloud platform computing center module is in order to receive and to process the signal parameter of data collecting module collected; Described CAE setting loss data base manipulation CAE emulation technology, carry out Virtual Test Analysis for different automobile types, different emergency conditions, collect the various characteristic parameter of vehicle in accident process, the CAE setting loss database of Erecting and improving, and coordinate with cloud platform computing center module, carry out car damage identification coupling; Described output module generates long-range setting loss report, record collision time of origin, place, nearby maintenace point, accident responsibility side, part injury grade and maintenance price, and the client long-range setting loss report being sent to insurer and insurance company.
Described data acquisition module, comprising: 3-axis acceleration sensor, three-axis gyroscope, three axle magnetometers, pressure altimeter, GPS sensor, sound transducer, car fault diagnosis device, WIFI hot spot administration module, 4G wireless communication module and biomedical sensor; For the data message in collection vehicle driving process, coordinate cloud platform computing center module, complete the motion detection of vehicle, position navigation, driving behavior record, collision accident identification etc.
Described CAE setting loss database, comprises CAE simulation data base and setting loss assessment data storehouse.Described CAE simulation data base, comprises the information such as peak acceleration eigenwert, maximum angular rate eigenwert, the deflection of critical area, the stress and strain of parts under the different collision accident of different automobile types.Described setting loss assessment data storehouse is a set of evaluation system formulated for CAE simulation result and actual setting loss situation, comprises the judgement of division to part injury grade and part maintenance cost.
The unmanned damage identification method of vehicle remote based on CAE technology, realize based on said system, concrete steps are as follows:
S1: vehicle ignition, native system is started working;
S2: the signal parameter in data collecting module collected vehicle travel process, whether this signal parameter is the real-time fault diagnosis of vehicle, angular velocity, acceleration, position, sound, air pressure and driver's sign state (tired, drunk driving);
S3: cloud platform computing center module receives and processes the signal parameter of data collecting module collected, and by neural computing, mates with CAE simulation data base and setting loss assessment data storehouse;
S4: output module generates long-range setting loss report, record collision time of origin, place, accident responsibility side, nearby maintenace point, part injury grade and maintenance price, and the client long-range setting loss report being sent to insurer and insurance company.
The data information transfer related in the present invention and Data Matching are the common practise of those skilled in the art; The invention is intended to protect the damage identification method of the connection between modules and native system solve the setting loss of car damage identification industry not in time, the problem such as sharpness is not high, concertedness is poor, transparency is low.
The above; be only the present invention's preferably embodiment; but protection scope of the present invention is not limited thereto; anyly be familiar with those skilled in the art in the technical scope that the present invention discloses; be equal to according to technical scheme of the present invention and inventive concept thereof and replace or change, all should be encompassed within protection scope of the present invention.
Claims (7)
1. based on the unmanned loss assessment system of vehicle remote of CAE technology, it is characterized in that: comprise data acquisition module, cloud platform computing center module, CAE setting loss database and output module; Described cloud platform computing center module is connected with output module with data acquisition module, CAE setting loss database respectively;
Described data acquisition module is used for vehicle location, attitude signal in collection vehicle driving process, reads car fault diagnosis data in real time, record driving behavior and driver's biological property;
Described cloud platform computing center module, in order to receive and to process the signal parameter of data collecting module collected, is calculated by fuzzy neural network, matches with CAE setting loss database, completes car damage identification and calculates;
Described CAE setting loss database, is used for coordinating with cloud platform computing center module, jointly completes setting loss;
Described output module generates long-range setting loss report, record collision time of origin, place, accident responsibility side, nearby maintenace point, part injury grade and maintenance price, and the client long-range setting loss report being sent to insurer and insurance company.
2. the unmanned loss assessment system of the vehicle remote based on CAE technology according to claim 1, it is characterized in that: described data acquisition module, comprise 3-axis acceleration sensor, three-axis gyroscope, three axle magnetometers, pressure altimeter, GPS sensor, sound transducer, car fault diagnosis device, WIFI hot spot administration module, 4G wireless communication module and biomedical sensor.
3. the unmanned loss assessment system of the vehicle remote based on CAE technology according to claim 1, is characterized in that: described CAE setting loss database, comprises CAE simulation data base and setting loss assessment data storehouse.
4. the unmanned loss assessment system of the vehicle remote based on CAE technology according to claim 3, it is characterized in that: described CAE simulation data base, comprise peak acceleration eigenwert, maximum angular rate eigenwert, the deflection of critical area, the stress and strain information of parts under the different collision accident of different automobile types.
5. the unmanned loss assessment system of the vehicle remote based on CAE technology according to claim 3, is characterized in that: setting loss assessment data storehouse, comprises the judgement of division to part injury grade and part maintenance cost.
6. according to the arbitrary described unmanned loss assessment system of the vehicle remote based on CAE technology of claim 1-5, it is characterized in that: described cloud platform computing center module is calculated by fuzzy neural network and matches with CAE setting loss database.
7., based on the unmanned damage identification method of vehicle remote of CAE technology, be realize based on the unmanned loss assessment system of the vehicle remote based on CAE technology according to claim 1, concrete steps are as follows:
S1: vehicle ignition, native system is started working;
S2: the signal parameter in data collecting module collected vehicle travel process, this signal parameter is the angular velocity of vehicle, acceleration, position, sound, air pressure, real-time fault diagnosis and driver's sign state;
S3: cloud platform computing center module receives and processes the signal parameter of data collecting module collected, and is calculated by fuzzy neural network, mates with CAE simulation data base and setting loss assessment data storehouse;
S4: output module generates long-range setting loss report, record collision time of origin, place, accident responsibility side, nearby maintenace point, part injury grade and maintenance price, and the client long-range setting loss report being sent to insurer and insurance company.
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| CN105550756A (en) * | 2015-12-08 | 2016-05-04 | 优易商业管理成都有限公司 | Vehicle rapid damage determination method based on simulation of vehicle damages |
| CN105915853A (en) * | 2016-05-27 | 2016-08-31 | 大连楼兰科技股份有限公司 | Remote unmanned loss assessment method and remote unmanned loss assessment system based on infrared sensing |
| CN106021639A (en) * | 2016-04-29 | 2016-10-12 | 大连楼兰科技股份有限公司 | CAE simulation analysis result based damaged part damage determination and classification method, and maintenance hour estimation method |
| CN106056148A (en) * | 2016-05-27 | 2016-10-26 | 大连楼兰科技股份有限公司 | System and method for establishing target division remote damage assessment of different vehicle types based on artificial intelligence unsupervised learning sparse coding method |
| CN106056151A (en) * | 2016-05-27 | 2016-10-26 | 大连楼兰科技股份有限公司 | System and method for establishing part division remote damage assessment of different vehicle types based on artificial intelligence supervised learning support vector machine (SVM) method |
| CN106056145A (en) * | 2016-05-27 | 2016-10-26 | 大连楼兰科技股份有限公司 | System and method for establishing vehicle type division remote damage assessment based on artificial intelligence Apriori algorithm |
| CN106056149A (en) * | 2016-05-27 | 2016-10-26 | 大连楼兰科技股份有限公司 | System and method for establishing working condition division remote damage assessment of different vehicle types based on artificial intelligence unsupervised learning principal component analysis method |
| CN106056144A (en) * | 2016-05-27 | 2016-10-26 | 大连楼兰科技股份有限公司 | System and method for establishing region division remote damage assessment of different vehicle types based on artificial intelligence |
| CN106055779A (en) * | 2016-05-27 | 2016-10-26 | 大连楼兰科技股份有限公司 | Remote damage-assessment system and method established based on artificial intelligence semi-supervised learning logistic-regression method for different types of vehicles |
| CN106056150A (en) * | 2016-05-27 | 2016-10-26 | 大连楼兰科技股份有限公司 | System and method for establishing part division remote damage assessment of different vehicle types based on artificial intelligence random forest method |
| CN106056140A (en) * | 2016-05-27 | 2016-10-26 | 大连楼兰科技股份有限公司 | System and method for establishing working condition division remote damage assessment of different vehicle types based on artificial intelligence supervised learning linear regression method |
| CN106056153A (en) * | 2016-05-27 | 2016-10-26 | 大连楼兰科技股份有限公司 | System and method for establishing region division remote damage assessment of different vehicle types based on artificial intelligence supervised learning AdaBoost method |
| CN106055777A (en) * | 2016-05-27 | 2016-10-26 | 大连楼兰科技股份有限公司 | Remote damage-assessment system and method established based on artificial intelligence semi-supervised learning self-training method for parts in different types of vehicles |
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| CN106056152A (en) * | 2016-05-27 | 2016-10-26 | 大连楼兰科技股份有限公司 | System and method for establishing target division remote damage assessment of different vehicle types based on artificial intelligence semi-supervised learning BIRCH method |
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