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System and method for establishing target division remote damage assessment of different vehicle types based on artificial intelligence semi-supervised learning BIRCH method

A semi-supervised learning and artificial intelligence technology, applied in the field of vehicle loss assessment, to achieve the effect of improving accuracy

Inactive Publication Date: 2016-10-26
DALIAN ROILAND SCI & TECH CO LTD
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0004] In order to solve the problem of detecting the target of the colliding vehicle after the collision, the present invention proposes a remote damage assessment system and method based on artificial intelligence semi-supervised learning BIRCH method to establish different vehicle types and targets, so as to realize the damage determination process. target detection and judgment

Method used

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  • System and method for establishing target division remote damage assessment of different vehicle types based on artificial intelligence semi-supervised learning BIRCH method

Examples

Experimental program
Comparison scheme
Effect test

Embodiment 1

[0025] A BIRCH method based on artificial intelligence semi-supervised learning to establish a remote damage assessment system for different vehicle models, including:

[0026] The model selection subsystem selects the model data corresponding to the vehicle as the total data set;

[0027] The data classification subsystem reads CAE simulation data and real vehicle data, and classifies the data accordingly;

[0028] The collision detection subsystem judges whether the vehicle collides during driving; the collision detection subsystem learns the collision training data to generate a collision model, and the collision model is established using the intelligent semi-supervised learning BIRCH method;

[0029] The working condition detection subsystem judges all the working condition information of the collision; the working condition detection subsystem learns the working condition training data to generate a working condition model, and the working condition model is established ...

Embodiment 2

[0054] An artificial intelligence semi-supervised learning BIRCH method to establish a remote damage assessment method for different vehicle types according to the target, comprising the following steps:

[0055] Step 1. Select the model data corresponding to the vehicle as the total data set;

[0056] Step 2. Read the CAE simulation data and real vehicle data, and classify the data accordingly;

[0057] Step 3. Judging whether the vehicle collides during driving; the collision detection subsystem learns the collision training data so as to generate a collision model, and the collision model is set up using an intelligent semi-supervised learning BIRCH method;

[0058] Step 4. Judging all working condition information that collision occurs; The working condition detection subsystem learns working condition training data so as to generate a working condition model, and the working condition model is set up using an intelligent semi-supervised learning BIRCH method;

[0059] St...

Embodiment 3

[0091] Example 3: Have the technical scheme identical with embodiment 1 or 2, more specifically:

[0092] The overall data set in the above scheme: all are CAE simulation data and sports car data; it is divided into three parts as follows

[0093] 1. Training data set: it is used to train the model or determine the model parameters (CAE simulation data and sports car data).

[0094] 2. Verification data set: It is used for model selection, that is, for final optimization and determination of the model (CAE simulation data and sports car data).

[0095] 3. Test data set: it is purely to test the generalization ability of the trained model. (CAE simulation data and sports car data).

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Abstract

The invention relates to a system and method for establishing target division remote damage assessment of different vehicle types based on an artificial intelligence semi-supervised learning BIRCH method and belongs to the vehicle damage assessment field. The objective of the invention is to solve problems in target detection of collision vehicles after a collision. According to the technical schemes of the invention, a target detection subsystem is adopted to judge collision objects in the vehicle collision; and the target type detection subsystem learns target training data so as to generate a target model, wherein the target model is built by adopting the intelligent semi-supervised learning BIRCH algorithm. With the system and method provided by the technical schemes of the invention adopted, target 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 [0001] The invention belongs to the field of vehicle damage assessment, and relates to a remote damage assessment system and method based on artificial intelligence semi-supervised learning BIRCH method to establish targets for different vehicle types. Background technique [0002] Aiming at the problem of claim settlement disputes caused by frequent collisions of vehicles during low-speed movement (including low-speed road driving, vehicle parking, etc.), the remote damage assessment technology collects various signals (such as speed, acceleration, angular velocity, Sound, etc.) and analyzed with signal processing and machine learning techniques to determine whether a collision has occurred and the damage to the vehicle after the collision. [0003] After a vehicle collision accident, the front-end equipment can detect the occurrence of the collision and intercept the signal of the collision process, and send it to the cloud through the wireless network, and...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/62
CPCG06F18/24G06F18/214
Inventor 田雨农刘俊俍
Owner DALIAN ROILAND SCI & TECH CO LTD
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