Target remote damage assessment system for different vehicle models established based on artificial intelligence supervised learning decision tree method and target remote damage assessment method thereof

[0025] A supervised learning decision tree method based on artificial intelligence 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 an intelligent supervised learning decision tree 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 establi...

[0052] A supervised learning decision tree method based on artificial intelligence to establish a remote damage assessment method for different vehicle types with sub-targets, comprising the following steps:

[0053] Step 1. Select the model data corresponding to the vehicle as the total data set;

[0054] Step 2. Read the CAE simulation data and real vehicle data, and classify the data accordingly;

[0055] Step 3. Judging 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 an intelligent supervised learning decision tree method;

[0056] Step 4. Judging all the working condition information where the collision occurs; the working condition detection subsystem learns the working condition training data to generate a working condition model, and the working condition model is established using an intelligent supervised learning decision tree...

[0097] Have the technical scheme identical with embodiment 1 or 2, more specifically:

[0098] 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

[0099] 1. Training data set: it is used to train the model or determine the model parameters (CAE simulation data and sports car data).

[0100] 2. Verification data set: It is used for model selection (model selection), that is, for the final optimization and determination of the model (CAE simulation data and sports car data).

[0101] 3. Test data set: it is purely to test the generalization ability of the trained model. (CAE simulation data and sports car data).