Automobile body welding process quality score prediction method and device based on LSTM (Long Short Term Memory) model
A scoring prediction and welding process technology, applied in the field of auto body welding process quality scoring prediction, can solve the problems of lack of intelligent application, high labor cost, poor timeliness, etc., and achieve the effect of improving maintenance accuracy and reducing maintenance time.
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Embodiment 1
[0051] Example 1: Take the LightGBM model as an example
[0052] Such as figure 2 As shown, the LightGBM model is deployed on the online server. This model can identify the path of the data feature value transmitted to the server, and judge whether there is new data and what new data is generated by reading the feature value data on the fixed path in real time. ; If new data is found, follow up the identification scheme of the corresponding solder joint type in real time, extract the feature data set, and generate a data screening file to save to a fixed path; if not found, the online system will not be triggered to perform feature comparison Importance selection model.
[0053] After the collected solder joint information data passes through the LightGBM model, the characteristic value parameters will be generated. While saving the characteristic value parameters to the solder joint information database, the system will detect that new characteristic data is generated in th...
Embodiment 2
[0080] Example 2: Taking the XGBoost model as an example
[0081] Such as image 3 As shown, the XGBoost model is deployed on the online server. This model can identify the path of the data feature value transmitted to the server, and judge whether there is new data and what new data is generated by reading the feature value data on the fixed path in real time. ; If new data is found, follow up the identification scheme of the corresponding solder joint type in real time, extract the feature data set, and generate a data screening file to save to a fixed path; if not found, the online system will not be triggered to perform feature comparison Importance selection model.
[0082] After the collected solder joint information data passes through the XGBoost model, eigenvalue parameters will be generated. While saving the eigenvalue parameters to the solder joint information database, the system will detect that new feature data is generated in the fixed path, and then trigger LS...
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