LIBS inversion method based on EMA-CNN and weighted loss function

By introducing EMA-CNN and a weighted loss function into the LIBS inversion method, the problems of low accuracy and high computational complexity in LIBS quantitative inversion are solved, and efficient collaborative prediction and stable inversion of multi-component content are achieved.

CN122024928BActive Publication Date: 2026-07-07SHANGHAI INSTITUTE OF TECHNICAL PHYSICS CHINESE ACADEMY OF SCIENCES
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Patent Information

Application Number
CN202610124562.X
Authority / Receiving Office
CN · China
Patent Type
Patents(China)
Current Assignee / Owner
Filing Date
2026-01-29
Publication Date
2026-07-07
Estimated Expiration
2046-01-29

AI Technical Summary

Technical Problem

Existing LIBS quantitative inversion methods suffer from low accuracy, low computational efficiency, and high computational complexity when dealing with complex nonlinear mapping relationships and the synergistic prediction of multi-component content.

Method used

We employ the LIBS inversion method based on EMA-CNN and a weighted loss function, introduce an efficient multi-scale attention mechanism and a weighted mean square error loss function, construct a deep convolutional neural network model, and optimize the training process and prediction performance.

Benefits of technology

It improves the accuracy and stability of LIBS spectral quantitative inversion, reduces computational complexity, and is suitable for multi-component synchronous inversion tasks in scenarios with large data volumes.

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Abstract

The application discloses a LIBS inversion method based on EMA-CNN and a weighted loss function, and relates to deep learning and spectral analysis.A LIBS spectral inversion model is constructed based on a deep convolutional neural network (CNN), an efficient multi-scale attention mechanism (EMA) is introduced, a weighted mean square error loss function is designed, and multi-component content collaborative prediction is realized.Conventional deep learning methods adopt equal attention allocation for all spectral features, the EMA module of the application can effectively guide the model to focus more reasonable attention, and the extraction and learning efficiency of key features are significantly improved.The conventional loss function is prone to the problem of unbalanced loss components caused by the order of magnitude difference of each component content, and the loss function of the application can weight the corresponding loss components of each component to collaboratively improve the prediction accuracy of all components.The method has the advantages of accuracy, stability and high efficiency, and is suitable for LIBS multi-component synchronous inversion tasks in a large data volume scene.
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