Method for detecting pollutant concentration through deep ultraviolet Raman spectrum based on deep learning

By constructing an SSA-CNN-LSTM-Attention fusion model, the problems of spectral line overlap, weak trace signals, and nonlinear response in the detection of new pollutants by deep ultraviolet Raman spectroscopy are solved, realizing high-precision and highly anti-interference quantitative analysis of concentration, which is suitable for rapid detection of environmental and industrial samples.

CN120948439APending Publication Date: 2025-11-14XUZHOU NORMAL UNIVERSITY
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Patent Information

Application Number
CN202511187288.2
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2025-08-25
Publication Date
2025-11-14

AI Technical Summary

Technical Problem

Existing deep ultraviolet Raman spectroscopy techniques face challenges in detecting the concentration of new pollutants, including spectral line overlap, weak trace signals, complex background noise, and nonlinear response, making it difficult to achieve high-precision and robust quantitative concentration analysis.

Method used

A fusion model of SSA-CNN-LSTM-Attention is constructed, which combines spectral preprocessing and synthetic data augmentation strategies. It extracts local spectral features through 1D-CNN, captures long-range sequence dependencies through LSTM, enhances the feature peak response weights through the attention mechanism, and optimizes hyperparameters through SSA, thereby solving the problems of spectral line overlap interference, weak trace signals, and nonlinear response.

Benefits of technology

It achieves high-precision and highly interference-resistant quantitative analysis of pollutant concentrations, improves the anti-interference capability and generalization of new pollutant detection, and is suitable for rapid and high-precision detection of environmental and industrial samples.

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Abstract

The invention discloses a method for detecting pollutant concentration based on deep ultraviolet Raman spectrum of deep learning, and belongs to the technical field of spectral analysis. According to the method, aiming at the problems of spectral line overlapping interference, weak trace signals, strong background noise, nonlinear response and the like when a traditional deep ultraviolet Raman spectrum technology is used for detecting pollutants, high-precision quantitative analysis is realized by constructing an SSA-CNN-LSTM-Attention fusion model and combining a deep ultraviolet Raman spectrum pretreatment technology. The model adopts 1D-CNN to extract spectral local features, LSTM to capture sequence long-range dependence, an Attention mechanism to enhance feature peak response weight, SSA to optimize hyper-parameters, and a concentration prediction value is output through a weighted loss function optimization model. And meanwhile, by combining a synthetic data expansion strategy and environment matrix standard sample library training, the adaptability of the model to a complex scene is improved. According to the method, the anti-interference capability and generalization of new pollutant detection are effectively improved, and the method has important application value in the fields of environmental monitoring, industrial safety and the like.
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