一种基于光谱结构解耦的自适应拉曼测量方法及系统

By decoupling the spectral structure and using a probabilistic spectral proxy model, adaptive optimization of Raman spectroscopy measurement parameters was achieved, solving the problems of low efficiency and poor stability in existing technologies, and improving the automation of measurements and the consistency of results.

CN121978081BActive Publication Date: 2026-07-17CHINA JILIANG UNIV
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Patent Information

Authority / Receiving Office
CN · China
Patent Type
Patents(China)
Current Assignee / Owner
CHINA JILIANG UNIV
Filing Date
2026-04-07
Publication Date
2026-07-17

AI Technical Summary

Technical Problem

Existing Raman spectroscopy measurement methods rely on human experience or fixed parameters, resulting in low efficiency, poor repeatability, and difficulty in adapting to complex samples or variable measurement environments. Furthermore, existing machine learning methods ignore spectral structure information, making it difficult to achieve adaptive parameter optimization under limited measurement conditions.

Method used

By decoupling the spectral structure, the Raman spectrum is decomposed into scale components and structural components, a probabilistic spectral surrogate model is constructed, and the model is used to predict the spectral quality and calculate the uncertainty, thereby achieving adaptive adjustment of the measurement parameters.

Benefits of technology

It improves the automation level and result stability of Raman measurement, reduces the dependence on large-scale measurement data, and meets the real-time requirements of rapid on-site measurement and online detection.

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Abstract

本发明公开了一种基于光谱结构解耦的自适应拉曼测量方法及系统。该方法首先定义包含激光功率和积分时间的参数空间，并在其中进行少量初始测量，获取初始光谱数据集；然后对每条光谱进行解耦处理，分解为表征整体强度水平的尺度分量和表征相对形态特征的结构分量；基于测量参数及解耦后的尺度、结构分量，构建能够预测任意参数下光谱分布的概率光谱代理模型；利用该模型对候选测量参数进行光谱质量预测并计算不确定度；通过融合预测质量和不确定度的决策函数，选择下一次测量的目标参数；执行测量后更新数据集并重构模型，迭代直至满足终止条件，输出最优测量参数和光谱，显著提升拉曼测量的自动化程度、效率和结果稳定性。
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Citation Information

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