多模态传感与大数据驱动的矿物成分智能检测方法研究OA

Traditional mineral component detection methods have technical limitations such as single-source information acquisition and low detection efficiency in the analysis of complex mineral combinations.To address this issue,a multimodal sensing platform integrating X-ray Fluorescence(XRF)Spectroscopy,Raman Spectroscopy,Infrared(IR)Spectroscopy,and Laser-Induced Breakdown Spectroscopy(LIBS)was constructed,and a distributed storage and processing architecture for massive spectral data was established.Based on deep learning theory,a cross-modal feature correlation analysis algorithm was developed to realize the intelligent fusion of multi-dimensional spectral information and the accurate identification of mineral components.Through training with a standard mineral sample library and multi-scenario verification,this method achieves an accuracy of 97.2%in mineral species identification,controls the relative error of major element content detection within 1.8%,and shortens the detection time by 75%compared with traditional methods.It provides an efficient technical solution for the intelligent detection of mineral components.