2results about How to "Remove random noise" patented technology

This invention discloses a spectral analysis method, apparatus, device, and storage medium. It includes: acquiring raw spectral data to be analyzed; preprocessing the raw spectral data to obtain preprocessed spectral data; performing multi-peak fitting on the preprocessed spectral data using an iterative residual peak-finding fitting algorithm to obtain a final fitted spectrum and a final residual spectrum; and generating spectral analysis results based on the final fitted spectrum and the final residual spectrum. Preprocessing the raw spectral data effectively eliminates background interference, cosmic ray pseudo-signals, and random noise, improving the signal-to-noise ratio of the spectral data. Using the iterative residual peak-finding fitting algorithm for multi-peak fitting accurately identifies and fits all significant spectral peaks, avoiding underfitting and overfitting problems, and improving the accuracy of multi-peak fitting. The generated spectral analysis results can accurately extract core feature indicators, and the fitting quality can be verified through residual spectroscopy, ensuring that the analysis results are accurate and reliable, truly reflecting the structural characteristics of the material.

The application provides a college major talent outflow trend prediction method based on multi-channel feature fusion and adaptive weighting random forest, relates to the talent flow prediction and machine learning application technical field, and comprises the following steps: collecting college major enrollment data and corresponding macroeconomic data of each year, and preprocessing missing data; adopting a multi-channel feature decoupling and fusion mechanism; the multi-channel feature decoupling and fusion mechanism adopted by the application effectively solves the defects of weak feature expression ability and neglecting external economic driving factors in the prior art, decouples internal trends and external driving factors into three channels, comprehensively extracts enrollment sequence characteristics, regional macroeconomic characteristics and regional economic difference characteristics, realizes deep fusion of internal and external information, enhances the model's recognition ability to complex patterns, can fully capture the influence of macroeconomic variables on professional talent flow, and greatly improves the prediction accuracy.