The invention discloses a multi-component heavy
metal spectrum
deep learning detection method and
system, and belongs to the technical field of
analytical chemistry and instrument analysis. According to the method, the UV-Vis spectral difference of multi-component
heavy metals is enhanced through a combined
chemical probe color development technology, complex nonlinear spectral signals are analyzed in combination with a
deep learning model, and the generalization ability of the model is improved through a data enhancement strategy. The
system is integrated with a lightweight end-to-end detection platform, is adaptive to low-cost portable equipment, and realizes minute-level multi-component
synchronous detection. According to the method, a closed-loop technical path is formed through three core technologies (probe combination, spectrum enhancement and
deep learning), the technical
bottleneck of a traditional method in a multi-component, small-sample and complex scene is broken through, and an efficient, accurate and low-cost complete solution is provided for heavy
metal pollution prevention and control; the method solves the problems of expensive equipment, low detection efficiency and insufficient multi-
component analysis capability of a traditional method, and is suitable for scenes of
environmental monitoring,
sewage treatment and the like.