This invention discloses a method and
system for multi-
component analysis of aircraft exhaust plumes using micro-on-
chip spectral imaging. The method involves acquiring
multispectral image data of the exhaust plume region using a micro-on-
chip spectral imaging system. This invention unmixes the mixed spectra using a non-negative matrix factorization
algorithm and, combined with a pre-built fuel component spectral
library, accurately identifies and quantitatively calculates the concentration distribution of key components such as
carbon dioxide,
carbon monoxide,
nitrogen oxides, and unburned hydrocarbons in the exhaust plume. This method overcomes the errors caused by component overlap in traditional
spectral analysis. By establishing a three-dimensional spatial coordinate model, high-precision extraction of the exhaust plume region is achieved, avoiding background interference. Furthermore, convolutional neural networks are used to perform
spatiotemporal correlation modeling of the multi-component concentration data, generating a three-dimensional dynamic evolution map of the exhaust plume chemical components. This map reflects the
diffusion patterns of exhaust plume components over time and space in real time, providing crucial data support for
environmental impact assessment, aircraft emission control, and
accident emergency response.