Research on key technologies of atmospheric carbon emission monitoring and early warning based on big data and spectral measurement

Abstract

The distribution of carbon emissions varies greatly among regions and industries in China. In order to master the current situation and trend of carbon emissions in different regions and industries, and formulate and adjust relevant policies in a timely manner, it is necessary to conduct real-time, continuous, accurate and digital monitoring of carbon emissions in major regions and industries. Therefore, "carbon peaking" and "carbon neutralization" put forward higher requirements for the environmental monitoring industry and also brought greater opportunities. In this paper, the spectral information data in typical atmospheric samples are collected in real time, processed and stored structurally through the atmospheric spectral detection method based on mid far infrared spectrum and terahertz spectrum, the spectral feature extraction algorithm based on deep neural network, and the atmospheric carbon emission data analysis and visual monitoring based on big data technology; Run the spectral classification algorithm with low cost and high efficiency on the big data platform, obtain the detailed data of gas sample composition in real time, and display the analysis results visually. Through simulation experiments, the results show that the proposed method can better achieve the monitoring and early warning of atmospheric carbon emissions based on big data and spectral measurement.