Infrared spectrum identification method and system for polluted gases

Abstract

The invention disclose an infrared spectrum identification method and system for polluted gases. The method comprises steps of: extracting characteristics of target gas spectrum under a preset condition, forming a target gas spectrum characteristic data set, and recording the data set as a standard spectral library data set; extracting characteristics of actually measured spectrum of a target gasbased on sparse learning, so as to obtain actually measured spectrum characteristics; selecting spectrum matching the actually measured spectrum characteristics in the standard spectral library data set so as to reconstruct the actually measured spectrum; and determining components of the target gas according to the reconstructed actually measured spectrum. The infrared spectrum identification method and system for polluted gases have characteristics of highly adapting to complex environment under the actual measurement condition and identifying components of mixed gases highly precisely.

Description

technical field [0001] The invention relates to the field of spectral analysis, in particular to an infrared spectrum identification method and system for polluted gas. Background technique [0002] With the development of China's industry, the polluting gases emitted by factories have caused serious air pollution, and sudden chemical gas leakage incidents have seriously endangered people's health and even life safety. Therefore, an effective monitoring method is needed to facilitate the detection of polluted gases. Carry out on-site emergency monitoring. At the same time, in practical applications, in the event of sudden chemical gas leakage, the various components and hazards of the polluted gas are unknown, and it is difficult to directly sample and detect the pollution source. Therefore, a long-distance detection method is urgently needed. At present, passive infrared remote sensing technology can carry out remote sensing identification and quantitative analysis of poll...

AI Technical Summary

Problems solved by technology

The non-negative least squares method proposed by ChimanKwan to separate mixed components requires that the spectral characteristics of all gases are emission characteristics. However, under the condition of an open optical path, the target spectrum may be either emission characteristics (the target temperature is greater than the background temperature), or it may be Absorption characteristics (the target temperature is lower than the background temperature), in the method, the target feature extraction needs to accurately reconstruct the measured spectrum, and the spectral reconstruction needs to know the spectral shape of each component, whether the target exists and the shape of the spectrum is unknown , the unknown spectral shape cannot accurately reconstruct the measured spectrum, and the process of spectral signal processing falls into an "infinite loop".

[0006] The defect of the traditional method is: the spectral characteristics of the target are related to the concentration path length value (concentration * path length), the traditional method can only be set to a certain (or several) concentration path length value target characteristics, and cannot adaptively reconstruct the actual measurement Spectrum, extraction of target feature spectrum; for multi-component mixing, when the number of mixed components is greater than the spectral dimension, the traditional method will have numerical errors, and the multi-component target features cannot be separated, which affects multi-component recognition

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