An artificial intelligence olfactory dynamic response map gas detection and identification method

Abstract

The invention discloses an artificial intelligence olfactory dynamic response spectrum gas detection and recognition method. The method comprises the following steps: collecting leakage gas data through dynamic signals of the array sensors; standard dynamic response spectrum reconstruction is carried out; establishing a standardized data matrix and a vector spectrum for to-be-detected gas collected by a sensor array, performing feature extraction and training learning on picture data in a standard spectrum library, establishing a machine learning dynamic response spectrum recognition model, and performing quantitative and qualitative recognition on the gas by utilizing the machine learning spectrum recognition model. According to the invention, traditional single-sensor response identification is converted into a multi-dimensional sensor dynamic response spectrum; Gas detection and recognition are achieved through an automatic map recognition method, the defects of singleness and crossinterference of a traditional single sensor in the aspect of gas detection are overcome, different gases are rapidly and accurately detected through the same sensor array, the detection efficiency and precision are improved, and meanwhile the detection result is visualized and more visual.

Description

technical field [0001] The invention belongs to the fields of electronic information, artificial intelligence, sensor technology and gas detection, and in particular relates to an artificial intelligence olfactory dynamic response map gas detection and identification method. Background technique [0002] The detection and identification of trace gas is widely used in chemical industry, food, environment and other fields, so fast and accurate trace gas detection method is especially important. Traditional gas detection methods rely on a single gas sensor based on physical and chemical principles, such as metal-semiconductor gas sensors, electrochemical gas sensors, infrared gas sensors, and gas sensors based on magnetic properties. Sensors generally have cross-sensitivity, poor stability and selectivity, and the response characteristics are easily affected by environmental factors such as temperature and humidity. Moreover, accurate qualitative and quantitative identification...

AI Technical Summary

Problems solved by technology

However, this method is generally based on modeling calculations based on the input of individual response values ​​of sensors, without considering the dynamic response characteristics of sensors, and the model is usually used for qualitative identification of gases, and there are still big problems in quantitative identification.

[0005] It can be seen that there are still some problems to be solved in the aspect of gas detection and identification, and there is still room for further research and development in this area.

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