Tunnel air quality monitoring method based on convolutional neural network algorithm

Abstract

The invention discloses a tunnel air quality monitoring method based on a convolutional neural network algorithm. The tunnel air quality monitoring method comprises the following steps: step 1, arranging a plurality of air quality monitoring nodes in a tunnel; 2, performing real-time collection and transmission of tunnel air quality related data; and step 3, analyzing and processing the data to obtain the air quality condition of the tunnel. According to the method, the air quality prediction convolutional neural network model is adopted to predict the air quality condition of the tunnel, the method is simple in step and convenient to implement, and meanwhile the sulfide sensor, the gas concentration sensor and the dust sensor are adopted to monitor the air quality in the tunnel; the method can monitor the concentration of sulfide, gas and dust at the same time, and is complete in function, high in working reliability, high in practicability, good in using effect and convenient to popularize and use.

Description

technical field [0001] The invention belongs to the technical field of air quality control, and in particular relates to a tunnel air quality monitoring method based on a convolutional neural network algorithm. Background technique [0002] In traffic construction, tunnel construction will be an essential engineering project. During the excavation process of tunnel-based underground projects, due to the disturbance of the soil, the soil will release various gases that are unfavorable to personnel and equipment, and generate a large amount of dust, such as gas and dust. Because these gases are colorless, odorless and difficult to be detected during the release process, and the dust is difficult to settle, it is very difficult to monitor them specifically. And these harmful gases, gas, dust, etc. are extremely harmful to the human body, and in severe cases, may cause poisoning death. [0003] In order to solve the above problems, according to market research, we found that s...

AI Technical Summary

Problems solved by technology

Due to the fact that these gases are colorless, odorless and difficult to be detected during the release process, and the dust is difficult to settle, it is very difficult to monitor them specifically

And these harmful gases, gas, dust, etc. are extremely harmful to the human body, and in severe cases, may cause poisoning death

[0003] In order to solve the above problems, according to market research, we found that air quality monitoring has begun in some tunnels. However, the monitoring methods are mostly limited to the monitoring of single pollution sources, dust or gas by using instruments and equipment on site. The function is single and the air quality monitoring effect is poor.

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