Theoretical Study on the Influence of Infrasound of Rail Transit Radiation on Animal Body
In order to study the neurophysiological effects of high-speed railway noise on animal organisms, this paper samples the noise and background noise of intercity high-speed railways, and makes reasonable arrangements according to the train running timetables along the line. The equivalent noise level of the noise is adjusted to 70dB as experimental exposure, Sound source. ICR mice and SD rats, which are very similar to the human central nervous system, were selected as experimental subjects. The experimental animal model of animal behavior, the open field experiment box and the light and dark box, were used to investigate the neurobehavioral behavior of the mice. The rats were subjected to blood collection, and the plasma levels of norepinephrine (NE), serotonin (5-HT), dopamine (DA) and glutamic acid (Glu) were measured by high performance liquid chromatography-fluorescence and enzyme-linked immunosorbent assay. The content of the material was observed by transmission electron microscopy. The ultrastructure of hippocampus, leaf cortex and amygdala was detected by Western blotting. The expression of p-CaMKII protein in hippocampus, leaf cortex and amygdala was detected by Western blotting. . The results of neurobehavioral experiments showed that compared with the control group, the residence time of the experimental group in the open field behavior experiment increased (P<0.05), there was no significant difference in the number of cross-grid, the number of modification behaviors and the number of standing (Antelope 0.05); In the box experiment, after the long-term exposure of noise, the dead time and the number of boxes in the mice were significantly reduced compared with the control group (P<0.05). This result indicates that high-speed railway noise exposure has a negative impact on mouse mood and activity. The duration of noise exposure is prolonged, and the anxiety symptoms of mice are more obvious. At the same time, this paper selects computer vision technology to study the classification of noise generated in the track surface. First, image preprocessing includes image enhancement and image denoising. Image enhancement can increase image sharpness and reduce the difficulty of computer recognition in later classification. Secondly, this paper chooses convolutional neural network as the image classifier. The structure of convolutional neural network is diverse, and the connection between layers is tight, which can realize more complex classification and recognition. Through the track detection, the track structure that generates vibration and noise is identified to achieve the purpose of vibration and noise reduction.