基于近紅外光譜透射法的汽車駕駛員血液酒精含量無損檢測
[Abstract]:In the forensic investigation of traffic accidents all over the world, drunk driving is an important cause of traffic accidents. The detection and prevention of drunken driving has been paid more and more attention by many countries all over the world. At present, the more mature methods of alcohol detection are expiratory alcohol detection and blood sampling test. Because the blood sampling test belongs to the invasive detection method, the detection accuracy of breath method is also a problem, and the near infrared spectroscopy is a fast method. Accurate, non-destructive testing method, compared with the existing technology, has irreplaceable advantages. Near infrared spectroscopy (NIR) has been widely used to detect substances in human body, such as glucose, blood oxygen, etc. It can be said that the technology has entered the mature stage of development. However, near infrared spectroscopy (NIR) is still in the research stage to detect the alcohol content in the driver's blood at home and abroad. The purpose of this paper is to realize the nondestructive detection of alcohol content in the driver's body by near infrared spectroscopy (NIR), and combine it with the automobile driving system to realize the anti-drunken driving. The near infrared diffuse reflectance spectrum of alcohol in human body was analyzed and studied by wavelet analysis and partial least square method. On the basis of the analysis, the attenuation and absorption of blood alcohol in human body with time were analyzed. A quantitative model for the determination of alcohol content in human tissues by partial least square method was established and the prediction accuracy and stability of the model were tested. The near infrared spectrum signal of alcohol in human body contains strong noise, especially the signal collected directly on the surface of human skin. The noise interference is very strong and can not be used to establish the model directly. In this paper, wavelet transform and data smoothing are used to pre-process the near-infrared spectrum of alcohol. Under the condition of hard threshold, the matlab default de-noising method is selected, and the spectral data are de-noised, and the wavelength range of the transmission characteristic spectrum of alcohol is determined to be about 1550nm-1800nm. According to the pre-processed spectral data such as wavelet analysis, the partial least square method is used to establish the quantitative correction model, and a cross-check method is used to determine the optimal principal fraction. The correlation coefficient (R), root mean square error (RMSEC) is used as the evaluation parameter of the calibration model. The prediction results of the unknown samples are evaluated by the prediction mean variance (RMSEP) and the average relative error (MREP), and the repeatability of the model is verified. The detection of unknown alcohol concentration is realized. Finally, an anti-drunken driving warning system is designed. In the aspect of hardware, S3C2410A is used as the microcontroller of the main control node, MCP2515 is chosen as the CAN controller of the ARM master node, and PCA82C250 is chosen as the CAN transceiver. The relevant circuits are drawn, and the software program of the corresponding executing mechanism is programmed. When the alcohol content of the driver exceeds the standard, the actions such as issuing alarm tone, lighting the alarm light and emergency braking are completed, so as to effectively prevent drunken driving and avoid unnecessary loss.
【學位授予單位】:山東大學
【學位級別】:碩士
【學位授予年份】:2014
【分類號】:U492.8;TN219
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