本文選題：氣體預(yù)警穿戴系統(tǒng) + 電化學(xué)氣體傳感器��； 參考：《東華大學(xué)》2017年碩士論文

【摘要】：隨著社會經(jīng)濟(jì)的不斷發(fā)展,國內(nèi)各種行業(yè)建設(shè)規(guī)模的不斷提高,各種類型的作業(yè)現(xiàn)場,如工業(yè)生產(chǎn),市政維護(hù),礦業(yè)開采等,隨之而來也伴隨著各種有害氣體對作業(yè)人員的生命與健康的危害。其中CO氣體由于其產(chǎn)生范圍較廣,且無色無味無刺激并帶有劇毒性,不易被人體察覺,對現(xiàn)場作業(yè)人員的安全與健康造成嚴(yán)重的危害。得益于傳感器,機(jī)器學(xué)習(xí),電子及計(jì)算機(jī)行業(yè)的迅猛發(fā)展,針對特殊作業(yè)場合的毒害氣體檢測系統(tǒng)也有了較大的發(fā)展。其中穿戴式作業(yè)現(xiàn)場毒害氣體預(yù)警系統(tǒng)將CO氣體傳感器嵌入到現(xiàn)場作業(yè)防護(hù)裝備中,無需人工干預(yù)而獨(dú)立工作,提高作業(yè)人員安全與健康的時間分辨率和空間分辨率。本論文針對穿戴式作業(yè)現(xiàn)場毒害氣體預(yù)警系統(tǒng)對其使用的CO傳感器進(jìn)行靈敏度校正和濃度預(yù)測研究與實(shí)現(xiàn)。本論文研究使用的開放計(jì)算平臺是樹莓派Raspberry PI Zero,CO傳感器采用德國Solidsens公司的CO1000 Micro3,由于Raspberry PI Zero運(yùn)行Linux系統(tǒng),支持Python語言,且由于Python簡潔性、易讀性以及可擴(kuò)展性,擁有強(qiáng)大且豐富的庫,如Numpy,Matplotlib,Pandas和Skelarn,還集成了GUI等工具,相比于Matlab更適用于實(shí)際工程中。由于電化學(xué)傳感器的靈敏度會隨著環(huán)境中溫度,濕度及氣壓的影響,會對測量結(jié)果造成一定的影響,本文通過改進(jìn)的最小二乘法嶺回歸方法對電化學(xué)氣體傳感器的靈敏度進(jìn)行預(yù)測。由傳感器采集到的CO氣體濃度是系列隨著時間變化的序列。對時間序列的預(yù)測有多種,比如移動平均法,指數(shù)自回歸模型等經(jīng)典方法,近年來隨著機(jī)器學(xué)習(xí),人工神經(jīng)網(wǎng)絡(luò)等先進(jìn)算法的發(fā)展,機(jī)器學(xué)習(xí)也逐漸運(yùn)用到時間序列的預(yù)測中去,本文將使用決策樹回歸,支持向量回歸方法,移動平均模型,指數(shù)將加權(quán)移動平均模型對CO氣體濃度進(jìn)行預(yù)測,并比較四種種方法的效果,在綜合考慮處理器的運(yùn)行速度和算法擬合精度的條件下,支持向量回歸的表現(xiàn)更加優(yōu)越。
[Abstract]:With the continuous development of social economy, the construction scale of various industries in China is increasing, and various types of operation sites, such as industrial production, municipal maintenance, mining and mining, etc. Followed by a variety of harmful gases to the lives and health of workers. Due to its wide range of production, colorless, odorless and irritant, and highly toxic, CO gas is not easily detected by human body, which causes serious harm to the safety and health of field workers. Thanks to the rapid development of sensors, machine learning, electronics and computer industry, the toxic gas detection system for special work situations has also made great progress. The wearable gas warning system embeds the CO gas sensor into the field protection equipment and works independently without manual intervention to improve the temporal and spatial resolution of the safety and health of the workers. In this paper, the sensitivity correction and concentration prediction of CO sensors used in wearable field poison gas warning system are studied and realized. The open computing platform used in this thesis is the raspberry Pi Zeroy CO sensor using CO1000 Micro3 of Solidsens Company of Germany. Because Raspberry Pi Zero runs Linux system, supports Python language, and because of Python simplicity, readability and extensibility. It has powerful and rich libraries, such as Numpy MatplotlibPandas and Skelarn, and integrates tools such as GUI, which is more suitable for practical engineering than Matlab. Because the sensitivity of the electrochemical sensor will be affected by the temperature, humidity and pressure in the environment, it will have a certain impact on the measurement results. In this paper, the sensitivity of electrochemical gas sensor is predicted by the improved least square regression method. The concentration of CO gas collected by the sensor is a series of changes over time. There are many methods to predict time series, such as moving average method, exponential autoregressive model and so on. In recent years, with the development of advanced algorithms such as machine learning, artificial neural network, etc. Machine learning is also gradually applied to the prediction of time series. In this paper, decision tree regression, support vector regression, moving average model, exponential weighted moving average model are used to predict CO concentration. Compared with the results of the four methods, the performance of support vector regression is superior under the condition of considering the processor speed and the algorithm fitting accuracy.
【學(xué)位授予單位】：東華大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：TP212

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