發(fā)布時間：2018-04-19 19:31

  本文選題：道面溫度 + 紅外測溫�。� 參考：《中國科學技術(shù)大學》2017年碩士論文

【摘要】：道面溫度不僅是道面氣象要素之一,也是判斷道路表面狀態(tài)的重要依據(jù)。對道面溫度的實時監(jiān)測,有利于掌握道路狀態(tài)、維護道路安全。本文在全輻射測溫法的基礎上,提出了一種優(yōu)化的測溫方案,設計了一種用于道面交通氣象測量的非接觸式道面溫度測量系統(tǒng),在無需接觸道路表面的狀態(tài)下,能夠精準獲取其表面溫度,具有安裝簡單、維護方便、測量準確的優(yōu)點。首先,根據(jù)實際應用需求,確定系統(tǒng)的測量目標,并完成了系統(tǒng)總體方案設計和關(guān)鍵系數(shù)確定。選擇雙窗口的熱電堆紅外探測器作為本系統(tǒng)的紅外探測器,利用濾波片以及兩個窗口的濾波特性分別在兩個波段對外界目標輻射和系統(tǒng)內(nèi)部輻射進行測量,以此來減小外部環(huán)境對測量的影響,并進行光學及機械系統(tǒng)的設計。其次,完成信號調(diào)理系統(tǒng)和數(shù)字信號處理系統(tǒng)的設計。完成對目標溫度循環(huán)檢測的控制,對探測器兩個窗口兩個輸出信號的采集、放大、濾波、處理運算、外部存儲及串口通信。再次,進行系統(tǒng)軟件的設計并分析了系統(tǒng)誤差的影響因素,通過最小二乘法以及三層BP神經(jīng)網(wǎng)絡對系統(tǒng)進行進一步標定和分析。實驗結(jié)果表明:三層BP神經(jīng)網(wǎng)絡的擬合效果較好,測量溫度的最大誤差在±0.2℃。
[Abstract]:Pavement temperature is not only one of the meteorological elements of the pavement, but also an important basis for judging the surface state of the road. The real-time monitoring of road surface temperature is helpful to master the road state and maintain road safety. Based on the total radiation temperature measurement method, an optimized temperature measurement scheme is proposed in this paper. A non-contact road surface temperature measurement system is designed for road surface traffic meteorological measurement. It has the advantages of simple installation, convenient maintenance and accurate measurement. First of all, according to the practical application requirements, the measurement target of the system is determined, and the overall scheme design and the key coefficient determination of the system are completed. The dual-window thermoelectric stack infrared detector is chosen as the infrared detector of the system. The external target radiation and the internal radiation of the system are measured in two bands using the filter chip and the filtering characteristics of the two windows. In order to reduce the impact of external environment on the measurement, and optical and mechanical system design. Secondly, the design of signal conditioning system and digital signal processing system is completed. Complete the control of the temperature cycle detection of the target, the acquisition, amplification, filtering, processing operation, external storage and serial communication of two output signals of two windows of the detector. Thirdly, the system software is designed and the influencing factors of system error are analyzed. The system is calibrated and analyzed by least square method and three-layer BP neural network. The experimental results show that the three-layer BP neural network has a good fitting effect, and the maximum error of measuring temperature is 鹵0.2 鈩，

本文編號：1774416