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摘要:由于交通超限治理和計(jì)重收費(fèi)工作的不斷深入，汽車動(dòng)態(tài)稱重系統(tǒng)得到了越來(lái)越廣泛的應(yīng)用。就拿公路汽車動(dòng)態(tài)稱重系統(tǒng)來(lái)說，汽車的動(dòng)態(tài)稱量精度以及汽車通過的速度是最重要的性能指標(biāo)。目前國(guó)內(nèi)研發(fā)的動(dòng)態(tài)稱重系統(tǒng)，由于缺乏對(duì)影響汽車動(dòng)態(tài)稱重的各種因素作系統(tǒng)分析，沒有對(duì)檢測(cè)信號(hào)作深層次的處理，使得動(dòng)態(tài)稱重精度與汽車速度之間的矛盾難以協(xié)調(diào)。往往測(cè)量的精度高，允許的通行速度就比較低;當(dāng)通行速度高時(shí)，測(cè)量精度就達(dá)不到要求。因此，動(dòng)態(tài)稱重系統(tǒng)的開發(fā)工作主要解決的問題是測(cè)量精度與車輛通行速度之間的矛盾。目前，汽車動(dòng)態(tài)稱重系統(tǒng)主要有檢測(cè)軸載荷和輪載荷兩種稱重方法，本文使用的是軸載荷稱重的方法對(duì)動(dòng)態(tài)稱重進(jìn)行研究。 為了達(dá)到設(shè)計(jì)要求，我們首先分析了汽車動(dòng)態(tài)稱重系統(tǒng)中影響測(cè)量精度的各種因素，得出了影響靜態(tài)稱重的各種因素，其主要包括汽車速度、汽車的振動(dòng)、地面的不平整等一系列因素，然后提出了從硬件和軟件兩個(gè)方面來(lái)提高動(dòng)態(tài)精度的有效措施和動(dòng)態(tài)稱重系統(tǒng)的設(shè)計(jì)思想。 為了提高測(cè)量精度，用BP神經(jīng)網(wǎng)絡(luò)算法建立了動(dòng)態(tài)稱重系統(tǒng)的數(shù)學(xué)模型，并對(duì)動(dòng)態(tài)稱重系統(tǒng)的數(shù)據(jù)做了進(jìn)一步的處理。 動(dòng)態(tài)稱重系統(tǒng)的硬件部分的設(shè)計(jì)，包括稱重平臺(tái)的設(shè)計(jì)以及稱重硬件電路的設(shè)計(jì)。電路部分主要包括傳感器的信號(hào)調(diào)理電路、AD轉(zhuǎn)換接口電路設(shè)計(jì)、測(cè)速電路、顯示電路以及與單片機(jī)通信接口電路等。 關(guān)鍵詞:動(dòng)態(tài)稱重系統(tǒng)；稱重傳感器；單片機(jī)；數(shù)據(jù)采集；BP神經(jīng)網(wǎng)絡(luò)
Abstract:Vehicle Weigh-in-Motion (WIM) systems were rapidly developed following transportation investigation, controlling serious vehicle overload and weighing charge. For the highway weigh-in-motion systems, the weighing accuracy and the speed of vehicle moving were the most important standard. At present, when the WIM systems were studied in our country, the contradiction on identifying accuracy and vehicle passing speed can’t be resolved well all along, for the disturbing factors affecting the WIM systems were short of systemic analysis and the inspecting signal wasn’t processed further .So it was important to resolve the contradiction on identifying accuracy and vehicle passing speed.At present, the WIM systems were studied on the base of wheel load and axle load, this paper discussed the way of axle load. For improving identifying accuracy, first, this paper analyzed all kinds of influence factors to WIN systems, including vehicle speed, vehicle liberation, road surface, and so on. Second, the restraint ways of disturbance could be realized by both hardware method and software method. In order to improve the measurement accuracy, using BP neural network algorithm established mathematical model of dynamic weighing systems, weighing systems and dynamic data to do further processing. The design of hardware consisted of the design of weighing platform and the design of integrated circuit. The integrated circuit was mainly made of signal disposal, A/D converters, data collection, measuring velocity, display and data communication with personal computer circuit. Key words: Weigh-in-motion systems; Weigh sensor; MCU; Data acquisition; BP neural network.
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