【摘要】：隨著我國(guó)改革開(kāi)放的不斷深入,高速公路的在建設(shè)里程和實(shí)際的通車?yán)锍滩粩嘣黾?使得經(jīng)濟(jì)發(fā)展對(duì)于公路交通的依賴日益增強(qiáng)。然而,高速公路運(yùn)營(yíng)數(shù)量增多的同時(shí),一些運(yùn)輸企業(yè)或個(gè)人為了能夠獲取更大經(jīng)濟(jì)效益,在運(yùn)輸中進(jìn)行超載運(yùn)輸。這不僅給運(yùn)輸車輛和人員帶來(lái)危險(xiǎn),也給高速公路本身帶來(lái)嚴(yán)重的影響。因此,對(duì)于高速公路的超載治理也引起相關(guān)的管理部門的重視。當(dāng)前,動(dòng)態(tài)稱重技術(shù)是高速公路管理部門進(jìn)行超重治理的重要技術(shù),也是很多研究機(jī)構(gòu)和學(xué)者關(guān)注的熱點(diǎn)研究?jī)?nèi)容,其成為了高速公路管理部門進(jìn)行超載管理的有效措施。通過(guò)應(yīng)用動(dòng)態(tài)稱重技術(shù)能夠提高管理部門對(duì)于高速公路的管理水平,減少高速公路的維護(hù)成本的投入,減少因車輛超載問(wèn)題引起的人、車、路的損失。本文以研究高速公路動(dòng)態(tài)稱重系統(tǒng)構(gòu)建與數(shù)據(jù)處理研究為目標(biāo),通過(guò)研究相關(guān)背景、國(guó)內(nèi)外研究的現(xiàn)狀等問(wèn)題,從中找出本次的研究目標(biāo)與內(nèi)容。進(jìn)而以高速公路收費(fèi)站管理為目標(biāo)研究了高速公路動(dòng)態(tài)稱重系統(tǒng)的設(shè)計(jì)原則,分析其組成與基本工作原理,研究車道布局與工作流程等問(wèn)題。研究與分析高速公路動(dòng)態(tài)稱重系統(tǒng)誤差來(lái)源問(wèn)題;從而有針對(duì)性地進(jìn)行稱重傳感器的選擇;同時(shí),進(jìn)行稱重?cái)?shù)據(jù)采集、傳輸方案的設(shè)計(jì)以及抗干擾設(shè)計(jì)的研究;分析動(dòng)態(tài)稱重信號(hào)的小波變換、動(dòng)態(tài)稱重信號(hào)預(yù)處理方法,研究了基于RBF神經(jīng)網(wǎng)絡(luò)的動(dòng)態(tài)稱重處理涉及到的徑向基函數(shù)網(wǎng)絡(luò)模型、RBF神經(jīng)網(wǎng)絡(luò)的學(xué)習(xí)算法等;同時(shí)進(jìn)行了稱重?cái)?shù)據(jù)采集,進(jìn)行了RBF神經(jīng)網(wǎng)絡(luò)在動(dòng)態(tài)稱重算法流程、程序設(shè)計(jì)以及實(shí)際數(shù)據(jù)測(cè)試,進(jìn)而得出RBF神經(jīng)網(wǎng)絡(luò)在動(dòng)態(tài)稱重的結(jié)論。通過(guò)上述研究,RBF神經(jīng)網(wǎng)絡(luò)算法在高速公路動(dòng)態(tài)稱重中應(yīng)用具有較高的精度和可靠性,其具體應(yīng)用與研究有非常好的應(yīng)用前景。
[Abstract]:With the deepening of China's reform and opening up, the highway mileage in construction and actual mileage are increasing, which makes the economic development rely on highway traffic increasingly. However, while the number of expressway operation is increasing, some transportation enterprises or individuals carry out overload transportation in order to obtain more economic benefits. This not only brings the danger to the transport vehicles and personnel, but also brings the serious influence to the highway itself. Therefore, the overload of highway management also caused the attention of the relevant management departments. At present, dynamic weighing technology is an important technology for highway management departments to manage overweight, and it is also a hot research content of many research institutions and scholars. It has become an effective measure for highway management departments to carry out overload management. The application of dynamic weighing technology can improve the management level of expressway, reduce the cost of expressway maintenance, and reduce the loss of people, vehicles and roads caused by overloading of vehicles. The purpose of this paper is to study the construction and data processing of highway dynamic weighing system. Through the research of related background and the current situation of domestic and foreign research, the research objectives and contents are found out. Then, the design principle of expressway dynamic weighing system is studied with the aim of expressway toll station management. The composition and basic working principle of the system are analyzed, and the lane layout and work flow are studied. This paper studies and analyzes the error source of dynamic weighing system of freeway, and chooses the weighing sensor pertinently, at the same time, carries on the research of weighing data collection, transmission scheme design and anti-jamming design. The wavelet transform of dynamic weighing signal and the preprocessing method of dynamic weighing signal are analyzed. The learning algorithm of radial basis function network model related to dynamic weighing processing based on RBF neural network is studied. At the same time, the weighing data is collected, the algorithm flow of RBF neural network in dynamic weighing, the program design and the actual data test are carried out, and the conclusion that RBF neural network is weighing dynamically is obtained. The application of RBF neural network algorithm in expressway dynamic weighing has high accuracy and reliability, and its application and research have a very good application prospect.
【學(xué)位授予單位】：長(zhǎng)安大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：U492.321

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