高速公路动态称重系统构建与数据处理研究
发布时间：2018-09-01 16:52
【摘要】：随着我国改革开放的不断深入,高速公路的在建设里程和实际的通车里程不断增加,使得经济发展对于公路交通的依赖日益增强。然而,高速公路运营数量增多的同时,一些运输企业或个人为了能够获取更大经济效益,在运输中进行超载运输。这不仅给运输车辆和人员带来危险,也给高速公路本身带来严重的影响。因此,对于高速公路的超载治理也引起相关的管理部门的重视。当前,动态称重技术是高速公路管理部门进行超重治理的重要技术,也是很多研究机构和学者关注的热点研究内容,其成为了高速公路管理部门进行超载管理的有效措施。通过应用动态称重技术能够提高管理部门对于高速公路的管理水平,减少高速公路的维护成本的投入,减少因车辆超载问题引起的人、车、路的损失。本文以研究高速公路动态称重系统构建与数据处理研究为目标,通过研究相关背景、国内外研究的现状等问题,从中找出本次的研究目标与内容。进而以高速公路收费站管理为目标研究了高速公路动态称重系统的设计原则,分析其组成与基本工作原理,研究车道布局与工作流程等问题。研究与分析高速公路动态称重系统误差来源问题;从而有针对性地进行称重传感器的选择;同时,进行称重数据采集、传输方案的设计以及抗干扰设计的研究;分析动态称重信号的小波变换、动态称重信号预处理方法,研究了基于RBF神经网络的动态称重处理涉及到的径向基函数网络模型、RBF神经网络的学习算法等;同时进行了称重数据采集,进行了RBF神经网络在动态称重算法流程、程序设计以及实际数据测试,进而得出RBF神经网络在动态称重的结论。通过上述研究,RBF神经网络算法在高速公路动态称重中应用具有较高的精度和可靠性,其具体应用与研究有非常好的应用前景。
[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.
【学位授予单位】：长安大学
【学位级别】：硕士
【学位授予年份】：2017
【分类号】：U492.321
本文编号：2217795
[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.
【学位授予单位】：长安大学
【学位级别】：硕士
【学位授予年份】：2017
【分类号】：U492.321
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