【作者】 吴华程；

【导师】 张新政；

【作者基本信息】 广东工业大学 ， 系统工程， 2006， 硕士

【摘要】 为了提高水环境监测和管理的信息化和自动化水平,本论文研究由嵌入式Internet接入技术和GIS实现远程水质监测和水质信息管理。论文主要围绕远程嵌入式水质监测终端的软件平台和水环境信息管理系统两个方面展开讨论,内容涉及当前计算机技术的几个“热点”领域——嵌入式实时操作系统,嵌入式TCP/IP协议栈,地理信息系统,以及水质预测模型。 本论文为嵌入式水质监测设备引入一款优秀的实时操作系统——eCos,详细分析了这个开源实时操作系统的结构,移植原理,交叉工具链的构建方法,并针对嵌入式水质监测终端试验板的硬件资源,描述了eCos在该终端上移植、开发、调试的具体步骤;为了实现水质监测终端能接入Internet,论文讨论怎样将开源TCP/IP协议栈LwIP成功移植到eCos,使之成为该操作系统的一个标准模块。因为eCos和LwIP软件基础的支持,嵌入式水质监测设备应用程序开发得以简化,并可以保证应用程序的稳定可靠。 针对水环境系统中,河流、水库、湖泊等较强的地理特征,本论文尝试着将地理信息系统(GIS)融入水环境信息管理当中,改变当前水质数据单一的数据报表格式,使水环境信息平台更加直观形象,简单易用。论文详细分析了MapInfo系列工具实现数字地图的一次开发和二次开发步骤,以及地理信息系统与水环境信息管理系统的融合方法。 在文章的最后,给出了一种BP神经网络预测水质数据的建模方法,该方法针对河流的特点,采用时间序列加空间序列建模,预测模型输入输出之间有较强的因果关系,并能对突发污染事件的后续效应作出较准确的预测。 作者希望,以上几点研究内容能帮助改善日益恶化的水环境现状。更多还原

【Abstract】 To improve the informationization of water environment supervision, this thesis proposes a system of water environment remote monitor and water quality data management which bases on embedded internet connecting technic and GIS (Global Information System). This thesis includes primarily embedded operation system, embedded TCP/IP stacks and GIS which are hot fields of computer technic in these years. To connect the water remote monitor terminal on internet, firstly this thesis research the achicticture of eCos which is an excellent open source RTOS(real time operation system), and resolves how to port eCos on the board of the terminal;then I add TCP/IP packages to eCos by adoping LwIP, which is very small and compeled TCP/IP stacks, especially suitable for embedded application. Both eCos and LwIP can make the development of application software simple and robustioust. Because the rivers are geographical objects, I use GIS to design the water environment information management and connect GIS with database. This thesis discusses the way how to built GIS by MapInfo series tools combined with VC or ASP( Vbscript). When the users open this software, they would see a beautiful digital map, rather than any input boxs. Users can get data they need by operate the digital map, and the data of water quality even can display on the digital map by charts or different colors. In the last chapter, I adopt BP neural network to forecast the water quality data. BP neural network is wildly used to water quality data forecast, but in this thesis, I give a different method to design network. By this method, BP neural network has strong causation between input and output data. This method can improve the veracity of forecasting. Besides, this BP neural network is even able to forecast the consequence of faculative water pollution. I hope all of these can do something to stop the depravation of water environment.更多还原