海水富营养化监测中面向传感器网络的软传感器关键技术研究

项目名称： 海水富营养化监测中面向传感器网络的软传感器关键技术研究

项目编号： No.61273068

项目类型： 面上项目

立项/批准年度： 2013

项目学科： 自动化技术、计算机技术

项目作者： 张颖

作者单位： 上海海事大学

项目金额： 80万元

中文摘要： 海水富营养化形成因素众多而机理复杂，受技术和时空条件限制，现有传感器无法做到实时、远程、准确地直接测量其变化状态。本项目基于面向传感器网络的软传感器，研究解决这一问题的各项关键技术。拟通过运用改进的模糊最近邻聚类算法消除海量观测数据的冗余信息，实现样本数据精简；运用基于机理分析与主成分分析相结合的方法，确定软传感器的辅助测量变量，以降低模型的复杂度。拟运用具有偏差动态补偿的T-S模糊神经网络构造软传感器模型，以获得具有一定测量精度的软传感器；研究依据功能及信息处理的相对独立性对软传感器模型进行模块化解析的方法，形成分布式协同信息处理的机制以适应传感器网络的分布式节点计算资源；研究运用估值理论对传感器网络多点软传感器测量值进行基于空间的数据融合的方法。设计出适合传感器网络特点和实际应用环境的软传感器测量系统，并通过模拟实验和理论分析的手段评价和比较所设计机制与算法的性能。

中文关键词： 软传感器；海水富营养化；传感器网络；网络分簇路由；安全网络拓扑控制

英文摘要： There exist many factors affecting the causation of seawater eutrophication. As the condition limitation of technology and time & space, the existing sensors can not measure the state of seawater eutrophication real-timely,remotely and accurately by direct sensing.This project wants to research many key technologies to solve the problem based on soft seansor in sensor networks. The redundance information of the monitoring data will be removed by the algorithm of improved fuzzy nearest neighbor clustering, and the simplified sample data will be got. The assistant measuring variables need to be picked up by the method of mechanism analysis combined to Principal Component Analysis, and the complexity of the model will be reduced. Soft sensor with more measuring accuracy can be got by the model of T-S fuzzy neural network based on declination compensation dynamically.The method of parsing the model by modularization according to the independence of function and information processing will be studied, and the mechanism of distributed cooperative information processing will meet the computation resource of distributed nodes in sensor network. The method of data fusion based on space for the results of multi-soft sensor measuring based on valuation fusion theory will be researched. The soft sensor measuring system will

英文关键词： soft sensing；seawater eutrophication；sensor networks；network clustering routing；security network topology control