车联网条件下的混合动力客车车载传感器实时数据预处理研究

发布时间：2018-01-14 00:04

本文关键词：车联网条件下的混合动力客车车载传感器实时数据预处理研究　出处：《重庆大学》2014年硕士论文　论文类型：学位论文

【摘要】：在计算机、互联网以及移动通信出现以后，物联网引领了新一代的信息技术革命。2009年美国在首次提出“智慧地球”这一新颖概念的同时，将物联网作为振兴经济、提高国家实力的重点领域之一。同一年，我国温总理提出了“感知中国”，并把物联网正式列入国家五大新兴战略性支柱产业之一，同时写入政府工作报告。在信息社会，物联网主要应用在医疗、电子政务、电网、教育、交通、城市管理等领域。物联网应用的同时产生了数以万计的实时数据，随着数据量的急剧增长和数据种类的不断增加，催生了大数据时代的到来。车联网作为物联网在智能公交中的典型应用，车联网条件下的车载数据除了具有物联网大数据一般性的特征，而且具有自身的结构性质。本文在总结数据预处理、数据清洗和数据质量等情报定量分析的相关文献下，探讨了实时数据清洗处理定量分析的研究现状，分析了车联网实时数据的主要质量问题和特征。在研究一般传感数据和传感信息的处理方法下，基于滑动窗口借鉴使用各种情报定量分析方法对车载传感实时数据进行清洗等预处理。针对所研究的问题，本文介绍了车联网网络架构和车载数据框架，，结合车载数据的海量、异构、实时等主要特征，提出了车联网条件下的车载数据实时清洗的预处理框架，然后以连续型和开关量信号两种主要的车载类型实时数据的清洗处理算法。最后以城市混合动力客车的天然气气瓶压力为代表的连续型变量和刹车制动为代表的车载开关量信号实时数据为基础，分别研究分析了相关的实时清洗预处理算法。针对各自的数据特征，本文以滑动窗口来保证数据清洗的实时性，运用莱茵达准则、滑动平均滤波、小波变换、贝叶斯决策理论等多种定量分析方法进行混合清洗处理使用。运用这些情报定量分析方法对数据进行预处理，能够有效地处理掉错误、噪声、缺失等异常数据，得到准确的数据信息，为驾乘人员提供实时、准确、可靠的公共交通信息服务，从而改善公共交通的智能化运营管理。由于车载数据的特征和数据实时清洗算法的复杂性，加之写作时间比较仓促和自身的水平能力有限，文中有错误之处还请批评指正。
[Abstract]:After the advent of computers, the Internet of things and mobile communications, the Internet of things led a new generation of information technology revolutions. In 2009, the United States first proposed the new concept of "intelligent earth" at the same time. In the same year, Premier Wen proposed "perceiving China" and formally listed the Internet of things as one of the five emerging strategic pillar industries. In the information society, the Internet of things is mainly used in medical, e-government, power grid, education, transportation. The application of the Internet of things has produced tens of thousands of real-time data in the field of urban management. With the rapid growth of data volume and the constant increase of data types, the advent of the era of big data has been accelerated. As a typical application of the Internet of things in the intelligent public transportation, the vehicle-borne data under the condition of the vehicle networking has the general characteristics of the big data of the Internet of things. And has its own structural properties. This paper summarizes the data preprocessing, data cleaning and data quality and other information quantitative analysis literature, discusses the research status of real-time data cleaning quantitative analysis. This paper analyzes the main quality problems and characteristics of the real-time data of the vehicle network, and studies the processing methods of the general sensing data and sensing information. Based on sliding window, various intelligence quantitative analysis methods are used to preprocess the real time data of vehicle sensor. Aiming at the problems, this paper introduces the network architecture and data frame of vehicle network, combining with the main characteristics of vehicle data, such as mass, heterogeneity, real-time and so on. In this paper, a pre-processing framework for real-time cleaning of on-board data under the condition of vehicle networking is proposed. Finally, the continuous variable and brake brake are used as the representative of the gas cylinder pressure of the city hybrid electric bus. The on-board switch signal is based on real-time data. According to their data characteristics, this paper uses sliding window to ensure the real-time of data cleaning, using Rhinda criterion, moving average filter, wavelet transform. Bayesian decision theory and other quantitative analysis methods are used for mixed cleaning. Using these information quantitative analysis methods to preprocess the data can effectively deal with the abnormal data such as error noise and missing. Get accurate data information, provide real time, accurate and reliable public transportation information service for drivers, and improve the intelligent operation management of public transportation. Due to the characteristics of on-board data and the complexity of data real-time cleaning algorithm, coupled with the short writing time and limited ability of its own level, there are some errors in this paper, which should be criticized and corrected.
【学位授予单位】：重庆大学
【学位级别】：硕士
【学位授予年份】：2014
【分类号】：TP391.44;TN929.5;TP311.13

【参考文献】