机场特种车辆监控网络研究

发布时间：2018-03-03 04:37

本文选题：Android平台　切入点：数据采集终端　出处：《中国民航大学》2017年硕士论文　论文类型：学位论文

【摘要】：随着机场特种车辆数量越来越多及其日常运行保障工作的特殊性和独立性,现有系统无法满足管理单位对特种车辆的特殊管理需求。绝大部分的特种车辆的日常运营管理依旧依靠音频通话的方式及人工手动签单的模式,监控效率低,准确性差。机场日常运营智能化与信息化建设滞后的问题正成为制约机场保障能力提升的重要因素。为解决上述问题,本课题以Android平台和无线局域网技术为基础,设计了针对机场特种车辆的监控网络系统,描述了Android采集终端、无线局域网络及UCMap移动GIS系统三个重要组成部分。针对无线局域网络传输数据过程中延时和网络堵塞现象,本文对离散型粒子群算法进行改进,设计了基于改进型粒子群算法的无线数据帧格式,完成了网络层路由协议中路由择域信息库(路由表)的设计工作,将改进型粒子群优化算法中的控制因子和参数影响到整个路由节点路径选择的过程中,解决了网络堵塞问题的同时降低了传输延迟时间。通过仿真软件在两种实际情况下同时对本文改进型粒子群算法、随机法以及遗传算法进行仿真,比较三种算法的流量负载指标,进一步验证改进型粒子群优化算法对于解决路由负载不平衡问题相比于现有算法的优越性。实验和仿真结果显示,本文设计的机场特种车辆监控网路系统能够有效的完善机场原有的运营管理系统,提升机场智能化与信息化水平,具有一定的创新性和应用价值。
[Abstract]:With the increasing number of special vehicles in the airport and the particularity and independence of the daily operation support work, The existing system can not meet the special management needs of special vehicles. Most of the daily operation and management of special vehicles still rely on the mode of audio call and manual signing orders, so the monitoring efficiency is low. The problem of the intelligence of airport daily operation and the lag of information construction is becoming an important factor that restricts the promotion of airport support ability. In order to solve the above problems, this subject is based on Android platform and wireless LAN technology. The monitoring network system for special vehicles in airport is designed, and three important components of Android acquisition terminal, wireless local area network and UCMap mobile GIS system are described. In this paper, the discrete particle swarm optimization algorithm is improved, the wireless data frame format based on the improved particle swarm optimization algorithm is designed, and the routing alternative information base (routing table) in the network layer routing protocol is designed. The control factors and parameters in the improved particle swarm optimization (PSO) algorithm affect the path selection of the whole routing node. The network clogging problem is solved and the transmission delay time is reduced. The improved particle swarm optimization algorithm, stochastic algorithm and genetic algorithm are simulated by simulation software. By comparing the traffic load indexes of the three algorithms, the superiority of the improved particle swarm optimization algorithm in solving the problem of routing load imbalance is further verified. The experimental and simulation results show that the improved PSO algorithm is more effective than the existing algorithms in solving the problem of routing load imbalance. The airport special vehicle monitoring network system designed in this paper can effectively improve the original airport operation management system, improve the airport intelligence and information level, and has certain innovative and application value.
【学位授予单位】：中国民航大学
【学位级别】：硕士
【学位授予年份】：2017
【分类号】：TN925.93;TP277

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