民航危险源管理系统及其关键技术研究
发布时间：2019-07-08 12:51
【摘要】：随着现代经济的发展,越来越多的人选择飞机作为出行工具。为使乘客平安到达目的地,安全是民航空管中的重要主题,其中,危险源的识别和分析是安全的重要保证。由于飞机在飞行中涉及环境和设备的种类繁多并且参数复杂,导致危险源特征数据量大,深层特征较多并且具有较强的关联性,这对危险源识别和分析提出了挑战。如何处理海量的危险源数据并分析危险源的深层特征是危险源识别和分析的研究重点。本文以民航安全风险管理为背景,设计了民航危险源管理系统,并利用改进的深层极限学习机和粒子群优化算法完成危险源的识别和分析。本文的主要研究内容如下:(1)给出了民航危险源管理系统的总体框架和主要功能结构,并给出系统中关键技术的详细介绍。(2)设计了一种基于深层极限学习机的危险源识别算法,算法由多个深层栈式极限学习机(S-ELM)和一个单隐藏层极限学习机(ELM)构成的深层网络结构组成。多个S-ELM采用平行的结构,各自拥有不同的隐藏结点个数,按照危险源领域接受危险源状态信息,并将其最上一层的隐层输出作为ELM的输入。在单隐藏层ELM中引入反向传播算法并对其进行改进,提高算法的识别准确率。同时,改进S-ELM的输入权重分配方式并采用分别训练深层S-ELM的方法,缓解了高维数据训练的内存压力和节点过多产生的过拟合现象。在某民航危险源管理系统的数据库上对算法进行验证,结果表明该算法能够提高设层神经网络的训练效率和对危险源的识别精确度。(3)设计了一种基于加权的多种群粒子群优化的危险源原因分析算法。算法分为数据预处理阶段和危险源原因分析阶段。在数据预处理阶段,为危险源事务数据库中的项目分配权重,并定义加权的项目集范围产生有意义的危险源候选关联规则。在危险源原因分析阶段,算法使用加权的多种群粒子群优化产生危险源关联规则,并回溯产生的关联规则得到危险源原因。在算法中,采取多种群平行的搜索模式,并提供了种群间的交互机制,为了使得产生更有意义的危险源关联规则,为种群中的每个粒子引入权重的概念,同时在粒子速度更新公式中,引入粒子权重和全局局部最优解,增强粒子间的交互。(4)完成了民航危险源管理系统主要功能的开发,并将研究成果初步应用于民航危险源管理系统中,给出了设计算法及主要功能模块的详细技术实现及典型运行界面。
文内图片：
图片说明：算法精确度与参数C的关系
[Abstract]:With the development of modern economy, more and more people choose aircraft as a travel tool. In order to make passengers reach their destination safely, safety is an important theme in civil aviation air traffic control, in which the identification and analysis of dangerous sources is an important guarantee of safety. Because there are many kinds of environment and equipment involved in aircraft flight and the parameters are complex, the data of hazard source characteristics is large, the deep features are more and have strong correlation, which challenges the identification and analysis of hazard sources. How to deal with massive hazard data and analyze the deep characteristics of dangerous sources is the research focus of hazard identification and analysis. In this paper, based on civil aviation safety risk management, a civil aviation risk source management system is designed, and the improved deep limit learning machine and particle swarm optimization algorithm are used to identify and analyze the risk sources. The main research contents of this paper are as follows: (1) the overall framework and main functional structure of civil aviation hazard source management system are given, and the key technologies in the system are introduced in detail. (2) A hazard source identification algorithm based on deep limit learning machine is designed, which is composed of multiple deep stack limit learning machines (S-ELM) and a single hidden layer limit learning machine (ELM). Multiple S-ELM adopt parallel structure, each with different number of hidden nodes, accept the status information of hazard source according to the domain of hazard source, and use the hidden layer output of the top layer as the input of ELM. The back propagation algorithm is introduced and improved in single hidden layer ELM to improve the recognition accuracy of the algorithm. At the same time, the input weight allocation method of S-ELM is improved and the deep S-ELM training method is adopted respectively, which alleviates the memory pressure of high dimensional data training and the overfitting phenomenon caused by too many nodes. The algorithm is verified on the database of a civil aviation hazard source management system. The results show that the algorithm can improve the training efficiency of layered neural network and the accuracy of hazard source recognition. (3) A hazard source cause analysis algorithm based on weighted multi-particle swarm optimization is designed. The algorithm is divided into data preprocessing stage and hazard source cause analysis stage. In the stage of data preprocessing, the project weight is assigned to the dangerous source transaction database, and the weighted item set scope is defined to generate meaningful hazard source candidate association rules. In the stage of hazard source cause analysis, the algorithm uses weighted multi-swarm particle swarm optimization to generate hazard source association rules, and backtracking the association rules to obtain the risk source causes. In the algorithm, a variety of parallel search patterns are adopted, and the interaction mechanism between populations is provided. In order to generate more meaningful risk source association rules, the concept of weight is introduced for each particle in the population. At the same time, in the particle velocity updating formula, particle weight and global local optimal solution are introduced to enhance the interaction between particles. (4) the development of the main functions of civil aviation hazard source management system is completed. The research results are preliminarily applied to the civil aviation hazard source management system, and the design algorithm, the detailed technical implementation of the main functional modules and the typical operation interface are given.
【学位授予单位】：南京航空航天大学
【学位级别】：硕士
【学位授予年份】：2017
【分类号】：V328;TP315
本文编号：2511596
文内图片：
图片说明：算法精确度与参数C的关系
[Abstract]:With the development of modern economy, more and more people choose aircraft as a travel tool. In order to make passengers reach their destination safely, safety is an important theme in civil aviation air traffic control, in which the identification and analysis of dangerous sources is an important guarantee of safety. Because there are many kinds of environment and equipment involved in aircraft flight and the parameters are complex, the data of hazard source characteristics is large, the deep features are more and have strong correlation, which challenges the identification and analysis of hazard sources. How to deal with massive hazard data and analyze the deep characteristics of dangerous sources is the research focus of hazard identification and analysis. In this paper, based on civil aviation safety risk management, a civil aviation risk source management system is designed, and the improved deep limit learning machine and particle swarm optimization algorithm are used to identify and analyze the risk sources. The main research contents of this paper are as follows: (1) the overall framework and main functional structure of civil aviation hazard source management system are given, and the key technologies in the system are introduced in detail. (2) A hazard source identification algorithm based on deep limit learning machine is designed, which is composed of multiple deep stack limit learning machines (S-ELM) and a single hidden layer limit learning machine (ELM). Multiple S-ELM adopt parallel structure, each with different number of hidden nodes, accept the status information of hazard source according to the domain of hazard source, and use the hidden layer output of the top layer as the input of ELM. The back propagation algorithm is introduced and improved in single hidden layer ELM to improve the recognition accuracy of the algorithm. At the same time, the input weight allocation method of S-ELM is improved and the deep S-ELM training method is adopted respectively, which alleviates the memory pressure of high dimensional data training and the overfitting phenomenon caused by too many nodes. The algorithm is verified on the database of a civil aviation hazard source management system. The results show that the algorithm can improve the training efficiency of layered neural network and the accuracy of hazard source recognition. (3) A hazard source cause analysis algorithm based on weighted multi-particle swarm optimization is designed. The algorithm is divided into data preprocessing stage and hazard source cause analysis stage. In the stage of data preprocessing, the project weight is assigned to the dangerous source transaction database, and the weighted item set scope is defined to generate meaningful hazard source candidate association rules. In the stage of hazard source cause analysis, the algorithm uses weighted multi-swarm particle swarm optimization to generate hazard source association rules, and backtracking the association rules to obtain the risk source causes. In the algorithm, a variety of parallel search patterns are adopted, and the interaction mechanism between populations is provided. In order to generate more meaningful risk source association rules, the concept of weight is introduced for each particle in the population. At the same time, in the particle velocity updating formula, particle weight and global local optimal solution are introduced to enhance the interaction between particles. (4) the development of the main functions of civil aviation hazard source management system is completed. The research results are preliminarily applied to the civil aviation hazard source management system, and the design algorithm, the detailed technical implementation of the main functional modules and the typical operation interface are given.
【学位授予单位】：南京航空航天大学
【学位级别】：硕士
【学位授予年份】：2017
【分类号】：V328;TP315
【参考文献】
相关期刊论文 前1条
1 邓万宇;郑庆华;陈琳;许学斌;;神经网络极速学习方法研究[J];计算机学报;2010年02期
，本文编号：2511596
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