基于序列模式挖掘的公交车辆维修保养数据模型研究
发布时间：2018-10-15 09:12
【摘要】：公交车作为城市公共交通运输中重要的一员,担负着极其重要的角色,其与轨道交通在城市公共交通运输中是互相补充,互相依存的关系。公交车要为乘客提供方便、舒适、快捷的出行服务,其中一个重要前提是必须确保运输车辆有良好的车质车况。这样的要求,依赖于公交维修企业对运输车辆有十分到位的维修保养服务。另一方面,公交车辆的维修保养在公交企业经营管理中成本高达25%,所以,在确保良好车质车况的同时,又不得不考虑维修成本等的问题。车辆在日常的维修保养中,会产生大量的维修数据,依靠数据挖掘技术,运用一些方法和算法,找到隐藏其中的知识,用于优化公交车辆日常维修保养,为每一辆车都制定差异化的维修保养方案,使公交车辆在维修质量、维修效率和维修成本中找到一个合理的平衡点,以提升公交车辆服务乘客的质量,提高公交企业的经济效益。为解决上述提出的问题,本文首先通过分析公交车辆维修保养信息管理系统生成数据的构成及特点,结合公交车辆维修企业管理的实际需要,提出了公交车辆维修保养数据挖掘模型。然后介绍了序列模式挖掘相关理论、方法和算法,简要分析了各种算法的优劣、应用环境。经过对比与分析,本文选定了Apriori算法和FP-Growth算法作为上述模型的数据挖掘算法,并在第4章中对这两种算法进行了较为详尽的说明和实现。接着,在第5章中提出了本数据挖掘模型的技术线路图,并对技术线路图中问题定义、数据准备、数据选择、数据预处理、数据转换、数据挖掘、可视化模式规则和知识库生成等各个步骤进行了详细的说明,完整地介绍了公交车辆维修保养数据挖掘分析模型的实现过程。然后,根据数据挖掘模型输出的可视化模式规则生成了对应的知识数据库,该数据库能动态应用于公交车维修保养信息管理系统中。论文还对算法运行的结果进行了较为详尽的比较分析,说明了FP-Growth算法对比Apriori算法无论是从算法的执行效率,还是系统开销进行比较,都优于不少。最后,论文对公交车辆维修保养数据挖掘模型适用性进行了描述,即说明了该模型不但对车辆的报修数据能挖掘出可靠的知识数据库,还能对车辆维修数据和零配件使用数据进行挖掘。论文的结论部分,从模型的层级设计、挖掘算法、技术线路图和知识数据库使用等方面进行了评价,并得出了该数据挖掘模型能基本满足公交维修企业使用要求的结论。
[Abstract]:As an important member of urban public transportation, bus plays an extremely important role, and it and rail transit complement and depend on each other in urban public transportation. In order to provide convenient, comfortable and fast travel service for the passengers, one of the important prerequisites of the bus is to ensure that the transport vehicle is in good condition. Such requirements, rely on public transport maintenance enterprises to transport vehicles in place to repair and maintenance services. On the other hand, the maintenance of public transport vehicles in the operation and management of public transport enterprises costs as high as 25%, so in order to ensure good vehicle quality, but also have to consider the cost of maintenance and other issues. In the daily maintenance of vehicles, a large number of maintenance data will be generated, relying on data mining technology, using some methods and algorithms to find the hidden knowledge, which can be used to optimize the daily maintenance of public transport vehicles. In order to improve the service quality of public transport vehicles, a differentiated maintenance plan is made for each vehicle to find a reasonable balance among the maintenance quality, maintenance efficiency and maintenance cost. In order to improve the quality of bus service to passengers, a reasonable balance can be found among the maintenance quality, the maintenance efficiency and the maintenance cost of the public transport vehicles. Improve the economic benefits of public transport enterprises. In order to solve the above problems, this paper first analyzes the structure and characteristics of the data generated by the public transport vehicle maintenance information management system, combined with the actual needs of the public transport vehicle maintenance enterprise management. A data mining model for bus vehicle maintenance is proposed. Then, the related theories, methods and algorithms of sequential pattern mining are introduced, and the advantages and disadvantages of these algorithms are briefly analyzed. After comparison and analysis, this paper selects Apriori algorithm and FP-Growth algorithm as the data mining algorithm of the above model, and in chapter 4, the two algorithms are explained and implemented in detail. Then, in chapter 5, the technical circuit diagram of the data mining model is proposed, and the definition of technical circuit diagram, data preparation, data selection, data preprocessing, data conversion, data mining, Various steps such as visual pattern rules and knowledge base generation are explained in detail. The implementation process of the data mining and analysis model for bus vehicle maintenance data is introduced in detail. Then, the corresponding knowledge database is generated according to the visual pattern rule output from the data mining model, which can be dynamically applied to the bus maintenance information management system. This paper also makes a detailed comparison and analysis of the results of the operation of the algorithm, which shows that the FP-Growth algorithm is better than the Apriori algorithm in terms of the efficiency of the algorithm and the cost of the system, both in terms of the efficiency of the algorithm and the cost of the system. Finally, the paper describes the applicability of the data mining model for bus maintenance, that is, the model can not only mine a reliable knowledge database for vehicle repair data. Also can carry on the mining to the vehicle maintenance data and the spare parts use data. In the last part of the paper, the hierarchical design of the model, the mining algorithm, the technical circuit diagram and the use of the knowledge database are evaluated, and the conclusion that the data mining model can basically meet the requirements of the public transport maintenance enterprises is obtained.
【学位授予单位】：华南农业大学
【学位级别】：硕士
【学位授予年份】：2016
【分类号】：U472;TP311.13
本文编号：2272054
[Abstract]:As an important member of urban public transportation, bus plays an extremely important role, and it and rail transit complement and depend on each other in urban public transportation. In order to provide convenient, comfortable and fast travel service for the passengers, one of the important prerequisites of the bus is to ensure that the transport vehicle is in good condition. Such requirements, rely on public transport maintenance enterprises to transport vehicles in place to repair and maintenance services. On the other hand, the maintenance of public transport vehicles in the operation and management of public transport enterprises costs as high as 25%, so in order to ensure good vehicle quality, but also have to consider the cost of maintenance and other issues. In the daily maintenance of vehicles, a large number of maintenance data will be generated, relying on data mining technology, using some methods and algorithms to find the hidden knowledge, which can be used to optimize the daily maintenance of public transport vehicles. In order to improve the service quality of public transport vehicles, a differentiated maintenance plan is made for each vehicle to find a reasonable balance among the maintenance quality, maintenance efficiency and maintenance cost. In order to improve the quality of bus service to passengers, a reasonable balance can be found among the maintenance quality, the maintenance efficiency and the maintenance cost of the public transport vehicles. Improve the economic benefits of public transport enterprises. In order to solve the above problems, this paper first analyzes the structure and characteristics of the data generated by the public transport vehicle maintenance information management system, combined with the actual needs of the public transport vehicle maintenance enterprise management. A data mining model for bus vehicle maintenance is proposed. Then, the related theories, methods and algorithms of sequential pattern mining are introduced, and the advantages and disadvantages of these algorithms are briefly analyzed. After comparison and analysis, this paper selects Apriori algorithm and FP-Growth algorithm as the data mining algorithm of the above model, and in chapter 4, the two algorithms are explained and implemented in detail. Then, in chapter 5, the technical circuit diagram of the data mining model is proposed, and the definition of technical circuit diagram, data preparation, data selection, data preprocessing, data conversion, data mining, Various steps such as visual pattern rules and knowledge base generation are explained in detail. The implementation process of the data mining and analysis model for bus vehicle maintenance data is introduced in detail. Then, the corresponding knowledge database is generated according to the visual pattern rule output from the data mining model, which can be dynamically applied to the bus maintenance information management system. This paper also makes a detailed comparison and analysis of the results of the operation of the algorithm, which shows that the FP-Growth algorithm is better than the Apriori algorithm in terms of the efficiency of the algorithm and the cost of the system, both in terms of the efficiency of the algorithm and the cost of the system. Finally, the paper describes the applicability of the data mining model for bus maintenance, that is, the model can not only mine a reliable knowledge database for vehicle repair data. Also can carry on the mining to the vehicle maintenance data and the spare parts use data. In the last part of the paper, the hierarchical design of the model, the mining algorithm, the technical circuit diagram and the use of the knowledge database are evaluated, and the conclusion that the data mining model can basically meet the requirements of the public transport maintenance enterprises is obtained.
【学位授予单位】：华南农业大学
【学位级别】：硕士
【学位授予年份】：2016
【分类号】：U472;TP311.13
【参考文献】
相关期刊论文 前1条
1 张国凤;刘望球;;某型公交车二级保养决策优化[J];公路与汽运;2010年04期
，本文编号：2272054
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