隧道掘进机工程大数据管理平台关键技术及应用研究

为解决目前大多数信息化管理平台兼容性及扩展性不强、功能单一，满足不了当前工程管理需求的问题，从高质量的大数据管理平台架构、兼容性强的数据采集、扩展性强的分布式存储3个方面进行平台建设关键技术研究。采用分布式HBase列存储数据库和高性能Spark内存计算引擎，创建行业Hadoop集群生态架构，设计一种多协议、多通道且兼容性强的数据采集系统，为隧道掘进机大数据平台的采集、存储、搭建等提供技术支撑。建立集智能监测、综合分析、协同管理、大数据应用功能于一体的隧道掘进机工程大数据管理平台，从管理和技术层面为类似的大数据平台设计开发提供参考，提升隧道掘进机施工效率，提高智能化管理水平。

Study on Key Technologies and Application of Engineering Big DataManagement Platform of Tunnel Boring Machine

The existing major information management platforms are characterized by low compatibility and expansibility and single function, which cannot meet the current engineering management requirements. Hence, the key technologies for the platform construction are studied from the aspects of high quality platform framework of big data management, data acquisition with good compatibility and distributed storage with strong expansibility. Firstly, the industrial Hadoop cluster ecological framework is created by adopting the distributed HBase column storage database and high performance Spark memory computing engine. And then a data acquisition system with multi protocol, multi channel and strong compatibility is designed to provide technical support for the acquisition, storage and construction of the big data platform of tunnel boring machine. Finally, a big data management platform integrating the functions of intelligent monitoring, comprehensive analysis, collaborative management and big data application for tunnel boring machine is established, which can provide reference for similar big data platform design and development in terms of management and technology, and improve the construction efficiency and intelligent management level of tunnel boring machine.