一种面向工业互联网的云存储方法

工业互联网是工业信息化进程中最受关注的热点，海量异构数据管理是其中的重点之一。传统的关系数据库（RDB）对海量多源异构数据的读写和检索都存在性能瓶颈，而近年来兴起的云数据管理方法主要是针对“键-值”（K-V）模式，无法依靠主键以外的数据属性对数据进行快速查找。提出了一种面向工业互联网的云存储方法——StoreCDB，在异构采样数据统一表达数据模型基础上，实现非结构化存储管理，同时，利用两级索引实现海量数据的快速检索。通过实验，在分布式集群实验平台上，采用海量高铁列车运行模拟数据，验证了StoreCDB具有良好的异构数据存储和检索性能，为工业互联网提供了一种新的数据管理方法。 

A new approach of cloud storage for industrial Internet

With the development of industrial informatization, industrial Internet has attracted many attentions, and massive heterogeneous data management is one of the most important issues. However, traditional relational database (RDB) limits the performance of access and retrieval of massive and heterogeneous data, while cloud data management mainly focuses on key-value (K-V) queries, which cannot quickly search data by using any data property other than the prime key. In this paper, a cloud storage framework-StoreCDB is proposed for data management in the industrial Internet. In StoreCDB, the heterogeneous data are represented by a uniform data model firstly and then stored in a distributed file and parallel architecture as unstructured data. In addition, a double-level index is proposed to support both key-value queries and RDB queries. This paper adopts a distributed cluster experimental platform and massive high-speed train operation simulation data to verify the framework. The experimental results show that StoreCDB has satisfactory heterogeneous data access and retrieval performance and provides a good solution for industrial Internet data management.