Device and method for safely storing error big data of smart electricity meter based on fountain code

Abstract

The invention discloses a device and a method for safely storing error big data of a smart electricity meter based on a fountain code. The device comprises a smart electricity meter self-service error calibration device, an HDFS (Hadoop Distributed File System) client side and a Hadoop big data storage platform, wherein the smart electricity meter self-service error calibration device, the HDFS client side and the Hadoop big data storage platform are sequentially connected; the smart electricity meter self-service error calibration device is used for collecting data of a measured smart electricity meter; the HDFS client side is used for preprocessing the collected data of the smart electricity meter and coding and decoding the preprocessed data; the Hadoop big data storage platform is used for carrying out distributed storage on the error big data of the smart electricity meter. The device and the method disclosed by the invention have the technical effects that the storage efficiency is increased; the utilization rate of storage space is increased; the problem of stored data loss caused by DataNode failure in the prior art is solved; privacy protection of the data of a user is ensured.

Description

Technical field [0001] The invention relates to the storage technology field of electric energy metering and marketing big data, belongs to the information technology application field of distributed computing and information security, and specifically relates to a fountain code-based intelligent electric energy meter error big data safe storage device and method. Background technique [0002] In recent years, with the large-scale installation and application of smart energy meters, the data on the power user side has increased exponentially, and the storage of massive data has become more and more important. The traditional power data storage system based on large relational databases such as Oracle has the disadvantages of slow analysis speed, poor scalability and reliability, and it is difficult to adapt to the data storage and analysis needs of power big data in the smart grid era. It has become a smart grid construction Where the bottleneck lies. [0003] HDFS (HadoopDistribu...

AI Technical Summary

Problems solved by technology

However, such schemes usually have low data recovery and high complexity. In addition, the typical configuration of HDFS requires three copies of each file block, and the space utilization rate is only 33%. On the other hand, massive power The data is managed by the cloud server provided by the third-party resource platform, and the user eventually loses control over the data. The security of power data information and the protection of user privacy depend on the cloud service provider

Although cloud service providers can provide more reliable data backup and data recovery capabilities, it cannot be ruled out that due to their own interests and interference from external factors, cloud service providers may leak, hide, or lose data stored in the cloud by users or be blocked. Malicious tampering, etc.

Images