Big data storage management system

Abstract

The invention relates to the technical field of data management, and provides a big data storage management system. The system comprises a cloud data server and at least one intelligent terminal, wherein the cloud data server is in wireless communication connection with the intelligent terminal; and the cloud data server comprises a data acquisition unit, a data classification and numbering unit, a data parallel processing unit, a data recovery unit, a data storage unit and a cloud database. The system is reasonable in structure and stable in operation, and has the effects of improving the data processing efficiency and error detection rate, reducing the complexity of managing related data and lightening the operation load of the system.

Description

technical field [0001] The invention belongs to the field of data management, in particular to a big data storage management system. Background technique [0002] With the rapid development of computer technology, the data in all walks of life is growing geometrically. These data come from various sources, ranging from sensors collecting weather conditions, digital pictures, and online video materials, to online shopping transaction records, and mobile phone GPS signals. With the rapid expansion of data scale, the amount of data accumulated in various industries is becoming larger and larger, with more and more data types and more complex data structures, which have surpassed the capabilities of traditional data management systems and processing models. Row database systems have been difficult to adapt to the rapid growth of application requirements, and show obvious lack of capacity in production practice, unable to meet the data storage requirements in the era of big data...

AI Technical Summary

Problems solved by technology

With the rapid expansion of data scale, the amount of data accumulated in various industries is becoming larger and larger, with more and more data types and more complex data structures, which have surpassed the capabilities of traditional data management systems and processing models. Row database systems have been difficult to adapt to the rapid growth of application requirements, showing obvious lack of capacity in production practice, unable to meet the data storage requirements in the era of big data

[0003] In the existing technology, the centralized data storage solution has low data processing efficiency, low disaster recovery capability, and long system recovery time; the distributed data storage solution uses DHT to access user data and searches in unicast mode. When a node fails, it initiates a search request to another node. Operations such as updates are similar, and there are also low data processing efficiency and low disaster recovery capabilities. In addition, cumbersome HASH calculation and routing search processes are required during implementation, which is complex to implement. There may also be data inconsistencies

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