Internet of Things time series data storage and retrieval method of flash memory particle array

Abstract

The invention discloses an internet-of-things time series data storage and retrieval method of a flash memory particle array. Corresponding improvements are made specially for reading and writing of multiple types of time series data. The method comprises the following steps: establishing respective block-level address mapping tables for physical blocks of which the spaces are allocated in advancefrom a flash memory particle array according to the life cycles of various types of data; combining data into a storage unit by taking one page as a unit in a buffer area of a memory, caching a certain amount of data, writing the data into a physical block allocated for the data in advance, and writing one page of data into the physical block in sequence from a first logic block during writing toensure that the same offset address of each block in a round outputs write-in data; constructing a sensor change condition record table to record parameters such as data time points, logic page numbers, logic page numbers and logic block numbers, and substituting the data into each parameter of the sensor change condition record table according to the time points of the target data during data query to calculate the address where the target data is located to obtain the data.

Description

technical field [0001] The invention relates to the field of storage and retrieval of databases, in particular to a storage and retrieval method for time series data of the Internet of Things of a flash memory particle array. Background technique [0002] With the continuous popularization of Internet of Things technology and the continuous development of wireless sensor network technology, more and more sensors are used in the layout of sensor networks, the sampling rate of sensors is also higher and higher, and the amount of data generated is increasing. The most important feature of this kind of data is that it has time continuity and various types of data. How to quickly store and retrieve so many types of data has become a problem that needs to be solved. Especially for fast retrieval of specified data, common relational databases cannot meet the timeliness of data retrieval under large-scale data; while new non-relational databases cannot make good use of the time rela...

AI Technical Summary

Problems solved by technology

Existing time-series-based industrial data storage and retrieval methods have certain limitations in the storage and retrieval of time-series data. Existing methods only consider putting the same data in the same physical block and physically Blocks are combined into multiple data files to save data, and the physical address of data stored on the storage medium is not optimized for storage. For disk media, if multiple physical blocks in the file are too scattered on the disk, random The efficiency of reading and writing will be very poor. For flash memory arrays, if multiple physical blocks storing continuous data are concentrated in one flash particle, the reading and writing efficiency will also become very poor.

[0006] In addition, the existing method reserves data storage space to prevent the increase of collection points, and the increase of collection data lacks the effective use of storage space. If the number of collection points of the specified type does not increase, the overflow data area cannot be used to write other types of data , the storage space utilization efficiency is low

For data retrieval, the existing method uses first to find the data files of the corresponding time period, and then compares the time difference of each data from the data files to find the data of the corresponding time point. The existing method needs to find the data according to the time point from the file retrieval table Which file is the target data in? When the amount of data continues to increase, the increasing number of data files will lead to lower query efficiency

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