Time series data compression method based on attribute grouping

Abstract

The invention discloses a time series data compression method based on attribute grouping. The method includes the steps that data of each measurement point is saved on a buffer zone, and a time series data sequence is divided into a timestamp sequence, a value sequence and a quality code sequence; the starting time of the timestamp sequence is recorded, then timestamps with the same change periodare grouped in order, and the difference value between the starting timestamp of each group and the starting time of the buffer zone, the change period of the group and the number of the timestamps of the group are recorded; floating-point numbers are decomposed into 'symbol-index' bits and 'mantissa' bits, the floating-point numbers with the same 'symbol-index' bit are grouped in order, and the'symbol-index' bits, number of the floating-point numbers and sequence of the 'mantissa' bits of each group are recorded; the quality code sequences with the same quality code are grouped in order, and the value and number of each group are recorded; the grouped data is copied to a compression buffer zone, and lossless compression is carried out on the buffer zone by using a zlib algorithm. The method has the advantages that the compression ratio is high, the speed is quick, and the storage space can be effectively saved to use.

Description

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AI Technical Summary

Benefits of technology

This invention improves compressing ratios by increasing their number but also reducing losses during decompression compared with previous methods like LZW or ZF techniques. Groupings reduce redundancy between different parts of an image while still maintains its quality level. Time-series data has better compression performance even if it doesn't have any significant periods when being analyzed correctly due to temporal correlation from other sources such as audio signals.

Problems solved by technology

Technics Problem addressed by this patented technical problem include efficient handling of very big amounts of time sequence data without losing important details such as timing information during decompression operations. Traditional techniques like rotating doors can only partially reduce their effectiveness when compressed at higher rates compared to raw data alone.

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