Telephone number attribution data compression algorithm

Abstract

The invention provides a telephone number attribution data compression algorithm, which comprises the following steps: 1) sorting and recording all region names, and taking record numbers as the index values of the region names; 2) sorting number segment data, replacing the region name corresponding to each number segment into the index value; 3) sorting and segmenting the number segment data according to the number segment, and independently compressing the data of each block of number segment; 4) obtaining the number segment through a telephone number; 5) finding corresponding block information through the number segment; 6) decompressing block data to obtain the index value of the corresponding region name of the corresponding number segment; and 7) obtaining the region name through the index value. The number segment data is partitioned, and each block of data is independently compressed; during retrieval, the corresponding block can be firstly found through the number segment, and the data of the block is then decompressed to obtain the index value; the corresponding region name is finally found through the index value; and therefore, the decompressed data is small, a compression rate is high, and operation speed is high.

Description

Technical field [0001] The present invention relates to the field of computer programs, in particular to a data compression algorithm for the attribution of telephone numbers. Background technique [0002] The attribution function of a telephone number is a commonly used function on mobile phones at present, which is realized by searching for more than 200,000 telephone section data. These raw data occupies about 5M space, which takes up too much space for some devices with limited storage capacity and makes it take too long to read and search for these data. Therefore, it is necessary to compress this data. [0003] Some other similar compression algorithms use general algorithms such as zip and rar, and the compressed data is about 500K, but the decompression algorithm is relatively complicated and runs slowly. Other algorithms use indexing to compress, and the compressed data is about 460K. Their common problem is that they have requirements for the computing power and memory...

AI Technical Summary

Problems solved by technology

Their common problem is that they have requirements for the computing power and memory capacity (from tens of K to hundreds of K) of the device, so they are only suitable for use on platforms with high hardware costs.

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