CN115480756A - Template data compression method, device, equipment and storage medium - Google Patents

Abstract

Embodiments of the present application provide a template data compression method, device, device, and storage medium. The template data compression method includes: obtaining template data; performing repetitive character aggregation conversion on the template data through a preset data conversion algorithm to obtain Character string; encode the character string through a preset data encoding algorithm to obtain a data sequence string; compress and store the data sequence string according to a preset index compression algorithm to obtain compressed template data; the embodiment of the present application can solve the problem of compression in the prior art After the template data occupies a large storage space problem.

Description

Template data compression method, device, equipment and storage medium

technical field

The present application belongs to the field of software development, and in particular relates to a template data compression method, device, equipment and storage medium.

Background technique

The current software development mostly uses componentization and template-based methods. For some software that outputs configurable templates, such as poster editors and web page visual editors, it is often necessary to store a large amount of configuration template data. Existing template data storage The process is shown in Figure 1. This type of information contains a large amount of identical or similar attribute information, and at the same time, characters such as colons, commas, and semicolons are used more frequently, and has the characteristics of large volume, high similarity, and high repetition.

At this stage, as users continue to increase, custom template data will also continue to expand, and the software will inevitably face higher storage space requirements brought about by massive data. However, the current prior art has not designed a targeted compression method for template data characteristics, and still uses conventional compression software to compress it, such as WinRar, WinZip, etc., resulting in a large storage space for the compressed template data.

Contents of the invention

Embodiments of the present application provide a template data compression method, device, device, and storage medium, which can solve the problem in the prior art that compressed template data occupies a large storage space.

In the first aspect, the embodiment of the present application provides a method for compressing template data, including:

get template data;

Perform repeated character aggregation conversion on the template data through a preset data conversion algorithm to obtain a character string;

Encode the character string through the preset data encoding algorithm to obtain the data sequence string;

The stored data sequence string is compressed according to a preset index compression algorithm to obtain compressed template data.

Further, in an embodiment, the method also includes:

According to the preset suffix array construction algorithm, the data sequence string is constructed with a suffix array to obtain a suffix array;

Sampling the suffix array every preset data to obtain a sampling array;

The sampled data is determined as index data for comparison with query input data when being queried.

Further, in one embodiment, the preset data conversion algorithm includes:

Burrows Wheeler transform algorithm.

Further, in one embodiment, the preset data encoding algorithm includes:

Huffman coding algorithm.

Further, in one embodiment, the preset index compression algorithm includes:

RRR compressed index structure.

In the second aspect, the embodiment of the present application provides a template data compression device, including:

Obtain module, used to obtain template data;

The conversion module is used to perform repeated character aggregation conversion on the template data through a preset data conversion algorithm to obtain a character string;

An encoding module, configured to encode a character string through a preset data encoding algorithm to obtain a data sequence string;

The compression storage module is configured to compress and store the data sequence string according to a preset index compression algorithm to obtain compressed template data.

Further, in one embodiment, the device also includes:

A construction module, configured to construct a suffix array for the data sequence string according to a preset suffix array construction algorithm to obtain a suffix array;

The sampling module is used to sample the suffix array every preset data to obtain the sampling array;

A determination module, configured to determine the sampling data as index data, and compare it with the query input data when being queried.

Further, in one embodiment, the preset data conversion algorithm includes:

Burrows Wheeler transform algorithm.

In the third aspect, an embodiment of the present application provides a compression device for template data, including: a memory, a processor, and a computer program stored on the memory and operable on the processor. When the computer program is executed by the processor, the following claims are realized Compression method for template data in any of to.

In the fourth aspect, the embodiment of the present application provides a computer-readable storage medium, on which is stored a program for implementing information transfer, and when the program is executed by a processor, the implementation of the template data according to any one of the claims to compression method.

The template data compression method, device, equipment, and storage medium of the embodiment of the present application aim at the characteristics of high similarity and high repetition of the template data, and adopt a preset data conversion algorithm to perform aggregation and conversion of repeated characters on the template data to reduce the storage ratio of repeated bytes , to effectively improve the compressibility of the original template data; combine the preset data encoding algorithm, construct the wavelet tree, and the preset index compression algorithm to compress the template data after repeated character aggregation and conversion, further improve the compressibility of the template data, after compression The template data occupies less storage space; and because the data processing time complexity of the preset data conversion algorithm and wavelet tree is low, the compression time is less.

Description of drawings

In order to more clearly illustrate the technical solutions of the embodiments of the present application, the following will briefly introduce the accompanying drawings that need to be used in the embodiments of the present application. Additional figures can be derived from these figures.

FIG. 1 is a schematic diagram of an existing template data storage process provided by an embodiment of the present application;

FIG. 2 is a schematic flowchart of a method for compressing template data provided in an embodiment of the present application;

Fig. 3 is a schematic structural diagram of a wavelet tree provided by an embodiment of the present application;

FIG. 4 is a schematic structural diagram of a template data compression device provided in an embodiment of the present application;

Fig. 5 is a schematic structural diagram of a device for compressing template data provided by an embodiment of the present application.

detailed description

The characteristics and exemplary embodiments of various aspects of the application will be described in detail below. In order to make the purpose, technical solution and advantages of the application clearer, the application will be further described in detail below in conjunction with the accompanying drawings and specific embodiments. It should be understood that the specific embodiments described here are only configured to explain the application, not to limit the application. It will be apparent to one skilled in the art that the present application may be practiced without some of these specific details. The following description of the embodiments is only to provide a better understanding of the present application by showing examples of the present application.

It should be noted that in this article, relational terms such as first and second are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply that there is a relationship between these entities or operations. There is no such actual relationship or order between them. Furthermore, the term "comprises", "comprises" or any other variation thereof is intended to cover a non-exclusive inclusion such that a process, method, article, or apparatus comprising a set of elements includes not only those elements, but also includes elements not expressly listed. other elements of or also include elements inherent in such a process, method, article, or device. Without further limitations, an element defined by the statement "comprising..." does not exclude the presence of additional same elements in the process, method, article or device comprising said element.

The existing template data compression method has the following disadvantages:

(1) General data compression software fails to fully consider the characteristics of high similarity and high repetition of template data, resulting in repeated data occupying a large amount of storage space and low compression efficiency;

(2) Pure compression software cannot perform attribute query without decompression, and simple query operations require full-text decompression, which seriously affects the operating efficiency of the software;

(3) The template data is often large in size and will increase with the development of the software. If it is not compressed, it will inevitably encounter the problem of limited storage. Compression will make it impossible to query and cannot guarantee the long-term availability of the software.

In order to solve the problems in the prior art, the embodiments of the present application provide a template data compression method, device, device, and storage medium. Aiming at the characteristics of high similarity and repetition of template data, this application adopts the preset data conversion algorithm to carry out repeated character aggregation conversion on the template data, reduces the storage ratio of repeated bytes, and effectively improves the compressibility of the original template data; combined with the preset data Encoding algorithm, construction of wavelet tree, and preset index compression algorithm compress the template data after repeated character aggregation and conversion, further improving the compressibility of template data, and the compressed template data occupies less storage space; and because the preset data The data processing time complexity of the conversion algorithm and the wavelet tree is low, which makes the compression less time-consuming. The method for compressing template data provided by the embodiment of the present application will be firstly introduced below.

Fig. 2 shows a schematic flowchart of a method for compressing template data provided by an embodiment of the present application. As shown in Figure 2, the method may include the following steps:

S110, acquiring template data.

The template data will generate new template data according to the configuration modification of different users, so this kind of data will continue to expand at a high speed with the growth of users during the promotion and use of the software. Situation, which has the characteristics of high similarity and high repetition, can be obtained in the background of the software.

S220. Perform repeated character aggregation conversion on the template data by using a preset data conversion algorithm to obtain a character string.

The preset data conversion algorithm can perform repeated character clustering on the data.

In one embodiment, the preset data conversion algorithm may be selected as Burrows Wheeler transform (BWT) algorithm. The basic idea of BWT is to rotate the original string bit by bit to the left and sort each row of the obtained character matrix in lexicographical order, and finally take the last column of the sorted matrix as the transformation result. There will be no data loss in the whole transformation process, but the converted string will have multiple consecutive aggregations of the same characters, which further improves the compressibility of the original data.

Taking the template data T="{id:1; Fd:F;}" as an example, Table 1 lists the ASCII codes of all characters in the template data T, by comparing the ASCII codes of the characters from left to right, the smaller The higher the order, the sorting process is called lexicographical sorting of characters.

Table 1 ASCII codes of all characters of T

Table 2 shows the whole process of performing BWT transformation on the template data T and obtaining the L string, in which the matrix M is obtained by shifting the string to the left, F is the end character corresponding to each row; and the matrix M' is the matrix M The result of lexicographical sorting of all rows corresponding to strings, the last column of matrix M′ is the transformation result of the original string through BWT—string L string, L=

":dd1F;:iF{};".

It can be seen that BWT transformation can aggregate the repeated characters of the original string to a certain extent. The more repeated characters in the original string, the more obvious the aggregation effect is. Therefore, the high repetition characteristics of template data can be amplified after BWT transformation, and the overall compressibility.

Table 2 BWT transformation process

In the embodiment of the present application, by aggregating repeated characters, the feature of high repetition in the original template data is amplified, which indicates the compressibility of the template data.

S230. Encode the character string by using a preset data encoding algorithm to obtain a data sequence string.

The preset data encoding algorithm can realize the compression of the string by encoding the string.

In an embodiment, the preset data encoding algorithm may be selected as a Huffman encoding algorithm. And choose the balanced coding in the Huffman coding algorithm. Table 3 shows the encoding result of the character string L=":dd1F;:iF{};" after Huffman balance encoding.

Table 3 Encoding Results

When using the Huffman coding algorithm to encode a character string, it is first necessary to determine the space compression structure, and the embodiment of the present application chooses the wavelet tree structure. The wavelet tree is a space compression structure, which is generally a binary tree, and the 0/1 sequence information of its internal nodes is allocated by its parent node according to the previous character order, and each leaf node represents the original character string A distinguishable character in the character set of .

Figure 3 shows a schematic diagram of the structure of the wavelet tree. As shown in Figure 3, the root node of the wavelet tree is a binary sequence {0,1,1,0,0,0,0,1,0,1, 1,0}, according to the left and right subtree division rules and encoding methods, the left node is {0,0,1,1,0,1,1}, and the right node is {0,0,0,1,1} , and then divided into four nodes {1,0,1}, {1,0,1,0}, {0,0,1} and {0,1}, it can be seen that the 0 or 1 represents a specific character in the string, for example, all 0s in the first node represent the character "1", and all 1s represent the character ":".

In the embodiment of the present application, after the wavelet tree transformation, the character string is transformed into a 0/1 sequence string with a more concentrated distribution of 0 and 1, which further improves the compressibility of data.

S240. Compress and store the data sequence string according to a preset index compression algorithm to obtain compressed template data.

After the wavelet tree transformation, the embodiment of the present application selects a 0/1 sequence compression index structure to compress and store the obtained data sequence string.

Aiming at the characteristics of high similarity and repetition of template data, this application adopts the preset data conversion algorithm to carry out repeated character aggregation conversion on the template data, reduces the storage ratio of repeated bytes, and effectively improves the compressibility of the original template data; combined with the preset data Encoding algorithm, construction of wavelet tree, and preset index compression algorithm compress the template data after repeated character aggregation and conversion, further improving the compressibility of template data, and the compressed template data occupies less storage space; and because the preset data The data processing time complexity of the conversion algorithm and the wavelet tree is low, which makes the compression less time-consuming.

In one embodiment, what the present application adopts is the RRR compression index structure, which records the number of 1s in each block, and can realize the counting operation (rank) and positioning operation (rank) of 0/1 at a certain position of the string locate), providing a data basis for data query operations.

In one embodiment, the method may also include:

S250. Construct a suffix array on the data sequence string according to a preset suffix array construction algorithm to obtain a suffix array.

The suffix array construction algorithm can carry out suffix data construction on the data; the commonly used methods are multiplication method and DC3 method.

In one embodiment, the present application selects the multiplication method as the suffix array construction algorithm. The doubling method first sorts the substrings of length 2 starting from each position in lexicographical order, then uses this result to sort the substrings of length 4, and then sorts the substrings of length 8, and keeps multiplying until If the length is greater than or equal to n, the suffix array is obtained.

Take the string T as an example, let T[1..n]=t ₀ t ₁ t ₂ ···t _n-1 , T[ _i ] represents the character at position i of the string T, and T _i represents the character of T The i-th suffix, that is, the substring T _i,n-1 , the suffix array SA of the string T is the subscript index arranged according to the lexicographical order of each suffix of T. For example: string T={id:1; Fd:F;}, then its suffixes are arranged according to the dictionary sequence: T4, T3, T8, T5, T10, T6, T9, T2, T7, T1, T0, T11 , Table 4 shows the suffix array of T, as shown in Table 4, the corresponding suffix array SA=[4,3,8,5,10,6,9,2,7,1,0,11] .

Table 4 SA suffix array of T

S260. Sampling the suffix array every preset data to obtain a sampling array.

In order to ensure higher storage efficiency, the embodiment of the present application adds a sampling mechanism, which can sample the suffix array at intervals of preset data.

S270. Determine the sampling data as index data, which is used for comparison with the query input data when being queried.

The suffix array SA is to sort all the suffixes of the string T in alphabetical order from small to large. Therefore, the suffix array SA has the following properties:

TSA[0]<TSA[1]<···<TSA[n-1]

Therefore, when the user wants to query a certain attribute, such as id, he only needs to enter whether the pattern string P="id:1;" matches in the original text. Through the sorting of the suffix array, the longest matching string of the pattern string P in the original text can only appear in the string pointed to by the suffix array with at least the same character as the previous one.

The dichotomy method can be used to perform pattern matching on the pattern string P from left to right and obtain the corresponding suffix array position i, as shown in Algorithm 1 shown in Table 5, and then return SA[i] on the sampled SA array, and Starting from the current ranking, iteratively jumps to the suffix starting from the previous position until a certain sampling point, and converts the suffix range into the corresponding position of the original text through multiple matches. If the algorithm result outputs the corresponding position, it means that the original template data has the attribute "id: 1", which realizes the attribute query operation in the compressed state.

table 5

As shown in Table 5, when sampling the suffix array, SA ₁ is the sampling result of SA, and d represents the sampling distance of SA, that is, every d data of the entire SA array stores a sampling data. The whole process of Algorithm 1 requires on average d/2 visits to the compressed information of the original text, and each visit needs to start from the root of the wavelet tree, and call the counting operation on the 0/1 sequence layer by layer, so its time is complicated The degree is: O(d*log|∑|), where ∑ is the height of the wavelet tree, and the execution efficiency is high.

The template data compression method provided by the embodiment of the present application first improves the compressibility of the original template data through BWT, then uses the wavelet tree structure to compress and store, and then cooperates with the sampled and stored SA array to realize the attribute query under the compressed state, which effectively improves the Large-volume template data storage efficiency, and can support fast attribute query operations.

The method provided by the embodiment of the present application only needs to install the Java Runtime Environment (JRE) to be used directly on multiple platforms, and in the actual application process, it can effectively reduce the template data storage space and provide basic operations such as template classification and query. , it still maintains good performance in the process of increasing user-defined data, and can effectively guarantee the long-term availability and efficiency of template output software.

Figures 2-3 describe the template data compression method, and the device provided by the embodiment of the present application will be described below in conjunction with Figure 4 and Figure 5 .

Fig. 4 shows a schematic structural diagram of a template data compression device provided by an embodiment of the present application. Each module in the device shown in Fig. 4 has the function of implementing each step in Fig. 1 and can achieve its corresponding technical effect. As shown in Figure 4, the device may include:

An acquisition module 410, configured to acquire template data.

The conversion module 420 is configured to perform repeated character aggregation conversion on the template data through a preset data conversion algorithm to obtain a character string.

The encoding module 430 is configured to encode a character string by using a preset data encoding algorithm to obtain a data sequence string.

The compression storage module 440 is configured to compress and store the data sequence string according to a preset index compression algorithm to obtain compressed template data.

The template data compression device provided by the embodiment of the present application first improves the compressibility of the original template data through BWT, then uses the wavelet tree structure to compress and store, and then cooperates with the sampled and stored SA array to realize the attribute query in the compressed state, which effectively improves the Large-volume template data storage efficiency, and can support fast attribute query operations.

In one embodiment, the device may also include:

The construction module 450 is configured to construct a suffix array for the data sequence string according to a preset suffix array construction algorithm to obtain a suffix array.

The sampling module 460 is configured to sample the suffix array every preset data to obtain a sample array.

The determining module 470 is configured to determine the sampling data as index data for comparison with the query input data when being queried.

In one embodiment, the preset data conversion algorithm may include:

Burrows Wheeler transform algorithm.

In one embodiment, the preset data encoding algorithm may include:

Huffman coding algorithm.

In one embodiment, the preset index compression algorithm may include:

RRR compressed index structure.

The template data compression device of the embodiment of the present application, aiming at the characteristics of high similarity and high repetition of the template data, adopts the preset data conversion algorithm to carry out repeated character aggregation conversion on the template data, reduces the storage ratio of repeated bytes, and effectively improves the efficiency of the original template data. Compressibility: Combining the preset data encoding algorithm, constructing wavelet tree, and preset index compression algorithm to compress the template data after repeated character aggregation and conversion, further improving the compressibility of the template data, the storage space occupied by the compressed template data Smaller; and because the data processing time complexity of the preset data conversion algorithm and wavelet tree is low, the compression time is less.

FIG. 5 shows a schematic structural diagram of a device for compressing template data provided by an embodiment of the present application. As shown in Fig. 5, the device may include a processor 501 and a memory 502 storing computer program instructions.

Specifically, the above-mentioned processor 501 may include a central processing unit (Central Processing Unit, CPU), or a specific integrated circuit (Application Specific Integrated Circuit, ASIC), or may be configured to implement one or more integrated circuits of the embodiments of the present application .

Memory 502 may include mass storage for data or instructions. By way of example and not limitation, the memory 502 may include a hard disk drive (Hard Disk Drive, HDD), a floppy disk drive, a flash memory, an optical disk, a magneto-optical disk, a magnetic tape, or a Universal Serial Bus (Universal Serial Bus, USB) drive or two or more Combinations of multiple of the above. In one example, memory 502 may include removable or non-removable (or fixed) media, or memory 502 may be a non-volatile solid-state memory. The storage 502 can be inside or outside the comprehensive gateway disaster recovery device.

In one example, the memory 502 may be a read only memory (Read Only Memory, ROM). In one example, the ROM can be mask programmed ROM, programmable ROM (PROM), erasable PROM (EPROM), electrically erasable PROM (EEPROM), electrically rewritable ROM (EAROM), or flash memory or both. A combination of one or more of the above.

The processor 501 reads and executes the computer program instructions stored in the memory 502 to implement the method in the embodiment shown in FIG. 2 , and achieve the corresponding technical effect achieved by executing the method in the example shown in FIG. 2 , which is briefly described here No longer.

In an example, the template data compression device may further include a communication interface 503 and a bus 510 . Wherein, as shown in FIG. 5 , the processor 501 , the memory 502 , and the communication interface 503 are connected through a bus 510 to complete mutual communication.

The communication interface 503 is mainly used to implement communication between modules, devices, units and/or devices in the embodiments of the present application.

The bus 510 includes hardware, software or both, and couples the components of the online data traffic charging device to each other. By way of example and not limitation, the bus may include an Accelerated Graphics Port (AGP) or other graphics bus, an Enhanced Industry Standard Architecture (EISA) bus, a Front Side Bus (FSB), a Super Transmission (Hyper Transport, HT) interconnect, Industry Standard Architecture (Industry Standard Architecture, ISA) bus, Infiniband Interconnect, Low Pin Count (LPC) bus, memory bus, Micro Channel Architecture (MCA) bus, peripheral component interconnect PCI bus, PCI-Express (PCI-X) bus, Serial Advanced Technology Attachment (SATA) bus, Video Electronics Standards Association Local (VLB) bus, or other suitable bus or a combination of two or more of these combination. Bus 510 may comprise one or more buses, where appropriate. Although the embodiments of this application describe and illustrate a particular bus, this application contemplates any suitable bus or interconnect.

The device for compressing template data can execute the method for compressing template data in the embodiment of the present application, so as to achieve the corresponding technical effect of the method for compressing template data described in FIG. 2 .

In addition, in combination with the template data compression method in the foregoing embodiments, the embodiment of the present application may provide a computer storage medium for implementation. Computer program instructions are stored on the computer storage medium; when the computer program instructions are executed by a processor, any template data compression method in the above-mentioned embodiments is implemented.

It is to be understood that the application is not limited to the specific configurations and processes described above and shown in the figures. For conciseness, detailed descriptions of known methods are omitted here. In the above embodiments, several specific steps are described and shown as examples. However, the method process of the present application is not limited to the specific steps described and shown, and those skilled in the art may make various changes, modifications and additions, or change the order of the steps after understanding the spirit of the present application.

The functional blocks shown in the structural block diagrams described above may be implemented as hardware, software, firmware, or a combination thereof. When implemented in hardware, it may be, for example, an electronic circuit, an Application Specific Integrated Circuit (ASIC), appropriate firmware, a plug-in, a function card, and the like. When implemented in software, the elements of the present application are the programs or code segments employed to perform the required tasks. Programs or code segments can be stored in machine-readable media, or transmitted over transmission media or communication links by data signals carried in carrier waves. "Machine-readable medium" may include any medium that can store or transmit information. Examples of machine-readable media include electronic circuits, semiconductor memory devices, ROM, flash memory, erasable ROM (EROM), floppy disks, CD-ROMs, optical disks, hard disks, fiber optic media, Radio Frequency (RF) links, etc. . Code segments may be downloaded via a computer network such as the Internet, an Intranet, or the like.

It should also be noted that the exemplary embodiments mentioned in this application describe some methods or systems based on a series of steps or devices. However, the present application is not limited to the order of the above steps, that is, the steps may be performed in the order mentioned in the embodiment, or may be different from the order in the embodiment, or several steps may be performed simultaneously.

Aspects of the present application are described above with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the present application. It will be understood that each block of the flowchart and/or block diagrams, and combinations of blocks in the flowchart and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus to produce a machine such that execution of these instructions via the processor of the computer or other programmable data processing apparatus enables Implementation of the functions/actions specified in one or more blocks of the flowchart and/or block diagrams. Such processors may be, but are not limited to, general purpose processors, special purpose processors, application specific processors, or field programmable logic circuits. It can also be understood that each block in the block diagrams and/or flowcharts and combinations of blocks in the block diagrams and/or flowcharts can also be realized by dedicated hardware for performing specified functions or actions, or can be implemented by dedicated hardware and Combination of computer instructions to achieve.

The above is only a specific implementation of the present application, and those skilled in the art can clearly understand that for the convenience and brevity of description, the specific working process of the above-described systems, modules and units can refer to the foregoing method embodiments The corresponding process in , will not be repeated here. It should be understood that the protection scope of the present application is not limited thereto, and any person skilled in the art can easily think of various equivalent modifications or replacements within the technical scope disclosed in the application, and these modifications or replacements should cover all Within the protection scope of this application.

Claims (10)

1. A compression method for template data, comprising: get template data; Perform repeated character aggregation conversion on the template data by a preset data conversion algorithm to obtain a character string; Encoding the character string through a preset data encoding algorithm to obtain a data sequence string; The data sequence string is compressed and stored according to a preset index compression algorithm to obtain the compressed template data. 2. the compression method of template data as claimed in claim 1, is characterized in that, described method also comprises: performing a suffix array construction on the data sequence string according to a preset suffix array construction algorithm to obtain a suffix array; Sampling the suffix array every preset data to obtain a sampling array; The sampling data is determined as index data for comparison with query input data when being queried. 3. The compression method of template data as claimed in claim 1, is characterized in that, described preset data conversion algorithm comprises: BWT algorithm. 4. The compression method of template data as claimed in claim 1, is characterized in that, described preset data encoding algorithm comprises: Huffman coding algorithm. 5. The compression method of template data as claimed in claim 1, is characterized in that, described preset index compression algorithm, comprises: RRR compressed index structure. 6. A compression device for template data, comprising: Obtain module, used to obtain template data; A conversion module, configured to perform repeated character aggregation conversion on the template data through a preset data conversion algorithm to obtain a character string; An encoding module, configured to encode the character string through a preset data encoding algorithm to obtain a data sequence string; The compression storage module is configured to compress and store the data sequence string according to a preset index compression algorithm to obtain the compressed template data. 7. The compression device of template data as claimed in claim 1, is characterized in that, described device also comprises: A construction module, configured to construct a suffix array for the data sequence string according to a preset suffix array construction algorithm to obtain a suffix array; A sampling module, configured to sample the suffix array every preset data to obtain a sampling array; A determining module, configured to determine the sampled data as index data for comparison with query input data when being queried. 8. The compression device of template data as claimed in claim 1, wherein the preset data conversion algorithm comprises: BWT algorithm. 9. A compression device for template data, comprising: a memory, a processor, and a computer program stored on the memory and operable on the processor, the computer program being executed by the processor When realizing the compression method of the template data as described in any one in claim 1 to 5. 10. A computer-readable storage medium, characterized in that, the computer-readable storage medium is stored with an implementation program for information transmission, and when the program is executed by a processor, it realizes the implementation of any one of claims 1 to 5. The compression method of the template data described above.

Images