CN112506879A - Data processing method and related equipment - Google Patents
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Abstract
The embodiment of the invention discloses a data processing method, a data processing device, equipment and a computer storage medium, wherein the data processing method comprises the following steps: acquiring data to be processed; acquiring a preset data set with the same data type as the data to be processed; determining a compression algorithm according to the preset data set; and compressing the data to be processed according to the compression algorithm to obtain target data. According to the embodiment of the application, the preset data set with the same data type as that of the data to be processed is obtained, the compression algorithm is further determined according to the preset data set, and then the data to be processed is compressed to obtain the target data. By adopting the method, the corresponding compression algorithm is obtained by the preset data set based on the data type of the data to be processed, so that the efficiency of data processing and data compression is improved.
Description
Technical Field
The present invention relates to the field of compression technologies, and in particular, to a data processing method, a data processing apparatus, a device, and a computer storage medium.
Background
Compression techniques in the prior art make use of algorithms provided by classical information theory. For example, lossless compression is the result of using data redundancies found and removed in files. Classical compression algorithms, even new algorithms such as those using artificial intelligence and machine language, are concerned with redundancy. The higher the redundancy, the better the compression ratio.
For example, the Huffman and Run-Length algorithms tend to look for pure redundancy, meaning that they tend to find a piece of data (i.e., one character of text) and find as many identical copies as possible in a larger block of data. These algorithms work well to some extent, but they have developed into the bottleneck of compression, all of them are executed based on existing redundancies, relying only on existing redundancies and execution of small data blocks limits the performance of conventional compression algorithms, and the prior art does not provide an optimal way to further improve the efficiency of data processing in combination with historical experience.
Disclosure of Invention
Embodiments of the present invention provide a data processing method, a data processing apparatus, a device, and a computer storage medium, which can help improve the efficiency of data processing and data compression.
In a first aspect, an embodiment of the present invention provides a data processing method, including:
acquiring data to be processed;
acquiring a preset data set with the same data type as the data to be processed;
determining a compression algorithm according to the preset data set;
and compressing the data to be processed according to the compression algorithm to obtain target data.
According to the embodiment of the application, the preset data set with the same data type as that of the data to be processed is obtained, the compression algorithm is further determined according to the preset data set, and then the data to be processed is compressed to obtain the target data. By adopting the method, the corresponding compression algorithm is obtained by the preset data set based on the data type of the data to be processed, so that the efficiency of data processing and data compression is improved.
The acquiring a preset data set with the same data type as the data to be processed comprises:
processing the data to be processed according to a preset algorithm to obtain first data;
acquiring the data type of the first data;
and acquiring a preset data set with the same data type as the first data from a preset database, and determining the preset data set with the same data type as the first data as the preset data set with the same data type as the data to be processed.
The processing the data to be processed according to a preset algorithm to obtain first data includes:
determining whether the data type of the data to be processed is a first data type;
if so, segmenting the data to be processed according to different lengths to obtain a plurality of data sets, wherein the length of the data in each data set is different from the length of the data in other data sets;
obtaining a redundancy of each of the plurality of data sets;
determining the data set with the redundancy exceeding a first threshold value as a first data set, wherein the data in the first data set is the first data.
Wherein, when the first data set comprises at least two data sets, the obtaining the data type of the first data comprises:
respectively acquiring the lengths of the data of the at least two data sets;
acquiring weights corresponding to the lengths of the data of the at least two data sets respectively;
and obtaining the length of the data of the first data set according to the length of the data of the at least two data sets and the weight, wherein the length of the data of the first data set is the data type of the first data.
And the compression ratio corresponding to the preset data sets with the same data types of the data to be processed is higher than a second threshold value.
In a second aspect, an embodiment of the present invention provides a data processing apparatus, including:
the first acquisition module is used for acquiring data to be processed;
the second acquisition module is used for acquiring a preset data set with the same data type as the data to be processed;
the determining module is used for determining a compression algorithm according to the preset data set;
and the processing module is used for compressing the data to be processed according to the compression algorithm to obtain target data.
The second obtaining module is specifically configured to:
processing the data to be processed according to a preset algorithm to obtain first data;
acquiring the data type of the first data;
and acquiring a preset data set with the same data type as the first data from a preset database, and determining the preset data set with the same data type as the first data as the preset data set with the same data type as the data to be processed.
Wherein the second obtaining module is further configured to:
determining whether the data type of the data to be processed is a first data type;
if so, segmenting the data to be processed according to different lengths to obtain a plurality of data sets, wherein the length of the data in each data set is different from the length of the data in other data sets;
obtaining a redundancy of each of the plurality of data sets;
determining the data set with the redundancy exceeding a first threshold value as a first data set, wherein the data in the first data set is the first data.
In a third aspect, an embodiment of the present invention provides a data processing apparatus, including: a processor and a memory;
the processor is connected with the memory, wherein the memory is used for storing program codes, and the processor is used for calling the program codes to execute the data processing method.
In a fourth aspect, an embodiment of the present invention provides a computer storage medium storing a computer program, the computer program comprising program instructions that, when executed by a processor, perform the data processing method.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings needed to be used in the description of the embodiments are briefly introduced below, and it is obvious that the drawings in the following description are some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without creative efforts.
Fig. 1 is a schematic flow chart of a data processing method according to an embodiment of the present invention;
FIG. 2 is a flow chart illustrating a further data processing method according to an embodiment of the present invention;
FIG. 3 is a schematic structural diagram of a data processing apparatus according to an embodiment of the present invention;
fig. 4 is a schematic structural diagram of a data processing device according to an embodiment of the present invention.
Detailed Description
The technical solution in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention.
It should be understood that the terms "first," "second," and the like in the description and claims of this application and in the drawings are used for distinguishing between different objects and not for describing a particular order. Furthermore, the terms "include" and "have," as well as any variations thereof, are intended to cover non-exclusive inclusions. For example, a process, method, system, article, or apparatus that comprises a list of steps or elements is not limited to only those steps or elements listed, but may alternatively include other steps or elements not listed, or inherent to such process, method, article, or apparatus.
Reference in the specification to "an embodiment" means that a particular feature, structure, or characteristic described in connection with the embodiment can be included in at least one embodiment of the invention. The appearances of the phrase in various places in the specification are not necessarily all referring to the same embodiment, nor are separate or alternative embodiments mutually exclusive of other embodiments. It is explicitly and implicitly understood by the person skilled in the art that the described embodiments of the invention can be combined with other embodiments.
Referring to fig. 1, fig. 1 is a schematic flow chart of a data processing method according to an embodiment of the present invention. As shown in fig. 1, the method includes steps 101-104 as follows:
101. acquiring data to be processed;
the data to be processed may be any form of data, such as binary data, hexadecimal data, and the like, and this scheme is not particularly limited.
Preferably, the data to be processed may be data for compression processing. Such as video data, image data, etc.
102. Acquiring a preset data set with the same data type as the data to be processed;
the data type may refer to a binary classification of data, such as binary, decimal, hexadecimal, etc.; it may also be a length classification of the data; or a classification of use of the data; or size classification of data, etc. The present solution is not particularly limited to this.
The preset data set may be obtained from a preset database, and the preset database may be obtained through machine learning training. The recommendation of the corresponding data set can be further performed based on the data type through machine learning training.
Wherein the database comprises a plurality of data sets; the preset data set may be one data set or a plurality of data sets. The present solution is not particularly limited to this.
103. Determining a compression algorithm according to the preset data set;
each data set can correspond to different compression algorithms, and the compression algorithm is the compression algorithm with the highest compression ratio when the data set is compressed.
When the preset data set is a plurality of data sets, the data set corresponding to the compression algorithm with the highest compression ratio may be used as the target data set, that is, the compression algorithm with the highest compression ratio is determined as the compression algorithm.
104. And compressing the data to be processed according to the compression algorithm to obtain target data.
And compressing the data to be processed according to the determined compression algorithm with the highest compression ratio to obtain compressed target data.
According to the embodiment of the application, the preset data set with the same data type as that of the data to be processed is obtained, the compression algorithm is further determined according to the preset data set, and then the data to be processed is compressed to obtain the target data. By adopting the method, the corresponding compression algorithm is obtained by the preset data set based on the data type of the data to be processed, so that the efficiency of data processing and data compression is improved.
Fig. 2 is a schematic flow chart of another data processing method according to an embodiment of the present invention. The method comprises the following steps 201 and 209:
201. acquiring data to be processed;
the data to be processed may be any form of data, such as binary data, hexadecimal data, and the like, and this scheme is not particularly limited.
Preferably, the data to be processed may be data for compression processing. Such as video data, image data, etc.
202. Determining whether the data type of the data to be processed is a first data type;
the first data type may be, for example, binary data or the like.
And if the data type of the data to be processed is not the first data type, performing conversion processing on the data to be processed, and the like.
The present scheme is only described by taking the data type as an example, and may also be a file type, such as MP4, and the present scheme is not limited in this respect.
203. If so, segmenting the data to be processed according to different lengths to obtain a plurality of data sets, wherein the length of the data in each data set is different from the length of the data in other data sets;
for example, dividing N bits of binary data into M sets of binary data blocks, where the total number of bits of each set of binary data blocks is N, and each set of binary data blocks includes at least two binary data blocks, and any two binary data blocks in each set of binary data blocks have the same number of bits; the bits of the binary data blocks in any two binary data block sets are different;
the N-bit binary data is divided into M binary data block sets, that is, M different division processes are performed on the N-bit binary data to obtain M binary data block sets.
For example, 16-bit binary data 1001000011101001, which may be split into 4-bit binary data blocks, 1001, 0000, 1110, 1001. It can also be split into 2 binary data blocks of 8 bits, 10010000, 11101001, etc. Wherein 1001, 0000, 1110 and 1001 form a binary data block set; 10010000, 11101001 constitute another set of binary data blocks.
The above description is made by taking only two types of division methods as an example, and other arbitrary division processes may be used, and this is not particularly limited in this embodiment.
For another example, for a data block with N of 100, 100 bits are split into blocks of minimum 4 bits and maximum 50 bits. In this embodiment, the maximum bit length of 50 bits is because when all blocks (2 blocks in this example) have equal bit lengths, this is the only possible way to split a data block into multiple blocks. So in this example, there may be: 1)25 splits, each 4 bits. 2)20 splits, each 5 bits. 3)10 splits, each 10 bits. 4)4 splits, 25 bits each. 5)2 splits, each 50 bits.
In the above-mentioned division, the number of bits of any two binary data blocks in each binary data block set is the same, that is, the division is an average division; optionally, the method may also be non-average segmentation, and the present scheme is not particularly limited in this respect.
The binary data blocks in any two binary data block sets have different bits, that is, different divisions correspond to different binary data block sets.
204. Obtaining a redundancy of each of the plurality of data sets;
the redundancy of each data set is the sum of the ratio of the number of the repeated data in each data set to the number of the data in the data set.
For example, if a data set includes 10 data, of which 4 are identical, the redundancy of the data set is 4/10-40%.
205. Determining a data set with the redundancy exceeding a first threshold as a first data set, wherein the data in the first data set is the first data;
the first threshold may be any value, and the present solution is not particularly limited to this. For example, it may be 70%, 80%, etc.
206. Acquiring the data type of the first data;
the data type of the first data may be, for example, a data length, i.e., a data length corresponding to the first data after the first data is divided.
Wherein, when the first data set comprises at least two data sets, the obtaining the data type of the first data comprises:
respectively acquiring the lengths of the data of the at least two data sets;
acquiring weights corresponding to the lengths of the data of the at least two data sets respectively;
and obtaining the length of the data of the first data set according to the length of the data of the at least two data sets and the weight, wherein the length of the data of the first data set is the data type of the first data.
For example, the first data set includes a second data set, a third data set, and a fourth data set, each of which is a different length.
The weight W corresponding to the length of the data of each data set is obtained, for example, by the following algorithm:
W1+W2+W3＝1，W1P1 Q1+W2P2 Q2+W3P3 Q3＝C。
wherein, W1、W2、W3Weights corresponding to the second data set, the third data set and the fourth data set respectively; p1、P2、P3Length of data corresponding to the second data set, the third data set and the fourth data set, Q1, Q respectively2、Q3Compression ratios corresponding to the second data set, the third data set, and the fourth data set, respectively, which may be known data; c is a constant, for example, a positive number such as 1 or 2, and this embodiment is not particularly limited.
And obtaining the length of the data of the first data set according to the length of the data of the second data set, the third data set and the fourth data set and the weight. When the obtained value is a non-integer, the data can be processed based on an algorithm such as rounding, and then rounded to obtain the length of the data of the first data set.
207. Acquiring a preset data set with the same data type as the first data from a preset database, and determining the preset data set with the same data type as the first data as the preset data set with the same data type as the data to be processed;
and the compression ratio corresponding to the preset data sets with the same data types of the data to be processed is higher than a second threshold value. For example, the compression ratios corresponding to the data sets in the preset database are all higher, so as to obtain the preset data sets. Of course, the present solution is not particularly limited to this.
The second threshold may be any value, and the present solution is not particularly limited to this. For example, it may be 70%, 80%, 92%, etc.
208. Determining a compression algorithm according to the preset data set;
for example, based on training learning, a length of P is obtained1Corresponds to a first compression algorithm; length P2Corresponds to a second compression algorithm; length P3Corresponds to a third compression algorithm, etc. It may also be based on statistical analysis to obtain the above results, which is not specifically limited in this embodiment.
209. And compressing the data to be processed according to the compression algorithm to obtain target data.
According to the embodiment of the application, the first data are determined based on the redundancy, the preset data set is obtained according to the data type of the first data, the compression algorithm is determined according to the preset data set, and then the data to be processed are compressed to obtain the target data. By adopting the method, the corresponding compression algorithm is obtained by the preset data set based on the data type of the data to be processed, so that the efficiency of data processing and data compression is improved.
Based on the description of the above data processing method embodiment, the embodiment of the present invention further discloses a data processing apparatus, referring to fig. 3, fig. 3 is a schematic structural diagram of the data processing apparatus provided in the embodiment of the present invention, where the data processing apparatus includes a first obtaining module 301, a second obtaining module 302, a determining module 303, and a processing module 304; wherein:
a first obtaining module 301, configured to obtain data to be processed;
a second obtaining module 302, configured to obtain a preset data set with a data type that is the same as that of the data to be processed;
a determining module 303, configured to determine a compression algorithm according to the preset data set;
the processing module 304 is configured to compress the data to be processed according to the compression algorithm to obtain target data.
The second obtaining module 302 is specifically configured to:
processing the data to be processed according to a preset algorithm to obtain first data;
acquiring the data type of the first data;
and acquiring a preset data set with the same data type as the first data from a preset database, and determining the preset data set with the same data type as the first data as the preset data set with the same data type as the data to be processed.
Wherein the second obtaining module 302 is further configured to:
determining whether the data type of the data to be processed is a first data type;
if so, segmenting the data to be processed according to different lengths to obtain a plurality of data sets, wherein the length of the data in each data set is different from the length of the data in other data sets;
obtaining a redundancy of each of the plurality of data sets;
determining the data set with the redundancy exceeding a first threshold value as a first data set, wherein the data in the first data set is the first data.
It is to be noted that, for a specific implementation of the functions of the data processing apparatus, reference may be made to the description of the data processing method, and details are not described here. The units or modules in the data processing apparatus may be respectively or completely combined into one or several other units or modules to form one or several other units or modules, or some unit(s) or module(s) thereof may be further split into multiple functionally smaller units or modules to form the same operations, without affecting the achievement of the technical effects of the embodiments of the present invention. The above units or modules are divided based on logic functions, and in practical applications, the functions of one unit (or module) may also be implemented by a plurality of units (or modules), or the functions of a plurality of units (or modules) may be implemented by one unit (or module).
Based on the description of the method embodiment and the device embodiment, the embodiment of the invention also provides a data processing device.
Fig. 4 is a schematic structural diagram of a data processing apparatus according to an embodiment of the present invention. As shown in fig. 4, the data processing apparatus described above may be applied to the data processing device 400, and the data processing device 400 may include: a processor 401, a network interface 404 and a memory 405, and the data processing apparatus 400 may further include: a user interface 403, and at least one communication bus 402. Wherein a communication bus 402 is used to enable connective communication between these components. The user interface 403 may include a Display (Display) and a Keyboard (Keyboard), and the selectable user interface 403 may also include a standard wired interface and a standard wireless interface. The network interface 404 may optionally include a standard wired interface, a wireless interface (e.g., WI-FI interface). The memory 405 may be a high-speed RAM memory or a non-volatile memory (e.g., at least one disk memory). The memory 405 may alternatively be at least one storage device located remotely from the aforementioned processor 401. As shown in fig. 4, the memory 405, which is a type of computer storage medium, may include therein an operating system, a network communication module, a user interface module, and a device control application program.
In the data processing apparatus 400 shown in fig. 4, the network interface 404 may provide a network communication function; and the user interface 403 is primarily an interface for providing input to a user; and processor 401 may be used to invoke a device control application stored in memory 405 to implement:
acquiring data to be processed;
converting the data to be processed according to a preset algorithm to obtain M groups of candidate data, wherein M is an integer not less than 2;
respectively acquiring the similarity between each group of candidate data in the M groups of candidate data and K preset data blocks with different lengths, wherein K is an integer not less than 1;
and determining the group of candidate data with the similarity exceeding a first preset threshold value as target data.
In one embodiment, the processor 401 specifically performs the following steps when executed:
dividing N bits of binary data into M binary data block sets, wherein the total bits of each binary data block set are N, each binary data block set comprises at least two binary data blocks, and the bits of any two binary data blocks in each binary data block set are the same; the bits of the binary data blocks in any two binary data block sets are different;
determining the set of M binary data blocks as the M sets of candidate data.
It should be understood that the data processing apparatus 400 described in the embodiment of the present invention may perform the data processing method described above, and may also perform the description of the data processing apparatus described above, which is not described herein again. In addition, the beneficial effects of the same method are not described in detail.
Further, here, it is to be noted that: an embodiment of the present invention further provides a computer storage medium, where the computer storage medium stores a computer program executed by the aforementioned data processing apparatus, and the computer program includes program instructions, and when a processor executes the program instructions, the description of the data processing method can be executed, so that details are not repeated herein. In addition, the beneficial effects of the same method are not described in detail. For technical details not disclosed in the embodiments of the computer storage medium to which the present invention relates, reference is made to the description of the method embodiments of the present invention.
It will be understood by those skilled in the art that all or part of the processes of the methods of the embodiments described above can be implemented by hardware related to instructions of a computer program, which can be stored in a computer-readable storage medium, and when executed, the processes of the embodiments of the methods described above can be included. The storage medium may be a magnetic disk, an optical disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), or the like.
The above disclosure is only for the purpose of illustrating the preferred embodiments of the present invention, and it is therefore to be understood that the invention is not limited by the scope of the appended claims.
Claims (10)
1. A data processing method, comprising:
acquiring data to be processed;
acquiring a preset data set with the same data type as the data to be processed;
determining a compression algorithm according to the preset data set;
and compressing the data to be processed according to the compression algorithm to obtain target data.
2. The method according to claim 1, wherein the obtaining of the preset data set of the same data type as the data to be processed comprises:
processing the data to be processed according to a preset algorithm to obtain first data;
acquiring the data type of the first data;
and acquiring a preset data set with the same data type as the first data from a preset database, and determining the preset data set with the same data type as the first data as the preset data set with the same data type as the data to be processed.
3. The method according to claim 2, wherein the processing the data to be processed according to a preset algorithm to obtain the first data comprises:
determining whether the data type of the data to be processed is a first data type;
if so, segmenting the data to be processed according to different lengths to obtain a plurality of data sets, wherein the length of the data in each data set is different from the length of the data in other data sets;
obtaining a redundancy of each of the plurality of data sets;
determining the data set with the redundancy exceeding a first threshold value as a first data set, wherein the data in the first data set is the first data.
4. The method of claim 3, wherein when the first data set comprises at least two data sets, the obtaining the data type of the first data comprises:
respectively acquiring the lengths of the data of the at least two data sets;
acquiring weights corresponding to the lengths of the data of the at least two data sets respectively;
and obtaining the length of the data of the first data set according to the length of the data of the at least two data sets and the weight, wherein the length of the data of the first data set is the data type of the first data.
5. The method according to claim 4, wherein the compression ratio corresponding to the preset data set with the same data type of the data to be processed is higher than the second threshold.
6. A data processing apparatus, comprising:
the first acquisition module is used for acquiring data to be processed;
the second acquisition module is used for acquiring a preset data set with the same data type as the data to be processed;
the determining module is used for determining a compression algorithm according to the preset data set;
and the processing module is used for compressing the data to be processed according to the compression algorithm to obtain target data.
7. The apparatus of claim 6, wherein the second obtaining module is specifically configured to:
processing the data to be processed according to a preset algorithm to obtain first data;
acquiring the data type of the first data;
and acquiring a preset data set with the same data type as the first data from a preset database, and determining the preset data set with the same data type as the first data as the preset data set with the same data type as the data to be processed.
8. The apparatus of claim 7, wherein the second obtaining module is further configured to:
determining whether the data type of the data to be processed is a first data type;
if so, segmenting the data to be processed according to different lengths to obtain a plurality of data sets, wherein the length of the data in each data set is different from the length of the data in other data sets;
obtaining a redundancy of each of the plurality of data sets;
determining the data set with the redundancy exceeding a first threshold value as a first data set, wherein the data in the first data set is the first data.
9. A data processing apparatus, characterized by comprising: a processor and a memory;
the processor is connected to a memory, wherein the memory is used for storing program code and the processor is used for calling the program code to execute the data processing method according to any one of claims 1-5.
10. A computer storage medium, characterized in that the computer storage medium stores a computer program comprising program instructions that, when executed by a processor, perform the data processing method of any one of claims 1-5.
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| CN112769874A (en) * | 2021-04-07 | 2021-05-07 | 南京创芯慧联技术有限公司 | Data compression method and compression device thereof |
| CN113253026A (en) * | 2021-05-13 | 2021-08-13 | 北京三维天地科技股份有限公司 | Monitoring method and device for on-off state of instrument |
| CN113542225A (en) * | 2021-06-17 | 2021-10-22 | 深圳市合广测控技术有限公司 | Data compression method and device, terminal equipment and storage medium |
| CN113659992A (en) * | 2021-07-16 | 2021-11-16 | 深圳智慧林网络科技有限公司 | Data compression method and device and storage medium |
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