CN116881183A

CN116881183A - Method and device for processing decoded data

Info

Publication number: CN116881183A
Application number: CN202311141260.6A
Authority: CN
Inventors: 郑涛; 邱勇
Original assignee: Beijing Rongwei Technology Co ltd
Current assignee: Beijing Rongwei Technology Co ltd
Priority date: 2023-09-06
Filing date: 2023-09-06
Publication date: 2023-10-13

Abstract

The embodiment of the specification provides a method and a device for processing decoded data, wherein the method for processing the decoded data comprises the following steps: determining initial data, and reading data to be decoded from the initial data based on a preset processing rule; performing decoding processing on data to be decoded to obtain decoded data; dividing the decoding data based on a preset dividing rule to obtain divided data; wherein the slicing data are multi-row and multi-column; and storing the fragment data into the corresponding memory page based on the row and column information of the fragment data. Reading data to be decoded from the initial data based on a preset processing rule by determining the initial data; performing decoding processing on data to be decoded to obtain decoded data; dividing the decoding data based on a preset dividing rule to obtain divided data; wherein the slicing data are multi-row and multi-column; and storing the fragment data into the corresponding memory page based on the row and column information of the fragment data, ensuring the throughput of data processing and reducing the use of resources.

Description

Method and device for processing decoded data

Technical Field

The embodiment of the specification relates to the technical field of data processing, in particular to a decoding data processing method.

Background

The Product Code (TPC) is a two-dimensional code, and both the row component code and the column component code are linear block codes. The product code is encoded in two steps, namely row encoding and column encoding. The transmission is performed by first outputting the row and then outputting the column.

Decoding of product codes typically employs multi-step decoding. Column decoding is typically performed after row decoding. Each time the component decoding algorithm is binary hamming decoding, in which case the entire error pattern is correctable if and only if the remaining row uncorrectable error pattern is a column-correctable error pattern. The repeated iteration of the preceding and subsequent decoding is carried out for a plurality of times, and the good decoding effect can be obtained by taking 4-5 times of general iteration times, and the improvement obtained by continuing the iteration is very small.

Generally, decoding algorithms are classified into hard decision decoding and soft decision decoding. Hard decision decoding refers to that a decoder directly decides a received signal waveform according to a decision threshold and then outputs 0 or 1, and soft decision decoding refers to that soft information generated by a transmission channel is used. Hard decision is simple to implement, the performance is poor, soft decision is opposite, the implementation is complex, but the performance is good, and soft decision decoding is often used in engineering to ensure the performance.

When using a soft-decision decoding algorithm, the hamming distance is no longer the reference metric at decoding, but we choose other metrics. The most common metrics are euclidean distance, correlation and likelihood functions, etc. The most commonly used soft decision decoding algorithm is a chase decoding algorithm, which is a suboptimal algorithm with low complexity, and the basic idea is that the error probability of information at a position with lower reliability in a receiving sequence is considered to be relatively high. Based on the confidence level of each code element, the least reliable bit is found, thereby generating a heuristic sequence, and selecting the code word with the nearest soft distance to the receiving sequence, namely the decoding output.

Due to the specificity of the TPC code pattern, the row reading and writing of matrix TPC code sub-codes, and the column reading and writing are needed to be completed in the decoding process, and the process is repeated. When logic is implemented, if the logic is implemented by using the RAM, the throughput is greatly reduced due to the RAM read-write rule, and if the logic is implemented by using a register mode, the throughput is ensured, but the resource is extremely high.

Thus, a better solution is needed.

Disclosure of Invention

In view of this, the present embodiments provide a decoded data processing method. One or more embodiments of the present specification are also directed to a decoded data processing apparatus, a computing device, a computer readable storage medium, and a computer program, which solve the technical drawbacks of the prior art.

According to a first aspect of embodiments of the present disclosure, there is provided a decoded data processing method, including:

determining initial data, and reading data to be decoded from the initial data based on a preset processing rule;

performing decoding processing on data to be decoded to obtain decoded data;

dividing the decoding data based on a preset dividing rule to obtain divided data; wherein the slicing data are multi-row and multi-column;

and storing the fragment data into the corresponding memory page based on the row and column information of the fragment data.

In one possible implementation manner, determining initial data, reading data to be decoded from the initial data based on a preset processing rule, and including:

determining initial data; wherein the initial data is a plurality of rows and a plurality of columns;

reading first line data from the initial data to a first cache based on a preset processing rule, and taking the first line data as data to be decoded;

reading first column data from the initial data to a second buffer memory based on a preset processing rule, and taking the first column data as data to be decoded under the condition that decoding processing is carried out on the first row data;

and reading the second row data from the initial data to the first cache based on a preset processing rule, and executing the operation of determining the data to be decoded until the row data and the column data in the initial data are read.

In one possible implementation manner, decoding the data to be decoded to obtain decoded data includes:

and carrying out unreliable position judgment, candidate code hard judgment, euclidean distance calculation and external information calculation on the data to be decoded to obtain decoded data.

In one possible implementation manner, slicing the decoded data based on a preset slicing rule to obtain sliced data includes:

determining a segmentation size based on a preset segmentation rule, and determining row information and column information based on the segmentation size;

dividing the decoding data into a plurality of rows of data based on the row information, and dividing the decoding data into a plurality of columns of data based on the column information;

and arranging based on the multiple rows of data and the multiple columns of data to obtain the sliced data.

In one possible implementation, storing the fragment data into the corresponding memory page based on the rank information of the fragment data includes:

determining row information and column information of the fragment data, and determining a target memory page based on the row information and the column information;

and storing the fragment data into the target memory page.

In one possible implementation, storing the fragment data into the target memory page includes:

under the condition that the reading mode is row writing and column reading, determining first target row data corresponding to a first target memory page based on row information;

acquiring first target column data from first target row data based on column information, and storing the first target column data into a first target memory page;

determining second target column data corresponding to a second target memory page based on column information under the condition that the reading mode is row reading and column writing;

and acquiring second target row data from the second target column data based on the row information, and storing the second target row data into a second target memory page.

In one possible implementation, the slice size is a 2×2 data block, a 4×4 data block, or an 8×8 data block.

According to a second aspect of embodiments of the present specification, there is provided a decoded data processing apparatus comprising:

the data determining module is configured to determine initial data, and read data to be decoded from the initial data based on a preset processing rule;

the data decoding module is configured to decode the data to be decoded to obtain decoded data;

the data segmentation module is configured to segment the decoding data based on a preset segmentation rule to obtain segmented data; wherein the slicing data are multi-row and multi-column;

and the data storage module is configured to store the fragment data into the corresponding memory page based on the row and column information of the fragment data.

According to a third aspect of embodiments of the present specification, there is provided a computing device comprising:

a memory and a processor;

the memory is used for storing computer executable instructions, and the processor is used for executing the computer executable instructions, and the computer executable instructions realize the steps of the decoding data processing method when being executed by the processor.

According to a fourth aspect of embodiments of the present specification, there is provided a computer readable storage medium storing computer executable instructions which, when executed by a processor, implement the steps of the above-described method of decoding data processing.

According to a fifth aspect of the embodiments of the present specification, there is provided a computer program, wherein the computer program, when executed in a computer, causes the computer to perform the steps of the above-described decoded data processing method.

Drawings

Fig. 1 is a schematic view of a decoding data processing method according to an embodiment of the present disclosure;

FIG. 2 is a flow chart of a method of processing decoded data according to one embodiment of the present disclosure;

FIG. 3 is a data diagram illustrating a method for decoding data according to an embodiment of the present disclosure;

FIG. 4 is a schematic diagram of a method for processing decoded data according to an embodiment of the present disclosure;

FIG. 5 is a schematic diagram of a method for decoding data according to an embodiment of the present disclosure;

FIG. 6 is a schematic diagram of data storage of a method for processing decoded data according to an embodiment of the present disclosure;

FIG. 7 is a schematic diagram of a decoding data processing apparatus according to an embodiment of the present disclosure;

FIG. 8 is a block diagram of a computing device provided in one embodiment of the present description.

Detailed Description

In the following description, numerous specific details are set forth in order to provide a thorough understanding of the present description. This description may be embodied in many other forms than described herein and similarly generalized by those skilled in the art to whom this disclosure pertains without departing from the spirit of the disclosure and, therefore, this disclosure is not limited by the specific implementations disclosed below.

The terminology used in the one or more embodiments of the specification is for the purpose of describing particular embodiments only and is not intended to be limiting of the one or more embodiments of the specification. As used in this specification, the singular forms "a," "an," and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise. It should also be understood that the term "and/or" as used in one or more embodiments of the present specification refers to and encompasses any or all possible combinations of one or more of the associated listed items.

It should be understood that, although the terms first, second, etc. may be used in one or more embodiments of this specification to describe various information, these information should not be limited by these terms. These terms are only used to distinguish one type of information from another. For example, a first may also be referred to as a second, and similarly, a second may also be referred to as a first, without departing from the scope of one or more embodiments of the present description. The word "if" as used herein may be interpreted as "at … …" or "at … …" or "responsive to a determination", depending on the context.

First, terms related to one or more embodiments of the present specification will be explained.

FPGA (Field Programmable Gate Array) is a product of further development on the basis of programmable devices such as PAL (programmable array logic), GAL (generic array logic) and the like.

DMA, full name Direct Memory Access, direct memory access. DMA transfers copy data from one address space to another address space, providing high speed data transfer between a peripheral and a memory or between a memory and a memory.

Random access memory (English: random Access Memory, abbreviated: RAM), also called main memory, is an internal memory that exchanges data directly with the CPU.

Cache (cache), in its original sense, refers to a high-speed memory that has a faster access speed than a general Random Access Memory (RAM).

In the present specification, a decoded data processing method is provided, and the present specification relates to a decoded data processing apparatus, a computing device, and a computer-readable storage medium, which are described in detail one by one in the following embodiments.

Referring to fig. 1, fig. 1 is a schematic view of a decoding data processing method according to an embodiment of the present disclosure.

In the application scenario of fig. 1, the computing device 101 may determine initial data, and read the data to be decoded 102 from the initial data based on a preset processing rule. The computing device 101 may then perform a decoding process on the data to be decoded 102 to obtain decoded data 103. Thereafter, the computing device 101 may segment the decoded data 103 based on a preset segmentation rule to obtain the sliced data 104. Finally, computing device 101 may store the shard data into a corresponding memory page based on the rank information of shard data 104, as indicated by reference numeral 105.

The computing device 101 may be hardware or software. When the computing device 101 is hardware, it may be implemented as a distributed cluster of multiple servers or terminal devices, or as a single server or single terminal device. When the computing device 101 is embodied as software, it may be installed in the hardware devices listed above. It may be implemented as a plurality of software or software modules, for example, for providing distributed services, or as a single software or software module. The present invention is not particularly limited herein.

Referring to fig. 2, fig. 2 shows a flowchart of a method for processing decoded data according to an embodiment of the present disclosure, which specifically includes the following steps.

Step 201: and determining initial data, and reading data to be decoded from the initial data based on a preset processing rule.

In one possible implementation manner, determining initial data, reading data to be decoded from the initial data based on a preset processing rule, and including: determining initial data; wherein the initial data is a plurality of rows and a plurality of columns; reading first line data from the initial data to a first cache based on a preset processing rule, and taking the first line data as data to be decoded; reading first column data from the initial data to a second buffer memory based on a preset processing rule, and taking the first column data as data to be decoded under the condition that decoding processing is carried out on the first row data; and reading the second row data from the initial data to the first cache based on a preset processing rule, and executing the operation of determining the data to be decoded until the row data and the column data in the initial data are read.

In practical applications, referring to fig. 3, the initial data may be a product code, including row data, column data, row checksum and column checksum data. Wherein, the row data and the column data are 57 bits, the row check and the column check are 7 bits, and the square matrix is 64 bits in total. In communication, each bit of data in the received product code is represented by a llr (log likelihood ratio) value, and a llr value is comprised of 8 bit values. That is, a 64-bit square matrix is 512 bits by 512 bits of data at the receiving end, represented by 64×64 llr values.

Specifically, referring to fig. 4, in the process of data processing, a plurality of initial data may be received, a first initial data may be read and stored in the receiving information buffer RAM1 or the receiving information buffer RAM2, so that the processor obtains data from the storing receiving information buffer RAM1 or the storing receiving information buffer RAM2 to decode, and in the case of processing by the processor, the storing receiving information buffer RAM1 or the storing information buffer RAM2 of a second initial data is read. It should be noted that if the first initial data is stored in the RAM1, the second initial data is stored in the RAM2. Equivalent to performing ping-pong operation, the embodiments of the present disclosure will not be described in detail.

Further, after the data is read in the above manner, multiple iterations are required in the decoding process, see fig. 5, and the decoding process needs to complete the row reading and writing of the matrix TPC codeword subcodes, and then column reading and writing, and the process is repeated for iteration.

Step 202: and carrying out decoding processing on the data to be decoded to obtain decoded data.

In one possible implementation manner, decoding the data to be decoded to obtain decoded data includes: and carrying out unreliable position judgment, candidate code hard judgment, euclidean distance calculation and external information calculation on the data to be decoded to obtain decoded data.

Specifically, the decoding process of the data to be decoded may use conventional technical means in the art, which includes performing operations such as unreliable position determination, candidate code hard determination, euclidean distance calculation, and extrinsic information calculation on the llr value, which are not described in detail in the embodiment of the present disclosure.

Step 203: and slicing the decoded data based on a preset slicing rule to obtain sliced data. Wherein the slicing data is multi-row and multi-column.

In one possible implementation manner, slicing the decoded data based on a preset slicing rule to obtain sliced data includes: determining a segmentation size based on a preset segmentation rule, and determining row information and column information based on the segmentation size; dividing the decoding data into a plurality of rows of data based on the row information, and dividing the decoding data into a plurality of columns of data based on the column information; and arranging based on the multiple rows of data and the multiple columns of data to obtain the sliced data.

In practical application, after the decoded data is obtained, the obtained data can be segmented, so that the whole data can be stored in a cache according to a corresponding rule for use.

For example, the slicing size is 4×4, that is, 64×64 is sliced by 4×4, that is, the first four llr values of the first row of decoded data are grouped into the first group, the first four llr values of the second row of decoded data are grouped into the first group, the first four llr values of the third row of decoded data are grouped into the first group, and the first four llr values of the fourth row of decoded data are grouped into the first group, resulting in 4×4 sliced data. In general, a 64×64 matrix llr is split according to a 4×4 data block, so that each data is split into 16 sets of data, and the whole is 16×16 two-dimensional data.

It should be noted that, the preferred segmentation size is 2×2 data block, 4×4 data block or 8×8 data block, and the maximum size is 64×64 for the initial data of 64×64, and the smaller the segmentation size, the smaller the bandwidth resource is required, and the performance can be set according to the requirement in the implementation process, which is not limited in the embodiments of the present disclosure.

Step 204: and storing the fragment data into the corresponding memory page based on the row and column information of the fragment data.

In one possible implementation, storing the fragment data into the corresponding memory page based on the rank information of the fragment data includes: determining row information and column information of the fragment data, and determining a target memory page based on the row information and the column information; and storing the fragment data into the target memory page.

In practical applications, the reading mode needs to be changed after each iteration, that is, if the current algebra is row reading and row writing, the next generation is column reading and column writing, and in the conversion process, the reading is called row writing and column reading, or column reading and row writing.

Specifically, storing the fragment data into the target memory page includes: under the condition that the reading mode is row writing and column reading, determining first target row data corresponding to a first target memory page based on row information; acquiring first target column data from first target row data based on column information, and storing the first target column data into a first target memory page; determining second target column data corresponding to a second target memory page based on column information under the condition that the reading mode is row reading and column writing; and acquiring second target row data from the second target column data based on the row information, and storing the second target row data into a second target memory page.

For example, for the first four data in the first row, the four points are placed in the first column of RAM BANK 1 of BRAM according to the preset classification rule, for the first four data in the second row, the four points are placed in the second column of RAM BANK2 of BRAM according to the preset classification rule, … … in sequence, see fig. 6, finally, the first row 64 data is stored in the first column of 16 memory pages, and the second row 64 data is stored in the second column of 16 memory pages, … …. Therefore, the data of TPC can be read based on the register, and the resource use is reduced under the condition of ensuring the throughput.

Corresponding to the above method embodiments, the present disclosure further provides an embodiment of a decoding data processing apparatus, and fig. 7 shows a schematic structural diagram of a decoding data processing apparatus according to one embodiment of the present disclosure. As shown in fig. 7, the apparatus includes:

a data determining module 701 configured to determine initial data, and read data to be decoded from the initial data based on a preset processing rule;

the data decoding module 702 is configured to perform decoding processing on the data to be decoded to obtain decoded data;

the data slicing module 703 is configured to slice the decoded data based on a preset slicing rule to obtain sliced data; wherein the slicing data are multi-row and multi-column;

the data storage module 704 is configured to store the fragment data into the corresponding memory page based on the rank information of the fragment data.

In one possible implementation, the data determination module 701 is further configured to:

In one possible implementation, the data decoding module 702 is further configured to:

In one possible implementation, the data slicing module 703 is further configured to:

In one possible implementation, the data storage module 704 is further configured to:

and storing the fragment data into the target memory page.

the slice size is 2 x 2 data blocks, 4 x 4 data blocks, or 8 x 8 data blocks.

The embodiment of the specification provides a method and a device for processing decoded data, wherein the device for processing the decoded data comprises the following components: determining initial data, and reading data to be decoded from the initial data based on a preset processing rule; performing decoding processing on data to be decoded to obtain decoded data; dividing the decoding data based on a preset dividing rule to obtain divided data; wherein the slicing data are multi-row and multi-column; and storing the fragment data into the corresponding memory page based on the row and column information of the fragment data. Reading data to be decoded from the initial data based on a preset processing rule by determining the initial data; performing decoding processing on data to be decoded to obtain decoded data; dividing the decoding data based on a preset dividing rule to obtain divided data; wherein the slicing data are multi-row and multi-column; and storing the fragment data into the corresponding memory page based on the row and column information of the fragment data, ensuring the throughput of data processing and reducing the use of resources.

The above is a schematic scheme of a decoding data processing apparatus of the present embodiment. It should be noted that, the technical solution of the decoding data processing apparatus and the technical solution of the decoding data processing method belong to the same concept, and details of the technical solution of the decoding data processing apparatus that are not described in detail can be referred to the description of the technical solution of the decoding data processing method.

Fig. 8 illustrates a block diagram of a computing device 800 provided in accordance with one embodiment of the present description. The components of computing device 800 include, but are not limited to, memory 810 and processor 820. Processor 820 is coupled to memory 810 through bus 830 and database 850 is used to hold data.

Computing device 800 also includes access device 840, access device 840 enabling computing device 800 to communicate via one or more networks 860. Examples of such networks include public switched telephone networks (PSTN, public Switched Telephone Network), local area networks (LAN, local Area Network), wide area networks (WAN, wide Area Network), personal area networks (PAN, personal Area Network), or combinations of communication networks such as the internet. Access device 840 may include one or more of any type of network interface, wired or wireless, such as a network interface card (NIC, network interface controller), such as an IEEE802.11 wireless local area network (WLAN, wireless Local Area Network) wireless interface, a worldwide interoperability for microwave access (Wi-MAX, worldwide Interoperability for Microwave Access) interface, an ethernet interface, a universal serial bus (USB, universal Serial Bus) interface, a cellular network interface, a bluetooth interface, near field communication (NFC, near Field Communication).

In one embodiment of the present description, the above-described components of computing device 800, as well as other components not shown in FIG. 8, may also be connected to each other, such as by a bus. It should be understood that the block diagram of the computing device illustrated in FIG. 8 is for exemplary purposes only and is not intended to limit the scope of the present description. Those skilled in the art may add or replace other components as desired.

Computing device 800 may be any type of stationary or mobile computing device, including a mobile computer or mobile computing device (e.g., tablet, personal digital assistant, laptop, notebook, netbook, etc.), mobile phone (e.g., smart phone), wearable computing device (e.g., smart watch, smart glasses, etc.), or other type of mobile device, or a stationary computing device such as a desktop computer or personal computer (PC, personal Computer). Computing device 800 may also be a mobile or stationary server.

Wherein the processor 820 is configured to execute computer-executable instructions that, when executed by the processor, perform the steps of the method for processing decoded data described above. The foregoing is a schematic illustration of a computing device of this embodiment. It should be noted that, the technical solution of the computing device and the technical solution of the above decoding data processing method belong to the same concept, and details of the technical solution of the computing device, which are not described in detail, can be referred to the description of the technical solution of the above decoding data processing method.

An embodiment of the present disclosure also provides a computer-readable storage medium storing computer-executable instructions that, when executed by a processor, implement the steps of the above-described method for processing decoded data.

The above is an exemplary version of a computer-readable storage medium of the present embodiment. It should be noted that, the technical solution of the storage medium and the technical solution of the above decoding data processing method belong to the same concept, and details of the technical solution of the storage medium which are not described in detail can be referred to the description of the technical solution of the above decoding data processing method.

An embodiment of the present disclosure further provides a computer program, where the computer program, when executed in a computer, causes the computer to perform the steps of the above-described decoding data processing method.

The above is an exemplary version of a computer program of the present embodiment. It should be noted that, the technical solution of the computer program and the technical solution of the above-mentioned decoding data processing method belong to the same conception, and details of the technical solution of the computer program which are not described in detail can be referred to the description of the technical solution of the above-mentioned decoding data processing method.

The foregoing describes specific embodiments of the present disclosure. Other embodiments are within the scope of the following claims. In some cases, the actions or steps recited in the claims can be performed in a different order than in the embodiments and still achieve desirable results. In addition, the processes depicted in the accompanying figures do not necessarily require the particular order shown, or sequential order, to achieve desirable results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

The computer instructions include computer program code that may be in source code form, object code form, executable file or some intermediate form, etc. The computer readable medium may include: any entity or device capable of carrying the computer program code, a recording medium, a U disk, a removable hard disk, a magnetic disk, an optical disk, a computer Memory, a Read-Only Memory (ROM), a random access Memory (RAM, random Access Memory), an electrical carrier signal, a telecommunications signal, a software distribution medium, and so forth. It should be noted that the computer readable medium contains content that can be appropriately scaled according to the requirements of jurisdictions in which such content is subject to legislation and patent practice, such as in certain jurisdictions in which such content is subject to legislation and patent practice, the computer readable medium does not include electrical carrier signals and telecommunication signals.

It should be noted that, for simplicity of description, the foregoing method embodiments are all expressed as a series of combinations of actions, but it should be understood by those skilled in the art that the embodiments are not limited by the order of actions described, as some steps may be performed in other order or simultaneously according to the embodiments of the present disclosure. Further, those skilled in the art will appreciate that the embodiments described in the specification are all preferred embodiments, and that the acts and modules referred to are not necessarily all required for the embodiments described in the specification.

In the foregoing embodiments, the descriptions of the embodiments are emphasized, and for parts of one embodiment that are not described in detail, reference may be made to the related descriptions of other embodiments.

The preferred embodiments of the present specification disclosed above are merely used to help clarify the present specification. Alternative embodiments are not intended to be exhaustive or to limit the invention to the precise form disclosed. Obviously, many modifications and variations are possible in light of the teaching of the embodiments. The embodiments were chosen and described in order to best explain the principles of the embodiments and the practical application, to thereby enable others skilled in the art to best understand and utilize the invention. This specification is to be limited only by the claims and the full scope and equivalents thereof.

Claims

1. A method of processing decoded data, comprising:

performing decoding processing on the data to be decoded to obtain decoded data;

dividing the decoding data based on a preset dividing rule to obtain divided data; wherein the slicing data are in a plurality of rows and a plurality of columns;

and storing the fragment data into a corresponding memory page based on the row and column information of the fragment data.

2. The method of claim 1, wherein the determining the initial data, reading the data to be decoded from the initial data based on a preset processing rule, comprises:

reading first line data from the initial data to a first cache based on the preset processing rule, and taking the first line data as the data to be decoded;

reading first column data from the initial data to a second cache based on the preset processing rule, wherein the first column data is used as the data to be decoded under the condition that decoding processing is carried out on the first row data;

and reading second row data from the initial data to a first cache based on the preset processing rule, and executing the operation of determining the data to be decoded until the row data and the column data in the initial data are read.

3. The method of claim 1, wherein the decoding the data to be decoded to obtain decoded data comprises:

and judging unreliable positions of the data to be decoded, hard judging candidate codes, calculating Euclidean distance and calculating external information to obtain decoded data.

4. The method according to claim 1, wherein the slicing the decoded data based on a preset slicing rule to obtain sliced data comprises:

and arranging based on the multi-row data and the multi-column data to obtain the sliced data.

5. The method of claim 1, wherein storing the sliced data into the corresponding memory page based on the row and column information of the sliced data comprises:

and storing the fragment data into the target memory page.

6. The method of claim 5, wherein storing the shard data into the target memory page comprises:

under the condition that the reading mode is row writing and column reading, determining first target row data corresponding to a first target memory page based on the row information;

acquiring first target column data from the first target row data based on the column information, and storing the first target column data into the first target memory page;

determining second target column data corresponding to a second target memory page based on the column information under the condition that the reading mode is row reading and column writing;

and acquiring second target row data from the second target column data based on the row information, and storing the second target row data into the second target memory page.

7. The method of claim 4, wherein the slice size is 2 x 2 data blocks, 4 x 4 data blocks, or 8 x 8 data blocks.

8. A decoded data processing apparatus, comprising:

the data segmentation module is configured to segment the decoding data based on a preset segmentation rule to obtain segmented data; wherein the slicing data are in a plurality of rows and a plurality of columns;

9. A computing device, comprising:

a memory and a processor;

the memory is configured to store computer executable instructions, and the processor is configured to execute the computer executable instructions, which when executed by the processor, implement the steps of the method for processing decoded data as claimed in any one of claims 1 to 7.

10. A computer readable storage medium storing computer executable instructions which when executed by a processor perform the steps of the method of processing decoded data as claimed in any one of claims 1 to 7.