CN118227372A - Storage method based on rank metric error correction code and related products - Google Patents

Abstract

The invention relates to the technical field of data storage error correction, and discloses a storage method and related products based on rank metric error correction codes, wherein the method comprises the following steps: performing rank measurement hard decision storage on pre-acquired initial data to obtain a check matrix and stored data; performing hard decision storage verification on the stored data according to the verification matrix to obtain a primary verification result; performing normal base error check on the stored data according to the check matrix and the primary check result to obtain a secondary check result; and carrying out storage error correction on the storage data according to the secondary check result and the check matrix to obtain standard storage data. By implementing the invention, the rank metric coding and decoding constructed based on the normal basis can reduce the computational complexity of decoding during soft error correction storage, ensure the safety of data storage and improve the efficiency of data error correction coding storage.

Description

A storage method based on rank metric error correction code and related products

Technical Field

The present invention relates to the technical field of data storage error correction, and in particular to a storage method based on rank metric error correction code and related products.

Background technique

With the rapid development of information technology, storage systems have become an indispensable part of the infrastructure for enterprises and individuals. With the improvement of storage density and the increase of data volume, the need to ensure data integrity and system reliability has become more urgent. In this context, the application of error correction coding storage technology has become particularly critical. Error correction coding storage can restore the original data when errors occur during data transmission or storage, and is widely used in the field of data storage.

Most of the existing error correction coding storage methods are based on typical error correction algorithms, such as Bose-Chaudhuri-Hocquenghem Code (BCHC), Low-Density Parity-Check Code (LDPC) and Reed-Solomon Code (RSC). In real-time applications, storage methods based on typical error correction algorithms may have defects such as complex decoding process and high complexity of decoding algorithm, resulting in low efficiency in error correction coding storage of data.

Summary of the invention

The present invention provides a storage method and related products based on rank metric error correction code, the main purpose of which is to solve the problem of low efficiency in error correction coding and storage of data in the related art.

To achieve the above-mentioned purpose, the present invention provides a storage method based on rank metric error correction code, including: performing rank metric hard decision storage on the pre-acquired initial data to obtain a check matrix and stored data; performing hard decision storage check on the stored data according to the check matrix to obtain a primary check result; performing normal basis error check on the stored data according to the check matrix and the primary check result to obtain a secondary check result; performing storage error correction on the stored data according to the secondary check result and the check matrix to obtain standard stored data.

In order to solve the above problems, the present invention also provides a storage system based on rank metric error correction code, the system including: a rank metric storage module, used to perform rank metric hard decision storage on the pre-acquired initial data to obtain a check matrix and stored data; a hard storage check module, used to perform hard decision storage check on the stored data according to the check matrix to obtain a primary check result; a soft storage check module, used to perform normal basis error check on the stored data according to the check matrix and the primary check result to obtain a secondary check result; a storage error correction module, used to perform storage error correction on the stored data according to the secondary check result and the check matrix to obtain standard storage data.

In order to solve the above problem, the present invention further provides an electronic device, the electronic device comprising:

at least one processor; and,

a memory communicatively connected to at least one processor; wherein,

The memory stores a computer program that can be executed by at least one processor, and the computer program is executed by at least one processor so that the at least one processor can execute the above-mentioned storage method based on rank metric error correction code.

In order to solve the above problems, the present invention also provides a computer-readable storage medium storing a computer program, which implements the above-mentioned storage method based on rank metric error correction code when executed by a processor.

The embodiment of the present invention can use the linear normal basis to build a linear characteristic of the coding by performing rank metric hard decision storage, facilitate efficient mathematical operations on the finite field, and enable the addition and multiplication operations on the finite field to be realized by cyclic shift operations, thereby improving the efficiency of encoding and decoding. The hard decision storage check can directly perform fast data verification through the check matrix, thereby improving the efficiency of error correction verification. By performing normal basis error verification, the decoding multiplication in the soft storage check can be converted by addition using the normal basis and the linearized polynomial, thereby improving the efficiency of decoding verification. The wiring harness enhances the storage efficiency of the data. By performing storage error correction, the integrity and reliability of the data during data storage are significantly enhanced, the time for data recovery is reduced, and the response speed of data reading and writing is improved, thereby providing more efficient data processing capabilities for scenarios such as data centers and high-performance computing applications, thereby improving the efficiency of data error correction storage. Therefore, the storage method and related products based on the rank metric error correction code proposed in the present invention can solve the problem of low efficiency when error correction coding is stored for data.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG1 is a schematic flow chart of a storage method based on a rank metric error correction code provided by an embodiment of the present invention;

FIG2 is a schematic diagram of a process for performing hard decision storage of rank metrics according to an embodiment of the present invention;

FIG3 is a schematic diagram of a process of performing a regular basis error check according to an embodiment of the present invention;

FIG4 is a functional module diagram of a storage system based on rank metric error correction code provided by an embodiment of the present invention;

FIG5 is a schematic diagram of the structure of an electronic device for implementing a storage method based on a rank metric error correction code according to an embodiment of the present invention.

The realization of the purpose, functional features and advantages of the present invention will be further explained in conjunction with embodiments and with reference to the accompanying drawings.

Detailed ways

It should be understood that the specific embodiments described herein are only used to explain the present invention, and are not used to limit the present invention.

The embodiment of the present application provides a storage method based on a rank metric error correction code. The execution subject of the storage method based on the rank metric error correction code includes but is not limited to at least one of the electronic devices such as the server, the terminal, etc. that can be configured to execute the method provided by the embodiment of the present application. In other words, the storage method based on the rank metric error correction code can be executed by software or hardware installed on a terminal device or a server device. The server includes but is not limited to: a single server, a server cluster, a cloud server or a cloud server cluster, etc. The server can be an independent server, or it can be a cloud server that provides cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, content delivery networks (Content Delivery Network, CDN), and basic cloud computing services such as big data and artificial intelligence platforms.

1 is a flow chart of a storage method based on rank metric error correction code provided by an embodiment of the present invention. In this embodiment, the storage method based on rank metric error correction code includes:

S1. Perform rank metric hard decision storage on the pre-acquired initial data to obtain a check matrix and store data.

In detail, initial data refers to data that needs to be stored, stored data refers to data stored in flash memory, the check matrix is a matrix used for storage verification, and stored data refers to the data inside the NAND flash memory after the initial data is stored in the NAND flash memory. NAND flash memory is a type of flash memory that uses a nonlinear macro unit mode internally, providing a cheap and effective solution for the realization of solid-state large-capacity memory.

In an embodiment of the present invention, as shown in FIG. 2 , the above-mentioned specific implementation process of performing hard decision storage of rank metric on the pre-acquired initial data to obtain the check matrix and store the data includes:

S21, constructing a linear normal basis for the pre-acquired initial data to obtain a normal basis matrix;

S22, performing a check initialization operation on the normal basis matrix to obtain a check matrix;

S23, performing rank metric error correction coding on the initial data according to the normal basis matrix to obtain coded data;

S24, storing the encoded data in a preset storage flash memory to obtain storage data.

Specifically, the normal basis matrix is a matrix used for error correction coding. By using the normal basis matrix, the initial data can be encoded into a coding matrix, thereby realizing error correction code storage of the data. The check initialization operation includes extracting the message length and the codeword length from the normal basis matrix, taking the difference between the codeword length and the message length as the check length, and generating a check matrix with a length equal to the check length and a width equal to the codeword length, so that the product of the normal basis matrix and the transposed matrix of the check matrix is 0.

Specifically, a linear normal basis is constructed for the pre-acquired initial data to obtain a normal basis matrix, including: initializing an error correction code finite field according to the pre-acquired initial data; extracting parameters from the initial data to obtain storage message parameters; generating an initial generation matrix according to a preset codeword length and storage message parameters; and filling the initial generation matrix with a linear normal basis according to the error correction code finite field to obtain a normal basis matrix.

Specifically, the error correction code finite field refers to a field containing a finite number of elements, and initializing the error correction code finite field refers to extracting all data elements in the initial data, initializing the error correction code finite field according to all the data elements, so that all data elements in the initial data can be mapped to the error correction code finite field.

In detail, parameter extraction refers to extracting the message length from the initial data, where the message length refers to the length of the original data, that is, the length of the actual information that needs to be stored or transmitted.

In detail, an initial generator matrix is a matrix with a length of a message length and a width of a codeword length, and the message length is less than or equal to the codeword length. The linear normal basis filling refers to using a linearized polynomial algorithm to fill each element in the initial generator matrix with data elements in a finite field of an error correction code. The normal basis matrix is composed of coefficients of the linearized polynomial. The normal basis in the normal basis matrix is a set of basis vectors, such that each basis vector is a cyclic shift of several powers of an element. The linearized polynomial has the linear property of maintaining addition and scalar multiplication, and is particularly suitable for the construction and operation of rank-metric codes. The elements of the normal basis matrix are usually composed of coefficients of the linearized polynomial.

Specifically, rank metric error correction coding refers to generating a message vector according to initial data, and performing matrix multiplication on the message vector and a normal basis matrix to obtain encoded data.

In detail, in NAND flash memory, the basic unit for storing data is called a Cell. Each Cell records different data by injecting and releasing electrons. The electrons entering and leaving the Cell will cause damage to the Cell. As the degree of damage increases, the probability of electrons in the Cell escaping will continue to increase, which will cause the data stored in the Cell to jump. For example, a Cell initially stores binary data 10. When the Cell is read again after a period of time, the binary data may become 11. Therefore, the stored data is not necessarily the same as the encoded data.

In an embodiment of the present invention, by performing rank metric hard decision storage, a linear normal basis can be used to construct a coding that maintains linear characteristics, facilitates efficient mathematical operations on a finite field, and enables addition and multiplication operations on a finite field to be implemented through circular shift operations, thereby improving the efficiency of coding and decoding.

S2. Perform hard decision storage verification on the stored data according to the verification matrix to obtain a primary verification result.

In detail, hard decision storage verification refers to the use of hardware to verify the integrity and correctness of stored data, and the primary verification results include verification pass and verification fail.

In an embodiment of the present invention, hard decision storage check is performed on the stored data according to the check matrix to obtain a primary check result, including: discrete signal quantization of the stored data to obtain hard storage data; matrix transposition operation of the check matrix to obtain a transposed check matrix; syndrome calculation of the hard storage data using the transposed check matrix to obtain a hard storage syndrome vector; zero vector check of the hard storage syndrome vector to obtain a primary check result.

In detail, discrete signal quantization refers to directly reading and quantizing discrete signals from NAND flash memory, syndrome calculation refers to multiplying the transposed check matrix with the matrix corresponding to the hard storage data using matrix multiplication, and the hard storage syndrome vector refers to the syndrome vector corresponding to the hard storage check, wherein the syndrome vector is a vector calculated by the received codeword and the check matrix, which can help identify and locate errors.

Specifically, the zero vector check refers to determining whether the hard storage syndrome vector is a zero vector. If the hard storage syndrome vector is a zero vector, it is determined that the check passes; if the hard storage syndrome vector is not a zero vector, it is determined that the check fails.

In the embodiment of the present invention, the hard decision storage check can directly perform fast data check through the check matrix, thereby improving the efficiency of error correction check.

S3. Perform a normal basis error check on the stored data according to the check matrix and the primary check result to obtain a secondary check result.

In detail, the secondary verification result includes an error locator corresponding to error data in the stored data, where the error data refers to a portion of the initial data in which an error occurs during the storage process.

In an embodiment of the present invention, as shown in FIG. 3 , a specific implementation process of performing a normal basis error check on the stored data according to the check matrix and the primary check result to obtain a secondary check result includes:

S31, quantizing the confidence signal of the stored data according to the primary verification result to obtain soft storage data;

S32, performing an addition syndrome operation on the soft storage data according to the check matrix to obtain a soft storage syndrome vector;

S33, performing polynomial positioning on the soft storage data according to the soft storage syndrome vector to obtain an error polynomial;

S34, performing error base screening on the soft storage data according to the error polynomial to obtain a secondary verification result.

In detail, soft storage data refers to storage data that takes into account the confidence and probability of the data, and soft storage data can improve the accuracy of verification.

Specifically, according to the primary verification result, the confidence signal of the stored data is quantized to obtain soft storage data, including: judging whether the primary verification result is a verification failure; if not, ending the verification; if so, quantizing the detail signal of the stored data to obtain quantized storage data and data confidence; and confidence-weighting the quantized storage data according to the data confidence to obtain soft storage data.

Specifically, detail signal quantization refers to quantizing and storing data according to signal strength, and obtaining probability information corresponding to each data as data confidence. Confidence weighting refers to multiplying each data in the quantized storage data by the corresponding confidence in the data confidence to obtain weighted storage data, and using all the weighted storage data to update the quantized storage data into soft storage data.

Specifically, the addition complex operation refers to multiplying the check matrix by the soft storage data. However, since the check matrix is a matrix composed of a low-complexity normal basis, the corresponding matrix multiplication can be converted into an addition operation, which can greatly reduce the complexity of software decoding and improve decoding efficiency.

Specifically, polynomial positioning is performed on soft storage data according to a soft storage syndrome vector to obtain an error polynomial, including: performing an error position initialization operation on the soft storage data to obtain an error position expression; performing an error value initialization operation on the soft storage syndrome to obtain an error value expression; and performing value positioning on the error value expression according to the error position expression to obtain an error polynomial.

Specifically, the error position expression and the error value expression may be initialized using an error span polynomial (ESP) or an error locator polynomial (ELP), and value locating refers to multiplying the error position expression by the error value expression.

In detail, the error position expression is an expression used to characterize the error position, and the error position is data used to indicate the position of error data in the soft storage data. The error value expression is an expression used to characterize the error value of the error data in the soft storage data, and the error value is data used to indicate the error degree of the error data.

In detail, the soft storage data is screened for error basis according to the error polynomial to obtain a secondary verification result, including: performing additive linear mapping on the error polynomial to obtain a mapping linear matrix; performing left null space mapping on the mapping linear matrix to obtain a data error basis; and performing basis construction on the error polynomial according to the data error basis to obtain a secondary verification result.

In detail, additive linear mapping refers to using the error polynomial to calculate the matrix of the corresponding linear mapping, which requires multiple multiplication operations in the error correction code finite field. Due to the existence of normal basis in the error correction code finite field, these multiplication operations can be converted into addition operations, which can greatly reduce the complexity of software decoding and improve decoding efficiency.

Specifically, Gaussian elimination method can be used to perform left null space mapping. The data error basis is the basis of the left null space of the mapped linear matrix, that is, the vector composed of the error roots of the error polynomial. Basis construction refers to extracting the error root group from the data error basis, substituting the error root group into the error polynomial to obtain the secondary verification result.

In the embodiment of the present invention, by performing a normal basis error check, the decoding multiplication in the soft storage check can be converted into an addition using a normal basis and a linearized polynomial, thereby improving the efficiency of the decoding check and enhancing the data storage efficiency.

S4. Perform storage error correction on the stored data according to the secondary check result and the check matrix to obtain standard stored data.

In detail, standard storage data refers to storage data after error correction. The standard storage data is the same as the initial data. Storage error correction can ensure the accuracy of the stored data and improve the stability of data storage.

In an embodiment of the present invention, storage error correction is performed on stored data according to secondary check results and a check matrix to obtain standard stored data, including: performing multi-level iterative solution on the secondary check results according to the stored data to obtain an error locator; performing error screening on the stored data according to the error locator to obtain error-corrected data; and performing data restoration on the error-corrected data according to the check matrix to obtain standard stored data.

Specifically, multi-level iterative solution refers to solving the error polynomial in the secondary check result using the Berlekamp-Massey algorithm (BM). The error locator is

In detail, the stored data is error-screened according to the error locator to obtain debugging data, including: locating the errors of the stored data according to the error locator to obtain the error position; extracting the errors of the stored data according to the error position to obtain the error value; and error-screening the stored data according to the error position and the error value to obtain debugging data.

In detail, the error location can be determined by calculating the right inverse of the error locator, and error extraction refers to substituting the error position into the error polynomial to obtain the corresponding error value, and the error value refers to the error deviation degree of the error data.

Specifically, error screening refers to screening out erroneous data from stored data using an error position, restoring the erroneous data according to the error value, and obtaining debugged data, which is the same as the encoded data.

Specifically, restoring the debugged data according to the check matrix to obtain the standard storage data means multiplying the debugged data by the inverse matrix of the check matrix to obtain the restoration matrix, merging the restoration matrix to obtain the standard storage matrix, wherein the restoration matrix and are matrices obtained after data segmentation of the initial data.

In the embodiments of the present invention, by performing storage error correction, the integrity and reliability of data during data storage are significantly enhanced, the time for data recovery is reduced, the response speed of data reading and writing is improved, more efficient data processing capabilities are provided for scenarios such as data centers and high-performance computing applications, and the efficiency of data error correction storage is improved.

The embodiment of the present invention can use the linear normal basis to build a linear characteristic of the coding by performing rank metric hard decision storage, facilitate efficient mathematical operations on the finite field, and enable the addition and multiplication operations on the finite field to be realized by cyclic shift operations, thereby improving the efficiency of encoding and decoding. The hard decision storage check can directly perform fast data verification through the check matrix, thereby improving the efficiency of error correction verification. By performing normal basis error verification, the decoding multiplication in the soft storage check can be converted by addition using the normal basis and the linearized polynomial, thereby improving the efficiency of decoding verification. The wiring harness enhances the storage efficiency of the data. By performing storage error correction, the integrity and reliability of the data during data storage are significantly enhanced, the time for data recovery is reduced, and the response speed of data reading and writing is improved. It provides more efficient data processing capabilities for scenarios such as data centers and high-performance computing applications, thereby improving the efficiency of data error correction storage. Therefore, the storage method based on the rank metric error correction code proposed in the present invention can solve the problem of low efficiency when error correction coding is stored for data.

As shown in FIG. 4 , it is a functional module diagram of a storage system based on rank metric error correction code provided in one embodiment of the present invention.

The storage system 400 based on the rank metric error correction code of the present invention can be installed in an electronic device. According to the functions implemented, the storage system 400 based on the rank metric error correction code may include a rank metric storage module 401, a hard storage check module 402, a soft storage check module 403 and a storage error correction module 404. The module of the present invention can also be referred to as a unit, which refers to a series of computer program segments that can be executed by an electronic device processor and can complete fixed functions, which are stored in the memory of the electronic device.

In this embodiment, the functions of each module/unit are as follows:

The rank metric storage module 401 is used to perform rank metric hard decision storage on the pre-acquired initial data to obtain a check matrix and store data;

A hard storage check module 402 is used to perform hard decision storage check on the stored data according to the check matrix to obtain a primary check result;

The soft storage check module 403 is used to perform a normal basis error check on the stored data according to the check matrix and the primary check result to obtain a secondary check result;

The storage error correction module 404 is used to perform storage error correction on the stored data according to the secondary check result and the check matrix to obtain standard stored data.

In detail, each module in the storage system 400 based on rank metric error correction code in an embodiment of the present invention adopts the same technical means as the storage method based on rank metric error correction code in Figure 1 above when in use, and can produce the same technical effects, which will not be repeated here.

As shown in FIG5 , it is a schematic diagram of the structure of an electronic device for implementing a storage method based on rank metric error correction code provided by an embodiment of the present invention.

The electronic device 501 may include a processor 510, a memory 511, a communication bus 512, and a communication interface 513, and may also include a computer program stored in the memory 511 and executable on the processor 510, such as a stored program based on a rank metric error correction code.

In some embodiments, the processor 510 may be composed of an integrated circuit, for example, a single packaged integrated circuit, or a plurality of integrated circuits with the same or different functions, including one or more central processing units (CPUs), microprocessors, digital processing chips, graphics processors, and a combination of various control chips. The processor 510 is the control core (Control Unit) of the electronic device, and uses various interfaces and lines to connect the various components of the entire electronic device, and executes various functions of the electronic device and processes data by running or executing programs or modules stored in the memory 511 (for example, executing stored programs based on rank metric error correction codes, etc.), and calling data stored in the memory 511.

The memory 511 includes at least one type of readable storage medium, and the readable storage medium includes a flash memory, a mobile hard disk, a multimedia card, a card-type memory (for example, SD or DX memory, etc.), a magnetic memory, a disk, an optical disk, etc. In some embodiments, the memory 511 may be an internal storage unit of an electronic device, such as a mobile hard disk of the electronic device. In other embodiments, the memory 511 may also be an external storage device of an electronic device, such as a plug-in mobile hard disk, a smart memory card (Smart Media Card, SMC), a secure digital (Secure Digital, SD) card, a flash card (Flash Card), etc. equipped on the electronic device. Further, the memory 511 may also include both an internal storage unit of the electronic device and an external storage device. The memory 511 can not only be used to store application software and various types of data installed in the electronic device, such as the code of the storage program based on the rank metric error correction code, but also can be used to temporarily store data that has been output or is to be output.

The communication bus 512 may be a peripheral component interconnect (PCI) bus or an extended industry standard architecture (EISA) bus, etc. The bus may be divided into an address bus, a data bus, a control bus, etc. The bus is configured to realize connection and communication between the memory 511 and at least one processor 510, etc.

The communication interface 513 is used for communication between the above-mentioned electronic device and other devices, including a network interface and a user interface. Optionally, the network interface may include a wired interface and/or a wireless interface (such as a WI-FI interface, a Bluetooth interface, etc.), which is generally used to establish a communication connection between the electronic device and other electronic devices. The user interface may be a display (Display), an input unit (such as a keyboard (Keyboard)), and optionally, the user interface may also be a standard wired interface, a wireless interface. Optionally, in some embodiments, the display may be an LED display, a liquid crystal display, a touch-sensitive liquid crystal display, and an OLED (Organic Light-Emitting Diode, organic light-emitting diode) touch device, etc. Among them, the display may also be appropriately referred to as a display screen or a display unit, which is used to display information processed in the electronic device and to display a visual user interface.

The figure only shows an electronic device with components. Those skilled in the art will understand that the structure shown in the figure does not constitute a limitation on the electronic device, and may include fewer or more components than shown in the figure, or combine certain components, or arrange the components differently.

For example, although not shown, the electronic device may also include a power source (such as a battery) for supplying power to various components. Preferably, the power source may be logically connected to at least one processor 510 through a power management system, so that the power management system can realize functions such as charging management, discharging management, and power consumption management. The power source may also include any components such as one or more DC or AC power sources, recharging systems, power failure detection circuits, power converters or inverters, and power status indicators. The electronic device may also include a variety of sensors, Bluetooth modules, Wi-Fi modules, etc., which will not be repeated here.

It should be understood that the embodiment is for illustration only and the scope of the patent application is not limited by this structure.

Specifically, the specific implementation method of the processor 510 for the above instructions can refer to the description of the relevant steps in the corresponding embodiment of the accompanying drawings, which will not be repeated here.

Furthermore, if the module/unit integrated in the electronic device 501 is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. The computer-readable storage medium can be volatile or non-volatile. For example, the computer-readable medium can include: any entity or system capable of carrying computer program code, recording medium, USB flash drive, mobile hard disk, magnetic disk, optical disk, computer memory, read-only memory (ROM, Read-Only Memory).

It is obvious to those skilled in the art that the present invention is not limited to the details of the above exemplary embodiments, and that the present invention can be implemented in other specific forms without departing from the spirit or essential characteristics of the present invention.

Therefore, no matter from which point of view, the embodiments should be regarded as illustrative and non-restrictive, and the scope of the present invention is limited by the appended claims rather than the above description, so it is intended that all changes falling within the meaning and scope of the equivalent elements of the claims are included in the present invention. Any attached figure mark in the claims should not be regarded as limiting the claims involved.

Finally, it should be noted that the above embodiments are only used to illustrate the technical solution of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solution of the present invention can be modified or replaced by equivalents without departing from the spirit and scope of the technical solution of the present invention.

Claims (12)

1. A method of storing an error correction code based on a rank metric, the method comprising:

Performing rank measurement hard decision storage on pre-acquired initial data to obtain a check matrix and stored data;

performing hard decision storage verification on the stored data according to the verification matrix to obtain a primary verification result;

performing normal base error check on the stored data according to the check matrix and the primary check result to obtain a secondary check result;

And carrying out storage error correction on the storage data according to the secondary check result and the check matrix to obtain standard storage data.

2. The method for storing error correction codes based on rank metric according to claim 1, wherein said performing rank metric hard decision storage on pre-acquired initial data to obtain a check matrix and stored data comprises:

performing linear normal basis construction on initial data acquired in advance to obtain a normal basis matrix;

Performing verification initialization operation on the regular base matrix to obtain a verification matrix;

performing rank measurement error correction coding on the initial data according to the regular basis matrix to obtain coded data;

and storing the coded data into a preset storage flash memory to obtain storage data.

3. The method for storing error correction codes based on rank metric according to claim 2, wherein said performing linear normal basis construction on pre-acquired initial data to obtain a normal basis matrix comprises:

initializing an error correction code finite field according to initial data acquired in advance;

extracting parameters of the initial data to obtain stored message parameters;

Generating an initial generation matrix according to the preset codeword length and the stored message parameters;

And carrying out linear normal base filling on the initial generation matrix according to the error correction code finite field to obtain a normal base matrix.

4. The method for storing error correction codes based on rank metric according to claim 1, wherein said performing regular base error check on said stored data based on said check matrix and said primary check result to obtain a secondary check result comprises:

carrying out confidence signal quantization on the stored data according to the primary verification result to obtain soft stored data;

Performing addition syndrome operation on the soft storage data according to the check matrix to obtain a soft storage syndrome vector;

performing polynomial positioning on the soft storage data according to the soft storage syndrome vector to obtain an error polynomial;

And performing error base screening on the soft storage data according to the error polynomial to obtain a secondary check result.

5. The method for storing error correction codes based on rank metric according to claim 4, wherein said performing polynomial positioning on said soft storage data according to said soft storage syndrome vector to obtain an error polynomial comprises:

performing error position initialization operation on the soft storage data to obtain an error position expression;

Performing error value initialization operation on the soft storage syndrome to obtain an error value expression;

And carrying out value positioning on the error value expression according to the error position expression to obtain an error polynomial.

6. The method for storing error correction codes based on rank metric according to claim 4, wherein said performing error base screening on said soft stored data according to said error polynomial to obtain a secondary check result comprises:

Performing addition linear mapping on the error polynomial to obtain a mapping linear matrix;

performing left zero space mapping on the mapping linear matrix to obtain a data error base;

And performing base construction on the error polynomial according to the data error base to obtain a secondary check result.

7. The storage method of error correction code based on rank metric according to claim 1, wherein said performing storage error correction on said stored data based on said secondary check result and said check matrix to obtain standard stored data comprises:

Performing multistage iterative solution on the secondary verification result according to the stored data to obtain an error locator;

Performing error screening on the stored data according to the error locator to obtain error-removed data;

And carrying out data reduction on the debug data according to the check matrix to obtain standard storage data.

8. The method for storing error correction codes based on rank metric according to claim 7, wherein said error screening said stored data according to said error locator to obtain debug data comprises:

performing error positioning on the stored data according to the error locator to obtain an error position;

performing error extraction on the stored data according to the error position to obtain an error value;

And performing error screening on the stored data according to the error position and the error value to obtain debug data.

9. The storage method of error correction code based on rank metric according to claim 1, wherein said performing hard decision storage check on said stored data according to said check matrix to obtain a primary check result comprises:

Performing discrete signal quantization on the stored data to obtain hard stored data;

performing matrix transposition operation on the check matrix to obtain a transposed check matrix;

Carrying out syndrome calculation on the hard storage data by utilizing the transpose check matrix to obtain a hard storage syndrome vector;

And carrying out zero vector verification on the hard storage syndrome vector to obtain a primary verification result.

10. A storage system based on rank metric error correction codes, the system comprising:

the rank measurement storage module is used for carrying out rank measurement hard decision storage on the initial data acquired in advance to obtain a check matrix and stored data;

The hard storage check module is used for carrying out hard decision storage check on the storage data according to the check matrix to obtain a primary check result;

the soft storage check module is used for carrying out normal base error check on the stored data according to the check matrix and the primary check result to obtain a secondary check result;

and the storage error correction module is used for carrying out storage error correction on the storage data according to the secondary check result and the check matrix to obtain standard storage data.

11. An electronic device, the electronic device comprising:

At least one processor; and

A memory communicatively coupled to the at least one processor; wherein,

The memory stores a computer program executable by the at least one processor to enable the at least one processor to perform the method of storing a rank metric error correction code based on any one of claims 1 to 9.

12. A computer readable storage medium storing a computer program, wherein the computer program when executed by a processor implements the method of storing a rank metric error correction code according to any one of claims 1 to 9.