A storage device data distributed error correction tracing method

Abstract

This invention discloses a distributed error correction and tracing method for storage device data, relating to the field of distributed storage technology. The method includes: using data analysis methods to obtain traceable features and group traceability features of stored data; constructing a basic analysis table to obtain initial traceability standards and constructing a basic traceability method; and obtaining error correction parameters and error correction item names based on data change features and the basic traceability method for stored data. This invention addresses the problem in existing distributed error correction and tracing methods for storage device data. Traditional erasure coding often requires reading large amounts of data from multiple nodes for reconstruction when a node malfunctions or fails, resulting in high repair bandwidth and computational overhead. This leads to low efficiency in traceability and error correction in large-scale clusters, easily causing repair delays and performance bottlenecks.

Description

Technical Field

[0001] This invention relates to the field of distributed storage technology, specifically to a distributed error correction and tracing method for storage device data. Background Technology

[0002] Storage device data refers to all information stored on non-volatile storage media of computers or electronic systems. It includes not only files directly created by users, but also the underlying data necessary for system operation. Distributed error correction and traceability of storage device data is a comprehensive data management mechanism that combines distributed storage, error correction coding and data traceability technologies. It is the cornerstone technology for building next-generation highly reliable and secure data centers and blockchain storage systems.

[0003] Existing methods for distributed error correction and tracing of data in storage devices typically involve obtaining the actual stored data based on the write operation of the file to be stored, then acquiring the actual read data of the file to be stored, and comparing the actual stored data with the actual read data to obtain the data verification result. This achieves error correction and verification during the write and read processes. While this improved method enhances the error correction capability of the storage device and utilizes erasure coding for error correction and tracing, traditional erasure coding often requires reading large amounts of data from multiple nodes for reconstruction when a node fails or malfunctions. This results in high repair bandwidth and computational overhead, leading to low efficiency in tracing and error correction in large-scale clusters, and easily causing repair delays and performance bottlenecks. For example, as described in publication number CN116737... Patent application 457A discloses a data verification method based on distributed storage. This method improves the error correction capability of distributed storage by detecting and verifying errors that may occur during file storage and retrieval. Other improvements to distributed error correction and tracing methods for storage devices typically improve data loss tracing by partitioning the disk. However, these improvements still cannot solve the problem that traditional erasure coding often requires reading large amounts of data from multiple nodes for reconstruction when a node fails or malfunctions. This results in high repair bandwidth and computational overhead, leading to low tracing and error correction efficiency in large-scale clusters, which can easily cause repair delays and performance bottlenecks. Therefore, it is necessary to improve existing distributed error correction and tracing methods for storage devices. Summary of the Invention

[0004] This invention aims to at least partially solve one of the technical problems in the prior art by proposing a distributed error correction and tracing method for storage devices. This method addresses the issue that traditional erasure coding methods for distributed error correction and tracing of storage devices often require reading large amounts of data from multiple nodes for reconstruction when a node fails or malfunctions. This results in high repair bandwidth and computational overhead, leading to low efficiency in tracing and error correction in large-scale clusters, which can easily cause repair delays and performance bottlenecks.

[0005] To achieve the above objectives, this application provides a distributed error correction and tracing method for storage device data, comprising the following steps:

[0006] Based on the records of data accessed within the storage device, data analysis methods are used to obtain the traceable features and group traceable features corresponding to each stored data; based on the traceable features and group traceable features of the stored data, a basic analysis table for each stored data is constructed.

[0007] Based on the basic analysis table of stored data, the initial traceability standard of each cell containing parameters in the basic analysis table is obtained, and based on the initial traceability standard of all cells, the basic traceability method of stored data is constructed.

[0008] After the basic analysis table of stored data is established, when the records of stored data that are called are modified in accordance with regulations, the data change characteristics of the corresponding stored data are obtained; when performing error correction and tracing on any stored data, the error correction parameters and error correction project name of the stored data are obtained based on all data change characteristics of the stored data and the basic tracing method of the stored data.

[0009] Furthermore, based on the records of data accessed within the storage device, data analysis methods are used to obtain the traceable characteristics and group traceable characteristics corresponding to each stored data, including:

[0010] The data stored in the storage device is recorded as stored data; all records in the storage device that are accessed by the stored data are recorded as storage access records;

[0011] The data analysis method includes: for any stored data α, based on the order in which all storage call records of stored data α are generated, all storage call records are sequentially recorded as call record DJ1 to call record DJ. n ;

[0012] For any call record: the names of the devices that retrieve and return stored data α in the call record are respectively recorded as the retrieving device and the returning device;

[0013] For the device being retrieved, the time between the time the device submits the retrieval request and the time when the stored data α is retrieved from the storage device is recorded as the retrieval request time, and the eight-digit number consisting of the year, month, and day corresponding to the date the device retrieves the stored data α from the storage device is recorded as the retrieval date code.

[0014] Furthermore, data analysis methods also include:

[0015] For the returned device, the length of time that the stored data α in the call record is outside the storage device is recorded as the call-out usage time, and the eight-digit number consisting of the year, month and day corresponding to the date when the returned device returns the stored data to the storage device is recorded as the return date code.

[0016] The first letters of the names of the devices being retrieved and returned are respectively used as the first letters of the retrieval and return names.

[0017] Furthermore, data analysis methods also include:

[0018] Record the eight-bit code corresponding to the binary representation of the first letter of the device being retrieved as the retrieved device code, and record the eight-bit code corresponding to the binary representation of the first letter of the device being returned as the returned device code; add the numbers in the same position in the retrieved device code and the retrieved date code, and record the resulting eight-bit code as the retrieved feature code; add the numbers in the same position in the returned device code and the returned date code, and record the resulting eight-bit code as the returned feature code.

[0019] The space occupied by the stored data α when it is retrieved from the call record is recorded as the retrieved space value. The space occupied by the stored data α when it is returned to the call record is recorded as the returned space value. The value of the returned space value minus the retrieved space value is recorded as the call space difference.

[0020] Record the request time, usage time, and call space difference of the stored data α as the self-traceability features, and record the call feature code and return feature code of the stored data α as the group traceability features; obtain the self-traceability features and group traceability features of the stored data α in all call records.

[0021] Furthermore, based on the traceability features and group traceability features of the stored data, a basic analysis table is constructed for each stored data, including:

[0022] For any stored data α in the storage device, create a table with n+1 rows and 6 columns, denoted as the basic analysis table for stored data α; in the top row of the basic analysis table, except for the first cell, fill in the retrieval request time, retrieval usage time, retrieval space difference, retrieval feature code, and return feature code in sequence; in the leftmost column of the basic analysis table, fill in the names of all retrieval records corresponding to stored data α in sequence.

[0023] Based on the traceable features and group traceable features of the stored data α in all call records, fill in the corresponding parameters in the cells of the basic analysis table.

[0024] Furthermore, based on the basic analysis table of stored data, the initial tracing criteria for obtaining each cell containing parameters within the basic analysis table include:

[0025] For cell β containing any parameter γ that is filled in the basic analysis table, the cell with the smallest difference between the filled parameter and parameter γ among all cells in the basic analysis table other than cell β is recorded as the companion cell of cell β; all parameters in the row containing cell β are recorded as characteristic parameters, where the characteristic parameters include parameter γ.

[0026] In the basic analysis table, cells other than cell β, where the parameter filled in is exactly the same as any feature parameter, are denoted as secondary cells of cell β; the curve obtained by fitting the center point of cell β with the center points of all associated cells is denoted as the associated discrimination curve, and the curve obtained by fitting the center point of the associated cells with the center points of all secondary cells is denoted as the secondary discrimination curve.

[0027] Furthermore, based on the basic analysis table containing stored data, the initial tracing criteria for obtaining each cell containing parameters within the basic analysis table also include:

[0028] The accompanying discrimination curve and the secondary discrimination curve are recorded as the initial tracing standard of cell β. When the secondary cell is not obtained, the positions of cell β and the accompanying cell β are recorded as the initial tracing standard of cell β.

[0029] Obtain the initial tracing criteria for all parameters in the basic analysis table.

[0030] Furthermore, basic tracing methods include:

[0031] When performing error correction and tracing on any call record of stored data α, all cells in the row containing the call record in the basic analysis table, except for the leftmost cell, are recorded as cells to be corrected.

[0032] For any cell to be corrected, based on the latest basic analysis table of stored data α, obtain the primary and secondary discrimination curves of the cell to be corrected, and denot them as the correction curve A1 and correction curve A2. When the cell to be corrected has data change characteristics, adjust the correction curves A1 and A2 based on the data change characteristics of the cell to be corrected, and denot the adjusted correction curves A1 and A2 as correction curves B1 and B2, respectively.

[0033] Furthermore, basic tracing methods also include:

[0034] When the error-to-be-corrected curve B1 completely coincides with the secondary discrimination curve in the initial traceability standard of the cell to be corrected, and the error-to-be-corrected curve B2 completely coincides with the secondary discrimination curve in the initial traceability standard of the cell to be corrected, the cell to be corrected is recorded as an error-free cell.

[0035] When the cell to be corrected is not recorded as a cell without errors, the parameters in the cell to be corrected are recorded as correction parameters, and the name in the top cell of the column containing the cell to be corrected is recorded as the correction item name.

[0036] Furthermore, after the basic analysis table for stored data is established, when a record of the stored data is modified in accordance with regulations, the data change characteristics corresponding to the stored data are obtained, including:

[0037] For any stored data α: After the basic analysis table of stored data α is established, when the record of stored data α is called is modified in compliance, for any one compliance modification: based on all calling records corresponding to stored data α after compliance modification, obtain the latest traceable features and group traceable features corresponding to stored data α;

[0038] Based on the latest traceable features and group traceable features corresponding to the stored data α, the basic analysis table of the stored data α is updated; for any cell β with parameter γ filled in in the updated basic analysis table, the accompanying discrimination curve and secondary discrimination curve corresponding to cell β in the updated basic analysis table are obtained and recorded as the updated true curve and the updated accompanying curve, respectively.

[0039] The process of converting the updated true curve and the updated companion curve into the companion discriminant curve and the secondary discriminant curve in the initial traceability standard of cell β is denoted as the data change characteristic of cell β.

[0040] Obtain the data change characteristics of all cells with parameters filled in within the updated basic analysis table, and record them as the data change characteristics corresponding to the stored data α.

[0041] The beneficial effects of this invention are as follows: First, based on the records of data being accessed within the storage device, this application uses data analysis methods to obtain the traceability features and group traceability features corresponding to each stored data. Based on the traceability features and group traceability features of the stored data, a basic analysis table for each stored data is constructed. Then, based on the basic analysis table of the stored data, the initial traceability standard for each cell containing parameters in the basic analysis table is obtained. The advantage of this is that by constructing the basic analysis table of the stored data based on the traceability features and group traceability features of the stored data, the features corresponding to each stored data can be visually represented. Thus, by utilizing the parameters filled in the cells of the basic analysis table and the distribution characteristics of the data, the judgment criteria for traceability data that fits the characteristics of the stored data itself, i.e., the initial traceability standard for each cell, can be obtained. This achieves the goal of ensuring the efficiency of error correction and traceability even when facing large-scale data clusters.

[0042] This application also constructs a basic source tracing method for stored data based on the initial source tracing standards of all cells. Finally, after the basic analysis table of stored data is established, when the records of stored data that are called are modified in accordance with regulations, the data change characteristics corresponding to the stored data are obtained. When performing error correction and source tracing on any stored data, the error correction parameters and error correction item names of the stored data are obtained based on all data change characteristics of the stored data and the basic source tracing method of the stored data. The advantage of this is that by constructing a basic source tracing method for each stored data and obtaining the data change characteristics obtained after the stored data is called, when performing error correction and source tracing on stored data, the initial source tracing standards and data change characteristics of each cell in the basic analysis table corresponding to the stored data can be used to perform effective error correction analysis on each cell in the basic analysis table, and the source item where the error correction data is located can be obtained after the error correction analysis. This allows for more efficient acquisition of the error correction parameters and error correction item names corresponding to the stored data in the basic analysis table, thereby improving the efficiency of error correction and source tracing on stored data and avoiding the problems of repair delay and performance bottlenecks caused by multiple nodes reading a large amount of data for reconstruction. Attached Figure Description

[0043] Figure 1 This is a flowchart illustrating the steps of the method of the present invention;

[0044] Figure 2 This is a schematic diagram of the basic analysis table of the present invention;

[0045] Figure 3 This is a schematic diagram of the electronic device of the present invention. Detailed Implementation

[0046] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0047] Example 1, please refer to Figure 1 As shown, this application provides a distributed error correction and tracing method for storage device data, including the following steps:

[0048] Step S1: Based on the records of data accessed within the storage device, use data analysis methods to obtain the traceable features and group traceable features corresponding to each stored data; based on the traceable features and group traceable features of the stored data, construct a basic analysis table for each stored data.

[0049] Step S1 includes: Step S101, recording the data stored in the storage device as stored data; recording all records in the storage device that are accessed by the stored data as storage access records;

[0050] Step S102, the data analysis method includes: Step S1021, for any stored data α, based on the order in which all storage call records of stored data α are generated, record all storage call records sequentially as call record DJ1 to call record DJ. n ;

[0051] In the analysis of this embodiment, for example, the stored data being analyzed is the drug storage data in a hospital, and the stored data α is the stored data of "Ceftriaxone Sodium for Injection";

[0052] Step S1022: For any call record: Record the names of the devices that retrieve and return the stored data α in the call record as the retrieving device and the returning device, respectively;

[0053] In practical implementation, when the object of retrieving or returning stored data α is a person, the retrieving device and the returning device can refer to the corresponding operator; for example, in a call record corresponding to the stored data "Ceftriaxone Sodium for Injection" recorded in this example, the operator who retrieved and returned "Ceftriaxone Sodium for Injection" is Nurse Zhang, so the corresponding retrieving device and returning device are both Nurse Zhang; if the target corresponding to the stored data in the actual call record being analyzed is virtual data, such as "Medical Equipment Purchase Record", when "Medical Equipment Purchase Record" is retrieved, the retrieving device is Director Li's work computer, so the device name corresponding to Director Li's work computer can be recorded as the retrieving device;

[0054] Step S1023: For the device being retrieved, the time between the time the device submits the retrieval request and the time when the stored data α is retrieved from the storage device is recorded as the retrieval request time, and the eight-digit number consisting of the year, month and day corresponding to the date on which the device retrieves the stored data α from the storage device is recorded as the retrieval date code.

[0055] The data analysis method also includes: step S1024, for the return device, the length of time that the stored data α in the call record is outside the storage device is recorded as the call-out usage time, and the eight-digit number consisting of the year, month and day corresponding to the date when the return device returns the stored data to the storage device is recorded as the return date code;

[0056] In the data analysis of this embodiment, for example, regarding a call record in the stored data of "Ceftriaxone Sodium for Injection", the operator for both calling and returning "Ceftriaxone Sodium for Injection" is Nurse Zhang. The time when "Ceftriaxone Sodium for Injection" was retrieved from the storage device was 08:30:00 on 2023-10-24, and the time when "Ceftriaxone Sodium for Injection" was returned to the storage device was 16:00:00 on 2023-10-24. Through analysis, it can be found that the first letter of the names of both the retrieving and returning devices is "Z", that is, the first letter of both retrieving and returning devices is "Z". The retrieving device code and the returning device code are both 01011010; the retrieving date code and the returning date code are both 20231024. By adding the numbers in the same position in the retrieving device code and the retrieving date code, the retrieving feature code is 21242034. Similarly, the returning feature code is also 21242034.

[0057] Step S1025: Record the first letters of the names of the device being retrieved and the device being returned as the first letters of the retrieval and return names, respectively.

[0058] The data analysis method also includes: step S1026, recording the eight-bit code corresponding to the binary representation of the first letter of the call-out device as the call-out device code, and recording the eight-bit code corresponding to the binary representation of the first letter of the return device as the return device code; adding the numbers in the same position in the call-out device code and the call-out date code, and recording the resulting eight-bit code as the call-out feature code; adding the numbers in the same position in the return device code and the return date code, and recording the resulting eight-bit code as the return feature code;

[0059] Step S1027: Record the size of the space occupied by the stored data α when it is retrieved from the call record as the retrieved space value, record the size of the space occupied by the stored data α when it is returned to the call record as the returned space value, and record the value of the returned space value minus the retrieved space value as the call space difference.

[0060] In the data analysis of this embodiment, in the above-mentioned call record of "Ceftriaxone Sodium for Injection", Nurse Zhang submitted the call request for "Ceftriaxone Sodium for Injection" at 08:25:00 on 2023-10-24. The parameter corresponding to the space occupied by "Ceftriaxone Sodium for Injection" when it was called out was 120 bottles, and the parameter corresponding to the space occupied when it was returned was 114 bottles. Therefore, through analysis, it can be found that the call request time was 00:05:00, the call usage time was 07:30:00, and the call space difference was 6 bottles.

[0061] Step S1028: Record the request time, usage time and call space difference of the stored data α as the self-traceability feature, and record the call feature code and return feature code of the stored data α as the group traceability feature; obtain the self-traceability feature and group traceability feature corresponding to the stored data α in all call records.

[0062] Step S1 further includes: Step S103, for any stored data α in the storage device, create a table with n+1 rows × 6 columns, denoted as the basic analysis table of stored data α; in the top row of the basic analysis table, except for the first cell, fill in the retrieval request time, retrieval usage time, retrieval space difference, retrieval feature code, and return feature code in sequence; in the leftmost column of the basic analysis table, fill in the names of all retrieval records corresponding to stored data α in sequence;

[0063] Step S104: Based on the traceable features and group traceable features of the stored data α in all call records, fill in the corresponding parameters in the cells of the basic analysis table.

[0064] Step S2: Based on the basic analysis table of stored data, obtain the initial traceability standard for each cell containing parameters in the basic analysis table, and construct the basic traceability method for stored data based on the initial traceability standards of all cells.

[0065] Step S2 includes: Step S201, for cell β containing any parameter γ that has been filled in in the basic analysis table, the cell with the smallest difference between the filled parameter and parameter γ among the cells in the basic analysis table other than cell β is recorded as the companion cell of cell β; all parameters in the row containing cell β are recorded as characteristic parameters, wherein the characteristic parameters include parameter γ;

[0066] In step S202, the cells in the basic analysis table other than the row containing cell β, where the parameter filled in is exactly the same as any feature parameter, are recorded as secondary cells of cell β; the curve obtained by fitting the center point of cell β with the center points of all associated cells is recorded as the associated discrimination curve, and the curve obtained by fitting the center point of the associated cells with the center points of all secondary cells is recorded as the secondary discrimination curve.

[0067] Step S2 further includes: Step S203, recording the accompanying discrimination curve and the secondary discrimination curve as the initial tracing standard of cell β, wherein, when no secondary cell is obtained, the positions of cell β and the accompanying cell β are recorded as the initial tracing standard of cell β.

[0068] In the data analysis of this embodiment, for example, after analyzing all the call records of "Ceftriaxone Sodium for Injection" mentioned above, the basic analysis table corresponding to the 6 call records of "Ceftriaxone Sodium for Injection" is shown in Table 1: Ceftriaxone Sodium Table;

[0069] Table 1: Sodium Trithione Table

[0070] Retrieve application time Retrieve usage time Calling space difference retrieve the signature Return the signature Call Log 1 00:05:00 07:30:00 6 bottles 21242034 21242034 Call Record 2 00:04:00 16:00:00 12 bottles 20232034 21241034 Call Record 3 00:02:00 03:00:00 2 bottles 21242034 20232034 Call Log 4 00:03:00 04:30:00 3 bottles 21242024 21242023 Call record 5 00:05:00 06:30:00 4 bottles 21231034 21241034 Call record 6 00:04:00 09:30:00 6 bottles 21224203 21142034

[0071] When the cell β being analyzed is located at [Call Record 1, Retrieve Feature Code], data extraction reveals that the parameter γ within cell β is "21242034". Therefore, the locations of the accompanying cells for cell β are [Call Record 1, Return Feature Code] and [Call Record 3, Retrieve Feature Code]. Analysis of the parameters within the row containing cell β reveals that the corresponding feature parameters are "00:05:00", "07:30:00", "6 bottles", "21242034", and "21242034". Therefore, the locations of the secondary cells for cell β are [Call Record 5, Retrieve Application Time], [Call Record 6, Call Spatial Difference], and [Call Record 3, Retrieve Feature Code].

[0072] In the data analysis of this embodiment, the positions of the companion and secondary cells of cell β obtained through the above analysis are used to obtain the companion discrimination curve and secondary discrimination curve corresponding to cell β, as shown below. Figure 2 As shown by curves BQ and CQ, curves BQ and CQ can be recorded as the initial traceability standards for cell β. In the analysis of error correction and traceability, cell β can be identified and error correction and traceability can be performed through curves BQ and CQ. When the initial traceability standard corresponding to cell β cannot be obtained through all call records of "Ceftriaxone Sodium for Injection", it indicates that there is an anomaly in the data corresponding to cell β. The location of the error can be efficiently obtained by analyzing the data related to the parameters in cell β and the associated and secondary cells involved in the initial traceability standard of cell β.

[0073] Step S204: Obtain the initial traceability standard for the cells containing all parameters in the basic analysis table.

[0074] Step S205, the basic tracing method includes: Step S2051, when performing error correction and tracing on any call record of stored data α, all cells in the row where the call record is located in the basic analysis table, except for the leftmost cell, are recorded as cells to be corrected;

[0075] Step S2052: For any cell to be corrected, based on the latest basic analysis table of stored data α, obtain the secondary discrimination curve and the secondary discrimination curve of the cell to be corrected, and record them as the correction curve A1 and the correction curve A2; when the cell to be corrected has data change characteristics, adjust the correction curve A1 and the correction curve A2 based on the data change characteristics of the cell to be corrected, and record the adjusted correction curve A1 and the correction curve A2 as the correction curve B1 and the correction curve B2, respectively.

[0076] The basic traceability method also includes: step S2053, when the error-to-be-corrected curve B1 completely coincides with the secondary discrimination curve in the initial traceability standard of the cell to be corrected, and the error-to-be-corrected curve B2 completely coincides with the secondary discrimination curve in the initial traceability standard of the cell to be corrected, the cell to be corrected is recorded as an error-free cell.

[0077] In the specific implementation process, if the data change characteristics of the cell to be corrected can be used to make the error-correcting curve B1 completely coincide with the accompanying discrimination curve in the initial traceability standard of the cell to be corrected, and the error-correcting curve B2 completely coincide with the secondary discrimination curve in the initial traceability standard of the cell to be corrected, it indicates that the data corresponding to the cell to be corrected, as well as its accompanying and secondary cells, has only been modified in accordance with regulations, without any other abnormalities, and therefore can be recorded as an error-free cell; however, if the cell to be corrected is not recorded as an error-free cell, it indicates that the data of the correction cell, as well as its accompanying and secondary cells, has only been modified in accordance with regulations, without any other abnormalities, and therefore can be recorded as an error-free cell; The corresponding data contains data that has been modified in violation of regulations, which causes the error-to-be-corrected curve B1 to not completely coincide with the accompanying discrimination curve in the initial traceability standard of the error-to-be-corrected cell, or the error-to-be-corrected curve B2 to not completely coincide with the secondary discrimination curve in the initial traceability standard of the error-to-be-corrected cell. Therefore, the parameters in the error-to-be-corrected cell can be recorded as error correction parameters, and the name in the top cell of the column where the error-to-be-corrected cell is located can be recorded as the error correction item name. In a more specific error correction analysis, the data sources corresponding to the error-to-be-corrected cell and its accompanying and secondary cells can be traced to achieve error correction and traceability.

[0078] Step 2054: When the cell to be corrected is not recorded as a cell without errors, record the parameters in the cell to be corrected as correction parameters, and record the name in the top cell of the column containing the cell to be corrected as the correction item name.

[0079] Step S3: After the basic analysis table of the stored data is established, when the record of the stored data is called is modified in accordance with regulations, the data change characteristics corresponding to the stored data are obtained; when performing error correction and tracing on any stored data, the error correction parameters and error correction item name of the stored data are obtained based on all data change characteristics of the stored data and the basic tracing method of the stored data.

[0080] Step S3 includes: Step S301, for any stored data α: after the basic analysis table of stored data α is established, when the record of stored data α being called is modified in compliance, for any one compliance modification: based on all calling records corresponding to stored data α after compliance modification, obtain the latest traceable features and group traceable features corresponding to stored data α;

[0081] Step S302: Based on the latest traceable features and group traceable features corresponding to the stored data α, update the basic analysis table of the stored data α; for any cell β of parameter γ that is filled in in the updated basic analysis table, obtain the companion discriminant curve and the secondary discriminant curve corresponding to cell β in the updated basic analysis table, and record them as the updated true curve and the updated companion curve, respectively.

[0082] Step S303 is to convert the update true curve and update companion curve into the companion discriminant curve and secondary discriminant curve in the initial traceability standard of cell β, respectively, and denot it as the data change feature of cell β.

[0083] In the specific implementation process, by obtaining the data change characteristics of all cells corresponding to the updated true curve and the updated companion curve, it is possible to record the data changes caused by the compliant modification of the stored data, thereby realizing data error correction and accurate source tracing by using data change characteristics during error correction and source tracing.

[0084] Step S304: Obtain the data change characteristics of all cells with parameters filled in within the updated basic analysis table, and record them as the data change characteristics corresponding to the stored data α.

[0085] Example 2, please refer to Figure 3 As shown, Figure 3The example illustrates the structure of an electronic device, which may include a processor, a communication interface, a memory, and a communication bus, wherein the processor, the communication interface, and the memory communicate with each other through the communication bus. The memory stores computer-readable instructions, and the processor can call the instructions in the memory. When the computer-readable instructions are executed by the processor, the steps in a distributed error correction and tracing method for storage device data are performed to achieve the following functions: First, based on the records of data being called within the storage device, data analysis methods are used to obtain the traceability features and group traceability features corresponding to each stored data. Based on the traceability features and group traceability features of the stored data, a basic analysis table is constructed for each stored data. Then, based on the basic analysis table of the stored data, the initial traceability standard for each cell with parameters in the basic analysis table is obtained, and based on the initial traceability standards of all cells, a basic traceability method for the stored data is constructed. Finally, after the basic analysis table of the stored data is established, when the records of the stored data being called are modified in accordance with regulations, the data change features corresponding to the stored data are obtained. When performing error correction and tracing on any stored data, based on all data change features of the stored data and the basic traceability method of the stored data, the error correction parameters and error correction item names of the stored data are obtained.

[0086] Furthermore, when the logical instructions in the aforementioned memory can be implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0087] Example 3: This application also provides a computer program product, which includes a computer program stored on a computer-readable storage medium. The computer program includes program instructions. When the program instructions are executed by a computer, the computer can execute a distributed error correction and tracing method for storage device data provided by the above methods. The method includes: first, based on the records of data accessed in the storage device, using data analysis methods to obtain the traceable features and group traceability features corresponding to each stored data; based on the traceable features and group traceability features of the stored data, constructing a basic analysis table for each stored data; then, based on the basic analysis table of the stored data, obtaining the initial traceability standard for each cell with parameters in the basic analysis table, and constructing a basic traceability method for the stored data based on the initial traceability standards of all cells; finally, after the basic analysis table of the stored data is established, when the records of accessed stored data are modified in accordance with regulations, obtaining the data change features corresponding to the stored data; when performing error correction and tracing on any stored data, obtaining the error correction parameters and error correction item name of the stored data based on all data change features of the stored data and the basic traceability method of the stored data.

[0088] Example 4: This application also provides a computer-readable storage medium storing a computer program. When the computer program is executed by a processor, it performs the steps described above in the distributed error correction and tracing method for storage device data to achieve the following functions: First, based on the records of data accessed within the storage device, a data analysis method is used to obtain the traceable features and group traceability features corresponding to each stored data. Based on the traceable features and group traceability features of the stored data, a basic analysis table is constructed for each stored data. Then, based on the basic analysis table of the stored data, the initial traceability standard for each cell containing parameters in the basic analysis table is obtained, and based on the initial traceability standards of all cells, a basic traceability method for the stored data is constructed. Finally, after the basic analysis table of the stored data is established, when the records of accessed stored data are modified in accordance with regulations, the data change features corresponding to the stored data are obtained. When performing error correction and tracing on any stored data, based on all data change features of the stored data and the basic traceability method for the stored data, the error correction parameters and error correction item names of the stored data are obtained.

[0089] Based on the above description of the embodiments, the embodiments of the present invention can be provided as methods, systems, or computer program products. Based on this understanding, the above technical solutions, in essence or in terms of their contribution to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods described in the various embodiments or certain parts of the embodiments.

[0090] In the embodiments provided in this application, it should be understood that the disclosed system or method can be implemented in other ways. The embodiments described above are merely illustrative. For example, the division of modules or units is only a logical functional division, and there may be other division methods in actual implementation. Furthermore, multiple modules or units may be combined or integrated into another system, or some features may be ignored or not executed. Additionally, the coupling or direct coupling or communication connection shown or discussed may be through some communication interfaces. The indirect coupling or communication connection between systems, modules, and units may be electrical, mechanical, or other forms.

[0091] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application.

Claims

1. A distributed error correction and tracing method for storage device data, characterized in that, Includes the following steps: Based on the records of data accessed within the storage device, data analysis methods are used to obtain the traceable features and group traceable features corresponding to each stored data. Based on the traceability features of the stored data and the group traceability features, a basic analysis table is constructed for each stored data. Based on the basic analysis table of stored data, the initial traceability standard of each cell containing parameters in the basic analysis table is obtained, and based on the initial traceability standard of all cells, the basic traceability method of stored data is constructed. After the basic analysis table of the stored data is established, when the records of the stored data that are retrieved are modified in accordance with regulations, the data change characteristics corresponding to the stored data are obtained. When performing error correction and tracing on any stored data, the error correction parameters and error correction item name of the stored data are obtained based on all data change characteristics of the stored data and the basic tracing method of the stored data.

2. The distributed error correction and tracing method for storage device data according to claim 1, characterized in that, Based on the records of data accessed within the storage device, data analysis methods are used to obtain the traceable characteristics and group traceable characteristics corresponding to each stored data, including: The data stored in the storage device is recorded as stored data; all records in the storage device that are accessed by the stored data are recorded as storage access records; The data analysis method includes: for any stored data α, based on the order in which all storage call records of stored data α are generated, all storage call records are sequentially recorded as call record DJ1 to call record DJ. n ; For any call record: the names of the devices that retrieve and return stored data α in the call record are respectively recorded as the retrieving device and the returning device; For the device being retrieved, the time between the time the device submits the retrieval request and the time when the stored data α is retrieved from the storage device is recorded as the retrieval request time, and the eight-digit number consisting of the year, month, and day corresponding to the date the device retrieves the stored data α from the storage device is recorded as the retrieval date code.

3. The distributed error correction and tracing method for storage device data according to claim 2, characterized in that, Data analysis methods also include: For the returned device, the length of time that the stored data α in the call record is outside the storage device is recorded as the call-out usage time, and the eight-digit number consisting of the year, month and day corresponding to the date when the returned device returns the stored data to the storage device is recorded as the return date code. The first letters of the names of the devices being retrieved and returned are respectively used as the first letters of the retrieval and return names.

4. The distributed error correction and tracing method for storage device data according to claim 3, characterized in that, Data analysis methods also include: Record the eight-bit code corresponding to the binary representation of the first letter of the device being retrieved as the retrieved device code, and record the eight-bit code corresponding to the binary representation of the first letter of the device being returned as the returned device code; add the numbers in the same position in the retrieved device code and the retrieved date code, and record the resulting eight-bit code as the retrieved feature code; add the numbers in the same position in the returned device code and the returned date code, and record the resulting eight-bit code as the returned feature code. The space occupied by the stored data α when it is retrieved from the call record is recorded as the retrieved space value. The space occupied by the stored data α when it is returned to the call record is recorded as the returned space value. The value of the returned space value minus the retrieved space value is recorded as the call space difference. Record the request time, usage time, and call space difference of the stored data α as the self-traceability features, and record the call feature code and return feature code of the stored data α as the group traceability features; obtain the self-traceability features and group traceability features of the stored data α in all call records.

5. The distributed error correction and tracing method for storage device data according to claim 4, characterized in that, Based on the traceable and group traceable characteristics of the stored data, a basic analysis table is constructed for each stored data, including: For any stored data α in the storage device, create a table with n+1 rows and 6 columns, denoted as the basic analysis table for stored data α; in the top row of the basic analysis table, except for the first cell, fill in the retrieval request time, retrieval usage time, retrieval space difference, retrieval feature code, and return feature code in sequence; in the leftmost column of the basic analysis table, fill in the names of all retrieval records corresponding to stored data α in sequence. Based on the traceable features and group traceable features of the stored data α in all call records, fill in the corresponding parameters in the cells of the basic analysis table.

6. The distributed error correction and tracing method for storage device data according to claim 5, characterized in that, Based on the basic analysis table containing stored data, the initial tracing criteria for obtaining each cell containing parameters in the basic analysis table include: For cell β containing any parameter γ that is filled in the basic analysis table, the cell with the smallest difference between the filled parameter and parameter γ among all cells in the basic analysis table other than cell β is recorded as the companion cell of cell β; all parameters in the row containing cell β are recorded as characteristic parameters, where the characteristic parameters include parameter γ. In the basic analysis table, cells other than cell β, where the parameter filled in is exactly the same as any feature parameter, are denoted as secondary cells of cell β; the curve obtained by fitting the center point of cell β with the center points of all associated cells is denoted as the associated discrimination curve, and the curve obtained by fitting the center point of the associated cells with the center points of all secondary cells is denoted as the secondary discrimination curve.

7. A distributed error correction and tracing method for storage device data according to claim 6, characterized in that, Based on the basic analysis table containing stored data, the initial tracing criteria for obtaining each cell containing parameters within the basic analysis table also include: The accompanying discrimination curve and the secondary discrimination curve are recorded as the initial tracing standard of cell β. When the secondary cell is not obtained, the positions of cell β and the accompanying cell β are recorded as the initial tracing standard of cell β. Obtain the initial tracing criteria for all parameters in the basic analysis table.

8. The distributed error correction and tracing method for storage device data according to claim 7, characterized in that, Basic source tracing methods include: When performing error correction and tracing on any call record of stored data α, all cells in the row containing the call record in the basic analysis table, except for the leftmost cell, are recorded as cells to be corrected. For any cell to be corrected, based on the latest basic analysis table of stored data α, obtain the primary and secondary discrimination curves of the cell to be corrected, and denot them as the correction curve A1 and correction curve A2. When the cell to be corrected has data change characteristics, adjust the correction curves A1 and A2 based on the data change characteristics of the cell to be corrected, and denot the adjusted correction curves A1 and A2 as correction curves B1 and B2, respectively.

9. A distributed error correction and tracing method for storage device data according to claim 8, characterized in that, Basic tracing methods also include: When the error-to-be-corrected curve B1 completely coincides with the secondary discrimination curve in the initial traceability standard of the cell to be corrected, and the error-to-be-corrected curve B2 completely coincides with the secondary discrimination curve in the initial traceability standard of the cell to be corrected, the cell to be corrected is recorded as an error-free cell. When the cell to be corrected is not recorded as a cell without errors, the parameters in the cell to be corrected are recorded as correction parameters, and the name in the top cell of the column containing the cell to be corrected is recorded as the correction item name.

10. A distributed error correction and tracing method for storage device data according to claim 9, characterized in that, After the basic analysis table for stored data is established, when a record of the stored data is modified in accordance with regulations, the data change characteristics corresponding to the stored data are obtained, including: For any stored data α: After the basic analysis table of stored data α is established, when the record of stored data α is called is modified in compliance, for any one compliance modification: based on all calling records corresponding to stored data α after compliance modification, obtain the latest traceable features and group traceable features corresponding to stored data α; Based on the latest traceable features and group traceable features corresponding to the stored data α, the basic analysis table of the stored data α is updated; for any cell β with parameter γ filled in in the updated basic analysis table, the accompanying discrimination curve and secondary discrimination curve corresponding to cell β in the updated basic analysis table are obtained and recorded as the updated true curve and the updated accompanying curve, respectively. The process of converting the updated true curve and the updated companion curve into the companion discriminant curve and the secondary discriminant curve in the initial traceability standard of cell β is denoted as the data change characteristic of cell β. Obtain the data change characteristics of all cells with parameters filled in within the updated basic analysis table, and record them as the data change characteristics corresponding to the stored data α.

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