An artificial intelligence-assisted data error correction and recovery method and system

Abstract

The present invention discloses an artificial intelligence-assisted data error correction and recovery method and system, which relates to the field of data error correction and recovery technology. The method comprises: obtaining data information to be transmitted, obtaining a target data item, and obtaining storage allocation information of the target data item; performing a first cut on the target data item based on the storage allocation information to obtain a data cut item, wherein the data cut item is used to represent the number of cuts of the target data item. The present invention performs a first step of refinement on the data information and then a second step of refinement, splitting it into multiple parts again, and obtaining feature information of each part, comparing it with the initial information, and when a difference is found in the comparison result, replacing the different fragments, thereby achieving the error correction and recovery effect of the data information. Since the data information is a plurality of fragments obtained through double refinement, it is more efficient when performing error correction and recovery on the fragments therein.

Description

Technical Field

[0001] The present invention relates to the technical field of data error correction and recovery, and specifically to an artificial intelligence-assisted data error correction and recovery method and system. Background Art

[0002] Data error correction and recovery is a key technology in computer storage systems. It refers to the process of detecting and correcting data errors through specific algorithms and mechanisms, and restoring the original information when data is lost or damaged. Its core goal is to ensure data integrity and availability, which is especially important in scenarios such as storage medium failure, human error or network attack. Artificial intelligence-assisted data error correction and recovery methods refer to the use of artificial intelligence technology to assist in data error correction and recovery, improve error correction efficiency and the intelligent and automated level of recovery, thereby enhancing data integrity and reliability.

[0003] The flash memory data error correction method and device with patent publication number CN105740088A, when receiving a data read instruction, first uses the row check data of the data block to be read to perform error correction processing, and after the error correction of the data block to be read using the row check data fails, uses the column check data corresponding to the data block to be read to perform error correction processing, and then combines the row check data to perform error correction processing on the data to be read, thereby restoring the original data of the data to be read, increasing the number of error bits that the flash memory storage device can correct and improving the recovery rate of flash memory data.

[0004] When the above-mentioned and similar technical solutions are used to transmit data, the degree of physical aging of different storage hard disks is inconsistent due to the influence of different erase and write times, temperature effects, write amplification factors and other factors. As the erase and write times increase, the charge leakage of the hard disk flash memory units and the interference between units increase, causing the error rate to increase exponentially. At this time, conventional error correction schemes such as fixed-strength LDPC codes will greatly increase the data error correction and recovery time in the later stages of aging, thereby causing the data recovery failure rate to soar. Especially when the data volume is large, the data error correction and recovery time will be further aggravated, thereby increasing the data recovery failure rate. Summary of the Invention

[0005] The purpose of the present invention is to provide an artificial intelligence-assisted data error correction and recovery method and system to solve the problems raised in the above background technology.

[0006] To achieve the above objectives, the present invention provides the following technical solution: an artificial intelligence-assisted data error correction and recovery method and system, comprising:

[0007] Obtain the data information to be transmitted, obtain the target data item, and obtain the storage allocation information of the target data item;

[0008] Performing a first cut on the target data item based on the storage allocation information to obtain a data cut item, where the data cut item is used to indicate the cut quantity of the target data item;

[0009] Create a quantitative formula for hard disk aging, calculate the aging degree of storage allocation information through artificial intelligence calculation methods, and obtain the target aging set;

[0010] The aging degree is classified into grades to obtain aging grade items, and the aging grade of the storage allocation information is determined by a determination method to obtain target grade items. Segmentation values ​​are set based on the aging grade items, and corresponding segmentation values ​​are obtained based on the target grade items to obtain segmentation comparison sets, wherein the segmentation comparison sets correspond to the storage allocation information respectively.

[0011] Based on the data cutting items, the corresponding storage target information is obtained to obtain a storage target set, which is used to indicate that target data items cut into different quantities are stored separately;

[0012] Based on the segmentation reference set, the data segmentation items corresponding to the segmentation reference set are split twice, and then the segmentation plan of the data segmentation items in different storage locations is obtained, and the target segmentation set is obtained. Based on the target segmentation set, the data segmentation items are compared and corrected, and restored and reserved respectively through the matching method, thereby realizing error correction and rapid recovery of storage partition data at different aging levels with the assistance of artificial intelligence.

[0013] Furthermore, the quantification formula for the hard disk aging degree includes:

[0014] ;

[0015] in For the degree of aging, is Euler's constant, represents the erase attenuation coefficient, Indicates the current cumulative number of erase and write times. Indicates the maximum number of erase and write times. represents the temperature sensitivity coefficient, Indicates the hard disk operating temperature. Indicates the reference temperature, Indicates the amount of data written, represents the master optimization factor, Indicates the cumulative power-on time. represents the time decay weight, Indicates the remaining available capacity. Indicates the total capacity.

[0016] Furthermore, the method for obtaining the aging level item includes:

[0017] Setting at least two aging degree range data to obtain aging range items;

[0018] The aging range items are sorted in order from low to high to obtain aging sorting items, and at least two assessment levels are set to correspond to the aging sorting items respectively to obtain aging level items.

[0019] Furthermore, the method for setting the segmentation value includes:

[0020] Based on the aging level items, level corresponding values ​​are set respectively, and the level corresponding values ​​are times values, so as to obtain at least two level corresponding items;

[0021] Based on the target level item, obtain the corresponding level corresponding item, obtain the target corresponding item, obtain the average value of the target corresponding item, obtain the corresponding average item, use the corresponding average item as the initial score, set the incremental value, and obtain the score division item based on the combination result of the incremental value and the initial score. The score division item is respectively used as the cutting value corresponding to the target level item.

[0022] Furthermore, the method for obtaining the target segmentation set includes:

[0023] Based on the segmentation control set, respectively obtain the data segmentation items corresponding to the segmentation control set, and obtain the segmentation values ​​matching the data segmentation items;

[0024] Obtain data segments of the data cutting item, and divide the data segments into equal parts based on the matching segmentation value, cutting the data cutting item into data sub-segments of a number corresponding to the segmentation value. A data cutting item includes at least one data sub-segment, and the data sub-segments contained in the data cutting item are combined to obtain the target segmentation set.

[0025] Furthermore, the matching method includes:

[0026] Based on the target segmentation set, the target data item is divided into contrast segments, and initial segment feature information of the target data item is obtained to obtain an initial information item;

[0027] Obtain the fragment feature information of the target segmentation set to obtain the segmentation information item, compare the segmentation information item with the initial information item corresponding to the segmentation information item, and determine whether the segmentation information item is abnormal. When the segmentation information item is abnormal, obtain the abnormal fragment information to obtain the abnormal fragment item, and obtain the corresponding fragment in the initial information item based on the abnormal fragment item to obtain the replacement fragment item, and then replace and restore the abnormal fragment item.

[0028] Furthermore, the method for obtaining the data cutting items includes:

[0029] Acquire quantity information of storage allocation information to obtain storage quantity items, where the storage quantity items represent different types of storage modules used to store the target data item;

[0030] Based on the quantity information of the stored quantity items, a target cutting amount is obtained, and based on the target cutting amount, a corresponding amount of cutting is performed on the target data item to obtain a data cutting item.

[0031] Furthermore, the method for obtaining the storage target set includes:

[0032] The importance of the data cutting items is sorted to obtain data sorting items, which are used to indicate the order of importance of the data cutting items from high to low.

[0033] Based on the comparison and matching results of the target level item and the data sorting item, the comparison storage level of the data cutting item is obtained, and the corresponding storage target set information is obtained based on the comparison storage level.

[0034] Furthermore, an artificial intelligence-assisted data error correction and recovery system, using the artificial intelligence-assisted data error correction and recovery method described above, includes:

[0035] Information acquisition module: obtains the data information to be transmitted, obtains the target data item, obtains the storage allocation information of the target data item, performs the first segmentation on the target data item based on the storage allocation information, and obtains the data segmentation item;

[0036] Aging calculation module: Creates a quantitative formula for hard disk aging, calculates the aging degree of storage allocation information, obtains a target aging set, classifies the aging degree into grades, obtains aging grade items, and simultaneously determines the aging grade of storage allocation information using a determination method to obtain a target grade item;

[0037] Segmentation and storage module: Segmentation values ​​are set based on the aging level items, and corresponding segmentation values ​​are obtained based on the target level items to obtain segmentation reference sets. The segmentation reference sets correspond to the storage allocation information respectively. Based on the data segmentation items, the corresponding storage target information is obtained to obtain the storage target set. The storage target set is used to indicate that target data items segmented into different numbers are stored separately. Based on the segmentation reference set, the data segmentation items corresponding to the segmentation reference set are segmented twice to obtain segmentation schemes for data segmentation items in different storage locations and obtain the target segmentation set.

[0038] Error correction and recovery module: Based on the target segmentation set, the data segmentation items are compared, corrected, and restored through matching methods.

[0039] Compared with the prior art, the present invention has the following beneficial effects:

[0040] The artificial intelligence-assisted data error correction and recovery method and system preliminarily cuts the data information to be transmitted, stores it in different storage intervals, performs the first step of refining the data information, and then performs the second step of refining the refined data information, splitting it into multiple parts again, and obtaining characteristic information of each part, comparing it with the initial information. When the comparison results show a difference, the different fragments are replaced, thereby achieving error correction and recovery of the data information. Since the data information is multiple fragments obtained through double refinement, it is more efficient when correcting and recovering the fragments.

[0041] At the same time, the aging of the storage interval where the data information is about to be stored is calculated by creating a quantitative formula for the aging degree of the hard disk, and according to the aging calculation results, a different number of split values ​​are matched for each storage interval, and the data information stored in each storage interval is subdivided to different degrees according to the split values. Among them, the storage interval with a higher degree of aging requires more detailed subdivision to facilitate rapid error correction and recovery response, thereby achieving the effect of error correction and recovery according to the actual aging status of each storage interval. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] Figure 1 It is a schematic diagram of the overall process of the present invention;

[0043] Figure 2 Schematic diagram of the relationship between target data items and storage quantity items of the present invention;

[0044] Figure 3 This is a schematic diagram of the process for obtaining the score division items of the present invention;

[0045] Figure 4 A schematic diagram of the relationship between the segmentation values ​​obtained in the present invention;

[0046] Figure 5 This is a schematic diagram of obtaining data sub-segments of the present invention. DETAILED DESCRIPTION

[0047] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0048] During the data transmission process, the difference in the aging degree of storage hard disks is an important issue that cannot be ignored. Since different hard disks are affected by factors such as different erase and write times, temperature effects, and write amplification factors, their physical aging degrees are often different. As the number of erase and write times increases, the charge leakage in the hard disk flash memory unit, the interference between units and other phenomena will intensify, causing the error rate to increase exponentially. Conventional error correction schemes need to spend a lot of time on data correction and recovery in the later stage of aging. This is because the sharp increase in the error rate makes the error correction process extremely complicated and requires a lot of calculations and iterations. On the other hand, long error correction and recovery operations will greatly increase the failure rate of data recovery. Especially in the case of large amounts of data, the time required for error correction and recovery will be further extended, thereby further increasing the failure rate of data recovery. The problems caused by this aging difference are not only reflected in the error correction efficiency, but also in data reliability. Since different hard disks age at different rates, even if the same error correction scheme is used, the final data reliability will vary. This variation will bring great challenges to data management and maintenance, making it difficult to accurately evaluate and predict data reliability. The present application provides an artificial intelligence-assisted data error correction and recovery method, which performs a preliminary segmentation of the data information to be transmitted, stores it in different storage intervals, performs a first step of refinement on the data information, and then performs a second step of refinement on the refined data information, splits it again into multiple parts, and obtains feature information of each part, compares it with the initial information, and when a difference is found in the comparison result, replaces the different fragments, thereby achieving the error correction and recovery effect of the data information. Since the data information is a plurality of fragments obtained through double refinement, the efficiency is higher when performing error correction and recovery on the fragments. Figure 1 As shown, steps S100-S600 are included.

[0049] Step S100: Acquire data information to be transmitted, obtain a target data item, and acquire storage allocation information of the target data item.

[0050] It should be noted that when transmitting data, relevant information of the transmitted data is obtained. For example, when packaging and transmitting video, picture and other data, the relevant information obtained at this time is the number, name and other information of the video and picture, and then the target data item is obtained. At the same time, the storage allocation information of the target data item is obtained. For example, when storing the target data item in a hard disk, the storage allocation information obtained at this time is the information of the storage hard disk.

[0051] Step S200: Performing a first segmentation on the target data item based on the storage allocation information to obtain a data segmentation item.

[0052] It should be noted that the data cutting item is used to represent the number of cuts of the target data item. The method for obtaining the data cutting item includes: obtaining the quantity information of the storage allocation information to obtain the storage quantity item, and the storage quantity item represents the different types of storage modules used to store the target data item; based on the quantity information of the storage quantity item, obtaining the target cutting amount, and cutting the target data item by a corresponding amount based on the target cutting amount to obtain the data cutting item.

[0053] In the specific implementation process, Figure 2 As shown, ten videos need to be stored now. At this time, the target data items are ten videos. There are two storage allocation information items, namely disk A and disk B. At this time, the storage quantity item is 2, which is used to represent different storage modules for storing ten videos. At this time, the target cutting amount is also 2. The target data item is cut based on the target cutting amount, that is, the ten videos are divided into two parts, which are stored in disk A and disk B respectively to obtain data cutting items.

[0054] Step S300: creating a quantitative formula for the degree of hard disk aging.

[0055] It should be noted that the aging degree of storage allocation information is calculated through artificial intelligence calculation methods to obtain the target aging set. The quantification formula for the hard disk aging degree includes:

[0056] ;

[0057] in For the degree of aging, is Euler's constant, Indicates the erase attenuation coefficient, with a value of 0.01, which is used to control the slope of the curve. Indicates the current cumulative number of erase and write times. Indicates the maximum number of erase and write times. The maximum number of erase and write times for different hard drives is different. The maximum number of erase and write times for SLC is about 100,000 times, the maximum number of erase and write times for MLC is about 3,000-5,000 times, and the maximum number of erase and write times for TLC is about 1,000-3,000 times. Indicates the temperature sensitivity coefficient, with a value of 1.5–2.0, indicating that high temperature accelerates aging. Indicates the hard disk operating temperature. Indicates the reference temperature. The set reference temperature is 25℃. Indicates the amount of data written, Indicates the master control optimization factor, which is 5–10 for advanced master control and 2–3 for low-end master control. Indicates the cumulative power-on time. represents the time decay weight, ranging from 0.05 to 0.1, reflecting non-write aging. Indicates the remaining available capacity. Indicates the total capacity.

[0058] It's important to note that the number of erase / write cycles is a core parameter. Flash memory essentially stores data through the oxide tunneling effect, and each erase / write cycle damages the oxide layer. Therefore, the number of erase / write cycles is factored into the formula for quantifying hard drive aging. Furthermore, high temperatures exacerbate electron migration and interface state generation, accelerating the formation of silicon-oxide interface defects and hindering electron transport. Therefore, temperature is also factored into the formula for quantifying hard drive aging. Furthermore, write amplification represents a loss in efficiency. When data is updated, old blocks must be moved, generating additional writes. Furthermore, when the master controller distributes writes across different blocks, address mapping operations are increased. Therefore, space pressure is also a significant factor in hard drive aging. Fourth, even without power, electrons trapped in the floating gate can leak through quantum tunneling. The γ parameter is related to temperature through the TDDB equation, so the accumulated power-on time also needs to be considered as a factor in hard drive aging. Finally, when available space is low, not only does performance degrade, but the frequency of garbage collection also increases exponentially. Therefore, remaining space is also a factor in hard drive aging.

[0059] In the specific implementation process, it is necessary to store a video in an AA hard disk. The cumulative number of erase and write times of the AA hard disk during use is 800 times, and the AA hard disk is TLC, with a maximum erase and write time of 1500 times. At the same time, the operating temperature is 45°C, the data write volume is 3, the cumulative power-on time of the hard disk is 2.5 years, the remaining space is 50GB, and the total space of the hard disk is 512GB. At this time, according to the obtained data, is 800, is 1500, is 45, is 3, is 2.5, is 50, 512, while erasing the attenuation coefficient The value is 0.01, the temperature sensitivity coefficient The value is 1.8, the master optimization factor The value is 5, time decay weight Take 0.07, and then use the quantification formula based on the hard disk aging degree:

[0060] ;

[0061] Calculated as:

[0062] ;

[0063] The final calculation result is 0.62, which means that the aging degree of the hard disk is 62%.

[0064] Step S400: classify the aging degree into grades to obtain aging grade items.

[0065] It should be noted that the method for obtaining the aging level item includes: setting three aging degree range data to obtain three aging range items; sorting the aging range items in order from low to high to obtain aging ranking items, setting at least two assessment levels, corresponding to the aging ranking items respectively, and then obtaining the aging level item.

[0066] Specifically, three aging degree range data are set, namely range 1, range 2 and range 3, and the data of the three are 0-33%, 33%-66% and 66%-100% respectively. At this time, range 1, range 2 and range 3 are sorted in order from low to high, and the sorting result is range 1>range 2>range 3, thereby obtaining an aging ranking item, and setting three assessment levels, namely low aging, medium aging and high aging, which correspond to the aging ranking items respectively, thereby obtaining an aging level item.

[0067] The aging level of the storage allocation information is determined by a determination method to obtain a target level item, and the segmentation values ​​are set based on the aging level item.

[0068] It should be noted that if Figure 3 As shown, the method for setting the split value includes: based on the aging level item, setting the level corresponding value respectively, the level corresponding value increases by 1 starting from 1, and corresponding to the aging level item, the level corresponding value is the number value, and at least two level corresponding items are obtained; based on the target level item, obtaining the corresponding level corresponding item, obtaining the target corresponding item, obtaining the average value of the target corresponding item, obtaining the corresponding average item, using the corresponding average item as the initial score, setting the incremental value, the incremental value is also 1, based on the combination result of the incremental value and the initial score, obtaining the score division item, the score division item is respectively used as the split value corresponding to the target level item.

[0069] In the specific implementation process, Figure 4As shown in the figure, 100 videos need to be stored. The target data item is 100, and they are stored in 4 hard disks, namely disk a, disk b, disk c and disk d. The aging degree of disk a is calculated to be 54%, the aging degree of disk b is 24%, the aging degree of disk c is 44%, and the aging degree of disk d is 74% through the set hard disk aging degree quantitative formula. Since the set aging level items are low aging, medium aging and high aging, the corresponding data are 0-33%, 33%-66% and 66%-100% respectively. Therefore, disk a and disk c are both medium aging, and disk b is low aging. Disk D has advanced aging. Since the setting level corresponding values ​​start from 1 and increase by 1, and correspond to the aging level items, the level corresponding values ​​for low-level aging, medium-level aging, and high-level aging are 1, 2, and 3 respectively. At this time, the target corresponding items for disks A, B, C, and D are 2, 1, 2, and 3 respectively. The corresponding average value is 2. At this time, 2 is used as the initial score. According to the setting to the increment value, the increment value is 1, and the three score division items obtained are 2, 3, and 4, which correspond to low-level aging, medium-level aging, and high-level aging respectively. That is, the segmentation values ​​of disks A, B, C, and D are 3, 2, 3, and 4 respectively.

[0070] The corresponding segmentation value is obtained based on the target level item to obtain a segmentation control set.

[0071] It should be noted that the segmentation control sets correspond to the storage allocation information respectively.

[0072] Step S500: Based on the data segmentation item, obtain the corresponding storage target information to obtain a storage target set.

[0073] It should be noted that the storage target set is used to indicate the separate storage of target data items cut into different numbers. The method for obtaining the storage target set includes: sorting the importance of the data cutting items to obtain data sorting items, and the data sorting items are used to indicate the importance of the data cutting items in descending order; based on the comparison and matching results of the target level items and the data sorting items, obtaining the comparison storage level of the data cutting items, and obtaining the corresponding storage target set information based on the comparison storage level.

[0074] Step S600: Based on the segmentation comparison set, the data segmentation items corresponding to the segmentation comparison set are segmented twice, thereby obtaining segmentation schemes for the data segmentation items at different storage locations and obtaining a target segmentation set.

[0075] It should be noted that the method for obtaining the target segmentation set includes: based on the segmentation control set, respectively obtaining the data segmentation items corresponding to the segmentation control set, and obtaining the segmentation value matching the data segmentation item; obtaining the data fragments of the data segmentation item, and based on the matching segmentation value, dividing the data fragments into equal parts, and cutting the data segmentation item into a number of data sub-segments corresponding to the segmentation value. A data segmentation item includes at least one data sub-segment, and the data sub-segments contained in the data segmentation item are combined to obtain the target segmentation set.

[0076] In the specific implementation process, Figure 5 As shown, when 100 videos need to be stored, they are stored in 4 hard disks, namely disk a, disk b, disk c and disk d. It is obtained that disk a and disk c are both medium-level aging, disk b is low-level aging, and disk d is high-level aging. The segmentation values ​​of disk a, disk b, disk c and disk d are 3, 2, 3, and 4 respectively. At this time, the video divided into disk a needs to be divided into 3 parts, the video divided into disk b needs to be divided into 2 parts, the video divided into disk c needs to be divided into 3 parts, and the video divided into disk d needs to be divided into 4 parts.

[0077] Based on the target segmentation set, the data segmentation items are compared, corrected, and restored and reserved through the matching method.

[0078] It should be noted that error correction and rapid recovery of storage partition data at different degrees of aging are achieved through a matching method assisted by artificial intelligence. The matching method includes: based on the target segmentation set, the target data item is divided into comparison segments, and the initial segment feature information of the target data item is obtained to obtain the initial information item. Since the target data item is secondary cut according to the target segmentation set, the data after the initial segmentation is secondary segmented and further split into comparison segments; the segment feature information of the target segmentation set is obtained to obtain the segmentation information item, that is, the feature information of each segment, and the segmentation information item and the initial information item corresponding to the segmentation information item are compared to determine whether the segmentation information item is abnormal. When the segmentation information item is abnormal, the abnormal segment information is obtained to obtain the abnormal segment item, and the corresponding segment in the initial information item is obtained based on the abnormal segment item to obtain the replacement segment item, and then the abnormal segment item is replaced and restored.

[0079] An artificial intelligence-assisted data error correction and recovery system uses the above-mentioned artificial intelligence-assisted data error correction and recovery method, including: an information acquisition module: acquiring data information to be transmitted, obtaining target data items, obtaining storage allocation information of the target data items, performing a first segmentation on the target data items based on the storage allocation information, and obtaining data segmentation items; an aging calculation module: creating a quantitative formula for the degree of hard disk aging, calculating the degree of aging of the storage allocation information, obtaining a target aging set, classifying the degree of aging, obtaining aging grade items, and at the same time determining the aging grade of the storage allocation information through a determination method, and obtaining a target grade item; segmenting the storage allocation information, and performing a first segmentation on the target data items. Storage module: Set split values ​​based on aging level items, obtain corresponding split values ​​based on target level items, and obtain split comparison sets. The split comparison sets correspond to storage allocation information respectively. Based on data cutting items, obtain corresponding storage target information to obtain storage target sets. The storage target sets are used to indicate that target data items cut into different numbers are stored separately. Based on the split comparison set, perform secondary splitting on the data cutting items corresponding to the split comparison set, and then obtain splitting schemes for data cutting items in different storage locations to obtain target cutting sets. Error correction and recovery module: Based on the target cutting set, compare and correct the data cutting items and perform recovery reservations through matching methods.

[0080] Although embodiments of the present invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and variations may be made to these embodiments without departing from the principles and spirit of the invention, and that the scope of the invention is limited by the accompanying embodiments and their equivalents.

Claims

1. An artificial intelligence-assisted data error correction and recovery method, comprising: Obtain the data information to be transmitted, obtain the target data item, and obtain the storage allocation information of the target data item; Performing a first cut on the target data item based on the storage allocation information to obtain a data cut item, where the data cut item is used to indicate the cut quantity of the target data item; Its characteristics are: Create a quantitative formula for hard disk aging, calculate the aging degree of storage allocation information through artificial intelligence calculation methods, and obtain the target aging set; The aging degree is classified into grades to obtain aging grade items, and the aging grade of the storage allocation information is determined by a determination method to obtain target grade items. Segmentation values ​​are set based on the aging grade items, and corresponding segmentation values ​​are obtained based on the target grade items to obtain segmentation comparison sets, wherein the segmentation comparison sets correspond to the storage allocation information respectively. Based on the data cutting items, the corresponding storage target information is obtained to obtain a storage target set, which is used to indicate that target data items cut into different quantities are stored separately; Based on the segmentation reference set, the data segmentation items corresponding to the segmentation reference set are split twice, and then the segmentation plan of the data segmentation items in different storage locations is obtained, and the target segmentation set is obtained. Based on the target segmentation set, the data segmentation items are compared and corrected, and restored and reserved through the matching method, thereby realizing error correction and rapid recovery of storage partition data at different aging levels with the assistance of artificial intelligence.

2. The artificial intelligence-assisted data error correction and recovery method according to claim 1, characterized in that: The quantitative formula for the hard disk aging degree includes: ； in For the degree of aging, is Euler's constant, represents the erase attenuation coefficient, Indicates the current cumulative number of erase and write times. Indicates the maximum number of erase and write times. represents the temperature sensitivity coefficient, Indicates the hard disk operating temperature. Indicates the reference temperature, Indicates the amount of data written, represents the master optimization factor, Indicates the cumulative power-on time. represents the time decay weight, Indicates the remaining available capacity. Indicates the total capacity.

3. The artificial intelligence-assisted data error correction and recovery method according to claim 1, characterized in that: The method for obtaining the aging level item includes: Setting at least two aging degree range data to obtain aging range items; The aging range items are sorted in order from low to high to obtain aging sorting items, and at least two assessment levels are set to correspond to the aging sorting items respectively to obtain aging level items.

4. The artificial intelligence-assisted data error correction and recovery method according to claim 1, characterized in that: The method for setting the segmentation value includes: Based on the aging level items, level corresponding values ​​are set respectively, and the level corresponding values ​​are times values, so as to obtain at least two level corresponding items; Based on the target level item, obtain the corresponding level corresponding item, obtain the target corresponding item, obtain the average value of the target corresponding item, obtain the corresponding average item, use the corresponding average item as the initial score, set the incremental value, and obtain the score division item based on the combination result of the incremental value and the initial score. The score division item is respectively used as the cutting value corresponding to the target level item.

5. The artificial intelligence-assisted data error correction and recovery method according to claim 1, characterized in that: The method for acquiring the target segmentation set includes: Based on the segmentation control set, respectively obtain the data segmentation items corresponding to the segmentation control set, and obtain the segmentation values ​​matching the data segmentation items; Obtain data segments of the data cutting item, and divide the data segments into equal parts based on the matching segmentation value, cutting the data cutting item into data sub-segments of a number corresponding to the segmentation value. A data cutting item includes at least one data sub-segment, and the data sub-segments contained in the data cutting item are combined to obtain the target segmentation set.

6. The artificial intelligence-assisted data error correction and recovery method according to claim 1, characterized in that: The matching method includes: Based on the target segmentation set, the target data item is divided into contrast segments, and initial segment feature information of the target data item is obtained to obtain an initial information item; Obtain the fragment feature information of the target segmentation set to obtain the segmentation information item, compare the segmentation information item with the initial information item corresponding to the segmentation information item, and determine whether the segmentation information item is abnormal. When the segmentation information item is abnormal, obtain the abnormal fragment information to obtain the abnormal fragment item, and obtain the corresponding fragment in the initial information item based on the abnormal fragment item to obtain the replacement fragment item, and then replace and restore the abnormal fragment item.

7. The artificial intelligence-assisted data error correction and recovery method according to claim 1, characterized in that: The method for obtaining the data cutting item includes: Acquire quantity information of storage allocation information to obtain storage quantity items, where the storage quantity items represent different types of storage modules used to store the target data item; Based on the quantity information of the stored quantity items, a target cutting amount is obtained, and based on the target cutting amount, a corresponding amount of cutting is performed on the target data item to obtain a data cutting item.

8. The artificial intelligence-assisted data error correction and recovery method according to claim 1, characterized in that: The method for acquiring the storage target set includes: The importance of the data cutting items is sorted to obtain data sorting items, which are used to indicate the order of importance of the data cutting items from high to low. Based on the comparison and matching results of the target level item and the data sorting item, the comparison storage level of the data cutting item is obtained, and the corresponding storage target set information is obtained based on the comparison storage level.

9. An artificial intelligence-assisted data error correction and recovery system, characterized by: An artificial intelligence-assisted data error correction and recovery method according to any one of claims 1 to 8 is used, comprising: Information acquisition module: obtains the data information to be transmitted, obtains the target data item, obtains the storage allocation information of the target data item, performs the first segmentation on the target data item based on the storage allocation information, and obtains the data segmentation item; Aging calculation module: Creates a quantitative formula for hard disk aging, calculates the aging degree of storage allocation information, obtains a target aging set, classifies the aging degree into grades, obtains aging grade items, and simultaneously determines the aging grade of storage allocation information using a determination method to obtain a target grade item; Segmentation and storage module: Segmentation values ​​are set based on the aging level items, and corresponding segmentation values ​​are obtained based on the target level items to obtain segmentation reference sets. The segmentation reference sets correspond to the storage allocation information respectively. Based on the data segmentation items, the corresponding storage target information is obtained to obtain the storage target set. The storage target set is used to indicate that target data items segmented into different numbers are stored separately. Based on the segmentation reference set, the data segmentation items corresponding to the segmentation reference set are segmented twice to obtain segmentation schemes for data segmentation items in different storage locations and obtain the target segmentation set. Error correction and recovery module: Based on the target segmentation set, the data segmentation items are compared, corrected, and restored through matching methods.

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