Pre-reading method and device based on SSD fault prediction, equipment and storage medium

Abstract

The invention discloses a pre-reading method, device and equipment based on SSD fault prediction and a storage medium, and relates to the technical field of solid state disks. The method comprises the steps of collecting operation data of the solid state disk; performing fault prediction on the solid state disk based on the operation data of the solid state disk through a pre-trained fault prediction model to obtain a fault prediction result; determining a pre-reading strategy of the solid state disk based on the fault prediction result; and configuring pre-reading parameters of the solid state disk based on the pre-reading strategy. According to the method, the pre-reading strategy can be automatically adjusted based on the running condition of the solid state disk, and the data security is improved while the data access speed is ensured; the method has remarkable technical effects and advantages in the aspects of improving the data access efficiency, reducing the SSD fault risk, ensuring the data security, improving the system stability and reliability, optimizing the user experience, reducing the maintenance cost and the like, and has important practical value and application prospect.

Description

Technical Field

[0001] The present invention relates to the technical field of solid state drives, and in particular to a pre-reading method, device, equipment and storage medium based on SSD fault prediction. Background Art

[0002] Solid State Drive (SSD) plays an increasingly important role in modern storage systems due to its high-speed data read and write capabilities and low power consumption. As an effective means to improve the efficiency of SSD data access, read-ahead technology has been widely studied and applied.

[0003] Existing pre-reading technology mainly predicts data access patterns, by analyzing historical data access records, predicting data blocks that may be accessed in the future, and reading them into the cache in advance to reduce data access latency. This technology improves data access speed to a certain extent, but there are still the following problems in actual applications:

[0004] Lack of failure prediction capabilities: Existing read-ahead technologies focus mainly on data access patterns without fully considering the health status and failure risk of the SSD. When an SSD is about to fail, traditional read-ahead strategies may exacerbate SSD wear and increase the risk of data loss.

[0005] Single pre-reading strategy: Existing pre-reading strategies often use fixed pre-reading data block size and pre-reading depth, and cannot be dynamically adjusted according to the actual health status of the SSD and changes in access patterns. This results in the pre-reading strategy being too conservative when the SSD is healthy, and being too aggressive when the SSD is about to fail.

[0006] Insufficient data security: When an SSD is about to fail, existing pre-reading technologies often do not have an effective data protection mechanism and cannot ensure the security of critical data. Once an SSD fails, data may be lost or damaged, causing serious losses to users. Summary of the invention

[0007] Embodiments of the present invention provide a pre-reading method, apparatus, device and storage medium based on SSD fault prediction, aiming to solve at least one technical problem in the above-mentioned background technology.

[0008] In a first aspect, an embodiment of the present invention provides a pre-reading method based on SSD fault prediction, which includes:

[0009] Collect the operation data of the solid state drive;

[0010] Performing fault prediction on the solid state drive based on the operation data of the solid state drive using a pre-trained fault prediction model to obtain a fault prediction result;

[0011] Determining a pre-reading strategy for the solid state drive based on the fault prediction result;

[0012] The pre-reading parameters of the solid state drive are configured based on the pre-reading strategy.

[0013] A further technical solution is that the collecting of the operating data of the solid state hard disk includes:

[0014] The SMART attributes and I / O performance indicators of the solid-state drive are collected, wherein the SMART attributes include temperature, number of relocated sectors, and power-on time; and the I / O performance indicators include read and write speed and response time.

[0015] A further technical solution is that the fault prediction result includes a health status score and / or a fault probability of the solid state drive.

[0016] A further technical solution is that the pre-reading strategy of the solid state drive is determined based on the fault prediction result, including:

[0017] Determining a failure risk level of the solid state drive according to a health status score and / or a failure probability of the solid state drive;

[0018] A pre-reading strategy corresponding to the solid state drive is determined based on the failure risk level.

[0019] A further technical solution is that determining the operating state of the solid state drive according to the health status score and / or failure probability of the solid state drive includes:

[0020] The health status score of the solid state drive and / or the failure risk level of the solid state drive corresponding to the failure probability are determined based on a preset corresponding relationship.

[0021] A further technical solution is that the fault risk level of the solid state hard disk includes a normal state, a warning state and a dangerous state, and the method further includes:

[0022] If the failure risk level of the solid state drive is in a dangerous state, data migration and backup are performed on the solid state drive.

[0023] A further technical solution is that the method further comprises:

[0024] Build training samples based on historical operation data of SSDs;

[0025] The initial model is trained through training samples to obtain the pre-trained fault prediction model.

[0026] In a second aspect, an embodiment of the present invention further provides a pre-reading device based on SSD fault prediction, which includes a unit for executing the above method.

[0027] In a third aspect, an embodiment of the present invention further provides a computer device, which includes a memory and a processor, wherein a computer program is stored in the memory, and the processor implements the above method when executing the computer program.

[0028] In a fourth aspect, an embodiment of the present invention further provides a computer-readable storage medium, wherein the storage medium stores a computer program, and the computer program can implement the above method when executed by a processor.

[0029] The embodiments of the present invention provide a pre-reading method, device, equipment and storage medium based on SSD fault prediction. The method includes: collecting the operation data of the solid-state hard disk; predicting the fault of the solid-state hard disk based on the operation data of the solid-state hard disk through a pre-trained fault prediction model to obtain a fault prediction result; determining the pre-reading strategy of the solid-state hard disk based on the fault prediction result; and configuring the pre-reading parameters of the solid-state hard disk based on the pre-reading strategy. The present invention can automatically adjust the pre-reading strategy based on the operation status of the solid-state hard disk, improve the security of the data while ensuring the data access speed; it has significant technical effects and advantages in improving data access efficiency, reducing the risk of SSD failure, ensuring data security, improving system stability and reliability, optimizing user experience and reducing maintenance costs, and has important practical value and application prospects. BRIEF DESCRIPTION OF THE DRAWINGS

[0030] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying any creative work.

[0031] Figure 1 A schematic diagram of a flow chart of a pre-reading method based on SSD fault prediction provided by an embodiment of the present invention;

[0032] Figure 2 A block diagram of the system architecture of a pre-reading method based on SSD fault prediction provided by an embodiment of the present invention

[0033] Figure 3 A schematic block diagram of a computer device provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0034] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are 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 creative work are within the scope of protection of the present invention.

[0035] It should be understood that when used in this specification and the appended claims, the terms "include" and "comprises" indicate the presence of described features, integers, steps, operations, elements and / or components, but do not exclude the presence or addition of one or more other features, integers, steps, operations, elements, components and / or combinations thereof.

[0036] It should also be understood that the terms used in this specification of the present invention are only for the purpose of describing specific embodiments and are not intended to limit the present invention. As used in the specification of the present invention and the appended claims, unless the context clearly indicates otherwise, the singular forms "a", "an" and "the" are intended to include plural forms.

[0037] It should be further understood that the term "and / or" used in the present description and the appended claims refers to any and all possible combinations of one or more of the associated listed items, and includes these combinations.

[0038] As used in this specification and the appended claims, the term "if" may be interpreted as "when" or "upon" or "in response to determining" or "in response to detecting," depending on the context. Similarly, the phrases "if it is determined" or "if [described condition or event] is detected" may be interpreted as meaning "upon determination" or "in response to determining" or "upon detection of [described condition or event]" or "in response to detecting [described condition or event]," depending on the context.

[0039] See also Figure 1-Figure 2 An embodiment of the present invention provides a pre-reading method based on SSD fault prediction, which can automatically adjust the pre-reading strategy based on the operating status of the solid-state drive, thereby improving data security while ensuring data access speed.

[0040] See also Figure 2 The functions and connections between the modules in the system architecture of the present invention are as follows:

[0041] 1. SSD10: storage device, serving as the core of data storage.

[0042] 2. Fault prediction module 20: collects SSD operation data, performs feature extraction and fault prediction.

[0043] 3. Pre-reading strategy module 30: adjusting the pre-reading strategy according to the fault prediction result.

[0044] 4. Data access module 40: processes data access requests and reads data from the SSD.

[0045] 5. Data protection module 50: triggers data protection operation when the SSD fault prediction result is abnormal.

[0046] See also Figure 1 ,like Figure 1 As shown, the method comprises the following steps:

[0047] S1, collects the operating data of the solid state drive.

[0048] In a specific implementation, the operation data of the solid state hard disk is collected when it is working, and the operation data can reflect the operation status of the solid state hard disk.

[0049] For example, in some embodiments, the above step of "collecting operating data of the solid-state drive" specifically includes the following steps: collecting the SMART attributes and I / O performance indicators of the solid-state drive, the SMART attributes including temperature, number of relocated sectors and power-on time; the I / O performance indicators including read and write speed and response time.

[0050] In a specific implementation, the operation data of the solid-state drive includes the SMART attributes and I / O performance indicators of the solid-state drive. The SMART attributes include data such as temperature, number of relocated sectors, and power-on time. The I / O performance indicators include data such as read and write speed and response time.

[0051] Of course, the operation data of the solid state drive may also include other data that can reflect the operation status of the solid state drive, which is not specifically limited in the present invention.

[0052] S2, performing fault prediction on the solid state drive based on the operation data of the solid state drive using a pre-trained fault prediction model to obtain a fault prediction result.

[0053] In a specific implementation, the fault prediction model can be constructed based on a neural network model, which is not specifically limited in the present invention. By inputting the operating data of the solid-state drive into a pre-trained fault prediction model, the fault prediction model performs fault prediction on the solid-state drive based on the operating data of the solid-state drive to obtain a fault prediction result. Specifically, in some embodiments, the fault prediction result includes a health status score and / or a failure probability of the solid-state drive.

[0054] In the embodiment of the present invention, through pre-training, the fault prediction model has the ability to predict the presence of abnormalities in the SSD audit. Specifically, the health status score and / or failure probability of the SSD can be predicted. The lower the health status score, the higher the probability of failure.

[0055] For example, in some embodiments, in order to better implement the model training process, the method also includes: constructing training samples based on historical operation data of the solid-state drive; training the initial model through the training samples to obtain the pre-trained fault prediction model.

[0056] In the specific implementation, the historical operation data of the solid-state drive is collected, the collected data is preprocessed, the features that have an important impact on the health status of the SSD are extracted, and training samples are constructed (the training samples need to be labeled); the initial model is trained with the training samples to obtain the pre-trained fault prediction model.

[0057] S3, determining a pre-reading strategy for the solid state drive based on the fault prediction result.

[0058] In a specific implementation, the pre-reading strategy of the solid state drive is determined according to the fault prediction result, so that the pre-reading strategy of the solid state drive matches the fault state of the solid state drive.

[0059] For example, in some embodiments, the above step of "determining the pre-reading strategy of the solid-state drive based on the fault prediction result" specifically includes the following steps: determining the failure risk level of the solid-state drive according to the health status score and / or failure probability of the solid-state drive; determining the pre-reading strategy corresponding to the solid-state drive based on the failure risk level.

[0060] In a specific implementation, the health status score of the solid state drive and / or the failure probability corresponding to the fault risk level of the solid state drive are determined based on a preset corresponding relationship. The corresponding relationship can be set by a person skilled in the art according to requirements, and the embodiment of the present invention does not specifically limit this. The health status score of the solid state drive and / or the failure probability corresponding to the fault risk level can be determined through the corresponding relationship.

[0061] Further, the pre-reading strategy corresponding to the SSD is determined based on the fault risk level. Each fault risk level is pre-configured with a corresponding pre-reading strategy, so after determining the fault risk level of the SSD, the pre-reading strategy corresponding to the SSD can be determined.

[0062] In some embodiments, the failure risk level of the solid state drive includes a normal state, a warning state, and a dangerous state, and the method further includes: if the failure risk level of the solid state drive is a dangerous state, performing data migration and backup on the solid state drive.

[0063] In the specific implementation, if the SSD is in a normal state, the default pre-reading strategy is adopted;

[0064] If the SSD is in warning status, increase the pre-read depth or pre-read block size to improve data access efficiency;

[0065] If the SSD is in a critical state, data backup and migration are required immediately, and the read-ahead policy should be adjusted to reduce additional pressure on the SSD.

[0066] S4, configuring pre-reading parameters of the solid state drive based on the pre-reading strategy.

[0067] In a specific implementation, the pre-reading strategy includes pre-reading parameters, such as pre-reading depth, pre-reading block size, pre-reading triggering conditions, etc. In a specific implementation, the pre-reading depth, pre-reading block size, pre-reading triggering conditions, etc. of the solid state drive are configured based on the pre-reading strategy.

[0068] Further, after configuring the pre-reading parameters of the solid-state drive based on the pre-reading strategy, jump to step S1 to collect the operating data of the solid-state drive after the pre-reading strategy is configured. Monitor the performance of the SSD in real time after executing the new pre-reading strategy, including data reading speed, response time, etc. According to the performance monitoring results, fine-tune the pre-reading strategy to further optimize the performance.

[0069] By returning the process to the SSD status monitoring stage, the health status and performance of the SSD are continuously monitored, and the pre-reading strategy is dynamically adjusted to adapt to changes in the SSD based on new data and situations.

[0070] The embodiment of the present invention proposes a pre-reading method based on SSD fault prediction, including: collecting the operation data of the solid-state hard disk; predicting the fault of the solid-state hard disk based on the operation data of the solid-state hard disk through a pre-trained fault prediction model to obtain a fault prediction result; determining the pre-reading strategy of the solid-state hard disk based on the fault prediction result; configuring the pre-reading parameters of the solid-state hard disk based on the pre-reading strategy. The present invention can automatically adjust the pre-reading strategy based on the operation status of the solid-state hard disk, while ensuring the data access speed, improving the security of the data; it has significant technical effects and advantages in improving data access efficiency, reducing the risk of SSD failure, ensuring data security, improving system stability and reliability, optimizing user experience and reducing maintenance costs, and has important practical value and application prospects.

[0071] Corresponding to the above pre-reading method based on SSD fault prediction, the present invention also provides a pre-reading device based on SSD fault prediction. The pre-reading device based on SSD fault prediction includes a unit for executing the above pre-reading method based on SSD fault prediction, and the pre-reading device based on SSD fault prediction can be configured in a desktop computer, a tablet computer, a laptop computer, and other terminals. Specifically, the pre-reading device based on SSD fault prediction includes:

[0072] A collection unit, used for collecting operation data of the solid state hard disk;

[0073] A prediction unit, configured to perform fault prediction on the solid state drive based on the operation data of the solid state drive by using a pre-trained fault prediction model to obtain a fault prediction result;

[0074] A determination unit, configured to determine a pre-reading strategy of the solid state drive based on the fault prediction result;

[0075] A configuration unit is used to configure the pre-reading parameters of the solid state drive based on the pre-reading strategy.

[0076] In some embodiments, collecting the operating data of the solid state drive includes:

[0077] The SMART attributes and I / O performance indicators of the solid-state drive are collected, wherein the SMART attributes include temperature, number of relocated sectors, and power-on time; and the I / O performance indicators include read and write speed and response time.

[0078] In some embodiments, the failure prediction result includes a health status score and / or a failure probability of the solid state drive.

[0079] In some embodiments, determining the pre-reading strategy of the solid state drive based on the fault prediction result includes:

[0080] Determining a failure risk level of the solid state drive according to a health status score and / or a failure probability of the solid state drive;

[0081] A pre-reading strategy corresponding to the solid state drive is determined based on the failure risk level.

[0082] In some embodiments, determining the operating status of the solid state drive according to the health status score and / or failure probability of the solid state drive includes:

[0083] The health status score of the solid state drive and / or the failure risk level of the solid state drive corresponding to the failure probability are determined based on a preset corresponding relationship.

[0084] In some embodiments, the failure risk level of the solid state drive includes a normal state, a warning state, and a dangerous state, and the pre-reading device based on SSD failure prediction further includes:

[0085] The migration and backup unit is used to migrate and back up data on the solid state drive if the failure risk level of the solid state drive is in a dangerous state.

[0086] In some embodiments, the pre-reading device based on SSD failure prediction further includes:

[0087] A construction unit, used for constructing a training sample based on historical operation data of the solid state drive;

[0088] The training unit is used to train the initial model through training samples to obtain the pre-trained fault prediction model.

[0089] It should be noted that technicians in the relevant field can clearly understand that the specific implementation process of the above-mentioned pre-reading device based on SSD fault prediction and each unit can refer to the corresponding description in the aforementioned method embodiment, and for the convenience and brevity of description, it will not be repeated here.

[0090] The above-mentioned pre-reading device based on SSD fault prediction can be implemented in the form of a computer program. The computer program can be used in Figure 3 Runs on the computer device shown.

[0091] See also Figure 3 , Figure 3 5 is a schematic block diagram of a computer device provided in an embodiment of the present application. The computer device 500 may be a terminal or a server, wherein the terminal may be an electronic device with communication functions such as a smart phone, a tablet computer, a laptop computer, a desktop computer, a personal digital assistant, and a wearable device. The server may be an independent server or a server cluster composed of multiple servers.

[0092] The computer device 500 includes a processor 502 , a memory, and a network interface 505 connected via a system bus 501 , wherein the memory may include a non-volatile storage medium 503 and an internal memory 504 .

[0093] The non-volatile storage medium 503 may store an operating system 5031 and a computer program 5032. When the computer program 5032 is executed, the processor 502 may execute a pre-reading method based on SSD failure prediction.

[0094] The processor 502 is used to provide computing and control capabilities to support the operation of the entire computer device 500 .

[0095] The internal memory 504 provides an environment for the operation of the computer program 5032 in the non-volatile storage medium 503. When the computer program 5032 is executed by the processor 502, the processor 502 can execute a pre-reading method based on SSD fault prediction.

[0096] The network interface 505 is used to communicate with other devices over the network. Those skilled in the art will appreciate that the above structure is only a block diagram of a portion of the structure related to the present application solution, and does not constitute a limitation on the computer device 500 to which the present application solution is applied. The specific computer device 500 may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0097] The processor 502 is used to run the computer program 5032 stored in the memory to implement the following steps:

[0098] Collect the operation data of the solid state drive;

[0099] Performing fault prediction on the solid state drive based on the operation data of the solid state drive using a pre-trained fault prediction model to obtain a fault prediction result;

[0100] Determining a pre-reading strategy for the solid state drive based on the fault prediction result;

[0101] The pre-reading parameters of the solid state drive are configured based on the pre-reading strategy.

[0102] In some embodiments, collecting the operating data of the solid state drive includes:

[0103] The SMART attributes and I / O performance indicators of the solid-state drive are collected, wherein the SMART attributes include temperature, number of relocated sectors, and power-on time; and the I / O performance indicators include read and write speed and response time.

[0104] In some embodiments, the failure prediction result includes a health status score and / or a failure probability of the solid state drive.

[0105] In some embodiments, determining the pre-reading strategy of the solid state drive based on the fault prediction result includes:

[0106] Determining a failure risk level of the solid state drive according to a health status score and / or a failure probability of the solid state drive;

[0107] A pre-reading strategy corresponding to the solid state drive is determined based on the failure risk level.

[0108] In some embodiments, determining the operating status of the solid state drive according to the health status score and / or failure probability of the solid state drive includes:

[0109] The health status score of the solid state drive and / or the failure risk level of the solid state drive corresponding to the failure probability are determined based on a preset corresponding relationship.

[0110] In some embodiments, the failure risk level of the solid state drive includes a normal state, a warning state, and a dangerous state, and the method further includes:

[0111] If the failure risk level of the solid state drive is in a dangerous state, data migration and backup are performed on the solid state drive.

[0112] In some embodiments, the method further comprises:

[0113] Build training samples based on historical operation data of SSDs;

[0114] The initial model is trained through training samples to obtain the pre-trained fault prediction model.

[0115] It should be understood that in the embodiment of the present application, the processor 502 may be a central processing unit (CPU), and the processor 502 may also be other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. Among them, the general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc.

[0116] It is understood by those skilled in the art that all or part of the processes in the method for implementing the above embodiment can be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a storage medium, which is a computer-readable storage medium. The computer program is executed by at least one processor in the computer system to implement the process steps of the embodiment of the above method.

[0117] Therefore, the present invention also provides a storage medium. The storage medium may be a computer-readable storage medium. The storage medium stores a computer program. When the computer program is executed by a processor, the processor executes the following steps:

[0118] Collect the operation data of the solid state drive;

[0119] Performing fault prediction on the solid state drive based on the operation data of the solid state drive using a pre-trained fault prediction model to obtain a fault prediction result;

[0120] Determining a pre-reading strategy for the solid state drive based on the fault prediction result;

[0121] The pre-reading parameters of the solid state drive are configured based on the pre-reading strategy.

[0122] In some embodiments, collecting the operating data of the solid state drive includes:

[0123] The SMART attributes and I / O performance indicators of the solid-state drive are collected, wherein the SMART attributes include temperature, number of relocated sectors, and power-on time; and the I / O performance indicators include read and write speed and response time.

[0124] In some embodiments, the failure prediction result includes a health status score and / or a failure probability of the solid state drive.

[0125] In some embodiments, determining the pre-reading strategy of the solid state drive based on the fault prediction result includes:

[0126] Determining a failure risk level of the solid state drive according to a health status score and / or a failure probability of the solid state drive;

[0127] A pre-reading strategy corresponding to the solid state drive is determined based on the failure risk level.

[0128] In some embodiments, determining the operating status of the solid state drive according to the health status score and / or failure probability of the solid state drive includes:

[0129] The health status score of the solid state drive and / or the failure risk level of the solid state drive corresponding to the failure probability are determined based on a preset corresponding relationship.

[0130] In some embodiments, the failure risk level of the solid state drive includes a normal state, a warning state, and a dangerous state, and the method further includes:

[0131] If the failure risk level of the solid state drive is in a dangerous state, data migration and backup are performed on the solid state drive.

[0132] In some embodiments, the method further comprises:

[0133] Build training samples based on historical operation data of SSDs;

[0134] The initial model is trained through training samples to obtain the pre-trained fault prediction model.

[0135] The storage medium is a physical, non-transient storage medium, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a magnetic disk, or an optical disk, etc., which can store program codes. The computer-readable storage medium can be non-volatile or volatile.

[0136] Those of ordinary skill in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of the two. In order to clearly illustrate the interchangeability of hardware and software, the composition and steps of each example have been generally described in terms of function in the above description. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of the present invention.

[0137] In the several embodiments provided by the present invention, it should be understood that the disclosed devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of each unit is only a logical function division, and there may be other division methods in actual implementation. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed.

[0138] The steps in the method of the embodiment of the present invention can be adjusted in order, combined and deleted according to actual needs. The units in the device of the embodiment of the present invention can be combined, divided and deleted according to actual needs. In addition, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit.

[0139] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a storage medium. Based on this understanding, the technical solution of the present invention is essentially or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions for a computer device (which can be a personal computer, terminal, or network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention.

[0140] In the above embodiments, the description of each embodiment has its own emphasis. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0141] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include these modifications and variations.

[0142] The above is only a specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any technician familiar with the technical field can easily think of various equivalent modifications or replacements within the technical scope disclosed by the present invention, and these modifications or replacements should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention shall be based on the protection scope of the claims.

Claims

1. A pre-reading method based on SSD fault prediction, characterized in that: include: Collect the operation data of the solid state drive; Performing fault prediction on the solid state drive based on the operation data of the solid state drive using a pre-trained fault prediction model to obtain a fault prediction result; Determining a pre-reading strategy for the solid state drive based on the fault prediction result; The pre-reading parameters of the solid state drive are configured based on the pre-reading strategy.

2. The pre-reading method based on SSD fault prediction according to claim 1, characterized in that: The collecting of the operation data of the solid state drive includes: The SMART attributes and I / O performance indicators of the solid-state drive are collected, wherein the SMART attributes include temperature, number of relocated sectors, and power-on time; and the I / O performance indicators include read and write speed and response time.

3. The pre-reading method based on SSD fault prediction according to claim 1, characterized in that: The failure prediction result includes a health status score and / or a failure probability of the solid state drive.

4. The pre-reading method based on SSD fault prediction according to claim 3, characterized in that: The determining the pre-reading strategy of the solid state drive based on the fault prediction result includes: Determining a failure risk level of the solid state drive according to a health status score and / or a failure probability of the solid state drive; A pre-reading strategy corresponding to the solid state drive is determined based on the failure risk level.

5. The pre-reading method based on SSD fault prediction according to claim 4, characterized in that: Determining the operating state of the solid state drive according to the health state score and / or failure probability of the solid state drive includes: The health status score of the solid state drive and / or the failure risk level of the solid state drive corresponding to the failure probability are determined based on a preset corresponding relationship.

6. The pre-reading method based on SSD fault prediction according to claim 1, characterized in that: The failure risk level of the solid state drive includes a normal state, a warning state, and a dangerous state. The method further includes: If the failure risk level of the solid state drive is in a dangerous state, data migration and backup are performed on the solid state drive.

7. The pre-reading method based on SSD fault prediction according to claim 1, characterized in that: The method further comprises: Build training samples based on historical operation data of SSDs; The initial model is trained through training samples to obtain the pre-trained fault prediction model.

8. A pre-reading device based on SSD fault prediction, characterized in that: The method comprises a unit for executing the method according to any one of claims 1 to 7.

9. A computer device, characterized in that: The computer device includes a memory and a processor, the memory stores a computer program, and the processor implements the method according to any one of claims 1 to 7 when executing the computer program.

10. A computer-readable storage medium, characterized in that: The storage medium stores a computer program, and when the computer program is executed by a processor, the method according to any one of claims 1 to 7 can be implemented.

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