Mass data migration method and system for solid state disk and medium

Abstract

The invention provides a mass data migration method and system for a solid state disk and a medium. The method comprises the following steps: processing migration characteristic data to obtain migration capability state verification parameters, carrying out comprehensive processing in combination with a comparison result of a total value of data to be migrated and storage space data to obtain a data migration strategy, then executing data migration, and monitoring migration execution record data in real time, further analyzing and processing to obtain a migration execution state, if the migration execution state is normal execution, performing data verification processing on the migrated data, and if the verification is passed, judging that the data migration is completed; according to the method, migration time limit prediction data is analyzed and predicted and optimized, the migration time limit prediction data is compared with migration time limit demand data and a threshold value, migration capability state verification parameters are determined, a corresponding data migration strategy is generated, the data migration process is monitored and evaluated in real time, and finally the migrated data is comprehensively verified. Therefore, intelligent migration of mass data in the solid state disk is realized.

Description

Technical Field

[0001] The present application relates to the field of data storage and transmission technology, and in particular to a method, system, and medium for migrating massive data of a solid-state drive. Background Art

[0002] Solid-state drives (SSDs), a common tool for users to upgrade storage devices, replace system disks, or synchronize data, often store massive amounts of data. Traditional migration methods focus only on the start and end states of migration and lack effective control over the migration process. These methods suffer from low migration efficiency and a lack of intelligent processing methods. Migration strategies cannot be automatically optimized, and post-migration verification is relatively simple. Therefore, an efficient, reliable, and intelligent method for migrating massive amounts of data from SSDs is urgently needed.

[0003] In response to the above problems, effective technical solutions are urgently needed. Summary of the Invention

[0004] The purpose of this application is to provide a method, system and medium for migrating massive data in a solid-state hard drive. The method can analyze and predict the migration time prediction data and optimize the processing, compare it with the migration time requirement data and threshold, determine the migration capability status verification parameters, and then generate the corresponding data migration strategy. The data migration process is monitored and evaluated in real time, and finally the migrated data is comprehensively verified, thereby realizing the intelligent migration of massive data in the solid-state hard drive.

[0005] This application also provides a method for migrating massive amounts of data from a solid-state drive, comprising the following steps: Obtaining migration time requirement data, migration feature data, and storage space data of the migration target hard disk of the data to be migrated, and processing the migration feature data to obtain migration capability status verification parameters; Processing the migration capability status test parameters in combination with the storage space data to obtain a data migration strategy; Executing data migration according to the data migration policy, monitoring migration execution record data within a first preset time period, and processing the migration execution record data to obtain a migration execution status; The migrated data is verified. If the verification passes, the data migration is considered complete.

[0006] Optionally, in the method for migrating massive amounts of data from a solid-state drive described in the present application, obtaining migration time requirement data, migration feature data, and storage space data of the migration target hard drive for the data to be migrated, and processing the migration feature data to obtain migration capability status verification parameters includes: Obtaining migration time requirement data, migration feature data, and storage space data of the migration target hard disk for the data to be migrated; The migration feature data includes data category labels and corresponding data quantity and data value; Aggregating the data values ​​to obtain a total migration data value, and processing the data in combination with preset bandwidth data and bandwidth utilization to obtain migration time prediction data; Obtaining the time limit impact optimization coefficient of the migration time limit prediction data; Optimizing the migration time limit prediction data according to the time limit impact optimization coefficient to obtain migration time limit prediction optimization data; Comparing the migration time limit prediction optimization data with the migration time limit demand data to obtain a migration limit excess rate; The migration excess rate is compared with a preset migration excess warning threshold to obtain migration capacity status verification parameters, including sufficient migration capacity or insufficient migration capacity.

[0007] Optionally, in the method for mass data migration of a solid-state drive described in the present application, obtaining a time limit impact optimization coefficient of the migration time limit prediction data includes: Processing is performed based on the data value and the total value of the migrated data to obtain the data proportion corresponding to the data category label; According to the data category label, a mapping table of preset categories and migration time limit impact coefficients is searched to obtain the time limit impact coefficient corresponding to the data category label; The time limit impact coefficient is multiplied by the corresponding data proportion and aggregated to obtain the time limit impact optimization coefficient.

[0008] Optionally, in the method for migrating massive amounts of data in a solid-state drive described in the present application, the step of processing the migration capability status test parameter in combination with the storage space data to obtain a data migration strategy includes: comparing the storage space data with the total value of the migration data; If it is less than or equal to the total value of the migrated data, suspend data migration and output an early warning; If it is greater than the total value of the migration data, the over-capacity rate is obtained and compared with the preset over-capacity warning threshold; If it is less than or equal to the preset over-capacity warning threshold, data migration will be suspended and an alert will be issued; If the value is greater than the preset over-capacity warning threshold and the migration time limit prediction optimization data is less than or equal to the migration time limit requirement data, a first data migration strategy is generated; If it is greater than the preset over-capacity warning threshold and the migration capability status verification parameter indicates that the migration capability is sufficient, a second data migration strategy is generated; If it is greater than the preset over-capacity warning threshold and the migration capability status verification parameter is insufficient migration capability, a third data migration strategy is generated.

[0009] Optionally, the method for migrating massive amounts of data from a solid-state drive described in the present application further includes: Obtaining a migration test data set corresponding to the data category label, and processing the data set in combination with the bandwidth data, bandwidth utilization, and time limit impact optimization coefficient to obtain migration test time limit prediction data corresponding to the data category label; Perform data migration on the migration test data set according to the data migration strategy to obtain migration test time-consuming data corresponding to the data category label; Comparing the migration test time consumption data with the migration test time limit prediction data to obtain a migration test timeout rate, and comparing it with a preset migration test timeout threshold; If the value is less than or equal to the preset migration test timeout threshold, the migration test is considered normal. If it is greater than the preset migration test timeout threshold, the migration test is determined to be abnormal.

[0010] Optionally, in the method for mass data migration of a solid-state drive described in the present application, performing data migration according to the data migration policy, monitoring migration execution record data within a first preset time period, and processing the migration execution record data to obtain a migration execution status include: Executing data migration according to the data migration strategy and monitoring migration execution record data within a first preset time period; The migration execution record data includes the average migration rate and the average actual temperature written to the hard disk; Comparing the migration rate mean with the average migration rate in a second preset time period to obtain a migration rate fluctuation rate; Comparing the actual temperature average with the preset hard disk set operating temperature to obtain the over-temperature rate; Performing a weighted summation process on the migration rate fluctuation rate and the over-temperature rate to obtain a data migration evaluation parameter, and comparing the parameter with a preset migration status evaluation threshold; If the value is less than or equal to the preset migration status evaluation threshold, the migration execution status is determined to be normal; If it is greater than the preset migration status evaluation threshold, the migration execution status is determined to be abnormal execution.

[0011] Optionally, in the method for migrating massive amounts of data from a solid-state drive described in the present application, performing data verification on the migrated data and determining that the data migration is complete if the verification passes includes: Obtaining a migration data value of the migration completed data, and comparing it with the total migration data value; If they are the same, obtaining a second hash value of the migrated data and comparing it with a preset first hash value of the data to be migrated; If the comparison passes, the 4K alignment status of the data written to the hard disk is obtained; If aligned, the data migration is determined to be complete.

[0012] In a second aspect, the present application provides a system for mass data migration of a solid-state drive, the system comprising: a memory and a processor, the memory comprising a program for a method for mass data migration of a solid-state drive, the program for the method for mass data migration of a solid-state drive, when executed by the processor, implementing the following steps: Obtaining migration time requirement data, migration feature data, and storage space data of the migration target hard disk of the data to be migrated, and processing the migration feature data to obtain migration capability status verification parameters; Processing the migration capability status test parameters in combination with the storage space data to obtain a data migration strategy; Executing data migration according to the data migration policy, monitoring migration execution record data within a first preset time period, and processing the migration execution record data to obtain a migration execution status; The migrated data is verified. If the verification passes, the data migration is considered complete.

[0013] Optionally, in the massive data migration system for a solid-state drive described in the present application, obtaining migration time requirement data, migration feature data, and storage space data of the migration target hard drive of the data to be migrated, and processing the migration feature data to obtain migration capability status verification parameters includes: Obtaining migration time requirement data, migration feature data, and storage space data of the migration target hard disk for the data to be migrated; The migration feature data includes data category labels and corresponding data quantity and data value; Aggregating the data values ​​to obtain a total migration data value, and processing the data in combination with preset bandwidth data and bandwidth utilization to obtain migration time prediction data; Obtaining the time limit impact optimization coefficient of the migration time limit prediction data; Optimizing the migration time limit prediction data according to the time limit impact optimization coefficient to obtain migration time limit prediction optimization data; Comparing the migration time limit prediction optimization data with the migration time limit demand data to obtain a migration limit excess rate; The migration excess rate is compared with a preset migration excess warning threshold to obtain migration capacity status verification parameters, including sufficient migration capacity or insufficient migration capacity.

[0014] In a third aspect, the present application also provides a computer-readable storage medium, which stores a program for a method for migrating massive data for a solid-state hard drive. When the program for a method for migrating massive data for a solid-state hard drive is executed by a processor, the steps of the method for migrating massive data for a solid-state hard drive as described in any one of the above items are implemented.

[0015] From the above, it can be seen that the present application provides a method, system and medium for migrating massive data for solid-state hard drives. By analyzing and predicting migration time prediction data and optimizing the processing, comparing it with the migration time requirement data and threshold, it determines the migration capability status verification parameters, and then generates the corresponding data migration strategy. It monitors and evaluates the data migration process in real time, and finally performs comprehensive verification on the migrated data, thereby realizing the intelligent migration of massive data in solid-state hard drives.

[0016] Other features and advantages of the present application will be described in the following description, and in part will become apparent from the description, or understood by practicing the embodiments of the present application. The objectives and other advantages of the present application can be achieved and obtained through the structures particularly pointed out in the written description and the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the following is a brief introduction to the drawings required for use in the embodiments of the present application. It should be understood that the following drawings only show certain embodiments of the present application and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other relevant drawings can be obtained based on these drawings without creative work.

[0018] Figure 1 A flowchart of a method for migrating massive data from a solid-state drive provided in an embodiment of the present application; Figure 2 A flowchart of obtaining migration capability status test parameters of a method for migrating massive data of a solid-state drive provided in an embodiment of the present application; Figure 3 A flowchart of obtaining a time limit impact optimization coefficient for a method for migrating massive data on a solid-state drive provided in an embodiment of the present application; Figure 4 A flowchart of obtaining a migration execution status of a method for migrating massive data of a solid-state drive provided in an embodiment of the present application; Figure 5 1 is a high-level flow chart of various methods of the present application, which can be used for mass data migration methods of solid-state drives. DETAILED DESCRIPTION

[0019] The technical solutions in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all of the embodiments. The components of the embodiments of the present application generally described and shown in the drawings here can be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present application provided in the drawings is not intended to limit the scope of the application for protection, but merely represents the selected embodiments of the present application. Based on the embodiments of the present application, all other embodiments obtained by those skilled in the art without making creative work fall within the scope of protection of the present application.

[0020] It should be noted that similar reference numerals and letters represent similar items in the following drawings. Therefore, once an item is defined in one drawing, it does not need to be further defined or explained in subsequent drawings. At the same time, in the description of this application, the terms "first", "second", etc. are only used to distinguish the description and should not be understood as indicating or implying relative importance.

[0021] Please refer to Figure 1 , Figure 1 This is a flow chart of a method for migrating massive data from a solid-state drive (SSD) in some embodiments of the present application. This method for migrating massive data from a SSD is used in a terminal device, such as a computer or mobile phone. This method for migrating massive data from a SSD includes the following steps: S11, obtaining migration time requirement data, migration feature data, and storage space data of the migration target hard disk of the data to be migrated, and processing the migration feature data to obtain migration capability status verification parameters; S12, processing the migration capability status test parameter in combination with the storage space data to obtain a data migration strategy; S13: Execute data migration according to the data migration policy, monitor migration execution record data within a first preset time period, and process the migration execution record data to obtain a migration execution status; S14: Perform data verification on the migrated data. If the verification passes, the data migration is determined to be complete.

[0022] It should be noted that in order to achieve intelligent data migration, the migration feature data of the data to be migrated is first compared after migration time analysis and prediction and optimization, and the migration capability status test parameters are evaluated. At the same time, the corresponding data migration strategy is generated in combination with the storage space data of the migrated hard disk, such as normal execution, increased bandwidth data or offline migration. Afterwards, data migration is implemented according to the generated data migration strategy, and the migration execution record data is monitored in real time, analyzed and processed, and the migration execution status is evaluated, including normal execution or abnormal execution. Finally, after the migration is completed, a comprehensive verification of the data value, hash value and 4K alignment is performed. After the verification passes, the data migration is determined to be completed.

[0023] Please refer to Figure 2 , Figure 2 This is a flow chart of obtaining migration capability status verification parameters for a method for migrating massive amounts of data from a solid-state drive (SSD) in some embodiments of the present application. According to embodiments of the present invention, obtaining migration time requirement data, migration feature data, and storage space data of the migration target drive for the data to be migrated, and processing the migration feature data to obtain the migration capability status verification parameters includes: S21, obtaining migration time limit data, migration feature data, and storage space data of the migration target hard disk for the data to be migrated; S22, the migration feature data includes a data category label and corresponding data quantity and data value; S23: Aggregate the data values ​​to obtain a total migration data value, and process the data in combination with preset bandwidth data and bandwidth utilization to obtain migration time prediction data; S24. Obtaining a time limit impact optimization coefficient of the migration time limit prediction data; S25. Optimize the migration time limit prediction data according to the time limit impact optimization coefficient to obtain migration time limit prediction optimization data; S26. Compare the migration time limit prediction optimization data with the migration time limit requirement data to obtain a migration time limit excess rate; S27. Compare the migration excess rate with a preset migration excess warning threshold to obtain migration capacity status verification parameters, including sufficient migration capacity or insufficient migration capacity.

[0024] It should be noted that in order to assess the migration capability status and achieve accurate and effective data migration, first, migration feature data including data category labels and corresponding data quantity and data value are obtained. Among them, the data category label is used to indicate the data type, such as text data, log data, JSON data, image data, and video data, and the data value indicates the size of the data, such as a file of 4MB. The data values ​​of each data category label are aggregated to determine the total value of the migration data to be migrated. The theoretical time consumption is predicted based on the real-time bandwidth situation to obtain the migration time prediction data. For example, if the total value of the migration data is 10TB, the bandwidth data is a 10Gbps dedicated line, and the bandwidth utilization rate is 0.7, then (10x1024 4 ) / [(10x10 9 x0.7x3600) / 8]≈3.49h, which is the theoretical migration time prediction data. In practice, different file types and proportions will affect the migration time. Therefore, the time limit impact optimization coefficient of the migration time prediction data is obtained to optimize the migration time prediction data and obtain the migration time prediction optimization data. For example, if the migration time prediction data is 3.49h and the time limit impact optimization coefficient is 0.3, then (1+0.3)*3.49=4.537h, which is the migration time prediction optimization data. Finally, The migration excess rate is compared with the migration time limit demand data to obtain the migration excess rate, and compared with the preset migration excess warning threshold. If it is less than or equal to the preset migration excess warning threshold, the migration capacity status test parameter is judged to be sufficient migration capacity, otherwise it is insufficient migration capacity. The migration excess rate refers to the ratio of the difference between the migration time limit prediction optimization data and the migration time limit demand data to the migration time limit demand data. If it is a negative value, the migration excess rate is recorded as 0. The preset migration excess warning threshold is pre-set by technical personnel in this field and can be dynamically adjusted.

[0025] Please refer to Figure 3 , Figure 3 This is a flow chart of obtaining a time limit impact optimization coefficient for a method for migrating massive data on a solid-state drive in some embodiments of the present application. According to an embodiment of the present invention, obtaining the time limit impact optimization coefficient for migration time limit prediction data includes: S31. Process the data value and the total value of the migrated data to obtain the data proportion corresponding to the data category label; S32: Query a mapping table of preset categories and migration time limit impact coefficients according to the data category label to obtain a time limit impact coefficient corresponding to the data category label; S33: Multiply the time limit impact coefficient by the corresponding data proportion and perform aggregation processing to obtain a time limit impact optimization coefficient.

[0026] It should be noted that to evaluate the impact of different data types and data proportions on data migration time, we first query the preset category and migration time limit impact coefficient mapping table based on the data category label to obtain the time limit impact coefficient corresponding to the data category label. This is then multiplied by the corresponding data proportion and aggregated to obtain the time limit impact optimization coefficient. The data proportion is the ratio of the sum of data values ​​of the same data type to the total value of the migrated data. For example, the time limit impact coefficients corresponding to text data, log data, JSON data, image data, and video data are 0.1, 0.15, 0.1, 0.3, and 0.35, respectively, and the data proportions are 10%, 15%, 30%, 25%, and 20%, respectively. Then 0.1*0.1+0.15*0.15+0.1*0.3+0.3*0.25+0.35*0.2=0.2075 is the time limit impact optimization coefficient, among which the preset category and migration time limit impact coefficient mapping table is evaluated and set by technical personnel in this field based on a large amount of historical data migration, and can be dynamically adjusted.

[0027] According to an embodiment of the present invention, the step of processing the migration capability status test parameter in combination with the storage space data to obtain a data migration strategy includes: comparing the storage space data with the total value of the migration data; If it is less than or equal to the total value of the migrated data, suspend data migration and output an early warning; If it is greater than the total value of the migration data, the over-capacity rate is obtained and compared with the preset over-capacity warning threshold; If it is less than or equal to the preset over-capacity warning threshold, data migration will be suspended and an alert will be issued; If the value is greater than the preset over-capacity warning threshold and the migration time limit prediction optimization data is less than or equal to the migration time limit requirement data, a first data migration strategy is generated; If it is greater than the preset over-capacity warning threshold and the migration capability status verification parameter indicates that the migration capability is sufficient, a second data migration strategy is generated; If it is greater than the preset over-capacity warning threshold and the migration capability status verification parameter is insufficient migration capability, a third data migration strategy is generated.

[0028] It should be noted that in order to ensure that the hard disk to be migrated has sufficient storage space, the storage space data is first compared with the total value of the migrated data. If it is less than or equal to, it means that the storage space is insufficient, which will cause data migration to fail, and data migration will be suspended and an early warning will be issued. If it is greater than, the storage space redundancy will continue to be determined to ensure the performance of the solid-state hard disk and prevent insufficient space due to the expansion of temporary files. Then, the obtained overcapacity rate is compared with the preset overcapacity warning threshold. If it is less than or equal to the preset overcapacity warning threshold, it means that the space redundancy is insufficient, and data migration will be suspended and an early warning will be output. If it is greater than the preset overcapacity warning threshold, it means that the space redundancy is sufficient. Combined with the comparison of the migration time limit prediction optimization data and the migration time limit requirement data, the first data migration strategy, the second data migration strategy or the third data migration strategy is generated. Among them, the first data migration strategy refers to the normal execution of data migration, the second data migration strategy refers to increasing bandwidth data, and the third data migration strategy is to increase bandwidth data and migrate some files offline. The overcapacity rate refers to the ratio of the difference between the storage space data and the total value of the migrated data to the storage space data.

[0029] According to an embodiment of the present invention, the further embodiment includes: Obtaining a migration test data set corresponding to the data category label, and processing the data set in combination with the bandwidth data, bandwidth utilization, and time limit impact optimization coefficient to obtain migration test time limit prediction data corresponding to the data category label; Perform data migration on the migration test data set according to the data migration strategy to obtain migration test time-consuming data corresponding to the data category label; Comparing the migration test time consumption data with the migration test time limit prediction data to obtain a migration test timeout rate, and comparing it with a preset migration test timeout threshold; If the value is less than or equal to the preset migration test timeout threshold, the migration test is considered normal. If it is greater than the preset migration test timeout threshold, the migration test is determined to be abnormal.

[0030] It should be noted that in order to further ensure the reliability of migration, the data to be migrated for each data category label is divided into a partial migration test data set. For example, 10GB of text data is used as the migration test data set. The bandwidth data is 10Gbps and the bandwidth utilization rate is 0.7. The theoretical prediction time for data migration is (10x1024 3 ) / [(10x10 9x0.7) / 8]≈12.27s. Combined with the time limit impact optimization coefficient (the time limit impact coefficient for text data is 0.1), we obtain the migration test time limit prediction data corresponding to the data category label: (1+0.1)x12.27=13.497s. The recorded migration test duration is 15s. We then compare the migration test duration (15s) with the migration test time limit prediction data (13.497s) to obtain the migration test timeout rate: (15-13.497) / 13.497≈0.11. If the value is negative, it means that the migration test duration is less than the migration test time limit prediction data, and the migration test timeout rate is recorded as 0. Finally, we perform a threshold comparison. If the value is less than or equal to the threshold, it means that the migration test meets the threshold requirements and is considered normal. Otherwise, the migration test is considered abnormal. Similarly, we complete the tests for other data categories. If all pass, the formal data migration continues. Otherwise, an alert response is output, and the operation and maintenance personnel will further determine whether to allow the data migration.

[0031] Please refer to Figure 4 , Figure 4 This is a flow chart of obtaining a migration execution status for a method for mass data migration on a solid-state drive in some embodiments of the present application. According to embodiments of the present invention, executing data migration according to the data migration policy, monitoring migration execution record data within a first preset time period, and processing the migration execution record data to obtain the migration execution status include: S41, executing data migration according to the data migration policy, and monitoring migration execution record data within a first preset time period; S42, the migration execution record data includes the average migration rate and the average actual temperature written to the hard disk; S43, comparing the migration rate mean with the average migration rate in a second preset time period to obtain a migration rate fluctuation rate; S44, comparing the actual temperature average with the preset hard disk set operating temperature to obtain an over-temperature rate; S45, performing weighted sum processing on the migration rate fluctuation rate and the over-temperature rate to obtain a data migration evaluation parameter, and comparing the parameter with a preset migration status evaluation threshold; S46: If the value is less than or equal to the preset migration status evaluation threshold, the migration execution status is determined to be normal execution; S47: If the value is greater than the preset migration status evaluation threshold, the migration execution status is determined to be abnormal execution.

[0032] It should be noted that dynamic monitoring during data migration can detect transmission anomalies in advance and prevent sudden transmission interruptions. This embodiment monitors the average migration rate and the average actual temperature of the hard disk in real time within a first preset time period. Simultaneously, the average migration rate and the preset hard disk operating temperature within a second preset time period are obtained and compared to obtain a migration rate fluctuation rate and an over-temperature rate. In this embodiment, the first preset time period is 5 minutes and the second preset time period is 30 minutes. The migration rate fluctuation rate refers to the ratio of the absolute value of the difference between the average migration rate and the average migration rate to the average migration rate. The over-temperature rate refers to the ratio of the average actual temperature to the preset hard disk operating temperature. If a negative value indicates a low temperature, the over-temperature rate is recorded as 0. Finally, the obtained migration rate fluctuation rate and over-temperature rate are weighted and summed to obtain data migration evaluation parameters. The parameters are then compared with the preset migration status evaluation threshold to determine whether the migration execution status is normal or abnormal. The corresponding weight values ​​and the preset migration status evaluation threshold are pre-set by those skilled in the art and can be dynamically adjusted.

[0033] According to an embodiment of the present invention, performing data verification on the migrated data and determining that the data migration is completed if the verification passes includes: Obtaining a migration data value of the migration completed data, and comparing it with the total migration data value; If they are the same, obtaining a second hash value of the migrated data and comparing it with a preset first hash value of the data to be migrated; If the comparison passes, the 4K alignment status of the data written to the hard disk is obtained; If aligned, the data migration is determined to be complete.

[0034] It should be noted that after completing the data migration, in order to ensure data integrity, the data value, hash value comparison and 4K alignment status are verified respectively. If all pass, it means that the data migration is accurate. Otherwise, if any one item fails, it means that the data migration is wrong and an early warning response is output.

[0035] Please refer to Figure 5 , Figure 5 The following is a high-level flowchart of various embodiments of the present application, which can be used for mass data migration on solid-state drives. According to an embodiment of the present invention, for example, in step S53, the obtained migration time prediction optimization data is compared with the migration time requirement data to obtain a migration overrun rate. This is then compared with a threshold to determine whether the migration capability status verification parameter indicates sufficient or insufficient migration capability, which is then used to generate a corresponding migration strategy.

[0036] It is worth mentioning that according to an embodiment of the present invention, the present invention further includes: Comparing the data value of the data to be migrated with a preset data classification threshold to obtain the number of small file data that is smaller than the preset data value threshold; Comparing the amount of small file data with a preset amount threshold; If the number is greater than a preset threshold, the small file data is processed according to the number of small files combined with the preset small file input and output time to obtain the additional time consumption data of small file migration; The additional time consumption data of small file migration and the migration time limit prediction optimization data are summed to obtain the total migration prediction time consumption.

[0037] It should be noted that the size of the migrated data is the same, but the number of files is different, and the time requirements for data migration are different, especially for a large number of small files. Since the system addressing, transmission and protocol establishment seriously delay the migration time, therefore, the file value threshold comparison is first performed. For example, files smaller than 1M are determined to be small files. If the number of small file data is less than or equal to the preset number threshold, it means that there are not many small files and no additional analysis of the additional time consumption of small files is required. If it is greater than the preset number threshold, it means that there are many small files and additional analysis of the additional time consumption of migrating small files is required. For example, if the number of small file data is 100,000 and the input and output time of each small file is about 0.1s, then 100000x0.1=10000s, which is about 2.78h. Adding this to the previously obtained migration time limit prediction optimization data, the total migration prediction time is obtained, which is used to compare with the migration time limit requirement data to obtain the migration excess rate and optimize the accuracy of the migration capability assessment.

[0038] It is worth mentioning that according to an embodiment of the present invention, the present invention further includes: Classify the data to be migrated according to the data category labels to obtain a first migration data set and a second migration data set; compressing the first migration dataset using a preset compression method to obtain a compressed migration dataset; The compressed migration data set and the second migration data set are migrated using the data migration strategy.

[0039] It should be noted that the data to be migrated includes multiple categories, such as text data, log data, JSON data, image data, and video data. They are classified according to compressibility to obtain a first migration data set with high compression cost-effectiveness, including text data, log data, and JSON data, and a second migration data set with low compression cost-effectiveness, including image data and video data. The first migration data set is compressed first and then migrated, and the second data set is directly migrated, thereby improving the efficiency of data migration.

[0040] It is worth mentioning that according to an embodiment of the present invention, the present invention further includes: Obtaining a first data value and a compression rate average of the first migration data set, and processing the data to obtain compression time prediction data; Obtaining a second data value of the compressed migration data set; Get the third data value of the second migration dataset Processing the second data value and the third data value in combination with preset bandwidth data and bandwidth utilization to obtain compression migration time limit prediction data; The compression time consumption prediction data and the compression migration time limit prediction data are summed to obtain compression migration total time limit prediction data.

[0041] It should be noted that it takes time to compress the data before migration. After compression, the total value of the migrated data will become smaller, and the migration time limit prediction data will also become smaller. Therefore, first, predict the compression time consumption and obtain the compression time consumption prediction data, that is, divide the first data value by the average compression rate, and then calculate the compression migration time limit prediction data, that is, sum the second data value and the third data value and divide it by the product of the preset bandwidth data and the bandwidth utilization rate. Finally, sum the two and determine the total compression migration time limit prediction data, that is, the total compression and migration time consumption.

[0042] The present invention also discloses a massive data migration system for a solid-state hard drive, comprising a memory and a processor. The memory comprises a massive data migration method program for a solid-state hard drive. When the massive data migration method program for a solid-state hard drive is executed by the processor, the following steps are implemented: Obtaining migration time requirement data, migration feature data, and storage space data of the migration target hard disk of the data to be migrated, and processing the migration feature data to obtain migration capability status verification parameters; Processing the migration capability status test parameters in combination with the storage space data to obtain a data migration strategy; Executing data migration according to the data migration policy, monitoring migration execution record data within a first preset time period, and processing the migration execution record data to obtain a migration execution status; The migrated data is verified. If the verification passes, the data migration is considered complete.

[0043] It should be noted that in order to achieve intelligent data migration, the migration feature data of the data to be migrated is first compared after migration time analysis and prediction and optimization, and the migration capability status test parameters are evaluated. At the same time, the corresponding data migration strategy is generated in combination with the storage space data of the migrated hard disk, such as normal execution, increased bandwidth data or offline migration. Afterwards, data migration is implemented according to the generated data migration strategy, and the migration execution record data is monitored in real time, analyzed and processed, and the migration execution status is evaluated, including normal execution or abnormal execution. Finally, after the migration is completed, a comprehensive verification of the data value, hash value and 4K alignment is performed. After the verification passes, the data migration is determined to be completed.

[0044] According to an embodiment of the present invention, obtaining migration time requirement data, migration feature data, and storage space data of a migration target hard disk of the data to be migrated, and processing the migration feature data to obtain migration capability status verification parameters includes: Obtaining migration time requirement data, migration feature data, and storage space data of the migration target hard disk for the data to be migrated; The migration feature data includes data category labels and corresponding data quantity and data value; Aggregating the data values ​​to obtain a total migration data value, and processing the data in combination with preset bandwidth data and bandwidth utilization to obtain migration time prediction data; Obtaining the time limit impact optimization coefficient of the migration time limit prediction data; Optimizing the migration time limit prediction data according to the time limit impact optimization coefficient to obtain migration time limit prediction optimization data; Comparing the migration time limit prediction optimization data with the migration time limit demand data to obtain a migration limit excess rate; The migration excess rate is compared with a preset migration excess warning threshold to obtain migration capacity status verification parameters, including sufficient migration capacity or insufficient migration capacity.

[0045] It should be noted that in order to assess the migration capability status and achieve accurate and effective data migration, first, migration feature data including data category labels and corresponding data quantity and data value are obtained. Among them, the data category label is used to indicate the data type, such as text data, log data, JSON data, image data, and video data, and the data value indicates the size of the data, such as a file of 4MB. The data values ​​of each data category label are aggregated to determine the total value of the migration data to be migrated. The theoretical time consumption is predicted based on the real-time bandwidth situation to obtain the migration time prediction data. For example, if the total value of the migration data is 10TB, the bandwidth data is a 10Gbps dedicated line, and the bandwidth utilization rate is 0.7, then (10x1024 4 ) / [(10x10 9x0.7x3600) / 8]≈3.49h, which is the theoretical migration time prediction data. In practice, different file types and proportions will affect the migration time. Therefore, the time limit impact optimization coefficient of the migration time prediction data is obtained to optimize the migration time prediction data and obtain the migration time prediction optimization data. For example, if the migration time prediction data is 3.49h and the time limit impact optimization coefficient is 0.3, then (1+0.3)*3.49=4.537h, which is the migration time prediction optimization data. Finally, The migration excess rate is compared with the migration time limit demand data to obtain the migration excess rate, and compared with the preset migration excess warning threshold. If it is less than or equal to the preset migration excess warning threshold, the migration capacity status test parameter is judged to be sufficient migration capacity, otherwise it is insufficient migration capacity. The migration excess rate refers to the ratio of the difference between the migration time limit prediction optimization data and the migration time limit demand data to the migration time limit demand data. If it is a negative value, the migration excess rate is recorded as 0. The preset migration excess warning threshold is pre-set by technical personnel in this field and can be dynamically adjusted.

[0046] According to an embodiment of the present invention, obtaining a time limit impact optimization coefficient of migration time limit prediction data includes: Processing is performed based on the data value and the total value of the migrated data to obtain the data proportion corresponding to the data category label; According to the data category label, a mapping table of preset categories and migration time limit impact coefficients is searched to obtain the time limit impact coefficient corresponding to the data category label; The time limit impact coefficient is multiplied by the corresponding data proportion and aggregated to obtain the time limit impact optimization coefficient.

[0047] It should be noted that to evaluate the impact of different data types and data proportions on data migration time, we first query the preset category and migration time limit impact coefficient mapping table based on the data category label to obtain the time limit impact coefficient corresponding to the data category label. This is then multiplied by the corresponding data proportion and aggregated to obtain the time limit impact optimization coefficient. The data proportion is the ratio of the sum of data values ​​of the same data type to the total value of the migrated data. For example, the time limit impact coefficients corresponding to text data, log data, JSON data, image data, and video data are 0.1, 0.15, 0.1, 0.3, and 0.35, respectively, and the data proportions are 10%, 15%, 30%, 25%, and 20%, respectively. Then 0.1*0.1+0.15*0.15+0.1*0.3+0.3*0.25+0.35*0.2=0.2075 is the time limit impact optimization coefficient, among which the preset category and migration time limit impact coefficient mapping table is evaluated and set by technical personnel in this field based on a large amount of historical data migration, and can be dynamically adjusted.

[0048] According to an embodiment of the present invention, the step of processing the migration capability status test parameter in combination with the storage space data to obtain a data migration strategy includes: comparing the storage space data with the total value of the migration data; If it is less than or equal to the total value of the migrated data, suspend data migration and output an early warning; If it is greater than the total value of the migration data, the over-capacity rate is obtained and compared with the preset over-capacity warning threshold; If it is less than or equal to the preset over-capacity warning threshold, data migration will be suspended and an alert will be issued; If the value is greater than the preset over-capacity warning threshold and the migration time limit prediction optimization data is less than or equal to the migration time limit requirement data, a first data migration strategy is generated; If it is greater than the preset over-capacity warning threshold and the migration capability status verification parameter indicates that the migration capability is sufficient, a second data migration strategy is generated; If it is greater than the preset over-capacity warning threshold and the migration capability status verification parameter is insufficient migration capability, a third data migration strategy is generated.

[0049] It should be noted that in order to ensure that the hard disk to be migrated has sufficient storage space, the storage space data is first compared with the total value of the migrated data. If it is less than or equal to, it means that the storage space is insufficient, which will cause data migration to fail, and data migration will be suspended and an early warning will be issued. If it is greater than, the storage space redundancy will continue to be determined to ensure the performance of the solid-state hard disk and prevent insufficient space due to the expansion of temporary files. Then, the obtained overcapacity rate is compared with the preset overcapacity warning threshold. If it is less than or equal to the preset overcapacity warning threshold, it means that the space redundancy is insufficient, and data migration will be suspended and an early warning will be output. If it is greater than the preset overcapacity warning threshold, it means that the space redundancy is sufficient. Combined with the comparison of the migration time limit prediction optimization data and the migration time limit requirement data, the first data migration strategy, the second data migration strategy or the third data migration strategy is generated. Among them, the first data migration strategy refers to the normal execution of data migration, the second data migration strategy refers to increasing bandwidth data, and the third data migration strategy is to increase bandwidth data and migrate some files offline. The overcapacity rate refers to the ratio of the difference between the storage space data and the total value of the migrated data to the storage space data.

[0050] According to an embodiment of the present invention, the further embodiment includes: Obtaining a migration test data set corresponding to the data category label, and processing the data set in combination with the bandwidth data, bandwidth utilization, and time limit impact optimization coefficient to obtain migration test time limit prediction data corresponding to the data category label; Perform data migration on the migration test data set according to the data migration strategy to obtain migration test time-consuming data corresponding to the data category label; Comparing the migration test time consumption data with the migration test time limit prediction data to obtain a migration test timeout rate, and comparing it with a preset migration test timeout threshold; If the value is less than or equal to the preset migration test timeout threshold, the migration test is considered normal. If it is greater than the preset migration test timeout threshold, the migration test is determined to be abnormal.

[0051] It should be noted that in order to further ensure the reliability of migration, the data to be migrated for each data category label is divided into a partial migration test data set. For example, 10GB of text data is used as the migration test data set. The bandwidth data is 10Gbps and the bandwidth utilization rate is 0.7. The theoretical prediction time for data migration is (10x1024 3 ) / [(10x10 9 x0.7) / 8]≈12.27s. Combined with the time limit impact optimization coefficient (the time limit impact coefficient for text data is 0.1), we obtain the migration test time limit prediction data corresponding to the data category label: (1+0.1)x12.27=13.497s. The recorded migration test duration is 15s. We then compare the migration test duration (15s) with the migration test time limit prediction data (13.497s) to obtain the migration test timeout rate: (15-13.497) / 13.497≈0.11. If the value is negative, it means that the migration test duration is less than the migration test time limit prediction data, and the migration test timeout rate is recorded as 0. Finally, we perform a threshold comparison. If the value is less than or equal to the threshold, it means that the migration test meets the threshold requirements and is considered normal. Otherwise, the migration test is considered abnormal. Similarly, we complete the tests for other data categories. If all pass, the formal data migration continues. Otherwise, an alert response is output, and the operation and maintenance personnel will further determine whether to allow the data migration.

[0052] According to an embodiment of the present invention, executing data migration according to the data migration policy, monitoring migration execution record data within a first preset time period, and processing the migration execution record data to obtain a migration execution status includes: Executing data migration according to the data migration strategy and monitoring migration execution record data within a first preset time period; The migration execution record data includes the average migration rate and the average actual temperature written to the hard disk; Comparing the migration rate mean with the average migration rate in a second preset time period to obtain a migration rate fluctuation rate; Comparing the actual temperature average with the preset hard disk set operating temperature to obtain the over-temperature rate; Performing a weighted summation process on the migration rate fluctuation rate and the over-temperature rate to obtain a data migration evaluation parameter, and comparing the parameter with a preset migration status evaluation threshold; If the value is less than or equal to the preset migration status evaluation threshold, the migration execution status is determined to be normal; If it is greater than the preset migration status evaluation threshold, the migration execution status is determined to be abnormal execution.

[0053] It should be noted that dynamic monitoring during data migration can detect transmission anomalies in advance and prevent sudden transmission interruptions. This embodiment monitors the average migration rate and the average actual temperature of the hard disk in real time within a first preset time period. Simultaneously, the average migration rate and the preset hard disk operating temperature within a second preset time period are obtained and compared to obtain a migration rate fluctuation rate and an over-temperature rate. In this embodiment, the first preset time period is 5 minutes and the second preset time period is 30 minutes. The migration rate fluctuation rate refers to the ratio of the absolute value of the difference between the average migration rate and the average migration rate to the average migration rate. The over-temperature rate refers to the ratio of the average actual temperature to the preset hard disk operating temperature. If a negative value indicates a low temperature, the over-temperature rate is recorded as 0. Finally, the obtained migration rate fluctuation rate and over-temperature rate are weighted and summed to obtain data migration evaluation parameters. The parameters are then compared with the preset migration status evaluation threshold to determine whether the migration execution status is normal or abnormal. The corresponding weight values ​​and the preset migration status evaluation threshold are pre-set by those skilled in the art and can be dynamically adjusted.

[0054] According to an embodiment of the present invention, performing data verification on the migrated data and determining that the data migration is completed if the verification passes includes: Obtaining a migration data value of the migration completed data, and comparing it with the total migration data value; If they are the same, obtaining a second hash value of the migrated data and comparing it with a preset first hash value of the data to be migrated; If the comparison passes, the 4K alignment status of the data written to the hard disk is obtained; If aligned, the data migration is determined to be complete.

[0055] It should be noted that after completing the data migration, in order to ensure data integrity, the data value, hash value comparison and 4K alignment status are verified respectively. If all pass, it means that the data migration is accurate. Otherwise, if any one item fails, it means that the data migration is wrong and an early warning response is output.

[0056] According to an embodiment of the present invention, for example, in step S53, the obtained migration time prediction optimization data is compared with the migration time requirement data to obtain the migration excess rate, and a threshold comparison is performed to determine whether the migration capacity status test parameter is sufficient or insufficient, which is used for subsequent generation of the corresponding migration strategy.

[0057] It is worth mentioning that according to an embodiment of the present invention, the present invention further includes: Comparing the data value of the data to be migrated with a preset data classification threshold to obtain the number of small file data that is smaller than the preset data value threshold; Comparing the amount of small file data with a preset amount threshold; If the number is greater than a preset threshold, the small file data is processed according to the number of small files combined with the preset small file input and output time to obtain the additional time consumption data of small file migration; The additional time consumption data of small file migration and the migration time limit prediction optimization data are summed to obtain the total migration prediction time consumption.

[0058] It should be noted that the size of the migrated data is the same, but the number of files is different, and the time requirements for data migration are different, especially for a large number of small files. Since the system addressing, transmission and protocol establishment seriously delay the migration time, therefore, the file value threshold comparison is first performed. For example, files smaller than 1M are determined to be small files. If the number of small file data is less than or equal to the preset number threshold, it means that there are not many small files and no additional analysis of the additional time consumption of small files is required. If it is greater than the preset number threshold, it means that there are many small files and additional analysis of the additional time consumption of migrating small files is required. For example, if the number of small file data is 100,000 and the input and output time of each small file is about 0.1s, then 100000x0.1=10000s, which is about 2.78h. Adding this to the previously obtained migration time limit prediction optimization data, the total migration prediction time is obtained, which is used to compare with the migration time limit requirement data to obtain the migration excess rate and optimize the accuracy of the migration capability assessment.

[0059] It is worth mentioning that according to an embodiment of the present invention, the present invention further includes: Classify the data to be migrated according to the data category labels to obtain a first migration data set and a second migration data set; compressing the first migration dataset using a preset compression method to obtain a compressed migration dataset; The compressed migration data set and the second migration data set are migrated using the data migration strategy.

[0060] It should be noted that the data to be migrated includes multiple categories, such as text data, log data, JSON data, image data, and video data. They are classified according to compressibility to obtain a first migration data set with high compression cost-effectiveness, including text data, log data, and JSON data, and a second migration data set with low compression cost-effectiveness, including image data and video data. The first migration data set is compressed first and then migrated, and the second data set is directly migrated, thereby improving the efficiency of data migration.

[0061] It is worth mentioning that according to an embodiment of the present invention, the present invention further includes: Obtaining a first data value and a compression rate average of the first migration data set, and processing the data to obtain compression time prediction data; Obtaining a second data value of the compressed migration data set; Get the third data value of the second migration dataset Processing the second data value and the third data value in combination with preset bandwidth data and bandwidth utilization to obtain compression migration time limit prediction data; The compression time consumption prediction data and the compression migration time limit prediction data are summed to obtain compression migration total time limit prediction data.

[0062] It should be noted that it takes time to compress the data before migration. After compression, the total value of the migrated data will become smaller, and the migration time limit prediction data will also become smaller. Therefore, first, predict the compression time consumption and obtain the compression time consumption prediction data, that is, divide the first data value by the average compression rate, and then calculate the compression migration time limit prediction data, that is, sum the second data value and the third data value and divide it by the product of the preset bandwidth data and the bandwidth utilization rate. Finally, sum the two and determine the total compression migration time limit prediction data, that is, the total compression and migration time consumption.

[0063] A third aspect of the present invention provides a readable storage medium, which stores a program for a method for migrating massive data for a solid-state hard drive. When the program for the method for migrating massive data for a solid-state hard drive is executed by a processor, the steps of the method for migrating massive data for a solid-state hard drive as described in any one of the above items are implemented.

[0064] The present invention discloses a method, system and medium for migrating massive data in a solid-state drive. The method analyzes and predicts migration time prediction data and optimizes the processing, compares it with migration time requirement data and thresholds, determines migration capability status verification parameters, and then generates corresponding data migration strategies. The data migration process is monitored and evaluated in real time, and finally, a comprehensive verification is performed on the migrated data, thereby realizing intelligent migration of massive data in a solid-state drive.

[0065] In the several embodiments provided in this application, it should be understood that the disclosed devices and methods can be implemented in other ways. The device embodiments described above are merely schematic. For example, the division of the units is merely a logical function division. In actual implementation, there may be other division methods, such as: multiple units or components can be combined, or can be integrated into another system, or some features can be ignored or not executed. In addition, the coupling, direct coupling, or communication connection between the components shown or discussed can be through some interfaces, and the indirect coupling or communication connection of the devices or units can be electrical, mechanical or other forms.

[0066] The units described above as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units; they may be located in one place or distributed across multiple network units; some or all of the units may be selected according to actual needs to achieve the purpose of the scheme of this embodiment.

[0067] In addition, all functional units in the embodiments of the present invention may be integrated into one processing unit, or each unit may be separately used as a unit, or two or more units may be integrated into one unit; the above-mentioned integrated units may be implemented in the form of hardware or in the form of hardware plus software functional units.

[0068] Those skilled in the art will appreciate that all or part of the steps of the above-mentioned method embodiments may be implemented by hardware related to program instructions, and the aforementioned program may be stored in a readable storage medium. When the program is executed, the program executes the steps of the above-mentioned method embodiments. The aforementioned storage medium includes various media that can store program codes, such as mobile storage devices, read-only memories (ROMs), random access memories (RAMs), magnetic disks, or optical disks.

[0069] Alternatively, if the integrated units described above are implemented as software functional modules and sold or used as standalone products, they can also be stored on a readable storage medium. Based on this understanding, the technical solutions of the embodiments of the present invention, or the portion that contributes to the prior art, can be embodied in the form of a software product. This software product, stored on a storage medium, includes instructions for enabling a computer device (such as a personal computer, server, or network device) to execute all or part of the methods described in the various embodiments of the present invention. The aforementioned storage media include various media capable of storing program code, such as removable storage devices, ROM, RAM, magnetic disks, or optical disks.

Claims

1. A method for migrating massive data from a solid-state drive, characterized in that: The following steps are involved: Obtaining migration time requirement data, migration feature data, and storage space data of the migration target hard disk of the data to be migrated, and processing the migration feature data to obtain migration capability status verification parameters; Processing the migration capability status test parameters in combination with the storage space data to obtain a data migration strategy; Executing data migration according to the data migration policy, monitoring migration execution record data within a first preset time period, and processing the migration execution record data to obtain a migration execution status; The migrated data is verified. If the verification passes, the data migration is considered complete.

2. The method for migrating massive amounts of data from a solid-state drive according to claim 1, wherein: The step of obtaining migration time requirement data, migration feature data, and storage space data of a migration target hard disk of the data to be migrated, and processing the migration feature data to obtain migration capability status verification parameters includes: Obtaining migration time requirement data, migration feature data, and storage space data of the migration target hard disk for the data to be migrated; The migration feature data includes data category labels and corresponding data quantity and data value; Aggregating the data values ​​to obtain a total migration data value, and processing the data in combination with preset bandwidth data and bandwidth utilization to obtain migration time prediction data; Obtaining the time limit impact optimization coefficient of the migration time limit prediction data; Optimizing the migration time limit prediction data according to the time limit impact optimization coefficient to obtain migration time limit prediction optimization data; Comparing the migration time limit prediction optimization data with the migration time limit demand data to obtain a migration limit excess rate; The migration excess rate is compared with a preset migration excess warning threshold to obtain migration capacity status verification parameters, including sufficient migration capacity or insufficient migration capacity.

3. The method for migrating massive amounts of data from a solid-state drive according to claim 2, wherein: The time limit impact optimization coefficient for obtaining the migration time limit prediction data includes: Processing is performed based on the data value and the total value of the migrated data to obtain the data proportion corresponding to the data category label; According to the data category label, a mapping table of preset categories and migration time limit impact coefficients is searched to obtain the time limit impact coefficient corresponding to the data category label; The time limit impact coefficient is multiplied by the corresponding data proportion and aggregated to obtain the time limit impact optimization coefficient.

4. The method for migrating massive amounts of data from a solid-state drive according to claim 3, wherein: The step of processing the migration capability status test parameter in combination with the storage space data to obtain a data migration strategy includes: comparing the storage space data with the total value of the migration data; If it is less than or equal to the total value of the migrated data, suspend data migration and output an early warning; If it is greater than the total value of the migration data, the over-capacity rate is obtained and compared with the preset over-capacity warning threshold; If it is less than or equal to the preset over-capacity warning threshold, data migration will be suspended and an alert will be issued; If the value is greater than the preset over-capacity warning threshold and the migration time limit prediction optimization data is less than or equal to the migration time limit requirement data, a first data migration strategy is generated; If it is greater than the preset over-capacity warning threshold and the migration capability status verification parameter indicates that the migration capability is sufficient, a second data migration strategy is generated; If it is greater than the preset over-capacity warning threshold and the migration capability status verification parameter is insufficient migration capability, a third data migration strategy is generated.

5. The method for migrating massive amounts of data from a solid-state drive according to claim 4, wherein: Also includes: Obtaining a migration test data set corresponding to the data category label, and processing the data set in combination with the bandwidth data, bandwidth utilization, and time limit impact optimization coefficient to obtain migration test time limit prediction data corresponding to the data category label; Perform data migration on the migration test data set according to the data migration strategy to obtain migration test time-consuming data corresponding to the data category label; Comparing the migration test time consumption data with the migration test time limit prediction data to obtain a migration test timeout rate, and comparing it with a preset migration test timeout threshold; If the value is less than or equal to the preset migration test timeout threshold, the migration test is considered normal. If it is greater than the preset migration test timeout threshold, the migration test is determined to be abnormal.

6. The method for migrating massive amounts of data from a solid-state drive according to claim 5, wherein: The performing of data migration according to the data migration policy, monitoring migration execution record data within a first preset time period, and processing the migration execution record data to obtain a migration execution status includes: Executing data migration according to the data migration strategy and monitoring migration execution record data within a first preset time period; The migration execution record data includes the average migration rate and the average actual temperature written to the hard disk; Comparing the migration rate mean with the average migration rate in a second preset time period to obtain a migration rate fluctuation rate; Comparing the actual temperature average with the preset hard disk set operating temperature to obtain the over-temperature rate; Performing a weighted summation process on the migration rate fluctuation rate and the over-temperature rate to obtain a data migration evaluation parameter, and comparing the parameter with a preset migration status evaluation threshold; If the value is less than or equal to the preset migration status evaluation threshold, the migration execution status is determined to be normal; If it is greater than the preset migration status evaluation threshold, the migration execution status is determined to be abnormal execution.

7. The method for migrating massive amounts of data from a solid-state drive according to claim 6, wherein: The data migration is completed by performing data verification processing on the migrated data. If the verification passes, the data migration is determined to be completed, including: Obtaining a migration data value of the migration completed data, and comparing it with the total migration data value; If they are the same, obtaining a second hash value of the migrated data and comparing it with a preset first hash value of the data to be migrated; If the comparison passes, the 4K alignment status of the data written to the hard disk is obtained; If aligned, the data migration is determined to be complete.

8. A massive data migration system for a solid state drive, characterized in that: The system comprises a memory and a processor, wherein the memory comprises a program of a method for migrating massive data from a solid-state drive, and when the program of the method for migrating massive data from a solid-state drive is executed by the processor, the following steps are implemented: Obtaining migration time requirement data, migration feature data, and storage space data of the migration target hard disk of the data to be migrated, and processing the migration feature data to obtain migration capability status verification parameters; Processing the migration capability status test parameters in combination with the storage space data to obtain a data migration strategy; Executing data migration according to the data migration policy, monitoring migration execution record data within a first preset time period, and processing the migration execution record data to obtain a migration execution status; The migrated data is verified. If the verification passes, the data migration is considered complete.

9. The massive data migration system for solid state drives according to claim 8, wherein: The step of obtaining migration time requirement data, migration feature data, and storage space data of a migration target hard disk of the data to be migrated, and processing the migration feature data to obtain migration capability status verification parameters includes: Obtaining migration time requirement data, migration feature data, and storage space data of the migration target hard disk for the data to be migrated; The migration feature data includes data category labels and corresponding data quantity and data value; Aggregating the data values ​​to obtain a total migration data value, and processing the data in combination with preset bandwidth data and bandwidth utilization to obtain migration time prediction data; Obtaining the time limit impact optimization coefficient of the migration time limit prediction data; Optimizing the migration time limit prediction data according to the time limit impact optimization coefficient to obtain migration time limit prediction optimization data; Comparing the migration time limit prediction optimization data with the migration time limit demand data to obtain a migration limit excess rate; The migration excess rate is compared with a preset migration excess warning threshold to obtain migration capacity status verification parameters, including sufficient migration capacity or insufficient migration capacity.

10. A computer-readable storage medium, characterized in that The computer-readable storage medium stores a program for a method for migrating massive data from a solid-state drive. When the program for migrating massive data from a solid-state drive is executed by a processor, the steps of a method for migrating massive data from a solid-state drive as described in any one of claims 1 to 7 are implemented.

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