Intelligent storage device data monitoring system and method based on artificial intelligence

Abstract

The invention discloses a storage device data intelligent monitoring system and method based on artificial intelligence, and relates to the technical field of artificial intelligence, and the method comprises the steps: deploying a hard disk monitoring tool on a computer device, and obtaining an erasing record generated by the computer device; establishing and training an artificial intelligence neural network model; calling a file storage path corresponding to each piece of application software on the computer equipment to obtain the storage influence degree of each piece of application software on the hard disk; performing different marking processing on the hard disk according to the storage influence degree; obtaining the predicted number of erasing times of the hard disk in the planned use time period, and correcting the number; and obtaining an early warning value of the hard disk based on the corrected number of erasing times, and performing early warning prompt on the hard disk according to the early warning value. Related personnel are helped to check the use condition of the disk better, and the situation that the production efficiency of an enterprise is reduced due to hard disk faults caused by the fact that the number of erasing times reaches a certain number can be prevented.

Description

Technical Field

[0001] The present invention relates to the field of artificial intelligence technology, and in particular to an artificial intelligence-based storage device data intelligent monitoring system and method. Background Art

[0002] With the advent of the big data era, storage devices are crucial for data storage. In order to store various types of data in a timely manner, computer equipment is usually equipped with a variety of storage devices such as hard disks. The number of hard disk erases and writes is closely related to its lifespan. In actual use, the erase and write conditions of different hard disks vary greatly. For example, surveillance cameras will regularly generate and delete surveillance videos, which will cause data to be constantly updated and the hard disk needs to be read and written frequently; while for hard disks that store backup data and small-capacity documents, the data rarely changes after being written, and the number of erases and writes is relatively small. The problem of uneven erase and write times will cause the hard disks on computer equipment to be unable to be fully and reasonably used, resulting in differences in lifespan and waste of resources, which brings great challenges to the efficiency of hard disk use and affects economic benefits. Summary of the invention

[0003] The purpose of the present invention is to provide a storage device data intelligent monitoring system and method based on artificial intelligence to solve the problems raised in the prior art.

[0004] To achieve the above object, the present invention provides the following technical solutions: An artificial intelligence-based storage device data intelligent monitoring method comprises the following steps: Step S100: deploying a hard disk monitoring tool on a computer device, monitoring the total number of hard disk erasures after a computer program initiates data writing and deleting operation instructions to the hard disk through the hard disk monitoring tool, and obtaining all erasure records corresponding to each hard disk based on the change in the total number of erasures; extracting and analyzing the erasure time corresponding to the erasure record to obtain a feature record set corresponding to each hard disk; establishing an artificial intelligence neural network model, and training the neural network model based on the feature record set; Step S200: Retrieve the file storage path corresponding to each application software on the computer device, and extract the hard disk corresponding to the file storage path; according to the file capacity size generated between two adjacent erase records of each application software, obtain the storage impact of each application software on the hard disk; Generally speaking, each application software has a corresponding file storage location, and there is one in which the downloaded files of the application software are stored. For example, common social software, browsers, network disks, and various monitoring software all have fixed file storage locations, and the file storage paths of these file storage locations correspond to a certain hard disk; Step S300: marking the hard disk differently according to the storage impact; setting the planned use period of the hard disk from the current moment onwards, obtaining the estimated number of erase and write times of the hard disk in the planned use period according to the trained neural network model, and correcting the number of erase and write times based on the marked hard disk; Step S400: Obtain the hard disk related technical manual to obtain the estimated number of erasures and writes when the hard disk reaches the end of its life, obtain the hard disk warning value based on the corrected number of erasures and writes, and issue a warning prompt to the hard disk according to the warning value.

[0005] Furthermore, step S100 includes: Step S110: deploying a hard disk monitoring tool on the computer device, monitoring the total number of hard disk erasures after the computer program initiates data writing and deleting operation instructions to the hard disk through the hard disk monitoring tool, and taking the time when the total number of erasures changes as the time corresponding to an erasure; numbering the hard disks on the computer device, extracting the hard disk number and erasure time corresponding to each erasure record, and establishing an erasure record set corresponding to each hard disk according to the order of the erasure time from the front to the back in the erasure record corresponding to each hard disk; There are many tools on the market that can check the hard disk erasure status. These tools can be used to quickly and easily check the hard disk usage, such as the Windows version of CrystalDiskInfo, some SSD management tools, and the Linux version of smartctl. After installation, keep the erasure tool in the boot-up state. The erasure tool will record the total number of hard disk erasures in the background, but because the erasure tool cannot obtain the detailed content of the erasure, it can only obtain the total number of erasures. Therefore, this solution uses the time when the erasure number changes as the erasure time; Step S120: According to the erase record set G corresponding to a hard disk h h , get the erase record set G h The erase interval time between all two adjacent erase records; If the variance between N consecutive erase intervals corresponding to a hard disk h is less than a preset variance threshold, N≥2, then according to the N+1 erase records corresponding to the N consecutive erase intervals, the erase records are sorted in the order of the erase time from the beginning to the end, and a feature record set is established, and then according to the erase record sets of several hard disks, several feature record sets are obtained; Step S130: Establish a neural network model, take the 1st to nth erasure times in a certain feature record set as the first sequence, and the n+1th to Nth erasure times as the second sequence, 2≤n<N; and use the first sequence as input and the second sequence as output, substitute them into the neural network model for training, and obtain the final trained neural network model.

[0006] Further, step S200 includes: Step S210: Retrieve the file storage path corresponding to each application software on the computer device, and determine the storage hard disk corresponding to each application software according to the file storage path; use the hard disk monitoring tool deployed on the computer device to detect the file capacity generated under each file storage path in real time, and add up the capacity of all files generated by a certain application software within a certain erase and write interval as the write capacity of the certain application software; The policies for creating and deleting files of each application program are set to remain unchanged, and the write capacity of all applications belonging to the same storage hard disk within a certain erase / write interval is added together to obtain the total write capacity of the storage hard disk within a certain erase / write interval; and the write capacity of each application program is divided by the total write capacity to obtain the degree of erase / write influence of each application program on the storage hard disk within a certain erase / write interval; Step S220: Obtain all the erase / write intervals corresponding to a hard disk, the number is M, and sort the erase / write intervals in order from front to back, and obtain the weight of each erase / write interval as , where W m is the weight of the mth erase interval, 1≤r≤M; Then we can get the impact of a certain application software on the storage of a certain hard disk as , where W m is the weight of the mth erase interval, P m It is the impact degree of a certain application on a certain hard disk during the mth erase / write interval.

[0007] For SSDs, the internal Flash Translation Layer (FTL) manages data storage and deletion. If the total write capacity within a certain erase / write interval is 10G, and under normal circumstances, if the file generation and deletion strategies of each application remain unchanged, and the garbage collection mechanism and wear leveling mechanism of the SSD are operating normally, then under normal circumstances, the total write capacity of the SSD will remain around 10G within each erase / write interval. This is because the FTL of the SSD can effectively use the space released by deleted files for the storage of new files, and the garbage collection and other operations performed in the background do not have a significant impact on the total write capacity, so the total write capacity will be relatively stable.

[0008] Furthermore, step S300 includes: Step S310: Add the storage impacts of all application software corresponding to each hard disk to obtain the adjustment coefficient of each hard disk, and use the hard disk with an adjustment coefficient greater than the adjustment coefficient threshold as the first marked hard disk, and the hard disk with an adjustment coefficient less than the adjustment coefficient threshold as the second marked hard disk; Set the planned usage period of the hard disk from the current time to F 2 , according to the previous time period F 1 All the erasing and writing moments of a hard disk in the period F are obtained, and the first erasing and writing sequence corresponding to the hard disk is obtained. The first erasing and writing sequence is used as input and substituted into the neural network model to obtain the hard disk in the period F. 2 A second erasing sequence in the first erasing sequence, taking the number of erasing times in the second erasing sequence as Y; Step S320: Add the storage impacts of all application software corresponding to a hard disk to obtain the sum of the impacts of the hard disk X 0 , if a hard disk is the first marked hard disk, the corrected number of erase and write times is Z=⌈Y*X 0 ⌉, where ⌈⌉ is rounded up; if a hard disk is the second marked hard disk, the corrected number of erase times is Z=⌊Y*X 0 ⌋, where ⌊⌋ is rounded down.

[0009] It should be noted that the first marked hard disk here is a hard disk whose adjustment coefficient is greater than the adjustment coefficient threshold. If the erase and write impact of a certain application becomes increasingly greater, it means that when the application is actually used, the capacity of the generated files is also increasing, resulting in a greater storage impact. In other words, the file generation capacity of the application corresponding to the first marked hard disk is increasing. Under normal circumstances, the total write capacity of the solid-state hard disk is maintained at a relatively fixed size during each erase and write interval. Therefore, it can be concluded that the number of erase and write times has increased accordingly, so the number of erase and write times needs to be corrected.

[0010] Further, step S400 includes: obtaining the hard disk in the planned use period F according to the number of erasure times b currently used by the hard disk, the estimated erasure times a obtained according to the technical specification, and the revised erasure times Z. 2 The warning value after completion is V=(b+Z) / a, and the hard disk with a warning value greater than the warning threshold will be warned, and the relevant personnel will be prompted to back up or balance the data of the hard disk in advance.

[0011] An artificial intelligence-based storage device data intelligent monitoring system, including a neural network model training module, a storage impact degree calculation module, an erase and write times correction module and a hard disk early warning prompt module; Neural network model training module: used to deploy hard disk monitoring tools on computer equipment, monitor the total number of hard disk erases and writes after the computer program initiates data write and delete operation instructions to the hard disk through the hard disk monitoring tool, and obtain all erase records corresponding to each hard disk based on the change of the total erase times; extract and analyze the erase time corresponding to the erase record to obtain the feature record set corresponding to each hard disk; establish an artificial intelligence neural network model, and train the neural network model based on the feature record set; Storage impact calculation module: used to retrieve the file storage path corresponding to each application software on the computer device, and extract the hard disk corresponding to the file storage path; according to the file capacity size generated between two adjacent erase records of each application software, the storage impact degree of each application software on the hard disk is obtained; The erase and write times correction module is used to mark the hard disk differently according to the storage impact degree; set the planned use period of the hard disk from the current moment onwards, obtain the estimated erase and write times of the hard disk in the planned use period according to the trained neural network model, and correct the erase and write times based on the marked hard disk; Hard disk early warning prompt module: used to obtain the hard disk related technical manual, obtain the estimated number of erasures and writes when the hard disk reaches the end of its life, and based on the corrected number of erasures and writes, obtain the hard disk's early warning value, and issue an early warning prompt to the hard disk according to the early warning value.

[0012] Further, the neural network model training module includes an erase record set establishment unit, a feature record set establishment unit and a neural network model training unit; The erase record set establishment unit is used to deploy the hard disk monitoring tool on the computer device to obtain each erase time of the hard disk; extract the hard disk number and erase time corresponding to each erase record, and establish the erase record set corresponding to each hard disk; The characteristic record set establishing unit is used to obtain the erase and write interval time according to the erase and write record set corresponding to the hard disk; and obtain several characteristic record sets according to the erase and write interval time; Neural network model training unit: used to establish a neural network model, obtain a first sequence and a second sequence according to a feature record set, and train the neural network model based on the first sequence and the second sequence.

[0013] Further, the erase and write times correction module includes a hard disk mark processing unit and an erase and write times correction unit; The hard disk marking processing unit is used to add up the storage impact of all application software corresponding to each hard disk to obtain the adjustment coefficient of each hard disk, and to mark the hard disk with an adjustment coefficient greater than the adjustment coefficient threshold as the first marked hard disk, and the hard disk with an adjustment coefficient less than the adjustment coefficient threshold as the second marked hard disk; Erasing and writing times correction unit: used to obtain the estimated erasing and writing times of the hard disk during the planned use period according to the trained neural network model, and to correct the erasing and writing times based on the marked hard disk.

[0014] Compared with the prior art, the beneficial effects of the present invention are as follows: the present invention provides a storage device data intelligent monitoring system and method based on artificial intelligence, including: deploying a hard disk monitoring tool on a computer device to obtain the erase and write records generated by the computer device; establishing and training an artificial intelligence neural network model; retrieving the file storage path corresponding to each application software on the computer device to obtain the storage impact of each application software on the hard disk; performing different marking processing on the hard disk according to the storage impact; obtaining the number of erase and write times expected for the hard disk during the planned use period, and making corrections; obtaining the hard disk warning value based on the corrected number of erase and write times, and giving a warning prompt to the hard disk according to the warning value. The present invention estimates the usage of each hard disk during the planned use period by analyzing the historical erase and write records of the hard disk and the usage of the hard disk in each application software, and giving a warning prompt to the hard disk according to the usage, and prompting the relevant personnel to back up the data or balance the hard disk in advance in time, which not only helps the relevant personnel to better view the disk usage, but also prevents the hard disk failure caused by the erase and write times reaching a certain number, resulting in a reduction in the production efficiency of the enterprise. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Figure 1 A schematic diagram of a flow chart of a storage device data intelligent monitoring method based on artificial intelligence according to the present invention; Figure 2 This is a structural diagram of an artificial intelligence-based storage device data intelligent monitoring system of the present invention. DETAILED DESCRIPTION

[0016] 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 only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0017] Example: Figure 1 As shown, the present invention provides a technical solution for a storage device data intelligent monitoring method based on artificial intelligence, comprising the following steps: Step S100: deploying a hard disk monitoring tool on a computer device, monitoring the total number of hard disk erasures after a computer program initiates data writing and deleting operation instructions to the hard disk through the hard disk monitoring tool, and obtaining all erasure records corresponding to each hard disk based on the change in the total number of erasures; extracting and analyzing the erasure time corresponding to the erasure record to obtain a feature record set corresponding to each hard disk; establishing an artificial intelligence neural network model, and training the neural network model based on the feature record set; Step S110: deploying a hard disk monitoring tool on the computer device, monitoring the total number of hard disk erasures after the computer program initiates data writing and deleting operation instructions to the hard disk through the hard disk monitoring tool, and taking the time when the total number of erasures changes as the time corresponding to an erasure; numbering the hard disks on the computer device, extracting the hard disk number and erasure time corresponding to each erasure record, and establishing an erasure record set corresponding to each hard disk according to the order of the erasure time from the front to the back in the erasure record corresponding to each hard disk; There are many tools on the market that can check the hard disk erasure status. These tools can be used to easily and quickly query the hard disk usage, such as the Windows version of CrystalDiskInfo, some SSD management tools, and the Linux version of smartctl. After installation, keep the erasure tool in the self-starting state when the computer is turned on. The erasure tool will record the total number of hard disk erasures in the background. However, since the erasure tool cannot obtain the detailed content of the erasure, it can only obtain the total number of erasures. Therefore, this solution uses the moment when the number of erasures changes as the erasure moment. SMART is a self-monitoring system built into storage devices such as hard disks, which can record multiple key information of the device in real time. It covers the health status of the device, operating temperature, and the number of programmable / erase cycles (P / Ecycles) used. The erasure tool accurately obtains the erasure status of the hard disk by reading the SMART data in the hard disk.

[0018] Step S120: According to the erase record set G corresponding to a hard disk h h , get the erase record set G h The erase interval time between all two adjacent erase records; If the variance between N consecutive erase intervals corresponding to a hard disk h is less than a preset variance threshold, N≥2, then according to the N+1 erase records corresponding to the N consecutive erase intervals, the erase records are sorted in the order of the erase time from the beginning to the end, and a feature record set is established, and then according to the erase record sets of several hard disks, several feature record sets are obtained; Step S130: Establish a neural network model, take the 1st to nth erasure times in a certain feature record set as the first sequence, and the n+1th to Nth erasure times as the second sequence, 2≤n<N; and use the first sequence as input and the second sequence as output, substitute them into the neural network model for training, and obtain the final trained neural network model.

[0019] Step S200: Retrieve the file storage path corresponding to each application software on the computer device, and extract the hard disk corresponding to the file storage path; according to the file capacity size generated between two adjacent erase records of each application software, obtain the storage impact of each application software on the hard disk.

[0020] Step S210: Retrieve the file storage path corresponding to each application software on the computer device, and determine the storage hard disk corresponding to each application software according to the file storage path; use the hard disk monitoring tool deployed on the computer device to detect the file capacity generated under each file storage path in real time, and add up the capacity of all files generated by a certain application software within a certain erase and write interval as the write capacity of the certain application software; The policies for creating and deleting files of each application program are set to remain unchanged, and the write capacity of all applications belonging to the same storage hard disk within a certain erase / write interval is added together to obtain the total write capacity of the storage hard disk within a certain erase / write interval; and the write capacity of each application program is divided by the total write capacity to obtain the degree of erase / write influence of each application program on the storage hard disk within a certain erase / write interval; Step S220: Obtain all the erase / write intervals corresponding to a hard disk, the number is M, and sort the erase / write intervals in order from front to back, and obtain the weight of each erase / write interval as , where W m is the weight of the mth erase interval, 1≤r≤M; Then we can get the impact of a certain application software on the storage of a certain hard disk as , where W m is the weight of the mth erase interval, P m It is the impact degree of a certain application on a certain hard disk during the mth erase / write interval.

[0021] Step S300: Mark the hard disk differently according to the degree of storage impact; set the planned usage period of the hard disk from the current moment onwards, obtain the estimated number of erase and write times of the hard disk during the planned usage period based on the trained neural network model, and correct the number of erase and write times based on the marked hard disk.

[0022] Step S310: Add the storage impacts of all application software corresponding to each hard disk to obtain the adjustment coefficient of each hard disk, and use the hard disk with an adjustment coefficient greater than the adjustment coefficient threshold as the first marked hard disk, and the hard disk with an adjustment coefficient less than the adjustment coefficient threshold as the second marked hard disk; Set the planned usage period of the hard disk from the current time to F 2 , according to the previous time period F 1 All the erasing and writing moments of a hard disk in the period F are obtained, and the first erasing and writing sequence corresponding to the hard disk is obtained. The first erasing and writing sequence is used as input and substituted into the neural network model to obtain the hard disk in the period F. 2 A second erasing sequence in the first erasing sequence, taking the number of erasing times in the second erasing sequence as Y; Step S320: Add the storage impacts of all application software corresponding to a hard disk to obtain the sum of the impacts of the hard disk X 0 , if a hard disk is the first marked hard disk, the corrected number of erase and write times is Z=⌈Y*X 0 ⌉, where ⌈⌉ is rounded up; if a hard disk is the second marked hard disk, the corrected number of erase times is Z=⌊Y*X 0 ⌋, where ⌊⌋ is rounded down.

[0023] It should be noted that the first marked hard disk here is a hard disk whose adjustment coefficient is greater than the adjustment coefficient threshold. If the erasure impact of a certain application is getting bigger and bigger, it means that when the application is actually used, the capacity of the generated file is also getting bigger and bigger, resulting in a larger storage impact. In other words, the file generation capacity of the application corresponding to the first marked hard disk is increasing. Since the total write capacity of the solid-state hard disk is generally maintained at a relatively fixed size during each erasure interval, it can be concluded that the number of erasures increases accordingly, so the number of erasures needs to be corrected. In this embodiment, the adjustment coefficient threshold is 1, Step S400: Obtain the hard disk related technical manual to obtain the estimated number of erasures and writes when the hard disk reaches the end of its life, obtain the hard disk warning value based on the corrected number of erasures and writes, and issue a warning prompt to the hard disk according to the warning value.

[0024] According to the number of erase and write times b currently used by the hard disk, the estimated number of erase and write times a obtained from the technical specification, and the revised number of erase and write times Z, the hard disk’s planned usage period F is obtained. 2 The warning value after completion is V=(b+Z) / a, and the hard disk with a warning value greater than the warning threshold will be warned, and the relevant personnel will be prompted to back up or balance the data of the hard disk in advance.

[0025] This solution estimates the usage of each hard disk during the planned usage period by analyzing the hard disk's historical erase and write records and the hard disk's usage in various application software. It also issues early warning prompts for the hard disk based on the usage and prompts relevant personnel to back up or balance the hard disk data in advance. This not only helps relevant personnel better view disk usage, but also prevents hard disk failures caused by a certain number of erase and write times, which can reduce corporate production efficiency.

[0026] It will be apparent to those skilled in the art that the invention is not limited to the details of the exemplary embodiments described above and that the invention can be implemented in other specific forms without departing from the spirit or essential features of the invention. Therefore, the embodiments should be considered exemplary and non-limiting in all respects, and the scope of the invention is defined by the appended claims rather than the foregoing description, and it is intended that all variations falling within the meaning and scope of the equivalent elements of the claims be included in the invention. Any reference numeral in a claim should not be considered as limiting the claim to which it relates.

Claims

1. A storage device data intelligent monitoring method based on artificial intelligence, characterized in that: The following steps are involved: Step S100: deploying a hard disk monitoring tool on a computer device, monitoring the total number of hard disk erasures after a computer program initiates data write and delete operation instructions to the hard disk through the hard disk monitoring tool, and obtaining all erasure records corresponding to each hard disk based on the change in the total number of erasures; extracting and analyzing the erasure time corresponding to the erasure record, and obtaining a feature record set corresponding to each hard disk; Establish an artificial intelligence neural network model and train the neural network model based on the feature record set; Step S200: Retrieve the file storage path corresponding to each application software on the computer device, and extract the hard disk corresponding to the file storage path; according to the file capacity size generated between two adjacent erase records of each application software, obtain the storage impact of each application software on the hard disk; Step S300: performing different marking processes on the hard disk according to the storage impact degree; Set the planned usage period of the hard disk from the current moment onwards, obtain the estimated number of erase and write times of the hard disk in the planned usage period according to the trained neural network model, and correct the number of erase and write times based on the marked hard disk; Step S400: Obtain the hard disk related technical manual to obtain the estimated number of erasures and writes when the hard disk reaches the end of its life, obtain the hard disk warning value based on the corrected number of erasures and writes, and issue a warning prompt to the hard disk according to the warning value.

2. The method for intelligently monitoring storage device data based on artificial intelligence according to claim 1, characterized in that: Step S100 includes: Step S110: deploying a hard disk monitoring tool on the computer device, monitoring the total number of hard disk erasures after the computer program initiates data writing and deleting operation instructions to the hard disk through the hard disk monitoring tool, and taking the time when the total number of erasures changes as the time corresponding to an erasure; numbering the hard disks on the computer device, extracting the hard disk number and erasure time corresponding to each erasure record, and establishing an erasure record set corresponding to each hard disk according to the order of the erasure time from the front to the back in the erasure record corresponding to each hard disk; Step S120: According to the erase record set G corresponding to a hard disk h h , get the erase record set G h The erase interval time between all two adjacent erase records; If the variance between the N consecutive erase intervals corresponding to the hard disk h is less than a preset variance threshold, N≥2, then according to the N+1 erase records corresponding to the N consecutive erase intervals, the erase records are sorted in the order of the erase time from the front to the back, and a feature record set is established, and then according to the erase record sets of the several hard disks, several feature record sets are obtained; Step S130: Establish a neural network model, take the 1st to nth erasure times in a certain feature record set as the first sequence, and the n+1th to Nth erasure times as the second sequence, 2≤n<N; and use the first sequence as input and the second sequence as output, substitute them into the neural network model for training, and obtain the final trained neural network model.

3. The method for intelligently monitoring storage device data based on artificial intelligence according to claim 2, characterized in that: Step S200 includes: Step S210: Retrieve the file storage path corresponding to each application software on the computer device, and determine the storage hard disk corresponding to each application software according to the file storage path; use the hard disk monitoring tool deployed on the computer device to detect the file capacity generated under each file storage path in real time, and add up the capacity of all files generated by a certain application software within a certain erase and write interval as the write capacity of the certain application software; The policies for creating and deleting files of each application program are set to remain unchanged, and the write capacity of all applications belonging to the same storage hard disk within a certain erase / write interval is added together to obtain the total write capacity of the storage hard disk within a certain erase / write interval; and the write capacity of each application program is divided by the total write capacity to obtain the degree of erase / write influence of each application program on the storage hard disk within a certain erase / write interval; Step S220: Obtain all the erase / write intervals corresponding to a hard disk, the number is M, and sort the erase / write intervals in order from front to back, and obtain the weight of each erase / write interval as , where W m is the weight of the mth erase interval, 1≤r≤M; Then we can get the impact of a certain application software on the storage of a certain hard disk as , where W m is the weight of the mth erase interval, P m It is the impact degree of a certain application on a certain hard disk during the mth erase / write interval.

4. The method for intelligently monitoring storage device data based on artificial intelligence according to claim 1, characterized in that: Step S300 includes: Step S310: Add the storage impacts of all application software corresponding to each hard disk to obtain the adjustment coefficient of each hard disk, and use the hard disk with an adjustment coefficient greater than the adjustment coefficient threshold as the first marked hard disk, and the hard disk with an adjustment coefficient less than the adjustment coefficient threshold as the second marked hard disk; Set the planned use period of the hard disk after the current moment as F2, obtain the first erasing sequence corresponding to the hard disk according to all erasing moments of the hard disk in the period F1 before the current moment, and substitute the first erasing sequence into the neural network model as input to obtain the second erasing sequence of the hard disk in the period F2, and take the number of erasing times in the second erasing sequence as Y; Step S320: Add up the storage impact degrees of all application software corresponding to a certain hard disk to obtain the sum X0 of the impact degrees of the certain hard disk. If the certain hard disk is a first-marked hard disk, the corrected number of erase and write times is Z=⌈Y*X0⌉, where ⌈⌉ is rounded up; if the certain hard disk is a second-marked hard disk, the corrected number of erase and write times is Z=⌊Y*X0⌋, where ⌊⌋ is rounded down.

5. The method for intelligently monitoring storage device data based on artificial intelligence according to claim 4, characterized in that: Step S400 includes: obtaining a warning value V=(b+Z) / a of the hard disk after the planned use period F2 ends according to the number of erase and write times b currently used by the hard disk, as well as the estimated number of erase and write times a and the corrected number of erase and write times Z obtained according to the technical specification, and issuing a warning prompt for the hard disk whose warning value is greater than the warning threshold, and prompting relevant personnel to back up data or balance the hard disk in advance.

6. A storage device data intelligent monitoring system, used to execute the storage device data intelligent monitoring method based on artificial intelligence according to any one of claims 1 to 5, characterized in that: The system includes a neural network model training module, a storage impact degree calculation module, an erase and write times correction module and a hard disk early warning prompt module; Neural network model training module: used to deploy hard disk monitoring tools on computer equipment, monitor the total number of hard disk erases and writes after the computer program initiates data write and delete operation instructions to the hard disk through the hard disk monitoring tool, and obtain all erase records corresponding to each hard disk based on the change of the total erase times; extract and analyze the erase time corresponding to the erase record to obtain the feature record set corresponding to each hard disk; establish an artificial intelligence neural network model, and train the neural network model based on the feature record set; Storage impact calculation module: used to retrieve the file storage path corresponding to each application software on the computer device, and extract the hard disk corresponding to the file storage path; according to the file capacity size generated between two adjacent erase records of each application software, the storage impact degree of each application software on the hard disk is obtained; Erasing and writing times correction module: used to mark the hard disk differently according to the degree of storage impact; Set the planned usage period of the hard disk from the current moment onwards, obtain the estimated number of erase and write times of the hard disk in the planned usage period according to the trained neural network model, and correct the number of erase and write times based on the marked hard disk; Hard disk early warning prompt module: used to obtain the hard disk related technical manual, obtain the estimated number of erasures and writes when the hard disk reaches the end of its life, and based on the corrected number of erasures and writes, obtain the hard disk's early warning value, and issue an early warning prompt to the hard disk according to the early warning value.

7. The storage device data intelligent monitoring system according to claim 6, characterized in that: The neural network model training module includes an erase record set establishment unit, a feature record set establishment unit and a neural network model training unit; The erase record set establishment unit is used to deploy the hard disk monitoring tool on the computer device to obtain each erase time of the hard disk; extract the hard disk number and erase time corresponding to each erase record, and establish the erase record set corresponding to each hard disk; The characteristic record set establishing unit is used to obtain the erase and write interval time according to the erase and write record set corresponding to the hard disk; and obtain several characteristic record sets according to the erase and write interval time; Neural network model training unit: used to establish a neural network model, obtain a first sequence and a second sequence according to a feature record set, and train the neural network model based on the first sequence and the second sequence.

8. The storage device data intelligent monitoring system according to claim 6, characterized in that: The erasure number correction module includes a hard disk mark processing unit and an erasure number correction unit; The hard disk marking processing unit is used to add up the storage impact of all application software corresponding to each hard disk to obtain the adjustment coefficient of each hard disk, and to mark the hard disk with an adjustment coefficient greater than the adjustment coefficient threshold as the first marked hard disk, and the hard disk with an adjustment coefficient less than the adjustment coefficient threshold as the second marked hard disk; Erasing and writing times correction unit: used to obtain the estimated erasing and writing times of the hard disk during the planned use period according to the trained neural network model, and to correct the erasing and writing times based on the marked hard disk.

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