Monitoring method of active storage device

Abstract

The invention discloses a monitoring method of an active storage device. The monitoring method of the active storage device comprises the following steps: training a prediction model by using a cloud server; the storage device actively sends the health state data of the storage device to a cloud server; and utilizing the cloud server to predict the future state of the storage device by using the prediction model according to the health state data of the storage device.

Description

Technical Field

[0001] The present invention relates to a storage device, and particularly to a monitoring method for an active storage device. Background Art

[0002] With the expansion of information and the progress of technology, storage devices of various specifications, such as solid-state drives, are widely used in various fields to store different data. However, when a storage device fails, the data stored in the storage device may be lost.

[0003] The existing health monitoring method for storage devices is to passively provide the health status data of the storage device by the storage device. When the health of the storage device is detected to be abnormal, the storage device may be close to the situation where it cannot be used. Thus, it causes a heavy burden on the maintenance cost. Summary of the Invention

[0004] The present invention provides a monitoring method for an active storage device in view of the deficiencies of the prior art. The monitoring method for the active storage device of the present invention includes the following steps: using a cloud server to train a prediction model; actively sending the health status data of the storage device to the cloud server by the storage device; and using the cloud server to use the prediction model to predict the future state of the storage device according to the health status data of the storage device.

[0005] As described above, the present invention provides a monitoring method for an active storage device, which actively sends its own health status data to a cloud server by the storage device, so that the cloud server can monitor the change of the health status data of the storage device in real time, dynamically predict the future state of the storage device in real time, and can give a warning and perform processing operations such as data backup of the storage device when it is predicted that a failure state will occur in the future.

[0006] To further understand the features and technical content of the present invention, please refer to the following detailed description and drawings of the present invention. However, the provided drawings are only for reference and illustration, and are not used to limit the present invention. Brief Description of the Drawings

[0007] Figure 1 It is a block diagram of a monitoring system for an active storage device according to an embodiment of the present invention.

[0008] Figure 2 It is a flowchart of the step of actively notifying update information by a storage device included in the monitoring method for an active storage device according to an embodiment of the present invention.

[0009] Figure 3A flowchart of the step of the host confirming whether the cloud server supports the storage device included in the monitoring method of the active storage device according to an embodiment of the present invention.

[0010] Figure 4 A flowchart of the step of the cloud server performing fault prediction on the storage device included in the monitoring method of the active storage device according to an embodiment of the present invention.

[0011] Figure 5 A flowchart of the step of the cloud server backing up data of the storage device included in the monitoring method of the active storage device according to an embodiment of the present invention.

[0012] Figure 6 A schematic diagram of a curve showing the change of the erasure count value of the storage device over time established by the monitoring method of the active storage device according to an embodiment of the present invention.

[0013] Figure 7 A schematic diagram of a curve showing the change of the write count value of the storage device over time established by the monitoring method of the active storage device according to an embodiment of the present invention. Detailed implementation manners

[0014] The following are specific embodiments to illustrate the implementation manners of the present invention. Those skilled in the art can understand the advantages and effects of the present invention from the content disclosed in this specification. The present invention can be implemented or applied through other different specific embodiments, and various details in this specification can also be modified and changed based on different viewpoints and applications without departing from the concept of the present invention. Additionally, the drawings of the present invention are only for simple schematic illustration and are not drawn according to actual dimensions, which is stated in advance. The following implementation manners will further detail the related technical content of the present invention, but the disclosed content is not used to limit the protection scope of the present invention. Additionally, the term "or" used herein may include any one or a combination of more of the associated listed items depending on the actual situation.

[0015] Please refer to Figure 1 and Figure 2 , where Figure 1 is a block diagram of the monitoring system of the active storage device according to an embodiment of the present invention, Figure 2 is a flowchart of the step of the storage device actively notifying update information included in the monitoring method of the active storage device according to an embodiment of the present invention.

[0016] The monitoring system of the active storage device of the present invention may include a host HS and a cloud server CLU as shown in Figure 1 , which are applied to manage one or more storage devices, such as Figure 1 the multiple storage devices SRD1 to SRDn shown.

[0017] It should be understood that each storage device includes at least a storage component and a controller. For example Figure 1 the storage device SRD1 shown includes a storage component ST1 and a controller CR1, the storage device SRD2 includes a storage component ST2 and a controller CR2, and the storage device SRDn includes a storage component STn and a controller CRn. In practice, the storage component ST1 may include multiple storage units, and the present invention is not limited thereto, so the internal architecture of the storage component ST1 will not be described in detail herein.

[0018] For the convenience of description, it is exemplified that the cloud server CLU of the monitoring system of the active storage device of the present invention may include a database DB, a training model building component PR, and a state prediction component FA, which can be replaced by other components or include more components in practice.

[0019] The database DB of the cloud server CLU stores a large amount of reference data collected. The training model building component PR of the cloud server CLU obtains the reference data from the database DB, performs big data analysis, and executes an artificial intelligence machine learning algorithm to train a prediction model.

[0020] The controllers CR1 to CRn of the storage devices SRD1 to SRDn actively send the health status data of the storage devices SRD1 to SRDn (including the health status data of the controllers CR1 to CRn of the storage devices SRD1 to SRDn respectively, the health status data of the storage components ST1 to STn of the storage devices SRD1 to SRDn respectively, or a combination thereof), and sequentially transmit them to the state prediction component FA through the database DB and the training model building component PR of the cloud server CLU.

[0021] The state prediction component FA of the cloud server CLU uses a prediction model established by the training model building component PR, and based on the health status data of the storage devices SRD1 to SRDn, predicts the future states of the storage devices SRD1 to SRDn, including predicting whether the storage devices SRD1 to SRDn will fail in the future and predicting the future failure states of the storage devices SRD1 to SRDn.

[0022] More specifically, the monitoring system of the active storage device of the present invention can execute the monitoring method of the active storage device of the present invention. The order and content of the steps of the monitoring method of the active storage device described herein can be appropriately adjusted or omitted according to actual needs, and the present invention is not limited thereto.

[0023] The monitoring method of the active storage device of the present invention can be applied synchronously or asynchronously to multiple storage devices SRD1 to SRDn, or to any one of the multiple storage devices SRD1 to SRDn. For the convenience of description, hereinafter, only the monitoring method of the active storage device of the present invention applied to the storage device SRD1 is described. It should be understood that the same or similar description can also be applied to other storage devices SRD2 to SRDn.

[0024] First, as Figure 2 shown, the monitoring method of the active storage device according to an embodiment of the present invention may include steps S101 to S106.

[0025] In step S101, the host HS sends the data of the storage component ST1 to be written into the storage device SRD1 to the controller CR1 of the storage device SRD1.

[0026] In step S102, the controller CR1 of the storage device SRD1 stores the total capacity C of the predefined storage device SRD1 and sets a capacity critical ratio P. The multiple total capacities C of the multiple storage devices SRD1 to SRDn may be different from each other.

[0027] In step S103, the controller CR1 of the storage device SRD1 calculates the data amount HWD of the data that the host HS wants to write into the storage component ST1 of the storage device SRD1.

[0028] In step S104, the controller CR1 of the storage device SRD1 multiplies the total capacity C of the storage device SRD1 by a capacity critical ratio P to calculate a capacity critical value. The controller CR1 of the storage device SRD1 determines whether the data amount HWD of the data that the host HS wants to write into the storage component ST1 of the storage device SRD1 is greater than or equal to this capacity critical value. This capacity critical value may be the current remaining capacity / usable capacity of the storage component ST1 of the storage device SRD1.

[0029] If the data amount HWD of the data that the host HS wants to write into the storage component ST1 of the storage device SRD1 is less than a capacity critical value, step S103 is executed. That is, the data currently sent by the host HS is written into the storage component ST1 of the storage device SRD1, the controller CR of the storage device SRD1 continuously receives new data sent by the host HS later, and the controller CR1 of the storage device SRD1 calculates the data amount HWD of the new data that the host HS wants to write into the storage component ST1 of the storage device SRD1.

[0030] On the contrary, if the data amount HWD of the data that the host HS wants to write into the storage component ST1 of the storage device SRD1 is greater than or equal to a capacity critical value, step S105 is executed.

[0031] In step S105, the controller CR1 of the storage device SRD1 allows a portion of the data sent by the host HS to be written into the storage component ST1 of the storage device SRD1, and calculates the amount of data (and its proportion of the total capacity C of the storage device SRD1) that has been written into the storage component ST1 of the storage device SRD1.

[0032] In step S106, without waiting for an inquiry from the host HS, the controller CR1 of the storage device SRD1 actively and in real-time sends the data write / update status information of the storage component ST1 of the storage device SRD1 (including the data content, data volume, and its proportion of the total capacity C of the storage device SRD1 that has been written into the storage component ST1 of the storage device SRD1, etc.) to the host HS, so as to actively notify the host HS and enable the host HS to know the usage / update status of the storage device SRD1 in real-time.

[0033] Please refer to Figure 1 and Figure 3 where Figure 1 is a block diagram of the monitoring system of the active storage device according to an embodiment of the present invention, Figure 3 is a flowchart of the step of the host confirming whether the cloud server supports the storage device included in the monitoring method of the active storage device according to an embodiment of the present invention.

[0034] The monitoring method of the active storage device according to an embodiment of the present invention may include steps S201 to S206 as shown in Figure 2 shown.

[0035] In step S201, the host HS requests the controller CR1 of the storage device SRD1 to report parameters, including the accumulated number of erase operations and write operations performed by the storage device SRD1.

[0036] In step S202, the controller CR1 of the storage device SRD1 counts the number of erase operations of the storage device SRD1 to generate an erase count value EC, and counts the number of write operations of the storage device SRD1 to generate a write count value FWC.

[0037] The controller CR1 of the storage device SRD1 sends an erase count value EC and a write count value FWC of the storage device SRD1 to the host HS. In practice, the controller CR1 of the storage device SRD1 can actively send an erase count value EC and a write count value FWC of the storage device SRD1 to the host HS.

[0038] In step S203, the host HS sends a support confirmation instruction to the controller CR1 of the storage device SRD1, which is transmitted to the cloud server CLU through the controller CR1 of the storage device SRD1.

[0039] In step S204, the training model establishment component PR of the cloud server CLU receives a support confirmation instruction through the database DB, determines whether it supports (manages or subscribes to) the storage device SRD1, and sends a support confirmation return message to the controller CR1 of the storage device SRD1 through the database DB.

[0040] If the controller CR1 of the storage device SRD1 receives a support confirmation return message from the cloud server CLU indicating that the cloud server CLU supports (manages or subscribes to) the storage device SRD1, step S205 is executed.

[0041] Conversely, if the controller CR1 of the storage device SRD1 receives a support confirmation return message from the cloud server CLU indicating that the cloud server CLU does not support (manage or subscribe to) the storage device SRD1, step S206 is executed.

[0042] In step S205, the controller CR1 of the storage device SRD1 transmits the support confirmation return message received from the cloud server CLU to the host HS to report to the host HS that the cloud server CLU supports (manages or subscribes to) the storage device SRD1.

[0043] When the host HS confirms that the cloud server CLU supports (manages or subscribes to) the storage device SRD1, the host HS outputs a data providing instruction to the controller CR1 of the storage device SRD1 to instruct the controller CR1 of the storage device SRD1 to transmit the health status data of the storage device SRD1 (including the above-mentioned erase count value EC and write count value FWC and other parameter values) to the database DB of the cloud server CLU for storage. In this way, the training model establishment component of the cloud server CLU can monitor the health status data of the storage device SRD1.

[0044] In step S206, the controller CR1 of the storage device SRD1 transmits the support confirmation return message received from the cloud server CLU to the host HS to report to the host HS that the cloud server CLU does not support (manage or subscribe to) the storage device SRD1. In this case, the host HS does not instruct the controller CR1 of the storage device SRD1 to transmit the health status data of the storage device SRD1 (including the above-mentioned erase count value EC and write count value FWC and other parameter values) to the cloud server CLU.

[0045] Please refer to Figure 1 、 Figure 4 、 Figure 6 and Figure 7 , where Figure 1 is a block diagram of the monitoring system of the active storage device according to an embodiment of the present invention. Figure 4A flowchart showing the steps of a failure prediction of a storage device performed by a cloud server in the monitoring method of an active storage device according to an embodiment of the present invention. Figure 6 A schematic diagram showing a curve of the number of erase counts of a storage device changing with time established in the monitoring method of an active storage device according to an embodiment of the present invention. Figure 7 A schematic diagram showing a curve of the number of write counts of a storage device changing with time established in the monitoring method of an active storage device according to an embodiment of the present invention.

[0046] The monitoring method of the active storage device according to an embodiment of the present invention may include steps S301 to S307 as Figure 3 shown.

[0047] In step S301, a training model building component PR of a cloud server CLU trains a prediction model.

[0048] In step S302, a database DB of the cloud server CLU receives the number of write counts of the storage device SRD1 counted at different time points from a controller CR1 of the storage device SRD1 to obtain a write count value FWC at each different time point. The training model building component PR of the cloud server CLU establishes a write count change curve of the storage device SRD1 changing with time in a write count change curve graph. The training model building component PR of the cloud server CLU calculates the slope of this write count change curve.

[0049] In addition, the database DB of the cloud server CLU receives the number of erase counts of the storage device SRD1 counted at different time points from the controller CR1 of the storage device SRD1 to obtain an erase count value EC at each different time point. The training model building component PR of the cloud server CLU establishes an erase count change curve of the storage device SRD1 changing with time in an erase count change curve graph. The training model building component PR of the cloud server CLU calculates the slope of this erase count change curve.

[0050] In step S303, the training model building component PR of the cloud server CLU sets a write count standard curve slope and a write count slope tolerance error. In addition, the training model building component PR of the cloud server CLU sets an erase count standard curve slope and an erase count slope tolerance error.

[0051] For the convenience of description, curves are used throughout the text for description, but any curve described herein can be replaced by a straight line in practice, depending on the change of data with time during actual application.

[0052] The slopes of the multiple write count standard curves of each of the multiple storage devices SRD1 to SRDn may be different from each other. For example, Figure 7 The slopes CU71 and CU72 of the two write count change curves of two of the multiple storage devices SRD1 to SRDn shown are different from each other.

[0053] The slopes of the multiple erase count standard curves of each of the multiple storage devices SRD1 to SRDn may also be different from each other, as Figure 6 The slopes of the erase count change curves CU61 and CU62 of two of the multiple storage devices SRD1 to SRDn shown are different from each other.

[0054] That is, different threshold values can be set for different storage devices SRD1 to SRDn.

[0055] In step S304, the training model building component PR of the cloud server CLU determines whether a prediction model has been trained.

[0056] The controller CR1 of the storage device SRD1 can calculate the difference between the slope of a write count change curve of the storage device SRD1 and the slope of a write count standard curve as a write count error. The controller CR1 of the storage device SRD1 can calculate the difference between the slope of an erase count change curve of the storage device SRD1 and the slope of an erase count standard curve as an erase count error.

[0057] When a write count error of the storage device SRD1 is greater than a write count slope tolerance error or an erase count error is greater than an erase count slope tolerance error, the training model building component PR of the cloud server CLU can retrain a prediction model.

[0058] If the training model building component PR of the cloud server CLU has not finished training a prediction model, return to step S302. On the contrary, if the training model building component PR of the cloud server CLU has finished training a prediction model, execute step S305.

[0059] In step S305, the training model building component PR of the cloud server CLU maintains or updates the slope of a write count change curve and the slope of an erase count change curve calculated in step S302.

[0060] In step S306, the training model building component PR of the cloud server CLU determines whether the slope of a write count change curve of the storage device SRD1 is the same as the slope of a write count standard curve and whether the slope of an erase count change curve of the storage device SRD1 is the same as the slope of an erase count standard curve to determine whether the usage state of the storage device SRD1 violates the standard.

[0061] If the slope of a write count change curve of the storage device SRD1 is the same as the slope of a write count standard curve (i.e., a standard value) and it is determined that the slope of an erase count change curve of the storage device SRD1 (i.e., a standard value) is the same as the slope of an erase count standard curve, the training model building component PR of the cloud server CLU determines that the usage state of the storage device SRD1 meets the standard, and then step S307 is executed.

[0062] On the contrary, if the slope of a write count change curve of the storage device SRD1 is different from the slope of a write count standard curve or the slope of an erase count change curve of the storage device SRD1 is different from the slope of an erase count standard curve, the training model building component PR of the cloud server CLU determines that the usage state of the storage device SRD1 violates the standard, and returns to execute step S305. At this time, in step S305, the training model building component PR of the cloud server CLU performs more data writes or erasures on the storage device SRD1 obtained from the database DB to recalculate / update the slope of a write count standard curve and the slope of an erase count change curve. Then, step S306 is executed to determine whether the slopes of the updated write count standard curve and erase count change curve of the storage device SRD1 meet the above standard values.

[0063] In step S307, the training model building component PR of the cloud server CLU predicts whether the storage device SRD1 may have a failure state in the future and predicts the future failure state data of the storage device SRD1 (i.e., the health state data of the storage device SRD1 when a failure occurs in the future) based on the health state data of the storage device SRD1 (such as the write count error and erase count error of the storage device SRD1 described above).

[0064] Please refer to Figure 1 and Figure 5 , where Figure 1 is a block diagram of a monitoring system for an active storage device according to an embodiment of the present invention, Figure 5 is a flowchart of steps for backing up data of a storage device by a cloud server included in a monitoring method for an active storage device according to an embodiment of the present invention.

[0065] The monitoring method for an active storage device according to an embodiment of the present invention may include steps S401 to S404 as shown in Figure 4 , which can be executed after it is predicted that the storage device SRD1 may have a failure state in the future.

[0066] In step S401, the database DB of the cloud server CLU obtains the data of the storage device SRD1 from the controller CR1 of the storage device SRD1. In addition to the health status data of the storage device SRD1 and multiple setting parameters (including the above-mentioned erasure count value EC and write count value FWC), other data stored in the storage component ST1 of the storage device SRD1 can also be obtained, especially when it is predicted that the storage device SRD1 may fail as described above.

[0067] In step S402, the database DB of the cloud server CLU receives data from the controller CR1 of the storage device SRD1 (including the data stored in the storage component ST1 of the storage device SRD1, the operation data of the controller CR1 of the storage device SRD1, or a combination thereof), and backs up the received data of the storage device SRD1. If necessary, the training model building component PR of the cloud server CLU can encrypt the data of the storage device SRD1 backed up in the database DB.

[0068] In step S403, the training model building component PR of the cloud server CLU determines whether the database DB of the cloud server CLU has completed the data backup of the storage device SRD1.

[0069] If the database DB of the cloud server CLU has not completed the data backup of the storage device SRD1, step S402 is continuously executed. On the contrary, if the database DB of the cloud server CLU has completed the data backup of the storage device SRD1, step S404 is continuously executed.

[0070] In step S404, the cloud server CLU issues a potential failure notification message for the storage device SRD1 with a potential for failure.

[0071] In summary, the present invention provides a monitoring method for an active storage device, which actively sends its own health status data to a cloud server, enabling the cloud server to monitor the change of the health status data of the storage device in real time, dynamically predict the future state of the storage device in real time, and can give a warning and perform data backup and other processing operations on the storage device when it is predicted that a failure state will occur in the future.

[0072] The content disclosed above is only a preferred feasible embodiment of the present invention, and does not limit the claims of the present invention. Therefore, all equivalent technical changes made by using the content of the specification and drawings of the present invention are included in the claims of the present invention.

Claims

1. A monitoring method for an active storage device, characterized in that The monitoring method of the active storage device described above includes the following steps: Using a cloud server to train a prediction model; The storage device actively sends a health status data of the storage device to the cloud server; And Using the cloud server, using the prediction model, based on the health status data of the storage device, to predict the future state of the storage device.

2. The monitoring method of the active storage device according to claim 1, wherein The monitoring method of the active storage device further includes the following steps: Using the storage device to determine whether the amount of data sent by a host to the storage device is greater than or equal to a capacity threshold value. If not, allow the host to write data to the storage device. If so, calculate the amount of data allowed to be written by the host to the storage device, calculate the amount of data that the host has written to the storage device, and send a data update notification message to the host.

3. The monitoring method of the active storage device according to claim 2, characterized in that, The monitoring method of the active storage device further includes the following steps: Using the storage device to multiply the total capacity of the storage device by a capacity threshold ratio to calculate the capacity threshold value.

4. The monitoring method of the active storage device according to claim 1, wherein The monitoring method of the active storage device further includes the following steps: A host sends a support confirmation instruction to the storage device; The storage device transmits the support confirmation instruction received from the host to the cloud server; The cloud server determines whether it supports the storage device according to the received support confirmation instruction, and sends a support confirmation return message to the storage device; Through the storage device, the support confirmation return message of the cloud server is transmitted to the host; And The host determines whether the cloud server supports the storage device according to the support confirmation return message. If so, outputs a data providing instruction to the storage device to instruct the storage device to send the health status data to the cloud server. If not, does not instruct the storage device to send the health status data to the cloud server.

5. The monitoring method of the active storage device according to claim 1, wherein The monitoring method of the active storage device further includes the following steps: The storage device counts the number of erasure operations performed by the storage device to generate an erasure count value; and The storage device sends the erasure count value to a host.

6. The monitoring method of the active storage device according to claim 5, wherein The monitoring method of the active storage device further includes the following steps: Using the cloud server to establish an erasure count change curve of the storage device changing with time in an erasure count change curve graph.

7. The monitoring method of the active storage device according to claim 6, characterized in that, The monitoring method of the active storage device further includes the following steps: Using the cloud server to calculate the slope of the erasure count change curve of the storage device changing with time; Using the cloud server to set a standard slope of the erasure count curve; Using the cloud server to determine whether the slope of the erasure count change curve of the storage device is the same as the standard slope of the erasure count curve. If so, perform the next step. If not, do not perform the next step; And Using the cloud server, predict the future failure state of the storage device according to the difference between the slope of the erasure count change curve of the storage device and the slope of the erasure count standard curve.

8. The monitoring method of the active storage device according to claim 7, characterized in that, The monitoring method of the active storage device further includes the following steps: Using the cloud server, set an erasure count slope tolerance error. Using the cloud server, calculate the difference between the slope of the erasure count change curve of the storage device and the slope of the erasure count standard curve as an erasure count error; and Using the cloud server, compare the erasure count error with the erasure count slope tolerance error to determine whether to predict the future failure state of the storage device.

9. The monitoring method of the active storage device according to claim 8, wherein The monitoring method of the active storage device further includes the following steps: Using the cloud server, use the prediction model to predict the future potential failure possibility of the storage device according to the difference between the slope of the erasure count change curve of the storage device and the slope of the erasure count standard curve. If so, perform the next step; if not, do not perform the next step; And Using the cloud server, obtain and back up the data of the storage device.

10. The monitoring method of the active storage device according to claim 9, characterized in that, The monitoring method of the active storage device further includes the following steps: Using the cloud server, encrypt the data of the storage device with potential failure possibility.

11. The monitoring method of the active storage device according to claim 9, wherein The monitoring method of the active storage device further includes the following steps: Using the cloud server, issue a potential failure notification message for the storage device with potential failure possibility.

12. The monitoring method of the active storage device according to claim 1, wherein The monitoring method of the active storage device further includes the following steps: By the storage device, count the write times of the storage device to generate a write count value; and By the storage device, send the write count value to a host.

13. The monitoring method of the active storage device according to claim 12, characterized in that, The monitoring method of the active storage device further includes the following steps: Using the cloud server, establish a write count change curve showing how the write count value of the storage device changes over time in a write count change curve graph.

14. The monitoring method of the active storage device according to claim 13, characterized in that, The monitoring method of the active storage device further includes the following steps: Using the cloud server, calculate the slope of the write count change curve showing how the write count value of the storage device changes over time. Using the cloud server, set a write count standard curve slope. Using the cloud server, determine whether the slope of the write count change curve of the storage device is the same as the write count standard curve slope. If so, perform the next step; if not, do not perform the next step; And Using the cloud server, predict the future failure state of the storage device according to the difference between the slope of the write count change curve of the storage device and the slope of the write count standard curve.

15. The monitoring method of the active storage device according to claim 14, characterized in that, The monitoring method of the active storage device further includes the following steps: Using the cloud server, set a write count slope tolerance error. Using the cloud server, calculate the difference between the slope of the write count change curve of the storage device and the slope of the write count standard curve as a write count error; And Using the cloud server, compare the write count error with the write count slope tolerance error to determine whether to predict the future failure state of the storage device.

16. The monitoring method of the active storage device according to claim 15, characterized in that, The monitoring method of the active storage device further includes the following steps: Using the cloud server, using the prediction model, based on the difference between the slope of the write count change curve of the storage device and the slope of the write count standard curve, to predict whether there is a potential possibility of future failure of the storage device. If so, perform the next step. If not, do not perform the next step; And Using the cloud server, obtain and back up the data of the storage device.

17. The monitoring method of the active storage device according to claim 16, characterized in that, The monitoring method of the active storage device further includes the following steps: Using the cloud server, encrypt the data of the storage device with a potential possibility of failure.

18. The monitoring method of the active storage device according to claim 16, wherein The monitoring method of the active storage device further includes the following steps: Using the cloud server, issue a potential failure notification message for the storage device with a potential possibility of failure.

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