A method for data storage and preservation

Abstract

The present invention discloses a data storage and preservation method, which relates to the technical field of information storage, and includes: obtaining the operation environment data of the hardware medium to be written, and evaluating the operation environment impact coefficient of the hardware medium to be written; obtaining the historical storage data of the hardware medium to be written, and evaluating the operation performance index and the dynamic load status index of the hardware medium to be written; initializing the health value of the internal storage unit of the hardware medium to be written based on the operation performance status index of the hardware medium to be written and the operation environment impact coefficient of the hardware medium to be written; correcting the initialized health value of the internal storage unit of the hardware medium to be written based on the dynamic load status index of the hardware medium to be written to obtain the dynamic health value of the storage unit of the hardware medium to be written; screening out that the dynamic health value of the storage unit of the hardware medium to be written meets the storage requirements of the data to be written for dynamic storage. The advantages of the present invention are: improving the stability of data storage and reducing the risk of data loss.

Description

Technical Field

[0001] The present invention relates to the technical field of information storage, and specifically relates to a data storage and preservation method. Background Art

[0002] Data storage and preservation refers to the process of storing information, materials or documents in digital form in a specific hardware medium (such as hard disk, SSD, mechanical hard disk, etc.) so that they can be accessed, retrieved and used at any time in the future.

[0003] Currently, since most enterprises deployed their servers earlier and the overall return on updating and iterating the server cluster is low, but the risk of data loss increases with the passage of the server deployment time. Therefore, a dynamic storage scheme for controlling data is proposed based on the comprehensive analysis and decision-making of the multi-factor status of the server itself. Summary of the Invention

[0004] To solve the above technical problems, a data storage and preservation method is provided. This technical solution solves the problem of the above-mentioned currently, since most enterprises deployed their servers earlier and the overall return on updating and iterating the server cluster is low, but the risk of data loss increases with the passage of the server deployment time. Therefore, a dynamic storage scheme for controlling data is proposed based on the comprehensive analysis and decision-making of the multi-factor status of the server itself.

[0005] To achieve the above objectives, the technical solution adopted by the present invention is as follows:

[0006] A data storage and preservation method, including:

[0007] Obtain the operating environment data of the hardware medium to be written, analyze the influence trend of the operating environment data on the hardware medium to be written, and evaluate the operating environment influence coefficient of the hardware medium to be written;

[0008] Obtain the historical storage data of the hardware medium to be written;

[0009] According to the historical storage data of the hardware medium to be written, analyze the storage quality of the hardware medium to be written, and evaluate the operating performance index of the hardware medium to be written;

[0010] Based on the operating performance status index of the hardware medium to be written and the operating environment influence coefficient of the hardware medium to be written, initialize the health value of the internal storage unit of the hardware medium to be written;

[0011] According to the historical storage data of the hardware medium to be written, analyze the storage load trend of the hardware medium to be written, and evaluate the dynamic load status index of the hardware medium to be written;

[0012] Based on the dynamic load status indicators of the hardware medium to be written, correct the health values of the internal storage units of the hardware medium to be written to obtain the dynamic health values of the storage units of the hardware medium to be written;

[0013] Based on the dynamic health values of the storage units of the hardware medium to be written, filter out the storage units of the hardware medium to be written whose dynamic health values meet the storage requirements of the data to be written for dynamic storage;

[0014] Among them, the health values of the internal storage units of the hardware medium to be written are specifically:

[0015] R = S·G

[0016] In the formula, R is the initialized health value of each internal storage unit of the hardware medium to be written, S is the operating environment impact coefficient of the hardware medium to be written, and G is the operating performance index of the hardware medium to be written;

[0017] Among them, the dynamic health values of the storage units of the hardware medium to be written are specifically:

[0018] U = F·R

[0019] In the formula, U is the dynamic health value of the storage units of the hardware medium to be written, and F is the dynamic load status indicator of the storage medium to be stored per unit time in the future.

[0020] Preferably, obtaining the operating environment data of the hardware medium to be written, analyzing the influence trend of the operating environment data on the hardware medium to be written, and evaluating the operating environment impact coefficient of the hardware medium to be written specifically include:

[0021] Perform standardization processing based on the operating environment data of the hardware medium to be written;

[0022] Based on the ideal standardized working environment data of the hardware medium to be written after standardization processing, determine the ideal standardized working environment parameter array of the hardware medium to be written; the manufacturing calibrated ideal working environment includes: working temperature, working humidity, working voltage;

[0023] Perform difference operation based on the ideal standardized working environment parameter array of the hardware medium to be written and the operating environment data of the hardware medium to be written to obtain the operating environment deviation parameter of the hardware medium to be written;

[0024] Based on the operating environment deviation parameter of the hardware medium to be written and the ideal standardized working environment parameter array of the hardware medium to be written, calculate the entropy value of the operating environment deviation parameter of the hardware medium to be written;

[0025] Determine the coefficient of variation of the operating environment deviation parameters according to the entropy value of the operating environment deviation parameters to be written into the hardware medium, and calculate the deviation weight of the operating environment deviation parameters to be written into the hardware medium;

[0026] Calculate the operating environment influence coefficient of the hardware medium to be written according to the deviation weight of the operating environment deviation parameters of the hardware medium to be written and the operating environment deviation parameters of the hardware medium to be written;

[0027] Among them, the operating environment influence coefficient of the hardware medium to be written is specifically:

[0028]

[0029] In the formula, S is the operating environment influence coefficient of the hardware medium to be written, and P ij is the deviation probability distribution of the j-th deviation parameter of the i-th operating environment of the hardware medium to be written, and E ij is the entropy value of the j-th deviation parameter of the i-th operating environment of the hardware medium to be written, and d ij is the coefficient of variation of the j-th deviation parameter of the i-th operating environment of the hardware medium to be written, and w ij is the deviation weight of the j-th deviation parameter of the i-th operating environment of the hardware medium to be written, and V ij is the value of the j-th deviation parameter of the i-th operating environment of the hardware medium to be written.

[0030] Preferably, according to the historical storage data of the hardware medium to be written, analyze the storage quality of the hardware medium to be written, and the evaluation of the operating performance indicators of the hardware medium to be written specifically includes:

[0031] Based on the historical storage data of the hardware medium to be written, screen out the storage quality characteristic data in the historical storage data according to the unit time, and obtain the storage quality characteristic time series data of the hardware medium to be written; the storage quality characteristic data includes: read / write speed data, access time data;

[0032] Perform normalization processing on the storage quality characteristic time series data of the hardware medium to be written to obtain the normalized storage quality characteristic time series data of the hardware medium to be written;

[0033] Based on AR autoregression, construct a prediction model for the performance change trend of the hardware medium to be written;

[0034] Substitute the normalized storage quality characteristic time series data of the hardware medium to be written into the prediction model for the performance change trend of the hardware medium to be written at intervals of time units to evaluate the operating performance indicators of the hardware medium to be written;

[0035] Among them, the prediction model for the performance change trend of the hardware medium to be written is specifically:

[0036]

[0037] Wherein, G is the operating performance index to be written into the hardware medium, and x i't-k is the i'-th storage quality characteristic data at the (t - k)-th unit time lag learning order of the hardware medium to be written, α0 is the intercept, and α k is the autoregressive coefficient, and ε t is the error term at the t-th unit time lag learning order, and q is the total number of lag learning orders.

[0038] Preferably, based on the historical storage data of the hardware medium to be written, analyze the storage load trend of the hardware medium to be written, and evaluate the dynamic load status index of the hardware medium to be written, which specifically includes:

[0039] Based on the historical storage data of the hardware medium to be written, mark the load action events of the storage medium to be stored at each unit time, and obtain the historical load action event characteristic data of the storage medium; the historical load action events include: read / write events, IOPS events;

[0040] According to the historical load action event characteristic data of the storage medium, construct a historical load action event curve graph of the storage medium to be stored;

[0041] Based on the historical load action event curve graph of the storage medium to be stored, mark the peak-valley load trend of the historical load action events, and obtain the historical load trend characteristic parameters of the storage medium to be stored;

[0042] Construct an autoregressive load evaluation model for the hardware medium to be written;

[0043] Based on the autoregressive load evaluation model of the hardware medium to be written, use the historical load trend characteristic parameters of the storage medium to be stored within a unit time as the independent variable input, and use the predicted dynamic load status index of the storage medium to be stored in the future unit time as the dependent variable output;

[0044] Among them, the autoregressive load evaluation model of the hardware medium to be written is specifically:

[0045]

[0046] Wherein, F is the dynamic load status index of the storage medium to be stored in the future unit time, is the l-th historical load trend characteristic parameter at the t-th unit time, β0 is the intercept, and β1, β2, β l are all regression coefficients, and ∈ is the error term.

[0047] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0048] The present invention proposes a data storage and preservation solution, which analyzes the initial health value of the storage unit of the hardware medium to be written in the operating environment and its own storage quality status; performs regression analysis based on the change trend of historical storage data to determine the load state trend of the storage unit of the hardware medium to be written, obtains the dynamic load state index of the hardware medium to be written, and uses this index to correct the initial health value of each storage unit inside the hardware medium to be written, so as to obtain the dynamic health value of the storage unit of the hardware medium to be written, providing guiding parameters for real-time data storage. The advantages of the present invention are: improving the stability of data storage and reducing the risk of data loss. Description of the Drawings

[0049] Figure 1 It is a flowchart of a data storage and preservation method;

[0050] Figure 2 It is a flowchart of a method for evaluating the influence coefficient of the operating environment of the hardware medium to be written;

[0051] Figure 3 It is a flowchart of a method for evaluating the operating performance index of the hardware medium to be written;

[0052] Figure 4 It is a flowchart of a method for evaluating the dynamic load state index of the hardware medium to be written. Detailed Embodiment

[0053] The following description is used to disclose the present invention so that those skilled in the art can implement the present invention. The preferred embodiments in the following description are only examples, and those skilled in the art can think of other obvious variations.

[0054] Refer to Figure 1 As shown, a data storage and preservation method includes:

[0055] Obtain the operating environment data of the hardware medium to be written, analyze the influence trend of the operating environment data on the hardware medium to be written, and evaluate the influence coefficient of the operating environment of the hardware medium to be written;

[0056] Obtain the historical storage data of the hardware medium to be written;

[0057] Based on the historical storage data of the hardware medium to be written, analyze the storage quality of the hardware medium to be written, and evaluate the operating performance index of the hardware medium to be written;

[0058] Based on the operating performance state index of the hardware medium to be written and the influence coefficient of the operating environment of the hardware medium to be written, initialize the health value of the internal storage unit of the hardware medium to be written;

[0059] Analyze the storage load trend of the hardware medium to be written based on the historical storage data, and evaluate the dynamic load status index of the hardware medium to be written;

[0060] Based on the dynamic load status index of the hardware medium to be written, correct the health value of the internal storage unit of the initialized hardware medium to be written to obtain the dynamic health value of the storage unit of the hardware medium to be written;

[0061] Based on the dynamic health value of the storage unit of the hardware medium to be written, screen out the storage units of the hardware medium to be written whose dynamic health value meets the storage requirements of the data to be written for dynamic storage.

[0062] Among them, the health value of the internal storage unit of the initialized hardware medium to be written is specifically:

[0063] R = S·G

[0064] In the formula, R is the initialized health value of each internal storage unit of the hardware medium to be written, S is the operating environment impact coefficient of the hardware medium to be written, and G is the operating performance index of the hardware medium to be written;

[0065] Among them, the dynamic health value of the storage unit of the hardware medium to be written is specifically:

[0066] U = F·R

[0067] In the formula, U is the dynamic health value of the storage unit of the hardware medium to be written, and F is the dynamic load status index of the storage medium to be stored in the future unit time.

[0068] It should be noted that the storage requirements of the data to be written can be obtained according to the meta-information of the data itself. The purpose of this solution is to solve how to store data safely and optimally, and it is familiar and non-disputable to those skilled in the art through the meta-information of the data itself, so no more description will be given here.

[0069] This solution analyzes the initialized health value of the storage unit of the hardware medium to be written under the operating environment and its own storage quality status; conducts regression analysis based on the change trend of historical storage data to determine the load status trend of the storage unit of the hardware medium to be written, obtains the dynamic load status index of the hardware medium to be written, and uses this index to correct the initialized health value of each internal storage unit of the hardware medium to be written to obtain the dynamic health value of the storage unit of the hardware medium to be written, providing guiding parameters for real-time data storage; the advantages of the present invention are: improving the stability of data storage and reducing the risk of data loss.

[0070] Refer to Figure 2As shown, obtaining the operating environment data of the hardware medium to be written, analyzing the influence trend of the operating environment data on the hardware medium to be written, and evaluating the operating environment influence coefficient of the hardware medium to be written specifically include:

[0071] Perform standardization processing based on the operating environment data of the hardware medium to be written;

[0072] Based on the manufacturing-calibrated ideal working environment data of the hardware medium to be written after standardization processing, determine the ideal standardized working environment parameter array of the hardware medium to be written; the manufacturing-calibrated ideal working environment includes: working temperature, working humidity, and working voltage;

[0073] Perform a difference operation based on the ideal standardized working environment parameter array of the hardware medium to be written and the operating environment data of the hardware medium to be written to obtain the operating environment deviation parameter of the hardware medium to be written;

[0074] Based on the operating environment deviation parameter of the hardware medium to be written and the ideal standardized working environment parameter array of the hardware medium to be written, calculate the entropy value of the operating environment deviation parameter of the hardware medium to be written;

[0075] According to the entropy value of the operating environment deviation parameter of the hardware medium to be written, determine the coefficient of variation of the operating environment deviation parameter, and calculate the deviation weight of the operating environment deviation parameter of the hardware medium to be written;

[0076] According to the deviation weight of the operating environment deviation parameter of the hardware medium to be written and the operating environment deviation parameter of the hardware medium to be written, calculate the operating environment influence coefficient of the hardware medium to be written;

[0077] Among them, the operating environment influence coefficient of the hardware medium to be written is specifically:

[0078]

[0079] In the formula, S is the operating environment influence coefficient of the hardware medium to be written, P ij is the deviation probability distribution of the jth deviation parameter of the ith operating environment of the hardware medium to be written, E ij is the entropy value of the jth deviation parameter of the ith operating environment of the hardware medium to be written, d ij is the coefficient of variation of the jth deviation parameter of the ith operating environment of the hardware medium to be written, w ij is the deviation weight of the jth deviation parameter of the ith operating environment of the hardware medium to be written, V ij is the value of the jth deviation parameter of the ith operating environment of the hardware medium to be written.

[0080] It should be noted that the manufacturing-calibrated ideal working environment data are all obtained through heterogeneous sensors deployed in the server;

[0081] This solution standardizes the actual and ideal operating environment data, calculates the deviation parameters, and uses the entropy method to analyze the degree of deviation variation to determine the weights. Finally, a comprehensive evaluation is carried out to obtain the operating environment impact coefficient of the hardware medium, providing key data for the subsequent optimized writing and stable operation of the hardware medium.

[0082] Refer to Figure 3 As shown, according to the historical storage data of the hardware medium to be written, the storage quality of the hardware medium to be written is analyzed. The evaluation of the operating performance indicators of the hardware medium to be written specifically includes:

[0083] Based on the historical storage data of the hardware medium to be written, the storage quality characteristic data in the historical storage data are screened according to the unit time to obtain the storage quality characteristic time series data of the hardware medium to be written; the storage quality characteristic data include: read and write speed data, access time data.

[0084] Normalize the storage quality characteristic time series data of the hardware medium to be written to obtain the normalized storage quality characteristic time series data of the hardware medium to be written.

[0085] Based on AR autoregression, construct a prediction model for the performance change trend of the hardware medium to be written.

[0086] Substitute the normalized storage quality characteristic time series data of the hardware medium to be written into the prediction model for the performance change trend of the hardware medium to be written at intervals of time units to evaluate the operating performance indicators of the hardware medium to be written.

[0087] Among them, the prediction model for the performance change trend of the hardware medium to be written is specifically:

[0088]

[0089] In the formula, G is the operating performance indicator of the hardware medium to be written, x i't-k is the i'-th storage quality characteristic data at the t-k-th unit time lag learning order of the hardware medium to be written, α0 is the intercept, α k is the autoregressive coefficient, ε t is the error term at the t-th unit time lag learning order, and q is the total number of lag learning orders.

[0090] This solution analyzes the storage quality characteristic data such as read and write speed and access time in the historical storage data of the hardware medium to be written, normalizes it after time series, and then uses the AR autoregressive model to construct a prediction model for the performance change trend to evaluate the operating performance indicators of the hardware medium to be written; providing a quantitative reference index for subsequent data storage.

[0091] Refer to Figure 4As shown in the figure, based on the historical storage data of the hardware medium to be written, analyzing the storage load trend of the hardware medium to be written, and evaluating the dynamic load status index of the hardware medium to be written specifically includes:

[0092] Based on the historical storage data of the hardware medium to be written, mark the load action events of the storage medium to be stored at each unit time, and obtain the characteristic data of the historical load action events of the storage medium; the historical load action events include: read / write events, IOPS events;

[0093] According to the characteristic data of the historical load action events of the storage medium, construct a historical load action event curve graph of the storage medium to be stored;

[0094] Based on the historical load action event curve graph of the storage medium to be stored, mark the peak-valley load trend of the historical load action events, and obtain the historical load trend characteristic parameters of the storage medium to be stored;

[0095] Construct an autoregressive load evaluation model for the hardware medium to be written;

[0096] Based on the autoregressive load evaluation model of the hardware medium to be written, use the historical load trend characteristic parameters of the storage medium to be stored within a unit time as the independent variable input, and use the predicted dynamic load status index of the storage medium to be stored in the future unit time as the dependent variable output;

[0097] Among them, the autoregressive load evaluation model of the hardware medium to be written is specifically:

[0098]

[0099] In the formula, F is the dynamic load status index of the storage medium to be stored in the future unit time, is the historical l-th load trend characteristic parameter at the t-th unit time, β0 is the intercept, β1, β2, β l are all regression coefficients, and ∈ is the error term.

[0100] It can be understood that since the storage units inside the storage medium in the server will have different degrees of stability fluctuations under different load states, this solution analyzes the historical storage data of the hardware medium to be written, marks and obtains the characteristic data of the load action events of the storage medium (such as read / write events, IOPS events), and then constructs a historical load action event curve graph, and marks the peak-valley trend characteristic parameters of the historical load. Based on these characteristic parameters, an autoregressive load evaluation model is constructed to predict the dynamic load status index of the storage medium to be stored in the future unit time, so as to achieve the advance evaluation and prediction of the load status of the storage medium, thereby improving the storage efficiency.

[0101] The basic principles, main features and advantages of the present invention have been shown and described above. Those skilled in the art should understand that the present invention is not limited by the above embodiments, and what is described in the above embodiments and the specification is only the principle of the present invention. Without departing from the spirit and scope of the present invention, various changes and improvements will occur to the present invention, and these changes and improvements all fall within the scope of the present invention claimed. The scope of protection required by the present invention is defined by the appended claims and their equivalents.

Claims

1. A data storage and preservation method, characterized in that, Including: Obtain the operating environment data of the hardware medium to be written, analyze the influence trend of the operating environment data on the hardware medium to be written, and evaluate the operating environment influence coefficient of the hardware medium to be written; Obtain the historical storage data of the hardware medium to be written; According to the historical storage data of the hardware medium to be written, analyze the storage quality of the hardware medium to be written, and evaluate the operating performance index of the hardware medium to be written; Based on the operating performance status index of the hardware medium to be written and the operating environment influence coefficient of the hardware medium to be written, initialize the health value of the internal storage unit of the hardware medium to be written; According to the historical storage data of the hardware medium to be written, analyze the storage load trend of the hardware medium to be written, and evaluate the dynamic load status index of the hardware medium to be written; Based on the dynamic load status index of the hardware medium to be written, correct the initialized health value of the internal storage unit of the hardware medium to be written to obtain the dynamic health value of the storage unit of the hardware medium to be written; Based on the dynamic health value of the storage unit of the hardware medium to be written, filter out the storage units of the hardware medium to be written whose dynamic health value meets the storage requirements of the data to be written for dynamic storage; Among them, the specific method for initializing the health value of the internal storage unit of the hardware medium to be written is: R = S·G In the formula, R is the initialized health value of each internal storage unit of the hardware medium to be written, S is the operating environment influence coefficient of the hardware medium to be written, and G is the operating performance index of the hardware medium to be written; Among them, the specific method for the dynamic health value of the storage unit of the hardware medium to be written is: U = F·R In the formula, U is the dynamic health value of the storage unit of the hardware medium to be written, and F is the dynamic load status index of the storage medium to be stored in the future unit time; Among them, according to the historical storage data of the hardware medium to be written, analyzing the storage load trend of the hardware medium to be written and evaluating the dynamic load status index of the hardware medium to be written specifically include: Based on the historical storage data of the hardware medium to be written, mark the load action events of the storage medium to be stored in each unit time, and obtain the characteristic data of the historical load action events of the storage medium; the historical load action events include: read / write events, IOPS events; According to the characteristic data of the historical load action events of the storage medium, construct a historical load action event curve graph of the storage medium to be stored; Based on the historical load action event curve graph of the storage medium to be stored, mark the peak-valley load trend of the historical load action events to obtain the historical load trend characteristic parameters of the storage medium to be stored; Construct an autoregressive load evaluation model for the hardware medium to be written; Based on the autoregressive load evaluation model of the hardware medium to be written, use the historical load trend characteristic parameters of the storage medium to be stored in the unit time as the independent variable input, and use the predicted dynamic load status index of the storage medium to be stored in the future unit time as the dependent variable output; Among them, the specific method for the autoregressive load evaluation model of the hardware medium to be written is: where F is the dynamic load status index of the storage medium to be stored in the future unit time, is the historical l-th load trend characteristic parameter at the t-th unit time, β0 is the intercept, β1, β2, β l are all regression coefficients, and ∈ is the error term.

2. A method for data storage and preservation according to claim 1, characterized in that, Obtain the operating environment data of the hardware medium to be written, analyze the influence trend of the operating environment data on the hardware medium to be written, and evaluate the operating environment influence coefficient of the hardware medium to be written specifically include: Perform standardization processing based on the operating environment data of the hardware medium to be written; Based on the ideal standardized working environment data for manufacturing calibration of the hardware medium to be written after standardization processing, determine the ideal standardized working environment parameter array of the hardware medium to be written; the manufacturing calibration ideal working environment includes: working temperature, working humidity, and working voltage; Perform a difference operation based on the ideal standardized working environment parameter array of the hardware medium to be written and the operating environment data of the hardware medium to be written to obtain the operating environment deviation parameter of the hardware medium to be written; Based on the operating environment deviation parameter of the hardware medium to be written and the ideal standardized working environment parameter array of the hardware medium to be written, calculate the entropy value of the operating environment deviation parameter of the hardware medium to be written; According to the entropy value of the operating environment deviation parameter of the hardware medium to be written, determine the coefficient of variation of the operating environment deviation parameter, and calculate the deviation weight of the operating environment deviation parameter of the hardware medium to be written; According to the deviation weight of the operating environment deviation parameter of the hardware medium to be written and the operating environment deviation parameter of the hardware medium to be written, calculate the operating environment influence coefficient of the hardware medium to be written.

3. A data storage and preservation method according to claim 2, characterized in that, The operating environment influence coefficient of the hardware medium to be written is specifically: Where S is the influence coefficient of the operating environment to be written into the hardware medium, and P ij is the deviation probability distribution of the j-th deviation parameter of the i-th operating environment of the hardware medium to be written, and E ij is the entropy value of the j-th deviation parameter of the i-th operating environment of the hardware medium to be written, and d ij is the coefficient of variation of the j-th deviation parameter of the i-th operating environment of the hardware medium to be written, and w ij is the deviation weight of the j-th deviation parameter of the i-th operating environment of the hardware medium to be written, and V ij is the value of the j-th deviation parameter of the i-th operating environment of the hardware medium to be written.

4. A data storage and preservation method according to claim 3, characterized in that, Based on the historical storage data of the hardware medium to be written, analyze the storage quality of the hardware medium to be written, and evaluate the operating performance indicators of the hardware medium to be written, specifically including: Based on the historical storage data of the hardware medium to be written, screen out the storage quality characteristic data in the historical storage data according to the unit time to obtain the storage quality characteristic time series data of the hardware medium to be written; the storage quality characteristic data includes: read and write speed data, access time data; Perform normalization processing on the storage quality characteristic time series data of the hardware medium to be written to obtain the normalized storage quality characteristic time series data of the hardware medium to be written; Based on AR autoregression, construct a performance change trend prediction model for the hardware medium to be written; Substitute the normalized storage quality characteristic time series data of the hardware medium to be written into the performance change trend prediction model of the hardware medium to be written at time unit intervals to evaluate the operating performance indicators of the hardware medium to be written.

5. A data storage and preservation method according to claim 4, characterized in that, The performance change trend prediction model of the hardware medium to be written is specifically: Wherein, G is the operating performance index to be written into the hardware medium, and x i't-k is the i ' th storage quality characteristic data at the (t - k)-th unit time lag learning order of the hardware medium to be written, α0 is the intercept, and α k is the autoregressive coefficient, ε t is the error term at the t-th unit time lag learning order, and q is the total number of lag learning orders.

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