A data optimization storage method based on artificial intelligence

Abstract

The present invention relates to the technical field of data storage, and specifically discloses a data optimization storage method based on artificial intelligence, which processes and analyzes the storage rate value of a storage unit to obtain a data storage performance value, and based on the storage performance value, if the storage performance value is less than a storage performance threshold, generates a storage abnormality signal; based on the storage abnormality signal, processes and analyzes the real-time data transmission rate value and the storage rate fluctuation period to obtain an impact synchronization value, and if the impact synchronization value is greater than the impact synchronization threshold, it is determined that the transmission rate affects the data storage rate abnormally, and the data storage rate of the current storage unit is adjusted according to the transmission rate adjustment value. The present invention deeply analyzes the transmission rate change in the data transmission process through the fluctuation of the storage rate, and reveals the potential connection between the data transmission rate and the storage rate abnormality through overlap analysis, which is conducive to using artificial intelligence technology to optimize storage system performance and improve the stability of data storage.

Description

Technical Field

[0001] The present invention relates to the technical field of data storage, and in particular to a data optimization storage method based on artificial intelligence. Background Art

[0002] With the rapid development of information technology, the amount of data generated by all walks of life has exploded. These data include structured data, unstructured data, and semi-structured data, ranging from simple text information to complex image, audio, and video data. Such a huge amount of data poses a severe challenge to the storage system. Traditional storage methods can no longer meet the requirements of high efficiency, low cost, and reliability.

[0003] The Chinese invention patent with publication number CN118605803A discloses an artificial intelligence-based data optimization storage method and big data system, including solving the problem that there may be multiple sets of formats in the corresponding format file, which will lead to mutual conversion between the read and write logics, resulting in its read and write rate being too slow, thereby causing the storage rate to slow down. The present invention adopts different read and write processing methods for files of different types of formats, and can fully guarantee its read and write rate. There is no need to convert the format back and forth between data, and the read and write rate is effectively enhanced. In the data acquisition stage before storage, the data acquisition rate is effectively enhanced, which further enhances the storage rate of the file in the entire stage.

[0004] However, the above-mentioned prior art mainly focuses on improving the reading and writing rate and storage rate by adopting different reading and writing processing methods for files of different types and formats, but lacks analysis of storage rate abnormalities. In practical applications, storage rate abnormalities may affect the stability of data storage. Summary of the invention

[0005] The purpose of the present invention is to provide a data optimization storage method based on artificial intelligence to solve the technical problems in the above background.

[0006] The purpose of the present invention can be achieved through the following technical solutions:

[0007] The present invention provides a data optimization storage method based on artificial intelligence, comprising:

[0008] Step 1: Process and analyze the storage rate value of the storage unit to obtain a data storage performance value CB. Based on the storage performance value CB, if the storage performance value CB is less than a storage performance threshold, a storage abnormality signal is generated;

[0009] Step 2: Based on the storage abnormality signal, the real-time data transmission rate value and the storage rate fluctuation period are processed and analyzed to obtain the impact synchronization value BT. If the impact synchronization value BT is greater than the impact synchronization threshold, it is determined that the transmission rate affects the data storage rate abnormally;

[0010] Step 3: If the transmission rate affects the data storage rate abnormally, determine a transmission rate adjustment value, and adjust the data storage rate of the current storage unit according to the transmission rate adjustment value.

[0011] As a further solution of the present invention: the storage performance value CB is obtained in the following manner:

[0012] Construct a storage rate variation curve based on the storage rate value, construct a storage rate baseline based on the storage rate standard value, and determine a storage rate fluctuation curve quantity ratio BS and a storage rate fluctuation area ratio BM according to the storage rate variation curve and the storage rate standard line;

[0013] The storage rate fluctuation curve quantity ratio BS and the storage rate fluctuation area ratio BM are processed by the formula: The storage performance value CB is calculated, wherein a1 and a2 are both preset proportional coefficients, and a1 and a2 are both greater than 0.

[0014] As a further solution of the present invention: the storage rate fluctuation curve quantity ratio BS is obtained in the following manner:

[0015] The part where the storage rate change curve overlaps with the storage rate baseline is marked as the storage rate overlap curve, the part of the storage rate change curve that is higher than the storage rate baseline is marked as the storage rate high fluctuation curve, and the part of the storage rate change curve that is lower than the storage rate baseline is marked as the storage rate low fluctuation curve. The number of storage rate high fluctuation curves and the number of storage rate low fluctuation curves are counted and summed to obtain the number of storage rate fluctuation curves. The number of storage rate fluctuation curves is ratioed with the number of all curve segments in the storage time period to obtain the storage rate fluctuation curve number ratio BS.

[0016] As a further solution of the present invention: the storage rate fluctuation area ratio BM is obtained in the following manner:

[0017] Measure the area between the storage rate high fluctuation curve and the storage rate baseline to obtain the storage rate high fluctuation area, measure the area between the storage rate low fluctuation curve and the storage rate baseline to obtain the storage rate low fluctuation area, sum up all the storage rate high fluctuation areas and the storage rate low fluctuation areas to obtain the storage rate fluctuation area, and ratio the storage rate fluctuation area with the storage rate fluctuation area threshold to obtain the storage rate fluctuation area ratio BM.

[0018] As a further solution of the present invention: comparing the storage performance value CB with the storage performance threshold, the specific process is:

[0019] If the storage performance value CB is less than the storage performance threshold, a storage abnormality signal is generated;

[0020] If the storage performance value CB is greater than or equal to the storage performance threshold, a storage normal signal is generated.

[0021] As a further solution of the present invention: the acquisition method of the influencing synchronization value BT is:

[0022] A real-time transmission rate fluctuation curve is constructed based on the data real-time transmission rate value, a transmission rate baseline is constructed based on the data real-time transmission rate standard value, and the overlap duration ratio QS and the overlap ratio BC of the degree of change are determined according to the real-time transmission rate fluctuation curve and the storage rate standard line;

[0023] The overlap duration ratio QS and the overlap ratio BC of the change degree are processed by the formula: The impact synchronization value BT is calculated, wherein b1 and b2 are both preset proportional coefficients, and both b1 and b2 are greater than 0.

[0024] As a further solution of the present invention: the overlap duration ratio QS is obtained in the following manner:

[0025] Mark the portion of the transmission rate change curve that is higher than the transmission rate baseline as a transmission rate high fluctuation curve, mark the portion of the transmission rate change curve that is lower than the transmission rate baseline as a transmission rate low fluctuation curve, obtain the time period corresponding to the high transmission rate fluctuation curve, mark it as a transmission rate high fluctuation period, obtain the time period corresponding to the low transmission rate fluctuation curve, mark it as a transmission rate low fluctuation period;

[0026] Based on the storage abnormality signal, obtaining a storage rate high fluctuation period and a storage rate low fluctuation period corresponding to when the storage abnormality signal is generated;

[0027] The period of overlap between the high fluctuation period of the transmission rate and the high fluctuation period of the storage rate is marked as a high overlap period, and the period of overlap between the low fluctuation period of the transmission rate and the low fluctuation period of the storage rate is marked as a low overlap period;

[0028] The high overlap period and the low overlap period are summed to obtain the overlap period, and the duration corresponding to the overlap period is marked as the overlap duration;

[0029] The overlap duration is processed by ratio with the total duration corresponding to the storage period to obtain the overlap duration ratio QS.

[0030] As a further solution of the present invention: the variation degree overlap ratio BC is obtained in the following manner:

[0031] The overlap period is divided into several analysis nodes, and the slope values ​​of the storage rate change curve and part of the real-time transmission rate fluctuation curve corresponding to the overlap period at the analysis nodes are obtained respectively, and the slope ratios at the analysis nodes are obtained by ratio processing, and the slope ratios at all the analysis nodes are summed and averaged to obtain the slope ratio within the overlap period;

[0032] The slope ratios of all the overlapping time periods are summed and averaged to obtain the slope ratio mean, the slope ratio is subtracted from the slope ratio mean, and the absolute value is taken to obtain the slope ratio deviation, and all the slope ratio deviations are summed and averaged to obtain the slope ratio deviation mean;

[0033] Compare the slope ratio deviation to the mean slope ratio deviation:

[0034] If the slope ratio deviation is greater than the slope ratio deviation mean, the coincidence period corresponding to the slope ratio deviation is marked as an abnormal coincidence period;

[0035] If the slope ratio deviation is less than or equal to the slope ratio deviation mean, the overlap period corresponding to the slope ratio deviation is marked as a normal overlap period;

[0036] The number of normal overlap periods in the overlap period is counted, and the ratio is processed with the number of overlap periods to obtain the overlap ratio BC of the change degree.

[0037] As a further solution of the present invention: comparing the synchronization impact value BT with the synchronization impact threshold, the specific process is:

[0038] If the synchronization impact value BT is greater than the synchronization impact threshold, it means that the data transmission rate is the reason affecting the data storage rate;

[0039] If the synchronization influencing value BT is less than or equal to the synchronization influencing threshold, it means that the data transmission rate is not the reason affecting the data storage rate, and other influencing factors need to be further analyzed.

[0040] As a further solution of the present invention: the process of obtaining the transmission rate adjustment value is:

[0041] The current storage rate value is obtained, and the difference is processed with the storage rate standard value to obtain the storage rate adjustment value, and the storage rate adjustment value is processed with the slope ratio in the overlap period to obtain the transmission rate adjustment value.

[0042] Beneficial effects of the present invention:

[0043] The present invention performs processing and analysis based on a storage rate value to obtain a data storage performance value CB. Based on the storage performance value CB, it is determined whether a storage signal is generated when storing data. The present invention monitors the storage rate in real time and dynamically evaluates the storage state based on the data storage performance value CB calculated thereby, thereby being able to timely discover potential problems in the storage process and improve the reliability of data storage. The present invention obtains a storage rate fluctuation period corresponding to when a storage abnormality signal is generated based on a storage abnormality signal, and obtains a data real-time transmission rate value corresponding to a storage node. Based on the data real-time transmission rate value and the storage rate fluctuation period, the present invention performs processing and analysis to obtain an impact synchronization value BT. Based on the impact synchronization value BT, it is determined whether the data transmission rate is the cause of the data storage rate. If the transmission rate is the cause of the data storage rate, the current storage rate is adjusted based on the transmission rate adjustment value. The present invention deeply analyzes the transmission rate change in the data transmission process through the fluctuation of the storage rate, and reveals whether the data transmission rate causes the data storage rate abnormality through overlap analysis, thereby being conducive to using artificial intelligence technology to optimize the performance of the storage system and improve the stability of data storage. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] The present invention will be further described below in conjunction with the accompanying drawings.

[0045] Figure 1 It is a flowchart of a data optimization storage method based on artificial intelligence according to an embodiment of the present invention;

[0046] Figure 2 It is a flowchart of a method for obtaining a storage performance value CB of a data optimization storage method based on artificial intelligence in an embodiment of the present invention. DETAILED DESCRIPTION

[0047] 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.

[0048] Embodiment 1:

[0049] See also Figure 1 , Figure 2 As shown, an artificial intelligence-based data optimization storage method according to an embodiment of the present invention comprises the following steps:

[0050] Step 1: During the storage period, the storage data of the storage unit is acquired in real time, wherein the storage data includes a storage rate value, and the storage rate value is processed and analyzed to obtain a data storage performance value CB. Based on the storage performance value CB, it is determined whether a storage signal is generated when storing data, and the storage signal includes a storage normal signal and a storage abnormal signal;

[0051] It should be noted that the storage rate value is measured using a professional storage device testing tool;

[0052] In the storage period, the storage period is divided into multiple storage nodes, the storage node is used as the X-axis, the storage rate value corresponding to the storage node is used as the Y-axis, an XY two-dimensional coordinate system is constructed, and the storage rate values ​​corresponding to the storage nodes are marked and connected in the XY coordinate system to obtain a storage rate change curve;

[0053] Mark the storage rate standard value on the Y axis to obtain the storage rate reference point, draw a straight line parallel to the X axis through the storage rate reference point, and mark it as the storage rate reference line;

[0054] It should be noted that the standard value of the storage rate is set by those skilled in the art based on past experience;

[0055] The portion where the storage rate change curve overlaps with the storage rate baseline is marked as the storage rate overlap curve, the portion of the storage rate change curve that is higher than the storage rate baseline is marked as the storage rate high fluctuation curve, and the portion of the storage rate change curve that is lower than the storage rate baseline is marked as the storage rate low fluctuation curve. The number of storage rate high fluctuation curves and the number of storage rate low fluctuation curves are counted and summed to obtain the number of storage rate fluctuation curves. The number of storage rate fluctuation curves is ratioed to the number of all curve segments in the storage period to obtain the storage rate fluctuation curve number ratio BS.

[0056] Measure the area between the storage rate high fluctuation curve and the storage rate baseline to obtain the storage rate high fluctuation area, measure the area between the storage rate low fluctuation curve and the storage rate baseline to obtain the storage rate low fluctuation area, sum all the storage rate high fluctuation areas and the storage rate low fluctuation areas to obtain the storage rate fluctuation area, and perform ratio processing on the storage rate fluctuation area and the storage rate fluctuation area threshold to obtain the storage rate fluctuation area ratio BM;

[0057] The storage rate fluctuation curve quantity ratio BS and the storage rate fluctuation area ratio BM are processed by the formula: The storage performance value CB is calculated, wherein a1 and a2 are both preset proportional coefficients, and a1 and a2 are both greater than 0;

[0058] Compare the storage performance value CB with the storage performance threshold:

[0059] If the storage performance value CB is less than the storage performance threshold, a storage abnormality signal is generated;

[0060] If the storage performance value CB is greater than or equal to the storage performance threshold, a storage normal signal is generated;

[0061] It should be noted that the storage performance value CB is obtained by processing the fluctuation curve number ratio BS and the fluctuation area ratio BM. The storage performance value CB reflects the data storage rate, the fluctuation curve number ratio BS reflects the number of curve segments where the actual data storage rate is different from the storage rate standard value during the storage time, and the fluctuation area ratio BM reflects the abnormality of the storage rate when the data is actually stored.

[0062] The technical solution of the embodiment of the present invention is mainly as follows: within a storage period, storage data of a storage unit is acquired in real time, wherein the storage data includes a storage rate value; processing and analysis are performed based on the storage rate value to obtain a data storage performance value CB; based on the storage performance value CB, it is determined whether a storage signal is generated when storing data; the storage signal includes a normal storage signal and an abnormal storage signal. The present invention monitors the storage rate in real time and dynamically evaluates the storage status based on the calculated data storage performance value CB, thereby being able to promptly discover potential problems in the storage process and improve the stability of data storage.

[0063] Embodiment 2:

[0064] Based on Example 1, please refer to Figure 1 As shown, the data optimization storage method based on artificial intelligence described in the embodiment of the present invention also includes the following steps:

[0065] Step 2: Based on the storage abnormality signal, obtain the storage rate fluctuation period corresponding to the generation of the storage abnormality signal, wherein the storage rate fluctuation period includes the storage rate high fluctuation period and the storage rate low fluctuation period, obtain the data real-time transmission rate value corresponding to the storage node, and process and analyze the data real-time transmission rate value and the storage rate fluctuation period to obtain the overlap duration ratio QS and the change degree overlap ratio BC, and process and analyze the overlap duration ratio QS and the change degree overlap ratio BC to obtain the impact synchronization value BT, and judge whether the data transmission rate is the cause of the data storage rate based on the impact synchronization value BT;

[0066] Specifically, an XY two-dimensional coordinate system is constructed with the storage node as the X-axis and the transmission data value corresponding to the storage node as the Y-axis, wherein the transmission data value includes the data real-time transmission rate value, the data real-time transmission rate value of the storage node is obtained, and it is marked in the XY two-dimensional coordinate system, and the marked data points of the data real-time transmission rate value are connected to obtain the real-time transmission rate fluctuation curve;

[0067] The standard value of the data real-time transmission rate is marked on the Y axis as a reference value, and a reference line parallel to the X axis is made through the data real-time transmission rate reference value, which is marked as the transmission rate reference line;

[0068] Mark the portion of the transmission rate change curve that is higher than the transmission rate baseline as a transmission rate high fluctuation curve, mark the portion of the transmission rate change curve that is lower than the transmission rate baseline as a transmission rate low fluctuation curve, obtain the time period corresponding to the high transmission rate fluctuation curve, mark it as a transmission rate high fluctuation period, obtain the time period corresponding to the low transmission rate fluctuation curve, mark it as a transmission rate low fluctuation period;

[0069] Based on the storage abnormality signal, obtaining a storage rate high fluctuation period and a storage rate low fluctuation period corresponding to when the storage abnormality signal is generated;

[0070] The period of overlap between the high fluctuation period of the transmission rate and the high fluctuation period of the storage rate is marked as a high overlap period, and the period of overlap between the low fluctuation period of the transmission rate and the low fluctuation period of the storage rate is marked as a low overlap period;

[0071] The high overlap period and the low overlap period are summed to obtain the overlap period, and the duration corresponding to the overlap period is marked as the overlap duration;

[0072] The overlap duration is processed by ratio with the total duration corresponding to the storage period to obtain the overlap duration ratio QS;

[0073] The overlap period is divided into several analysis nodes, and the slope values ​​of the storage rate change curve and part of the real-time transmission rate fluctuation curve corresponding to the overlap period at the analysis nodes are obtained respectively, and the slope ratios at the analysis nodes are obtained by ratio processing, and the slope ratios at all the analysis nodes are summed and averaged to obtain the slope ratio within the overlap period;

[0074] The slope ratios of all the overlapping time periods are summed and averaged to obtain the slope ratio mean, the slope ratio is subtracted from the slope ratio mean, and the absolute value is taken to obtain the slope ratio deviation, and all the slope ratio deviations are summed and averaged to obtain the slope ratio deviation mean;

[0075] Compare the slope ratio deviation to the mean slope ratio deviation:

[0076] If the slope ratio deviation is greater than the slope ratio deviation mean, the coincidence period corresponding to the slope ratio deviation is marked as an abnormal coincidence period;

[0077] If the slope ratio deviation is less than or equal to the slope ratio deviation mean, the overlap period corresponding to the slope ratio deviation is marked as a normal overlap period;

[0078] The number of normal overlap periods in the overlap period is counted, and the ratio is processed with the number of overlap periods to obtain the overlap ratio BC of the change degree;

[0079] The overlap duration ratio QS and the overlap ratio BC of the change degree are processed by the formula: The influence synchronization value BT is calculated, wherein b1 and b2 are both preset proportional coefficients, and b1 and b2 are both greater than 0;

[0080] Compare the impact synchronization value BT with the impact synchronization threshold:

[0081] If the synchronization impact value BT is greater than the synchronization impact threshold, it means that the data transmission rate has a greater impact on the data storage rate;

[0082] If the synchronization impact value BT is less than or equal to the synchronization impact threshold, it means that the data transmission rate has little impact on the data storage rate, and further analysis is needed on other influencing factors, including but not limited to transmission traffic, etc.

[0083] Step 3: If the transmission rate is the reason affecting the data storage rate, a transmission rate adjustment value is obtained, and the current storage rate is adjusted based on the transmission rate adjustment value;

[0084] Specifically, the current storage rate value is obtained, and a difference process is performed with the storage rate standard value to obtain a storage rate adjustment value, and the storage rate adjustment value is ratio-processed with the slope ratio in the overlap period to obtain a transmission rate adjustment value;

[0085] Adjusting the current transmission rate based on the transmission rate adjustment value;

[0086] The technical solution of the embodiment of the present invention is mainly as follows: based on the storage abnormality signal, the storage rate fluctuation period corresponding to the generation of the storage abnormality signal is obtained, wherein the storage rate fluctuation period includes a storage rate high fluctuation period and a storage rate low fluctuation period, the real-time data transmission rate value corresponding to the storage node is obtained, and the overlap duration ratio QS and the overlap ratio BC of the degree of change are obtained based on the processing and analysis of the real-time data transmission rate value and the storage rate fluctuation period, and the influence synchronization value BT is obtained based on the processing and analysis of the overlap duration ratio QS and the overlap ratio BC of the degree of change, and the influence synchronization value BT is obtained, and based on the influence synchronization value BT, it is determined whether the data transmission rate is the cause of the data storage rate, if the transmission rate is the cause of the data storage rate, the transmission rate adjustment value is obtained, and the current storage rate is adjusted based on the transmission rate adjustment value. The present invention deeply analyzes the transmission rate change in the data transmission process through the fluctuation of the storage rate, and reveals whether the data transmission rate causes the data storage rate abnormality through the overlap analysis, which is conducive to using artificial intelligence technology to optimize the performance of the storage system and improve the stability of data storage.

[0087] The above formulas are all dimensionless and numerical calculations. The formula is a formula for the most recent real situation obtained by collecting a large amount of data and performing software simulation. The preset parameters in the formula are set by technicians in this field according to actual conditions.

[0088] The above shows and describes the basic principles, main features and advantages of the present invention. It should be understood by those skilled in the art that the present invention is not limited to the above embodiments. The above embodiments and descriptions are only for explaining the principles of the present invention. Without departing from the spirit and scope of the present invention, the present invention may have various changes and improvements, which fall within the scope of the present invention. The scope of protection of the present invention is defined by the attached claims and their equivalents.

Claims

1. A data optimization storage method based on artificial intelligence, characterized in that: include: Step 1: Process and analyze the storage rate value of the storage unit to obtain a data storage performance value CB. Based on the storage performance value CB, if the storage performance value CB is less than a storage performance threshold, a storage abnormality signal is generated; Step 2: Based on the storage abnormality signal, the real-time data transmission rate value and the storage rate fluctuation period are processed and analyzed to obtain the impact synchronization value BT. If the impact synchronization value BT is greater than the impact synchronization threshold, it is determined that the transmission rate affects the data storage rate abnormally; Step 3: If the transmission rate affects the data storage rate abnormally, determine the transmission rate adjustment value, and adjust the data storage rate of the current storage unit according to the transmission rate adjustment value; The acquisition method of the impact synchronization value BT is: A real-time transmission rate fluctuation curve is constructed based on the data real-time transmission rate value, a transmission rate baseline is constructed based on the data real-time transmission rate standard value, and the overlap duration ratio QS and the overlap ratio BC of the degree of change are determined according to the real-time transmission rate fluctuation curve and the storage rate standard line; The overlap duration ratio QS and the overlap ratio BC of the change degree are processed by the formula: The influence synchronization value BT is calculated, wherein b1 and b2 are both preset proportional coefficients, and b1 and b2 are both greater than 0; The overlap duration ratio QS is obtained as follows: Mark the portion of the transmission rate change curve that is higher than the transmission rate baseline as a transmission rate high fluctuation curve, mark the portion of the transmission rate change curve that is lower than the transmission rate baseline as a transmission rate low fluctuation curve, obtain the time period corresponding to the high transmission rate fluctuation curve, mark it as a transmission rate high fluctuation period, obtain the time period corresponding to the low transmission rate fluctuation curve, mark it as a transmission rate low fluctuation period; Based on the storage abnormality signal, obtaining a storage rate high fluctuation period and a storage rate low fluctuation period corresponding to when the storage abnormality signal is generated; The period of overlap between the high fluctuation period of the transmission rate and the high fluctuation period of the storage rate is marked as a high overlap period, and the period of overlap between the low fluctuation period of the transmission rate and the low fluctuation period of the storage rate is marked as a low overlap period; The high overlap period and the low overlap period are summed to obtain the overlap period, and the duration corresponding to the overlap period is marked as the overlap duration; The overlap duration is processed by ratio with the total duration corresponding to the storage period to obtain the overlap duration ratio QS; The variation overlap ratio BC is obtained as follows: The overlap period is divided into several analysis nodes, and the slope values ​​of the storage rate change curve and part of the real-time transmission rate fluctuation curve corresponding to the overlap period at the analysis nodes are obtained respectively, and the slope ratios at the analysis nodes are obtained by ratio processing, and the slope ratios at all the analysis nodes are summed and averaged to obtain the slope ratio within the overlap period; The slope ratios of all the overlapping time periods are summed and averaged to obtain the slope ratio mean, the slope ratio is subtracted from the slope ratio mean, and the absolute value is taken to obtain the slope ratio deviation, and all the slope ratio deviations are summed and averaged to obtain the slope ratio deviation mean; Compare the slope ratio deviation to the mean slope ratio deviation: If the slope ratio deviation is greater than the slope ratio deviation mean, the coincidence period corresponding to the slope ratio deviation is marked as an abnormal coincidence period; If the slope ratio deviation is less than or equal to the slope ratio deviation mean, the overlap period corresponding to the slope ratio deviation is marked as a normal overlap period; The number of normal overlap periods in the overlap period is counted, and the ratio is processed with the number of overlap periods to obtain the overlap ratio BC of the change degree.

2. The data optimization storage method based on artificial intelligence according to claim 1 is characterized in that: The storage performance value CB is obtained in the following manner: Construct a storage rate variation curve based on the storage rate value, construct a storage rate baseline based on the storage rate standard value, and determine a storage rate fluctuation curve quantity ratio BS and a storage rate fluctuation area ratio BM according to the storage rate variation curve and the storage rate standard line; The storage rate fluctuation curve quantity ratio BS and the storage rate fluctuation area ratio BM are processed by the formula: The storage performance value CB is calculated, wherein a1 and a2 are both preset proportional coefficients, and a1 and a2 are both greater than 0; The storage rate fluctuation curve quantity ratio BS is obtained in the following manner: The portion where the storage rate change curve overlaps with the storage rate baseline is marked as the storage rate overlap curve, the portion of the storage rate change curve that is higher than the storage rate baseline is marked as the storage rate high fluctuation curve, and the portion of the storage rate change curve that is lower than the storage rate baseline is marked as the storage rate low fluctuation curve. The number of storage rate high fluctuation curves and the number of storage rate low fluctuation curves are counted and summed to obtain the number of storage rate fluctuation curves. The number of storage rate fluctuation curves is ratioed to the number of all curve segments in the storage period to obtain the storage rate fluctuation curve number ratio BS. The storage rate fluctuation area ratio BM is obtained as follows: Measure the area between the storage rate high fluctuation curve and the storage rate baseline to obtain the storage rate high fluctuation area, measure the area between the storage rate low fluctuation curve and the storage rate baseline to obtain the storage rate low fluctuation area, sum up all the storage rate high fluctuation areas and the storage rate low fluctuation areas to obtain the storage rate fluctuation area, and ratio the storage rate fluctuation area with the storage rate fluctuation area threshold to obtain the storage rate fluctuation area ratio BM.

3. The data optimization storage method based on artificial intelligence according to claim 2 is characterized in that: The storage performance value CB is compared with the storage performance threshold. The specific process is: If the storage performance value CB is less than the storage performance threshold, a storage abnormality signal is generated; If the storage performance value CB is greater than or equal to the storage performance threshold, a storage normal signal is generated.

4. The data optimization storage method based on artificial intelligence according to claim 1 is characterized in that: The synchronization impact value BT is compared with the synchronization impact threshold. The specific process is as follows: If the synchronization impact value BT is greater than the synchronization impact threshold, it means that the data transmission rate is the reason affecting the data storage rate; If the synchronization influencing value BT is less than or equal to the synchronization influencing threshold, it means that the data transmission rate is not the reason affecting the data storage rate, and other influencing factors need to be further analyzed.

5. The data optimization storage method based on artificial intelligence according to claim 1 is characterized in that: The process of obtaining the transmission rate adjustment value is as follows: The current storage rate value is obtained, and the difference is processed with the storage rate standard value to obtain the storage rate adjustment value, and the storage rate adjustment value is processed with the slope ratio in the overlap period to obtain the transmission rate adjustment value.

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