A method and system for off-site data migration and backup

Abstract

The present invention relates to the technical field of data processing. More specifically, the present invention relates to a method and system for off-site data migration and backup. The method includes: obtaining the temperature and current during machine operation and constructing a collaborative relationship curve; using the extreme points of the collaborative relationship curve to segment the collaborative relationship curve, calculating the difference between the normality of any data in any segment and the average normality of adjacent data, and obtaining the anomaly index of the data. Calculating the interference degree of the data, correcting the temperature or current at the corresponding moment based on the interference degree, the correction direction is towards the adjacent data, the correction amplitude is positively correlated with the interference degree, and using the Huffman coding algorithm to compress the corrected values of the temperature and current for migration and backup. The present invention can improve the compression efficiency of data and the accuracy of the compression result during the data migration and backup process.

Description

Technical Field

[0001] The present invention relates to the technical field of data processing. More specifically, the present invention relates to a method and system for off-site data migration and backup. Background Art

[0002] Off-site data migration and backup is a common and crucial data protection strategy. By backing up core data to a remote storage system or server, it can effectively prevent data loss or damage caused by emergencies. For machine monitoring data, especially data on key parameters such as temperature and current, off-site backup can quickly restore to the remote backup when the device fails or is damaged, ensuring the continuous availability of data and the stability of the system. And the monitoring data of machines usually has a large amount of data. When performing migration and backup, how to compress the data is particularly important.

[0003] In the related art, as disclosed in the patent application document with the publication number CN118964315A, a method and system for environmental temperature data acquisition and management based on big data are disclosed. The method includes: obtaining a first temperature data set of a preset time period in a target area; mapping the first temperature data set into a two-dimensional data table; obtaining a growth algorithm for data points of the two-dimensional data table, and performing region growth on the data points of the two-dimensional data table; using Huffman coding to compress the data after the growth is completed to obtain meteorological temperature storage data of a preset time period in the target area.

[0004] However, when the above solution compresses data using Huffman coding, it does not consider the influence of abnormal data. The existence of abnormal data may reduce the data compression efficiency, and since it is impossible to distinguish between normal data and abnormal data, there may be a risk of loss of normal data, thereby reducing the accuracy of the stored data. Summary of the Invention

[0005] In order to solve the problems of low data compression efficiency and low accuracy of the compression result during the data migration and backup process, the present invention provides a method and system for off-site data migration and backup.

[0006] According to a first aspect of the present invention, there is provided a method for off-site data migration and backup, including:

[0007] Obtaining the temperature and current during machine operation and constructing a collaborative relationship curve, where the abscissa of the collaborative relationship curve is the sampling time, and the ordinate represents the mutual influence situation of the temperature and current at each sampling time;

[0008] Using the extreme points of the collaborative relationship curve to segment the collaborative relationship curve, calculating the difference between the normality of any data in any segment and the average normality of adjacent data to obtain the anomaly index of the data; normality ; For the normality of the th data in the th segment; , respectively are the th data in two preset windows centered on two data symmetric about the th data in adjacent segments of the th segment; is the th data in the th segment; is the number of data in the window; is the normalization function;

[0009] Calculate the interference degree of this data. The interference degree is positively correlated with the difference in the anomaly index and the slope difference of the corresponding data in two preset windows centered on this data and the reference data. The reference data is symmetric with this data about the central position of this segment;

[0010] Based on the interference degree, correct the temperature or current at the corresponding moment. The correction direction is towards the adjacent data, and the correction amplitude is positively correlated with the interference degree. And use the Huffman coding algorithm to compress the corrected values of the temperature and current for migration backup.

[0011] Before compressing the data using the Huffman coding algorithm, the present invention corrects the data using the interference degree, which can eliminate the normal fluctuations of the data. At the same time, on the basis of retaining the abnormal fluctuation characteristics of the data, the abnormal fluctuations of the data are moderately smoothed. By correcting the data, the frequency of normal data increases and the frequency of abnormal data decreases, thereby improving the compression efficiency of the data; and correcting the data can enhance the distinguishability between normal data and abnormal data, thereby reducing the risk of loss of normal data and improving the accuracy of the stored data during data migration backup.

[0012] Preferably, the interference degree satisfies the following relational expression:

[0013] ;

[0014] In the formula, is the interference degree of the th data in the th segment; is the anomaly index of the th data in the window of this data; is the anomaly index of the th data in the window of the reference data of this data; is the slope of the th data in the window of this data; is the The slope of a data point; Is the absolute value symbol; Is the natural exponential function; Is the ceiling function; Is the normalization function; Is the number of data points within the window.

[0015] The present invention limits the value range of the interference degree to 0-1, which can reduce the computational complexity when correcting data based on the interference degree and lower the computational difficulty of the data correction value.

[0016] Preferably, the method for obtaining the anomaly index includes:

[0017] For any data point in any segment, calculate the average of the normality of the two adjacent data points of this data point, and take the absolute value of the difference between the normality of this data point and the average value as the anomaly index of this data point.

[0018] The present invention utilizes the characteristic that the mutual influence degree between temperature and current of the machine is relatively stable in the normal state, and can accurately evaluate the abnormal relationship between temperature and current at each moment.

[0019] Preferably, the method for obtaining the coordination relationship curve includes:

[0020] Taking the sampling time as the abscissa and the temperature or current at each sampling time as the ordinate, construct a temperature curve and a current curve;

[0021] Calculate the coordination parameter at any sampling time , is the coordination parameter at the th sampling time, is the temperature at the th sampling time, is the current at the th sampling time, is the slope of the temperature curve at the th sampling time, is the slope of the current curve at the th sampling time, is the normalization function; taking the sampling time as the abscissa and the coordination parameters at each sampling time as the ordinate, construct a coordination relationship curve.

[0022] The present invention takes into account the mutual influence relationship between the temperature and current of the machine, and then constructs a coordination relationship curve of the machine. And the present invention analyzes the abnormal state of the machine based on the coordination relationship curve, which can ensure the accuracy of the analysis result.

[0023] Preferably, the method for obtaining the correction amplitude includes:

[0024] Use the interference level of any data in any segment as the interference level of the corresponding data at the corresponding moment; use the difference between the temperature at any sampling moment and the average temperature at the adjacent sampling moment as the first difference, and use the difference between the current at this sampling moment and the average current at the adjacent sampling moment as the second difference;

[0025] Perform a multiplication operation on the interference level at this sampling moment and the first difference or the second difference to obtain the correction amplitude of the temperature or current at this sampling moment.

[0026] Preferably, use the Huffman coding algorithm to compress the correction values of temperature and current, including:

[0027] Use the sequence composed of the correction values of temperature at each sampling moment as the temperature correction sequence, and use the sequence composed of the correction values of current at each sampling moment as the current correction sequence;

[0028] Based on the frequencies of the data in the temperature correction sequence or the current correction sequence, construct the Huffman tree of the temperature correction sequence or the current correction sequence, so as to compress the temperature correction sequence based on the Huffman tree of the temperature correction sequence and compress the current correction sequence based on the Huffman tree of the current correction sequence.

[0029] The present invention can improve the compression efficiency of data and reduce the risk of normal data loss.

[0030] Preferably, use the extreme points of the synergy relationship curve to segment the synergy relationship curve, including:

[0031] Obtain all the extreme points in the synergy relationship curve, and use the data segment between adjacent extreme points in the synergy relationship curve as a segment, so as to divide the synergy relationship curve into multiple segments.

[0032] According to the second aspect of the present invention, a system for off-site data migration and backup is provided. The system includes a memory and a processor. A computer program is stored on the memory, and the processor executes the computer program to implement the steps of the first aspect of the present invention.

[0033] The present invention has the following effects:

[0034] 1. By calculating the interference level, the present invention can accurately measure the degree of interference of abnormal data on the machine at each moment. When using the interference level to correct the data, it can increase the frequency of normal data and reduce the frequency of abnormal data, thereby improving the compression efficiency of the data; and when correcting the data, it increases the difference between normal data and abnormal data, thereby reducing the risk of normal data loss and ensuring the accuracy of the data stored during data migration and backup.

[0035] 2. When calculating the interference degree of the present invention, the mutual influence degree between temperature and current is considered, and the characteristics that the mutual influence degree between the temperature and current of the machine under normal conditions is relatively stable, and there are significant differences in the mutual influence degree between the temperature and current of the machine under abnormal conditions are utilized. Therefore, the interference degree of the machine affected by abnormal data at each moment can be accurately measured, ensuring the accuracy of the interference degree. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] By reading the following detailed description with reference to the accompanying drawings, the above and other objects, features, and advantages of the exemplary embodiments of the present invention will become readily understood. In the drawings, several embodiments of the present invention are shown in an exemplary rather than restrictive manner, and the same or corresponding reference numerals represent the same or corresponding parts, wherein:

[0037] Figure 1 is a schematic flowchart of the steps of a method for off-site data migration and backup according to an embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0038] The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are some, but not all, of the embodiments of the present invention. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0039] The specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings.

[0040] Referring to Figure 1 , a method for off-site data migration and backup includes steps S1 - S5, specifically as follows:

[0041] S1: Obtain the temperature and current during the operation of the machine and construct a collaborative relationship curve. The abscissa of the collaborative relationship curve is the sampling time, and the ordinate represents the mutual influence of the temperature and current at each sampling time.

[0042] Specifically, a temperature sensor and a multimeter can be used to collect the temperature and current of the intelligent machine within one operation cycle at a certain frequency, such as 1 time / minute. Of course, the temperature and current within multiple operation cycles can also be collected. This embodiment does not make special limitations on the sampling period and sampling frequency.

[0043] It should be noted that during the operation of the machine, temperature and current affect each other. Specifically, when current passes through a conductor, the resistance of the conductor causes energy loss and generates heat. As the current increases, the resistance of the conductor usually also increases, resulting in a temperature rise. As the temperature rises, the resistance of the conductor usually increases, thereby restricting the flow of current. Therefore, the present invention analyzes the data characteristics of temperature and current at each sampling moment, and constructs a collaborative relationship curve of temperature and current within the sampling period to measure the change in the degree of mutual influence between temperature and current over time.

[0044] In an exemplary embodiment of the present invention, the construction of the collaborative relationship curve can be achieved through the following steps:

[0045] Taking the sampling moment as the abscissa and the temperature or current at each sampling moment as the ordinate, construct a temperature curve and a current curve; calculate the coordination parameter at any sampling moment; taking the sampling moment as the abscissa and the coordination parameter at each sampling moment as the ordinate, construct a collaborative relationship curve.

[0046] Specifically, the coordination parameter at any sampling moment satisfies the following relational expression:

[0047] ;

[0048] In the formula, is the coordination parameter at the th sampling moment; is the temperature at the th sampling moment; is the current at the th sampling moment; is the slope of the temperature curve at the th sampling moment; is the slope of the current curve at the th sampling moment; is the normalization function.

[0049] Optionally, the values and slopes of the data points in the curve can reflect the data characteristics of the corresponding moments. Therefore, the present invention calculates the differences in the data characteristics of each sampling moment in the two curves to reflect the mutual influence of the corresponding data in the corresponding two curves.

[0050] Optionally, when the coordination parameter at any sampling moment is large, it indicates that the degree of mutual influence between the temperature and current at that moment is large; on the contrary, when the coordination parameter at any sampling moment is small, it indicates that the degree of mutual influence between the temperature and current at that moment is small.

[0051] S2: Use the extreme points of the synergy relationship curve to segment the synergy relationship curve, calculate the difference between the normality of any data in any segment and the average normality of adjacent data, and obtain the anomaly index of this data.

[0052] In an exemplary embodiment of the present invention, normality refers to data for measuring the accuracy of the correlation between the temperature and current at the corresponding moment of any data in the synergy relationship curve.

[0053] It should be noted that when the machine is abnormal, due to the mutual influence between temperature and current, there is a lag in the change of the degree of mutual influence between temperature and current. Therefore, the present invention performs segmentation processing on the synergy relationship curve to utilize the characteristics of lag and measure the normality of each data in the synergy relationship curve.

[0054] In an exemplary embodiment of the present invention, the segmentation processing of the synergy relationship curve can be achieved through the following steps:

[0055] Obtain all the extreme points in the synergy relationship curve, and use the data segment between adjacent extreme points in the synergy relationship curve as a segment to divide the synergy relationship curve into multiple segments.

[0056] Optionally, using the data segment between adjacent extreme points as a segment can divide continuous data segments with the same change trend into one segment, so as to effectively identify and analyze the temperature and current change patterns of the machine in different states.

[0057] Furthermore, after the segmentation processing of the synergy relationship curve, the normality of any data in any segment can be calculated.

[0058] It should be noted that under normal circumstances, the temperature control system of the machine can maintain a strong correlation between current and temperature, and the change trend of the correlation is relatively stable. That is, under normal circumstances, the degree of mutual influence between current and temperature is large and the fluctuation is small. However, when the machine is abnormal, the changes in current and temperature are relatively chaotic, resulting in a weakening of the correlation between current and temperature. At the same time, the change trend of the correlation will also show large fluctuations. Therefore, the present invention utilizes this feature to evaluate the normality of the corresponding data by measuring the value and fluctuation of each data in the synergy relationship curve.

[0059] Specifically, the normality of any data in any segment satisfies the following relational expression:

[0060] ;

[0061] In the formula, is the normality of the th data in the th segment; , are respectively the th data in two preset windows centered on two data symmetric about the th data in adjacent segments of the th segment. For example, first in the th segment and the th segment, determine two data symmetric about the th data in the th segment, and then preset a window centered on any one of the two data, so as to obtain two preset windows, and determine and according to the corresponding data in the two preset windows; is the th data in the th segment; is the number of data in the window. In this embodiment, the size of the window is 5×5, then = 25; is a normalization function; is an absolute value symbol.

[0062] Among them, reflects the correlation of the data in the preset windows of two data symmetric about the symmetric point of the th data in the adjacent segments of the th segment. The smaller this value is, the stronger the correlation of the data in the preset windows of the corresponding two data is, and further it shows that the change of the th data in the th segment is relatively stable, and the normality of the corresponding data is relatively high.

[0063] Optionally, when is relatively large, it indicates that the temperature and current at the corresponding moment have a strong correlation. At this time, if is relatively small, it indicates that the change trend of the correlation between the temperature and the current is relatively stable, and further it shows that the machine is more likely to be in a normal state at this moment, and the normality of the th data in the th segment is relatively high.

[0064] Furthermore, after determining the normality of each data in the collaborative relationship curve by using the normality calculation formula, the anomaly index of each data in the collaborative relationship curve can be calculated.

[0065] In an exemplary embodiment of the present invention, the determination of the anomaly index of any data in any segment can be achieved through the following steps:

[0066] For any data in any segment, the average value of the normality of two adjacent data of the data is calculated, and the absolute value of the difference between the normality of the data and the average value is taken as the abnormality index of the data.

[0067] Optionally, the ratio of the normality of any data in any segment to the average normality of adjacent data may be calculated to obtain the abnormality index of the data.

[0068] S3: Calculate the interference degree of the data. The interference degree is positively correlated with the difference in abnormal index of corresponding data in two windows preset with the data and the reference data as the center, and the slope difference. The reference data and the data are symmetrical about the center position of the segment.

[0069] It should be noted that when the machine is operating normally, the temperature and current will change with the working requirements of the machine, but due to the automatic adjustment mechanism of the system, the data fluctuations are relatively stable and the changes are highly stable. However, when the machine is abnormal, the current and temperature will affect each other, and the changes will become more drastic. For example, abnormal operation of the machine may cause the temperature to rise. In order to reduce the temperature, the temperature control system increases the power, which leads to an increase in current. The increase in current will further increase the temperature of the machine, forming a vicious circle, causing the data changes under abnormal conditions to become more drastic and chaotic. Therefore, based on this feature, the present invention analyzes the correlation between the data in the synergy relationship curve to determine the interference situation of the machine.

[0070] In an exemplary embodiment of the present invention, the interference degree refers to data determined based on the correlation between data in the synergy relationship curve, and is used to measure the degree of interference to the machine. For example, when the correlation between any data in the synergy relationship curve and other data is strong, the degree of interference to the machine at the corresponding moment of the any data is relatively small; on the contrary, when the correlation between any data in the synergy relationship curve and other data is low, the degree of interference to the machine at the corresponding moment of the any data is relatively large.

[0071] Specifically, the interference degree of any data in the synergy relationship curve satisfies the following relationship:

[0072] ;

[0073] In the formula, For the In the segment The interference degree of individual data; is the first The abnormal index of each data; The first The abnormal index of each data; is the first The slope of a data; is the th data in the window of the reference data of this data is the absolute value symbol; is the natural exponential function; is the ceiling symbol; is the normalization function; is the number of data within the window. In this embodiment, the size of the window is 5×5, then = 25.

[0074] Among them, reflects the difference in anomaly indices of the corresponding data within two preset windows centered on the th data in the th segment and the reference data of this data; the larger this value, the poorer the correlation between the th data and the reference data of this data in the th segment, and then the greater the interference degree of the th data in the th segment.

[0075] reflects the difference in slopes of the corresponding data within two preset windows centered on the th data in the th segment and the reference data of this data; the larger this value, the greater the difference in the change conditions between the th data and the reference data of this data in the th segment, and further indicates that the correlation between the th data and the reference data of this data in the th segment is relatively low, and then the greater the interference degree of the th data in the th segment.

[0076] Furthermore, the interference degree of each data in the synergy relationship curve can be calculated through the calculation formula of the interference degree, so as to determine the degree of interference suffered by the machine at each moment.

[0077] S4: Based on the interference degree, correct the temperature or current at the corresponding moment. The correction direction is towards the direction of the data at the adjacent moment, and the correction amplitude is positively correlated with the interference degree.

[0078] In an exemplary embodiment of the present invention, the determination of the correction amplitude of the temperature or current at any moment can be achieved through the following steps:

[0079] Take the interference degree of any data in any segment as the interference degree of the corresponding data at the corresponding moment; take the difference between the temperature at any sampling moment and the average temperature at the adjacent sampling moment as the first difference, and take the difference between the current at this sampling moment and the average current at the adjacent sampling moment as the second difference; perform a multiplication operation on the interference degree at this sampling moment and the first difference or the second difference to obtain the correction amplitude of the temperature or current at this sampling moment.

[0080] Optionally, the difference between the temperature at any sampling moment and the average temperature at the adjacent sampling moment can be the absolute value of the difference between the temperature at this sampling moment and the average temperature at the adjacent sampling moment; it can also be the ratio of the temperature at this sampling moment to the average temperature at the adjacent sampling moment. This embodiment does not make a special limitation on the determination method of the first difference.

[0081] Among them, the determination method of the second difference is the same as that of the first difference, and this embodiment will not elaborate here.

[0082] Optionally, by using the interference degree to correct the temperature or current at each sampling moment, the fluctuations in the temperature curve or current curve can be effectively smoothed. Specifically, for normal fluctuations (i.e., moments with a small interference degree), the data can be smoothed to a value close to the average of adjacent data; for abnormal fluctuations (i.e., moments with a large interference degree), while retaining the abnormal characteristics, smoothing can be performed, thereby reducing the fluctuation amplitude and frequency of the fluctuating data, so that the processed temperature curve or current curve can eliminate normal fluctuations and, while retaining the characteristics of abnormal fluctuations, moderately smooth the abnormal fluctuations.

[0083] S5: Compress the corrected values of temperature and current using the Huffman coding algorithm for migration backup.

[0084] It should be noted that the Huffman Coding algorithm is an optimal prefix coding method based on the greedy algorithm, aiming to reduce the overall data length by reducing the coding length of symbols with a higher occurrence frequency. The compression principle is: for symbols with a higher occurrence frequency, use shorter codes; for symbols with a lower occurrence frequency, use longer codes. In this way, the total coding length can be compressed, thus saving storage space.

[0085] It should be further noted that if the Huffman coding algorithm is directly applied to compress the temperature data or current data of the machine, due to the mutual dependence between temperature and current, when the machine malfunctions, the Huffman coding algorithm may not be able to effectively distinguish normal data from abnormal data, resulting in low compression efficiency and increasing the risk of loss of normal data. This may cause deviations during data migration and backup processes, affecting the accuracy and reliability of the data. Therefore, before using this algorithm to compress the temperature data or current data of the machine, the present invention first corrects the data to eliminate normal fluctuations, while retaining the characteristics of abnormal fluctuations and moderately smoothing the abnormal fluctuations. By correcting the data, the occurrence frequency of normal data is relatively high, while the occurrence frequency of abnormal data is relatively low, thereby enhancing the distinguishability between normal data and abnormal data. When using the Huffman coding algorithm to compress the corrected values of the data, it can not only improve the compression efficiency of the data, but also effectively reduce the risk of loss of normal data.

[0086] In an exemplary embodiment of the present invention, the compression of the corrected values of the temperature or current at each sampling moment can be achieved through the following steps:

[0087] Form the sequence composed of the corrected values of the temperature at each sampling moment as the temperature correction sequence, and form the sequence composed of the corrected values of the current at each sampling moment as the current correction sequence; based on the frequencies of the data in the temperature correction sequence or the current correction sequence, construct the Huffman tree of the temperature correction sequence or the current correction sequence, so as to compress the temperature correction sequence based on the Huffman tree of the temperature correction sequence and compress the current correction sequence based on the Huffman tree of the current correction sequence.

[0088] Among them, the process of constructing the Huffman tree of the data sequence based on the frequencies of the data appearing in the data sequence to compress the data sequence is a prior art, and this embodiment will not be elaborated in detail here.

[0089] Furthermore, the compressed temperature correction sequence and the current correction sequence can be directly copied to a new machine or a new storage device, and according to the decoding steps in the Huffman coding algorithm, the compressed temperature correction sequence and the compressed current correction sequence are decoded into the new machine, thereby completing the migration or backup of the temperature data and the current data of the machine. It should be noted that the decoding steps in the Huffman coding algorithm are prior art, and this embodiment will not be elaborated in detail here.

[0090] The present invention also provides a system for off-site data migration and backup. The system includes a memory and a processor, and a computer program is stored on the memory. The computer program integrates the functions of a method for off-site data migration and backup. When the computer program is executed, the data compression efficiency and the accuracy of the compression result can be improved through a method for off-site data migration and backup.

[0091] In the description of this specification, the meanings of "a plurality of" and "several" are at least two, such as two, three or more, etc., unless otherwise specifically defined.

[0092] Although this specification has shown and described multiple embodiments of the present invention, it is obvious to those skilled in the art that such embodiments are provided only by way of example. Those skilled in the art will think of many changes, alterations and alternative ways without departing from the spirit and idea of the present invention. It should be understood that various alternative solutions to the embodiments of the present invention described herein can be adopted in the process of practicing the present invention.

Claims

1. A method for off-site data migration and backup, characterized in that: include: The temperature and current of the machine during operation are obtained and a synergistic relationship curve is constructed, wherein the abscissa of the synergistic relationship curve is the sampling time, and the ordinate represents the mutual influence of the temperature and the current at each sampling time; By using the extreme points of the synergy relationship curve, the synergy relationship curve is segmented, and the difference between the normality of any data in any segment and the average normality of the adjacent data is calculated to obtain the abnormal index of the data; the normality ; For the In the segment Normality of individual data; , Respectively Among the adjacent segments of the segment, The first individual data; For the In the segment individual data; is the number of data in the window; is the normalization function; Calculate the interference degree of the data, the interference degree is positively correlated with the difference in abnormal index and slope of corresponding data in two windows preset with the data and reference data as the center, and the reference data is symmetrical with the data about the center position of the segment; The temperature or current at the corresponding moment is corrected based on the interference degree, the correction direction is close to the direction of adjacent data, the correction amplitude is positively correlated with the interference degree, and the Huffman coding algorithm is used to compress the correction values ​​of temperature and current for migration backup.

2. The method for remote data migration and backup according to claim 1, characterized in that: The interference degree satisfies the following relationship: ； In the formula, For the In the segment The interference degree of individual data; is the first The abnormal index of each data; The first The abnormal index of each data; is the first The slope of the data; The first The slope of the data; is the absolute value symbol; is the natural exponential function; is the rounding symbol; is the normalization function; is the number of data in the window.

3. The method for remote data migration and backup according to claim 2, characterized in that: The method for obtaining the abnormality index includes: For any data in any segment, the average value of the normality of two adjacent data of the data is calculated, and the absolute value of the difference between the normality of the data and the average value is taken as the abnormality index of the data.

4. The method for remote data migration and backup according to claim 1, characterized in that: The method for obtaining the synergy relationship curve comprises: The temperature curve and the current curve are constructed by taking the sampling time as the horizontal axis and the temperature or current at each sampling time as the vertical axis; Calculate the coordination parameters at any sampling time , For the The coordination parameters at each sampling time, For the The temperature at the sampling time, For the The current at each sampling moment, The temperature curve The slope at the sampling moment, The current curve The slope at the sampling moment, is a normalized function; the synergy relationship curve is constructed with the sampling time as the horizontal coordinate and the coordination parameter at each sampling time as the vertical coordinate.

5. The method for remote data migration and backup according to claim 1, characterized in that: The method for obtaining the correction amplitude includes: The interference degree of any data in any segment is taken as the interference degree of the corresponding data at the corresponding time; the difference between the temperature at any sampling time and the average temperature at the adjacent sampling time is taken as the first difference, and the difference between the current at the sampling time and the average current at the adjacent sampling time is taken as the second difference; The interference degree at the sampling moment is multiplied by the first difference or the second difference to obtain a correction amplitude of the temperature or current at the sampling moment.

6. The method for remote data migration and backup according to claim 1, characterized in that: The method of compressing the temperature and current correction values ​​using the Huffman coding algorithm includes: The sequence composed of the correction values ​​of the temperature at each sampling moment is used as the temperature correction sequence, and the sequence composed of the correction values ​​of the current at each sampling moment is used as the current correction sequence; Based on the frequency of each data in the temperature correction sequence or the current correction sequence, a Huffman tree of the temperature correction sequence or the current correction sequence is constructed, so as to compress the temperature correction sequence based on the Huffman tree of the temperature correction sequence and compress the current correction sequence based on the Huffman tree of the current correction sequence.

7. The method for remote data migration and backup according to claim 1, characterized in that: The step of segmenting the synergy relationship curve by using the extreme points of the synergy relationship curve comprises: All extreme value points in the synergistic relationship curve are obtained, and the data segment between adjacent extreme value points in the synergistic relationship curve is used as a segment, so as to divide the synergistic relationship curve into a plurality of segments.

8. A system for remote data migration and backup, characterized in that: The off-site data migration and backup system includes a memory and a processor, the memory stores a computer program, and the processor executes the computer program to implement the steps of the method according to any one of claims 1 to 7.

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