Remote data migration backup method and system

Abstract

The invention relates to the technical field of data processing, in particular to a remote data migration backup method and system, and the method comprises the steps: obtaining the temperature and current during the operation of a machine, and constructing a cooperative relation curve; and segmenting the collaborative relation curve by using the extreme point of the collaborative relation curve, and 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. The interference degree of the data is calculated, the temperature or the current at the corresponding moment is corrected based on the interference degree, the correction direction is the direction close to the adjacent data, the correction amplitude is in positive correlation with the interference degree, and correction values of the temperature and the current are compressed through a Huffman coding algorithm so that migration backup can be conducted. According to the method, the data compression efficiency and the accuracy of the compression result in the data migration backup process can be improved.

Description

Technical Field

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

[0002] Offsite data migration and backup is a common and critical 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, offsite backup can quickly restore to remote backup when the equipment fails or is damaged, ensuring the continuous availability of data and the stability of the system. Machine monitoring data usually has a large amount of data, so it is particularly important to compress the data when performing migration and backup.

[0003] In the related technology, for example, a patent application document with publication number CN118964315A discloses an environmental temperature data collection and management method and system based on big data, the method comprising: obtaining a first temperature data set of a preset time period of 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 regional growth on the data points of the two-dimensional data table; and using Huffman coding to compress the data after the growth is completed, so as to obtain the meteorological temperature storage data of the preset time period of the target area.

[0004] However, the above scheme does not take into account the impact of abnormal data when compressing data using Huffman coding. The presence of abnormal data may reduce the compression efficiency of the data, and since it is impossible to distinguish between normal data and abnormal data, there may be a risk of normal data being lost, thereby reducing the accuracy of the stored data. Summary of the invention

[0005] In order to solve the problem of low data compression efficiency and low accuracy of compression results during data migration and backup, 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, a method for off-site data migration and backup is provided, comprising: The temperature and current of the machine during operation are obtained and a synergy relationship curve is constructed. The horizontal axis of the synergy relationship curve is the sampling time, and the vertical axis represents the mutual influence of the temperature and current at each sampling time. 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; 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, which 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 difference in slope, and the reference data and the data are symmetrical 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. The Huffman coding algorithm is used to compress the correction values ​​of temperature and current for migration backup.

[0007] The present invention uses interference to correct data before compressing data using the Huffman coding algorithm, which can eliminate normal fluctuations in data and moderately smooth abnormal fluctuations in data while retaining the abnormal fluctuation characteristics of data. By correcting the data, the frequency of normal data is increased and the frequency of abnormal data is reduced, thereby improving the compression efficiency of data; and correcting the data can enhance the distinction between normal data and abnormal data, thereby reducing the risk of normal data loss and improving the accuracy of data stored during data migration and backup.

[0008] Preferably, 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.

[0009] The present invention limits the value range of the interference degree to 0-1, which can reduce the amount of calculation when correcting data based on the interference degree and reduce the difficulty of calculating the data correction value.

[0010] Preferably, 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.

[0011] The present invention utilizes the characteristic that the mutual influence degree between temperature and current of a machine under normal conditions is relatively stable, and can accurately evaluate the abnormal relationship between temperature and current at each moment.

[0012] Preferably, the method for obtaining the synergy relationship curve includes: 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 sampling time is taken as the horizontal coordinate and the coordination parameter at each sampling time is taken as the vertical coordinate to construct a synergy relationship curve.

[0013] The present invention takes into account the mutual influence between the temperature and current of the machine, and then constructs a synergistic relationship curve of the machine. The present invention analyzes the abnormal state of the machine based on the synergistic relationship curve, and can ensure the accuracy of the analysis result.

[0014] Preferably, 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.

[0015] Preferably, the correction values ​​of temperature and current are compressed using a Huffman coding algorithm, including: 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, and the temperature correction sequence is compressed by the Huffman tree based on the temperature correction sequence, and the current correction sequence is compressed by the Huffman tree based on the current correction sequence.

[0016] The present invention can improve the data compression efficiency and reduce the risk of normal data loss.

[0017] Preferably, the synergy relationship curve is segmented by using the extreme points of the synergy relationship curve, including: All extreme value points in the synergy relationship curve are obtained, and the data segment between adjacent extreme value points in the synergy relationship curve is used as a segment to divide the synergy relationship curve into multiple segments.

[0018] According to a 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. The memory stores a computer program. The processor executes the computer program to implement the steps of the first aspect of the present invention.

[0019] The present invention has the following effects: 1. The present invention can accurately measure the degree to which the machine is interfered by abnormal data at each moment by calculating the interference degree, so that when the interference degree is used to correct the data, the frequency of normal data can be increased and the frequency of abnormal data can be reduced, thereby improving the data compression efficiency; and when the data is corrected, the difference between normal data and abnormal data is increased, thereby reducing the risk of normal data loss and ensuring the accuracy of the data stored during data migration and backup.

[0020] 2. When calculating the interference degree, the present invention takes into account the degree of mutual influence between temperature and current, and utilizes the characteristics that the degree of mutual influence between the temperature and current of the machine under normal conditions is relatively stable, while the degree of mutual influence between the temperature and current of the machine under abnormal conditions is greatly different. This can accurately measure the degree of interference of the machine caused by abnormal data at each moment, thereby ensuring the accuracy of the interference degree. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] 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 accompanying drawings, several embodiments of the present invention are shown in an exemplary and non-limiting manner, and the same or corresponding reference numerals represent the same or corresponding parts, wherein: Figure 1 The present invention is a flowchart of a method for off-site data migration and backup according to an embodiment of the present invention. DETAILED DESCRIPTION

[0022] 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 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 those skilled in the art without creative work are within the scope of protection of the present invention.

[0023] The specific embodiments of the present invention are described in detail below with reference to the accompanying drawings.

[0024] Reference Figure 1 , a method for off-site data migration and backup, including steps S1 to S5, specifically as follows: S1: Obtain the temperature and current when the machine is running and construct a synergy relationship curve. The horizontal axis of the synergy relationship curve is the sampling time, and the vertical axis represents the mutual influence of the temperature and current at each sampling time.

[0025] Specifically, a temperature sensor and a multimeter can be used to collect the temperature and current of the smart machine in one operation cycle at a certain frequency, such as once per minute. Of course, the temperature and current in multiple operation cycles can also be collected. This embodiment does not specifically limit the sampling period and sampling frequency.

[0026] It should be noted that during the operation of the machine, temperature and current have mutual influence. Specifically, when current passes through a conductor, the resistance of the conductor will cause energy loss and generate heat. As the current increases, the resistance of the conductor will usually increase, resulting in a temperature increase. As the temperature rises, the resistance of the conductor will usually increase, thereby limiting the flow of current. Therefore, the present invention constructs a synergistic relationship curve of temperature and current within the sampling period by analyzing the data characteristics of temperature and current at each sampling moment, so as to measure the change in the degree of mutual influence between temperature and current over time.

[0027] In an exemplary embodiment of the present invention, the construction of the synergy relationship curve can be achieved by the following steps: The temperature curve and the current curve are constructed with the sampling time as the horizontal coordinate and the temperature or current at each sampling time as the vertical coordinate; the coordination parameter at any sampling time is calculated; the coordination relationship curve is constructed with the sampling time as the horizontal coordinate and the coordination parameter at each sampling time as the vertical coordinate.

[0028] Specifically, the coordination parameters at any sampling time satisfy the following relationship: ; In the formula, For the Coordination parameters at each sampling moment; For the The temperature at each sampling moment; For the The current at each sampling moment; The temperature curve The slope at each sampling moment; The current curve The slope at each sampling moment; is the normalization function.

[0029] Optionally, the value and slope of each data point in the curve can reflect the data characteristics at the corresponding moment. Therefore, the present invention calculates the difference in data characteristics at each sampling moment in the two curves to reflect the mutual influence of the corresponding data in the two curves.

[0030] Optionally, when the coordination parameter at any sampling moment is larger, it means that the temperature and current at that moment influence each other more; conversely, when the coordination parameter at any sampling moment is smaller, it means that the temperature and current at that moment influence each other less.

[0031] 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 the adjacent data, and obtain the abnormal index of the data.

[0032] In an exemplary embodiment of the present invention, normality refers to data used to measure the accuracy of the correlation between temperature and current at any data corresponding to a time in the synergy relationship curve.

[0033] It should be noted that when the machine is abnormal, the temperature and current will affect each other, resulting in a hysteresis in the change of the mutual influence between the temperature and the current. Therefore, the present invention processes the synergy relationship curve in sections to use the hysteresis feature to measure the normality of each data in the synergy relationship curve.

[0034] In an exemplary embodiment of the present invention, the segmented processing of the synergy relationship curve can be implemented by the following steps: All extreme value points in the synergy relationship curve are obtained, and the data segment between adjacent extreme value points in the synergy relationship curve is used as a segment to divide the synergy relationship curve into multiple segments.

[0035] Optionally, the data segment between adjacent extreme value points is taken as a segment, and continuous data segments with the same change trend can be divided into a segment, so that the temperature and current change patterns of the machine under different states can be effectively identified and analyzed.

[0036] Furthermore, after the synergy relationship curve is segmented, the normality of any data in any segment can be calculated.

[0037] 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 trend of the correlation change is relatively stable. That is, under normal circumstances, the 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 more chaotic, resulting in a weakening of the correlation between current and temperature. At the same time, the trend of the correlation change will also show large fluctuations. Therefore, the present invention uses this feature to evaluate the normality of the corresponding data by measuring the values ​​and fluctuations of each data in the synergistic relationship curve.

[0038] Specifically, the normality of any data in any segment satisfies the following relationship: ; In the formula, For the In the segment Normality of individual data; , Respectively Among the adjacent segments of the segment, The first For example, you can first Segment and In each segment, determine The first Then, a window is preset with any one of the two data as the center, thereby obtaining two preset windows, and determining the corresponding data in the two preset windows. and ; For the In the segment individual data; is the number of data in the window. In this embodiment, the size of the window is 5×5. =25; is the normalization function; is the absolute value symbol.

[0039] in, Reflects the Among the adjacent segments of the segment, The smaller the value is, the stronger the correlation is between the data in the preset window of the two data points. In the segment The change of individual data is relatively stable, and the corresponding normality of the data is higher.

[0040] Optional, when When it is larger, it means that the temperature and current at the corresponding moment have a strong correlation. At this time, if If the value is smaller, it means that the correlation between temperature and current is changing steadily, which means that the machine is more likely to be in a normal state at this moment. In the segment The normality of the data is high.

[0041] Furthermore, after the normality of each data in the synergy relationship curve is determined using the normality calculation formula, the abnormality index of each data in the synergy relationship curve can be calculated.

[0042] In an exemplary embodiment of the present invention, the determination of the abnormal index of any data in any segment can be achieved by the following steps: 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.

[0043] 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.

[0044] 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.

[0045] 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.

[0046] 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.

[0047] Specifically, the interference degree of any data in the synergy relationship curve 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. In this embodiment, the size of the window is 5×5. =25.

[0048] in, Reflects the The first The larger the value, the more abnormal the index difference of the corresponding data in the two windows preset as the center of the reference data of the data. In the segment The correlation between the data and the reference data of the data is poor, then In the segment The data has a large interference.

[0049] Reflects the The first The slope difference of the corresponding data in the two windows preset as the center of the data and the reference data of the data; the larger the value, the The first The difference between the changes in the data and the reference data of the data is large, which shows that The first If the correlation between the data and the reference data of the data is low, In the segment The data has a large interference.

[0050] Furthermore, the interference degree of each data in the synergy relationship curve can be calculated by using the interference degree calculation formula, so as to determine the degree of interference to the machine at each moment.

[0051] S4: Correct the temperature or current at the corresponding moment based on the interference degree. The correction direction is the direction close to the data at the adjacent moment. The correction amplitude is positively correlated with the interference degree.

[0052] In an exemplary embodiment of the present invention, the correction amplitude of the temperature or current at any time can be determined by the following steps: The interference degree of any data in any segment is taken as the interference degree of the corresponding data at the corresponding moment; the difference between the temperature at any sampling moment and the average temperature at the adjacent sampling moments is taken as the first difference, and the difference between the current at the sampling moment and the average current at the adjacent sampling moments 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 the correction amplitude of the temperature or current at the sampling moment.

[0053] Optionally, the difference between the temperature at any sampling moment and the average temperature at adjacent sampling moments may be the absolute value of the difference between the temperature at the sampling moment and the average temperature at adjacent sampling moments; or may be the ratio of the temperature at the sampling moment to the average temperature at adjacent sampling moments. This embodiment does not specifically limit the method for determining the first difference.

[0054] The method for determining the second difference is the same as the method for determining the first difference, and is not described in detail in this embodiment.

[0055] Optionally, by using the interference 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 less interference), the data can be smoothed to a value close to the average value of adjacent data; and for abnormal fluctuations (i.e., moments with greater interference), smoothing can be performed while retaining the abnormal characteristics, thereby reducing the fluctuation amplitude and frequency of the fluctuation data, so that the processed temperature curve or current curve can eliminate normal fluctuations and moderately smooth abnormal fluctuations while retaining the characteristics of abnormal fluctuations.

[0056] S5: Use the Huffman coding algorithm to compress the correction values ​​of temperature and current for migration backup.

[0057] It should be noted that Huffman Coding is an optimal prefix coding method based on a greedy algorithm, which aims to reduce the overall data length by reducing the code length of symbols that appear more frequently. The compression principle is: for symbols that appear more frequently, a shorter code is used; for symbols that appear less frequently, a longer code is used. In this way, the total code length can be compressed, thereby saving storage space.

[0058] It should be further explained 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 is abnormal, 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 normal data loss, thereby causing deviations in the data migration and backup process, affecting the accuracy and reliability of the data. Therefore, before using the 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 abnormal fluctuations. By correcting the data, the frequency of normal data is relatively high, while the frequency of abnormal data is relatively low, thereby enhancing the distinction between normal data and abnormal data. When the Huffman coding algorithm is used to compress the corrected value of the data, not only can the data compression efficiency be improved, but also the risk of normal data loss can be effectively reduced.

[0059] In an exemplary embodiment of the present invention, the compression of the correction value of temperature or current at each sampling moment can be achieved by the following steps: The sequence composed of the temperature correction values ​​at each sampling moment is used as the temperature correction sequence, and the sequence composed of the current correction values ​​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, and the temperature correction sequence is compressed by the Huffman tree based on the temperature correction sequence, and the current correction sequence is compressed by the Huffman tree based on the current correction sequence.

[0060] The process of constructing a Huffman tree of the data sequence based on the frequency of occurrence of each data in the data sequence to compress the data sequence is a prior art and will not be described in detail in this embodiment.

[0061] 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 current data of the machine. It should be noted that the decoding steps in the Huffman coding algorithm are prior art and are not described in detail in this embodiment.

[0062] 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 in the memory, the computer program integrates the functions of a method for off-site data migration and backup, and when the computer program is executed, the method for off-site data migration and backup can improve the data compression efficiency and the accuracy of the compression results during the data migration and backup process.

[0063] In the description of this specification, "plurality" or "several" means at least two, such as two, three or more, etc., unless otherwise clearly and specifically defined.

[0064] Although this specification has shown and described a number of embodiments of the present invention, it will be apparent to those skilled in the art that such embodiments are provided by way of example only. Those skilled in the art will conceive of many modifications, changes and alternatives without departing from the ideas and spirit of the present invention. It should be understood that in the practice of the present invention, various alternatives to the embodiments of the present invention described herein may be employed.

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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