Cloud platform data management system and method

Abstract

The invention discloses a cloud platform data management system and method, relates to the technical field of data management, and solves the problems that data features of related data are not analyzed, and the related data with similar data features cannot be stored in the same place. According to the method, feature parameter confirmation is performed on data of different cloud platforms of the same type, similarity analysis is performed on related cloud platform data with the same feature parameters based on feature parameter differences among the data of the different platforms, and the similarity of the related cloud platform data with the same feature parameters is obtained by confirming the corresponding feature polygons. According to the method, similar cloud platform data is determined based on the shape features and the cross regions among the feature polygons and is stored in the same partition, so that the management effect of the data is guaranteed, the features of a plurality of cloud platform data are synchronously and fully classified, and a better data classification management effect is achieved.

Description

Technical Field

[0001] The present invention relates to the technical field of data management, and in particular to a cloud platform data management system and method. Background Art

[0002] Cloud computing platform, also known as cloud platform, refers to services based on hardware resources and software resources, providing computing, network and storage capabilities; cloud computing platforms can be divided into three categories: storage-type cloud platforms that focus on data storage, computing-type cloud platforms that focus on data processing, and comprehensive cloud computing platforms that take into account both computing and data storage processing.

[0003] The application with publication number CN113900801A discloses a cloud platform data management system, the system comprising: an input module for receiving input data; a format conversion module for normalizing the format of the input data to obtain intermediate data; a compression module for compressing the intermediate data to obtain compressed data; an encryption module for encrypting the compressed data to obtain encrypted compressed data; a storage module for storing the encrypted compressed data and converting the encrypted compressed data to obtain output data; an output module for outputting the output data. The present application also discloses a cloud platform data management method. By using the system of the present application, there is no need for an administrator to manage the data, there is no dependence on the administrator, and the intelligence level of cloud platform data management is improved.

[0004] In the process of relevant management of its cloud platform data, it is generally based on the relevant data received in the same period, and stores such relevant data without analyzing the data characteristics of the relevant data, and fails to store relevant data with similar data characteristics in the same place. The overall data management effect is not good, and it is not convenient for subsequent relevant personnel to coordinate the management of the data. Summary of the invention

[0005] In view of the deficiencies in the prior art, the present invention provides a cloud platform data management system and method, which solves the problem that the data features of the relevant data are not analyzed and the relevant data with the same data features cannot be stored in the same place.

[0006] To achieve the above objectives, the present invention is implemented through the following technical solutions: a cloud platform data management method, comprising the following steps: S1. Based on the identification features set by this platform, different cloud platform data with the same identification features are calibrated, and several groups of calibrated cloud platform data are divided into the same type of data. The specific method is as follows: Based on several groups of identification features set in this platform, all of which are preset features; Determine the corresponding data calibration features from the cloud platform data, perform feature recognition, and classify different cloud platform data with the same recognition features into the same type of data; S2. Perform feature analysis on several groups of different cloud platform data classified as data of the same type, confirm the numerical feature parameters of several groups of different cloud platform data, and sort the confirmed different numerical feature parameters to determine the feature parameter set of the corresponding cloud platform data. The specific sub-steps are: S21, preferentially performing mean processing on several groups of data associated in a single cloud platform data, determining a first numerical characteristic parameter T1, then selecting a maximum value and a minimum value from the associated several groups of data to determine a range, and the range = maximum value of the data - minimum value of the data, calibrating the range as a second numerical characteristic parameter T2, then performing variance processing on the associated several groups of data, determining a discrete value, and calibrating the discrete value as a third numerical characteristic parameter T3; S22. Sort several groups of data associated with a single cloud platform data based on the timestamp inside the data, and then determine the corresponding coordinate points in the two-dimensional coordinate system based on different data corresponding to different timestamps, wherein the horizontal coordinate axis of the two-dimensional coordinate system is the timeline, and the vertical coordinate axis is the data parameter, and determine the median line between several coordinate points, wherein the median line is perpendicular to the vertical coordinate axis, and the coordinate points above the median line are calibrated as upper points, and the coordinate points below the median line are calibrated as lower points, and the coordinate points intersecting the median line are not calibrated, and the sum of the vertical distances between several upper points and the median line is determined to be calibrated as H1, and then the sum of the vertical distances between several lower points and the median line is determined to be calibrated as H2, so that the median line moves up and down, and stops moving when H1=H2, and determines the position of the median line, and the data parameter corresponding to the median line at this position is calibrated as the fourth numerical feature parameter T4; S23, perform data clustering processing on several groups of data associated with a single group of cloud platform data, sort the several groups of data in ascending order, generate a data sequence, and segment the data sequence: determine the first group of parameters CS1 of the data sequence, confirm the data belonging to [CS1, CS1+Y1], and then confirm the data belonging to (CS1+Y1, CS1+2Y1], and then use the same interval confirmation method to divide the data sequence into several data segments, select a group of data segments with the largest number of data, and perform mean processing on several groups of data associated with this data segment, calibrate the processed value as the fifth numerical feature parameter T5, sort the confirmed numerical feature parameters one by one, and determine the feature parameter set corresponding to the cloud platform data, where Y1 is a preset value; S24: for several groups of different cloud platform data of the same type of data, the same method as steps S21-S23 is used to process, and the different numerical feature parameters determined by the different cloud platform data are calibrated as T1 i 、T2 i 、T3 i 、T4 i and T5 i , where i represents different cloud platform data, and determines the feature parameter set of the corresponding cloud platform data; S3. Based on the number of numerical characteristic parameters of the determined characteristic parameter set, a corresponding number of measurement lines are constructed, and the angles between each measurement line are equal. Based on the specific numerical characteristic parameters, specific points are determined on the measurement line, and then the determined specific points are connected to determine the characteristic polygon of the corresponding cloud platform data. The specific method is as follows: S31. According to the number of numerical characteristic parameters in the characteristic parameter set of the cloud platform data, a corresponding number of measurement lines are constructed, and the starting points of the measurement lines are consistent, and the angles between each group of measurement lines are consistent, and the numerical characteristic parameters measured by each group of measurement lines are limited; S32, based on different numerical feature parameters in the feature parameter set, find corresponding measurement points on the corresponding measurement lines starting from the initial point, and after the measurement points on each measurement line are confirmed, connect the corresponding measurement points on adjacent measurement lines to determine the characteristic polygon of this cloud platform data, where i represents different cloud platform data; S33, for the feature parameter sets corresponding to other cloud platform data, the same processing method as steps S31-S32 is adopted to determine the corresponding feature polygons on several identical metric lines; S4, based on the different feature polygons corresponding to different cloud platform data in the same type of data, based on the mutual intersection of the corresponding feature polygons, the cloud platform data with greater similarity are divided into data with the same feature, and the data with the same feature is stored in the same storage partition; the specific sub-steps are: S41. Based on different characteristic polygons corresponding to different cloud platform data, two groups of characteristic polygons are randomly selected for similarity confirmation, the overlapping areas of the two groups of characteristic polygons are identified, and based on the area proportions ZB1 and ZB2 of the overlapping areas in the corresponding characteristic polygons, ZB1 and ZB2 are averaged to determine the proportion mean ZJ. If ZJ≥Y2, the two groups of characteristic polygons are marked as the same type of characteristic polygons. If ZJ<Y2, it means that the two groups of characteristic polygons do not belong to the same type of characteristic polygons, where Y2 is a preset value. S42, processing any two groups of characteristic polygons one by one, locking the same type of characteristic polygons, if any two characteristic polygons are the same type of characteristic polygons, then any two characteristic polygons are marked as the same type of characteristic polygons; S43. Based on the determined similar characteristic polygons, the same characteristic data corresponding to the corresponding characteristic polygons are stored in the same storage partition.

[0007] Preferably, the method further comprises the following steps: S5. Based on the different cloud platform data stored in each different storage partition and the different characteristic polygons corresponding to each different cloud platform data, the mean polygon of this storage partition is locked, and then based on the numerical feature difference between the different characteristic polygons and the mean polygon, the different cloud platform data are segmented. The specific sub-steps are: S51, based on different characteristic polygons corresponding to different cloud platform data, based on measurement points corresponding to different corner points of different characteristic polygons, based on multiple groups of measurement points confirmed on the same measurement line, confirm their mean points, and then connect adjacent mean points to determine the mean polygon; S52, determine a set of characteristic polygons corresponding to cloud platform data, identify the area difference MC of the characteristic polygon and the mean polygon corresponding to the polygon area between adjacent metric lines, and MC≥0, then determine a set of metric lines as the starting point, sort them clockwise to the right, perform ratio processing on the MC of the corresponding polygon area in turn, and determine a sequence of area difference ratios of several polygon areas; S53, based on the determined ratio sequence, the corresponding cloud platform data is segmented, and a number of data segments are confirmed, and the capacity ratio columns of the data segments in the previous and next order are consistent with the ratio sequence, and the data segments belonging to the same type of cloud platform data are all marked with corresponding marks i. If the same ratio exists in the ratio sequence, the data segments of the same capacity are digitally marked, and the smaller the digital mark, the closer the corresponding data segment is to the front; S54: For other cloud platform data in this storage partition, segmentation is performed in the same manner as steps S51-S53, and several data segments completed in this storage partition are stored in a shuffled manner.

[0008] Preferably, the cloud platform data management system includes: The same type data classification end calibrates different cloud platform data with the same identification features based on the identification features set by this platform, and classifies the calibrated groups of cloud platform data into the same type of data; The feature parameter set confirmation end performs feature analysis on several groups of different cloud platform data classified as data of the same type, confirms the numerical feature parameters of several groups of different cloud platform data, and sorts the confirmed different numerical feature parameters to determine the feature parameter set of the corresponding cloud platform data; The feature polygon confirmation end constructs a corresponding number of measurement lines based on the number of numerical feature parameters of the determined feature parameter set, and the angles between each measurement line are equal. Based on the specific numerical feature parameters, a specific point is determined on the measurement line, and then the determined specific points are connected to determine the feature polygon corresponding to the cloud platform data; On the data classification side, based on the different feature polygons corresponding to different cloud platform data in the same type of data and the mutual intersection of the corresponding feature polygons, cloud platform data with great similarity are divided into data with the same feature, and the data with the same feature is stored in the same storage partition; The segmented encryption processing end, based on the different cloud platform data stored in each different storage partition and the different feature polygons corresponding to each different cloud platform data, locks the mean polygon of this storage partition, and then performs segmented processing on the different cloud platform data based on the numerical feature differences between the different feature polygons and the mean polygon.

[0009] The present invention provides a cloud platform data management system and method. Compared with the prior art, it has the following beneficial effects: The present invention confirms the characteristic parameters of data of different cloud platforms of the same type, and then performs similarity analysis on related cloud platform data with the same characteristic parameters based on the differences in characteristic parameters between data of different platforms. By confirming the corresponding characteristic polygons, similar cloud platform data is determined based on the shape characteristics and intersection areas between the characteristic polygons, and the similar cloud platform data is stored in the same partition, so that the data management effect is guaranteed, and the characteristics of several cloud platform data are fully classified simultaneously, so as to achieve better data classification management effect. Subsequently, for different cloud platform data stored in the same storage partition, feature processing is performed on different cloud platform data, and the relevant ratio sequence is determined based on the numerical difference between its feature polygon and the mean polygon. Subsequently, segmentation processing is performed on different cloud platform data based on the corresponding ratio sequence, so that the data is relatedly segmented to achieve better data segmentation processing effect, and the different cloud platform data stored in the storage partition are simultaneously associated and encrypted to ensure the overall associated encryption effect. BRIEF DESCRIPTION OF THE DRAWINGS

[0010] Figure 1 It is a schematic diagram of the process of the present invention; Figure 2 It is a schematic diagram of the principle framework of the present invention. DETAILED DESCRIPTION

[0011] 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.

[0012] Example 1 See also Figure 1 , this application provides a cloud platform data management method, comprising the following steps: S1. Confirm the cloud platform data of the same type (this confirmation process is confirmed by relevant personnel or data transmitted by the same transmission nodes. The specific confirmation process is determined by the specific scenario. For example, housing price data, different housing price data generated in different areas of a city at different time points belong to the same type of cloud platform data). Based on the identification features set by this platform, different cloud platform data with the same identification features are calibrated, and several groups of calibrated cloud platform data are divided into the same type of data. The specific sub-steps of identification include: Based on several groups of identification features set in this platform, the identification features are all preset features, which are all prepared in advance by the operator based on experience; Determine the corresponding data calibration features (e.g., data headers, such as housing prices, etc.) from the cloud platform data, perform feature recognition, and classify different cloud platform data with the same identification features into the same type of data; S2. Perform feature analysis on several groups of different cloud platform data classified as data of the same type, confirm the numerical feature parameters of several groups of different cloud platform data, and sort the confirmed different numerical feature parameters to determine the feature parameter set of the corresponding cloud platform data, wherein the specific sub-steps of confirming the numerical feature parameters of a single cloud platform data include: S21, preferentially perform mean processing on several groups of data associated in a single cloud platform data, determine the first numerical characteristic parameter T1, then select the maximum value and the minimum value of the data from the associated several groups of data to determine the range, and the range = the maximum value of the data - the minimum value of the data, calibrate the range as the second numerical characteristic parameter T2, then perform variance processing on the associated several groups of data, determine the discrete value, and calibrate the discrete value as the third numerical characteristic parameter T3, wherein the specific method of discrete processing is: calibrate several groups of data as SJ k , where k = 1, 2, ..., n, several sets of data SJ k , perform mean processing to determine the mean Jz, using Determine a discrete value F, which is the third numerical feature parameter T3; S22. Sort several groups of data associated with a single cloud platform data based on the timestamp inside the data, and then determine the corresponding coordinate points in a two-dimensional coordinate system based on different data corresponding to different timestamps, wherein the horizontal axis of the two-dimensional coordinate system is the timeline, and the vertical axis is the data parameter, and determine a median line between several coordinate points, wherein the median line is perpendicular to the vertical axis, and the coordinate points above the median line are marked as upper points, and the coordinate points below the median line are marked as lower points, and the coordinate points intersecting the median line are marked as lower points. Without any calibration, determine the sum of the vertical distances between several upper points and the median line as H1, and then determine the sum of the vertical distances between several lower points and the median line as H2, so that the median line moves up and down, and stops moving when H1=H2, determine the location of the median line, and calibrate the data parameter corresponding to the median line at this position (that is, the corresponding parameter on the vertical coordinate axis) as the fourth numerical feature parameter T4. Specifically, the median line and the determined mean are two data features, not the same data feature; S23, perform data clustering processing on several groups of data associated with a single group of cloud platform data, sort several groups of data in ascending order, generate a data sequence, and segment the data sequence: determine the first group of parameters CS1 of the data sequence, confirm the data belonging to [CS1, CS1+Y1], and then confirm the data belonging to (CS1+Y1, CS1+2Y1], and then use the same interval confirmation method to divide the data sequence into several data segments, select a group of data segments with the largest number of data, and perform mean processing on several groups of data associated with this data segment, and calibrate the processed value as the fifth numerical feature parameter T5. The group of data segments with the largest number of values ​​represents that the data clustering in this data segment is the most concentrated. Then, from the most concentrated related data clustered, its corresponding numerical feature can be determined, and according to the confirmed numerical feature parameters one by one, they are sorted to determine the feature parameter set corresponding to the cloud platform data, where Y1 is a preset value, and its specific value is determined by the operator based on experience; S24: for several groups of different cloud platform data of the same type of data, the same method as steps S21-S23 is used to process, and the different numerical feature parameters determined by the different cloud platform data are calibrated as T1 i 、T2 i 、T3 i 、T4 i and T5 i , where i represents different cloud platform data, and determines the feature parameter set of the corresponding cloud platform data; S3. Based on the number of numerical characteristic parameters of the determined characteristic parameter set, a corresponding number of measurement lines are constructed, and the angles between each measurement line are equal. Based on the specific numerical characteristic parameters, specific points are determined on the measurement line, and then the determined specific points are connected to determine the characteristic polygon corresponding to the cloud platform data, wherein the specific method of determination is: S31. According to the number of numerical characteristic parameters in the characteristic parameter set of the cloud platform data, a corresponding number of measurement lines are constructed, and the starting points of the measurement lines are consistent, and the angles between each group of measurement lines are consistent, and the numerical characteristic parameters measured by each group of measurement lines are limited; S32. Based on the different numerical feature parameters in the feature parameter set, find the corresponding measurement point on the corresponding measurement line starting from the initial point. After the measurement point on each measurement line is confirmed, connect the corresponding measurement points on the adjacent measurement lines to determine the characteristic polygon of this cloud platform data, where i represents different cloud platform data. For example, a set of feature parameter sets is {T1 i 、T2 i 、T3 i 、T4 i 、T5 i}, then there are five groups of measurement lines, each group of measurement lines corresponds to a group of numerical feature parameters. If T1 i If the value is 50, the point where the value 50 is located can be locked on the measurement line. This point is the determined measurement point. Each measurement line is marked with a corresponding measurement point. After the adjacent measurement points are connected, the corresponding feature polygon can be determined. S33, for the feature parameter sets corresponding to other cloud platform data, the same processing method as steps S31-S32 is adopted to determine the corresponding feature polygons on several identical metric lines; S4. Based on the different feature polygons corresponding to different cloud platform data in the same type of data, based on the mutual intersection of the corresponding feature polygons, the cloud platform data with greater similarity are divided into data with the same feature, and the data with the same feature is stored in the same storage partition, wherein the specific sub-steps of the division processing are: S41. Based on different characteristic polygons corresponding to different cloud platform data, two groups of characteristic polygons are randomly selected for similarity confirmation, the overlapping areas of the two groups of characteristic polygons are identified, and based on the area proportions ZB1 and ZB2 of the overlapping areas in the corresponding characteristic polygons, ZB1 and ZB2 are averaged to determine the proportion mean ZJ. If ZJ≥Y2, the two groups of characteristic polygons are marked as the same type of characteristic polygons. If ZJ<Y2, it means that the two groups of characteristic polygons do not belong to the same type of characteristic polygons, where Y2 is a preset value, and its specific value is determined by the operator based on experience. S42, process any two groups of characteristic polygons one by one, lock the same type of characteristic polygons, if any two characteristic polygons are all the same type of characteristic polygons, then any two characteristic polygons are marked as the same type of characteristic polygons, here combined with an example to understand: there are three polygons A, B, and C, after processing A and B belong to the same type of characteristic polygons, B and C belong to the same type of characteristic polygons, and A and C also belong to the same type of characteristic polygons, then A, B and C are all the same type of characteristic polygons; S43. Based on the determined similar characteristic polygons, the same characteristic data corresponding to the corresponding characteristic polygons are stored in the same storage partition.

[0013] Example 1 mainly focuses on the data feature processing of different cloud platform data, confirms the data features of different cloud platform data, and then based on the similarity between the data features, stores the cloud platform data belonging to the same feature edge in the same place to ensure the data management effect, and simultaneously fully classifies the features of several cloud platform data to achieve better data classification management effect.

[0014] Example 2 In the specific implementation process of this embodiment, compared with the above embodiment, this embodiment mainly focuses on the encryption process of different cloud platform data in different storage partitions; The following steps are also included: S5. Based on the different cloud platform data stored in each different storage partition and the different characteristic polygons corresponding to each different cloud platform data, the mean polygon of this storage partition is locked, and then based on the numerical feature difference between the different characteristic polygons and the mean polygon, the different cloud platform data are segmented and each different cloud platform data is encrypted; The specific sub-steps of performing segment encryption processing are: S51, based on different characteristic polygons corresponding to different cloud platform data, based on measurement points corresponding to different corner points of different characteristic polygons, based on multiple groups of measurement points confirmed on the same measurement line, confirm their mean points, and then connect adjacent mean points to determine the mean polygon; S52, determine a set of characteristic polygons corresponding to cloud platform data, identify the area difference MC of the characteristic polygon and the mean polygon corresponding to the polygon area between adjacent metric lines, and MC≥0, then determine a set of metric lines as the starting point, sort them clockwise to the right, perform ratio processing on the MC of the corresponding polygon area in turn, and determine a sequence of area difference ratios of several polygon areas; S53, based on the determined ratio sequence, the corresponding cloud platform data is segmented, and several data segments are confirmed, and the capacity ratio column of the data segments sorted in front and back is consistent with the ratio sequence, and the data segments belonging to the same cloud platform data are all marked with corresponding tags i. If the same ratio exists in the ratio sequence, then there are data segments with the same data capacity in the generated data segments. In order to distinguish between the front and the back, the data segments belonging to the front are marked with a specified tag 1, and the data segments belonging to the back are marked with a specified tag 2. If there are three groups, they can be sorted by tags 1, 2, and 3. The tags 1, 2, and 3 are corresponding digital tags. The smaller the digital tag, the closer the corresponding data segment is to the front. S54: For other cloud platform data in the storage partition, segmentation is performed in the same manner as steps S51-S53, and several data segments completed in the storage partition are stored in a shuffled manner; Specifically, when the designated cloud platform data is subsequently extracted, the system executes the same processing method, that is, the method of steps S51-S52, to determine the corresponding ratio sequence, and then extracts the data segments with the same mark i, and then sorts the data segments with different data capacities according to the ratio sequence. If there are data segments with the same capacity, they are sorted front and back according to the internally calibrated digital marks, and the sorted data segments are integrated to obtain the original cloud platform data.

[0015] Cloud platform data management system, including: The same type data classification end calibrates different cloud platform data with the same identification features based on the identification features set by this platform, and classifies the calibrated groups of cloud platform data into the same type of data; The feature parameter set confirmation end performs feature analysis on several groups of different cloud platform data classified as data of the same type, confirms the numerical feature parameters of several groups of different cloud platform data, and sorts the confirmed different numerical feature parameters to determine the feature parameter set of the corresponding cloud platform data; The feature polygon confirmation end constructs a corresponding number of measurement lines based on the number of numerical feature parameters of the determined feature parameter set, and the angles between each measurement line are equal. Based on the specific numerical feature parameters, a specific point is determined on the measurement line, and then the determined specific points are connected to determine the feature polygon corresponding to the cloud platform data; On the data classification side, based on the different feature polygons corresponding to different cloud platform data in the same type of data and the mutual intersection of the corresponding feature polygons, cloud platform data with great similarity are divided into data with the same feature, and the data with the same feature is stored in the same storage partition; The segmented encryption processing end, based on the different cloud platform data stored in each different storage partition and the different feature polygons corresponding to each different cloud platform data, locks the mean polygon of this storage partition, and then performs segmented processing on the different cloud platform data based on the numerical feature differences between the different feature polygons and the mean polygon.

[0016] Example 3 The specific implementation process of this embodiment includes the entire implementation process of the above two groups of embodiments.

[0017] Some of the data in the above formulas are dimensionless and numerically calculated. Meanwhile, the contents not described in detail in this specification belong to the prior art known to those skilled in the art.

[0018] The above embodiments are only used to illustrate the technical method of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical method of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical method of the present invention.

Claims

1. A cloud platform data management method, characterized in that: The following steps are involved: S1. Based on the identification features set by this platform, different cloud platform data with the same identification features are calibrated, and several groups of calibrated cloud platform data are divided into the same type of data; S2. Perform feature analysis on several groups of different cloud platform data classified as data of the same type, confirm the numerical feature parameters of several groups of different cloud platform data, and sort the confirmed different numerical feature parameters to determine the feature parameter set of the corresponding cloud platform data; S3. Based on the number of numerical feature parameters of the determined feature parameter set, a corresponding number of measurement lines are constructed, and the angles between each measurement line are equal. Based on the specific numerical feature parameters, specific points are determined on the measurement line, and then the determined specific points are connected to determine the feature polygon corresponding to the cloud platform data; S4. Based on the different feature polygons corresponding to different cloud platform data in the same type of data and based on the mutual intersection of the corresponding feature polygons, the cloud platform data with great similarity are divided into data with the same feature, and the data with the same feature are stored in the same storage partition.

2. The cloud platform data management method according to claim 1, characterized in that: In step S1, the specific method of identifying the same type of data is: Based on several groups of identification features set in this platform, all of which are preset features; Determine the corresponding data calibration features from the cloud platform data, perform feature recognition, and classify different cloud platform data belonging to the same identification features into the same type of data.

3. The cloud platform data management method according to claim 1, characterized in that: In step S2, the specific sub-steps of confirming the numerical characteristic parameters of the cloud platform data are: S21, preferentially performing mean processing on several groups of data associated in a single cloud platform data, determining a first numerical characteristic parameter T1, then selecting a maximum value and a minimum value from the associated several groups of data to determine a range, and the range = maximum value of the data - minimum value of the data, calibrating the range as a second numerical characteristic parameter T2, then performing variance processing on the associated several groups of data, determining a discrete value, and calibrating the discrete value as a third numerical characteristic parameter T3; S22. Sort several groups of data associated with a single cloud platform data based on the timestamp inside the data, and then determine the corresponding coordinate points in the two-dimensional coordinate system based on different data corresponding to different timestamps, wherein the horizontal coordinate axis of the two-dimensional coordinate system is the timeline, and the vertical coordinate axis is the data parameter, and determine the median line between several coordinate points, wherein the median line is perpendicular to the vertical coordinate axis, and the coordinate points above the median line are calibrated as upper points, and the coordinate points below the median line are calibrated as lower points, and the coordinate points intersecting the median line are not calibrated, and the sum of the vertical distances between several upper points and the median line is determined to be calibrated as H1, and then the sum of the vertical distances between several lower points and the median line is determined to be calibrated as H2, so that the median line moves up and down, and stops moving when H1=H2, and determines the position of the median line, and the data parameter corresponding to the median line at this position is calibrated as the fourth numerical feature parameter T4; S23, perform data clustering processing on several groups of data associated with a single group of cloud platform data, sort the several groups of data in ascending order, generate a data sequence, and segment the data sequence: determine the first group of parameters CS1 of the data sequence, confirm the data belonging to [CS1, CS1+Y1], and then confirm the data belonging to (CS1+Y1, CS1+2Y1], and then use the same interval confirmation method to divide the data sequence into several data segments, select a group of data segments with the largest number of data, and perform mean processing on several groups of data associated with this data segment, calibrate the processed value as the fifth numerical feature parameter T5, sort the confirmed numerical feature parameters one by one, and determine the feature parameter set corresponding to the cloud platform data, where Y1 is a preset value; S24: for several groups of different cloud platform data of the same type of data, the same method as steps S21-S23 is used to process, and the different numerical feature parameters determined by the different cloud platform data are calibrated as T1 i 、T2 i 、T3 i 、T4 i and T5 i , where i represents different cloud platform data and determines the feature parameter set of the corresponding cloud platform data.

4. The cloud platform data management method according to claim 1, characterized in that: In step S3, the specific method of determining the characteristic polygon corresponding to the cloud platform data is: S31. According to the number of numerical characteristic parameters in the characteristic parameter set of the cloud platform data, a corresponding number of measurement lines are constructed, and the starting points of the measurement lines are consistent, and the angles between each group of measurement lines are consistent, and the numerical characteristic parameters measured by each group of measurement lines are limited; S32, based on different numerical feature parameters in the feature parameter set, find corresponding measurement points on the corresponding measurement lines starting from the initial point, and after the measurement points on each measurement line are confirmed, connect the corresponding measurement points on adjacent measurement lines to determine the characteristic polygon of the cloud platform data; S33. For the feature parameter sets corresponding to other cloud platform data, the same processing method as steps S31-S32 is adopted to determine the corresponding feature polygons on several identical metric lines.

5. The cloud platform data management method according to claim 4, characterized in that: In step S4, the specific sub-steps of dividing the cloud platform data are as follows: S41. Based on different characteristic polygons corresponding to different cloud platform data, two groups of characteristic polygons are randomly selected for similarity confirmation, the overlapping areas of the two groups of characteristic polygons are identified, and based on the area proportions ZB1 and ZB2 of the overlapping areas in the corresponding characteristic polygons, ZB1 and ZB2 are averaged to determine the proportion mean ZJ. If ZJ≥Y2, the two groups of characteristic polygons are marked as the same type of characteristic polygons. If ZJ<Y2, it means that the two groups of characteristic polygons do not belong to the same type of characteristic polygons, where Y2 is a preset value. S42, processing any two groups of characteristic polygons one by one, locking the same type of characteristic polygons, if any two characteristic polygons are the same type of characteristic polygons, then any two characteristic polygons are marked as the same type of characteristic polygons; S43. Based on the determined similar characteristic polygons, the same characteristic data corresponding to the corresponding characteristic polygons are stored in the same storage partition.

6. The cloud platform data management method according to claim 5, characterized in that: The following steps are also included: S5. Based on the different cloud platform data stored in each different storage partition and the different characteristic polygons corresponding to each different cloud platform data, the mean polygon of this storage partition is locked, and then based on the numerical feature differences between the different characteristic polygons and the mean polygon, the different cloud platform data are segmented.

7. The cloud platform data management method according to claim 6, characterized in that: In step S5, the specific sub-steps of segmenting the data of different cloud platforms are: S51, based on different characteristic polygons corresponding to different cloud platform data, and based on the measurement points corresponding to different corner points of different characteristic polygons, based on multiple groups of measurement points confirmed on the same measurement line, confirm the mean point, and then connect adjacent mean points to determine the mean polygon; S52, determine a set of characteristic polygons corresponding to cloud platform data, identify the area difference MC of the corresponding polygonal regions between adjacent metric lines where the characteristic polygon and the mean polygon are located, and MC≥0, and then randomly determine a set of metric lines as the starting point, sort them clockwise to the right, perform ratio processing on the MC of the corresponding polygonal regions in turn, and determine a sequence of area difference ratios of several polygonal regions; S53, based on the determined ratio sequence, segment the corresponding cloud platform data, confirm a number of data segments, and the capacity ratio columns of the data segments in order are consistent with the ratio sequence, and the data segments belonging to the same type of cloud platform data are all marked with corresponding tags i; S54: For other cloud platform data in this storage partition, segmentation is performed in the same manner as steps S51-S53, and several data segments completed in this storage partition are stored in a shuffled manner.

8. The cloud platform data management method according to claim 7, characterized in that: In the step S53, if the same ratio exists in the ratio sequence, the data segments of the same capacity are digitally marked, and the smaller the digital mark, the closer the corresponding data segment is to the front.

9. A cloud platform data management system, which operates based on the cloud platform data management method according to any one of claims 1 to 8, characterized in that: include: The same type data classification end calibrates different cloud platform data with the same identification features based on the identification features set by this platform, and classifies the calibrated groups of cloud platform data into the same type of data; The feature parameter set confirmation end performs feature analysis on several groups of different cloud platform data classified as data of the same type, confirms the numerical feature parameters of several groups of different cloud platform data, and sorts the confirmed different numerical feature parameters to determine the feature parameter set of the corresponding cloud platform data; The feature polygon confirmation end constructs a corresponding number of measurement lines based on the number of numerical feature parameters of the determined feature parameter set, and the angles between each measurement line are equal. Based on the specific numerical feature parameters, a specific point is determined on the measurement line, and then the determined specific points are connected to determine the feature polygon corresponding to the cloud platform data; On the data classification side, based on the different feature polygons corresponding to different cloud platform data in the same type of data and the mutual intersection of the corresponding feature polygons, cloud platform data with great similarity are divided into data with the same feature, and the data with the same feature is stored in the same storage partition; The segmented encryption processing end, based on the different cloud platform data stored in each different storage partition and the different feature polygons corresponding to each different cloud platform data, locks the mean polygon of this storage partition, and then performs segmented processing on the different cloud platform data based on the numerical feature differences between the different feature polygons and the mean polygon.

Images