Cloud Platform Data Management System and Method

Abstract

The present invention discloses a cloud platform data management system and method. The present invention relates to the technical field of data management, and solves the problems that the data characteristics of relevant data are not analyzed and the relevant data with the same type of data characteristics are not stored in the same place. The present invention confirms the characteristic parameters of data from different cloud platforms of the same type, and then, based on the differences in characteristic parameters between data from different platforms, conducts a similarity analysis on the relevant cloud platform data with the same characteristic parameters. By confirming the corresponding characteristic polygons, and based on the shape characteristics and intersection areas between the characteristic polygons, similar cloud platform data are determined and stored in the same partition, so as to ensure the management effect of data, and at the same time fully classify the characteristics of several cloud platform data, achieving a better data classification management effect.

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:

[0007] 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:

[0008] Based on several groups of identification features set in this platform, all of which are preset features;

[0009] Determine the corresponding data calibration features from the cloud platform data, perform feature recognition, and divide different cloud platform data belonging to the same recognition feature into the same type of data;

[0010] S2. Perform feature analysis on several groups of different cloud platform data divided into the same type of data, 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 as follows:

[0011] S21. First, perform mean processing on several groups of data associated within a single cloud platform data to determine the first numerical feature parameter T1. Then, select the maximum value and the minimum value from the associated several groups of data to determine the range, and the range = maximum value of data - minimum value of data. Calibrate this range as the second numerical feature parameter T2. Then, perform variance processing on the associated several groups of data to determine the discrete value, and calibrate this discrete value as the third numerical feature parameter T3;

[0012] S22. Sort several groups of data associated within a single cloud platform data based on the timestamps inside the data. Then, based on the different data corresponding to different timestamps, determine the corresponding coordinate points in a two-dimensional coordinate system. The horizontal axis of the two-dimensional coordinate system is the time line, and the vertical axis is the data parameter. Determine the median line among several coordinate points. The median line is perpendicular to the vertical axis. Calibrate the coordinate points above the median line as the upper points, and calibrate the coordinate points below the median line as the lower points. Do not perform any calibration on the coordinate points intersecting with the median line. Determine the sum of the vertical distances between several upper points and this median line as H1, and then determine the sum of the vertical distances between several lower points and this median line as H2. Move the median line up and down. When H1 = H2, stop moving. Determine the position of this median line, and calibrate the data parameter corresponding to the median line at this position as the fourth numerical feature parameter T4;

[0013] S23. Perform data clustering processing on several groups of data associated within a single group of cloud platform data. Sort several groups of data in ascending order to generate a data sequence. Segment the data sequence: Determine the first group of parameters CS1 of the data sequence, confirm the data belonging to [CS1, CS1 + Y1], 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 data segment with the largest number of groups of data, and perform mean processing on several groups of data associated within this data segment. Calibrate the processed value as the fifth numerical feature parameter T5. Sort them according to the confirmed numerical feature parameters one by one to determine the feature parameter set of the corresponding cloud platform data, where Y1 is a preset value;

[0014] S24. For several groups of different cloud platform data in the same type of data, process them in the same way as steps S21 - S23, and calibrate the determined different numerical feature parameters of different cloud platform data as T1 i , T2 i , T3 i , T4 i and T5 i , where i represents different cloud platform data, and determine the set of feature parameters of its corresponding cloud platform data;

[0015] S3. Based on the number of numerical feature parameters in the determined set of feature parameters, construct the corresponding number of measurement lines, and the angle between each measurement line is equal. Based on the specific numerical feature parameters, determine specific points on the measurement lines, and then connect the determined several specific points to determine the characteristic polygon of the corresponding cloud platform data. The specific method is as follows:

[0016] S31. According to the number of numerical feature parameters in the set of feature parameters of the cloud platform data, construct the corresponding number of measurement lines, and the starting points of the measurement lines are the same, and the angle between each group of measurement lines is the same, and limit the numerical feature parameters measured by each group of measurement lines;

[0017] S32. Based on different numerical feature parameters in the set of feature parameters, find the corresponding measurement points on the corresponding measurement lines starting from the initial point. After the measurement points on each measurement line are 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;

[0018] S33. For the set of feature parameters corresponding to other cloud platform data, adopt the same processing method as steps S31 - S32 to determine the corresponding characteristic polygon on several identical measurement lines;

[0019] S4. Based on the different characteristic polygons corresponding to different cloud platform data in the same type of data, based on the mutual intersection situation of the corresponding characteristic polygons, divide the cloud platform data with greater similarity into the same characteristic data, and store the same characteristic data in the same storage partition; The specific sub - steps are as follows:

[0020] S41. Based on different characteristic polygons corresponding to data on different cloud platforms, randomly select two groups of characteristic polygons for similarity confirmation, identify the overlapping area of the two groups of characteristic polygons, and based on the area ratios ZB1 and ZB2 of the overlapping area in the corresponding characteristic polygons, perform mean processing on ZB1 and ZB2 to determine the ratio mean ZJ. If ZJ≥Y2, label the two groups of characteristic polygons 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;

[0021] S42. Process any two groups of characteristic polygons one by one to lock the same type of characteristic polygons. If any two pairs of characteristic polygons are of the same type of characteristic polygons, then any two pairs of characteristic polygons are labeled as the same type of characteristic polygons;

[0022] S43. Based on the determined same type of characteristic polygons, store the same characteristic data corresponding to the corresponding characteristic polygons in the same storage partition.

[0023] Preferably, the following steps are further included:

[0024] S5. Based on the different cloud platform data stored in each different storage partition, based on the different characteristic polygons corresponding to each different cloud platform data, lock the mean polygon of this storage partition, and then based on the numerical characteristic differences between the different characteristic polygons and the mean polygon, perform segmentation processing on the different cloud platform data; the specific sub-steps are as follows:

[0025] S51. Based on the different characteristic polygons corresponding to the different cloud platform data, based on the measurement points corresponding to different corner points of the different characteristic polygons, based on multiple groups of measurement points confirmed on the same measurement line, confirm their mean points, and then connect the adjacent mean points to determine the mean polygon;

[0026] S52. Determine the characteristic polygon corresponding to a group of cloud platform data, identify the area difference MC between the characteristic polygon and the corresponding polygon area between adjacent measurement lines of the mean polygon, and MC≥0. Then determine a group of measurement lines as the starting point, sort clockwise to the right, and perform ratio processing on the MC of the corresponding polygon areas in turn to determine a sequence of area difference ratios of several polygon areas;

[0027] S53. Based on the determined ratio sequence, segment the corresponding cloud platform data to confirm several data segments. The capacity ratio column of the data segments sorted before and after is consistent with the ratio sequence, and the data segments belonging to the same cloud platform data are all labeled with the corresponding label i. If there are the same ratios in the ratio sequence, perform digital marking on the divided data segments with the same capacity. The smaller the digital marking, the earlier the corresponding data segment is;

[0028] For other cloud platform data in this storage partition, use the same method as steps S51 - S53 for segmentation processing, and randomly store the completed data segments in this storage partition.

[0029] Preferably, the cloud platform data management system includes:

[0030] The same - type data division terminal, based on the recognition features set by this platform, calibrates different cloud platform data with the same recognition features, and divides the calibrated several groups of cloud platform data into the same - type data;

[0031] The characteristic parameter set confirmation terminal analyzes the characteristics of several groups of different cloud platform data divided into the same - type data, confirms the numerical characteristic parameters of several groups of different cloud platform data, and sorts the confirmed different numerical characteristic parameters to determine the characteristic parameter set corresponding to the cloud platform data;

[0032] The characteristic polygon confirmation terminal constructs the corresponding number of measurement lines based on the number of numerical characteristic parameters of the determined characteristic parameter set, and the angle between each measurement line is equal. Based on the specific numerical characteristic parameters, specific points are determined on the measurement lines, and then the determined several specific points are connected to determine the characteristic polygon corresponding to the cloud platform data;

[0033] The data classification terminal, based on the different characteristic polygons corresponding to different cloud platform data in the same - type data, and based on the mutual intersection situation of the corresponding characteristic polygons, divides the cloud platform data with greater similarity into the same - characteristic data, and stores the same - characteristic data in the same storage partition;

[0034] The segmented encryption processing terminal, based on the different cloud platform data stored in each different storage partition, and based on the different characteristic polygons corresponding to each different cloud platform data, locks the mean polygon of this storage partition, and then performs segmentation processing on the different cloud platform data based on the numerical characteristic differences between the different characteristic polygons and the mean polygon.

[0035] The present invention provides a cloud platform data management system and method. Compared with the prior art, it has the following beneficial effects:

[0036] The present invention confirms the characteristic parameters of different cloud platform data of the same type, and then based on the characteristic parameter differences between different platform data, performs similarity analysis on the related cloud platform data with the same characteristic parameters. By confirming the corresponding characteristic polygons, based on the shape characteristics and intersection areas between the characteristic polygons, similar cloud platform data are determined and stored in the same partition, ensuring the management effect of the data, and simultaneously fully classifying the characteristics of several cloud platform data, achieving a better data classification management effect;

[0037] Subsequently, for different cloud platform data stored in the same storage partition, perform feature processing on the different cloud platform data. Based on the numerical difference between its feature polygon and the mean polygon, determine the relevant ratio sequence. Subsequently, perform segmentation processing on the different cloud platform data based on the corresponding ratio sequence to segment the data, achieving a better data segmentation effect. At the same time, perform associated encryption on the different cloud platform data stored in the storage partition to ensure the overall associated encryption effect. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] Figure 1 It is a schematic flowchart of the method of the present invention;

[0039] Figure 2 It is a schematic diagram of the principle framework of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0040] The following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. 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.

[0041] Embodiment 1

[0042] Please refer to Figure 1 , the present application provides a cloud platform data management method, including the following steps:

[0043] S1. Confirm the cloud platform data of the same type (this part of the confirmation process is confirmed by relevant personnel or the data transmitted by the same type of transmission nodes, and its specific confirmation process is determined by the specific scenario. For example, housing price data, different housing price data generated in different regions of a certain city at different time nodes belong to the cloud platform data of the same type). Based on the identification features set on this platform, calibrate different cloud platform data with the same identification features, and divide the calibrated several groups of cloud platform data into the same type of data. Among them, the specific sub-steps for identification include:

[0044] Based on several groups of identification features set in this platform, the identification features are all preset features, which are all drawn up by the operator in advance according to experience;

[0045] Determine the corresponding data calibration features (such as data headers, such as housing prices) from the cloud platform data, perform feature identification, and divide different cloud platform data belonging to the same identification feature into the same type of data;

[0046] S2. Analyze the characteristics of several groups of different cloud platform data divided into the same type of data, confirm the numerical characteristic parameters of the several groups of different cloud platform data, and sort the confirmed different numerical characteristic parameters to determine the characteristic parameter set of the corresponding cloud platform data. The specific sub-steps for confirming the numerical characteristic parameters of a single cloud platform data include:

[0047] S21. First, perform mean processing on several groups of data associated within a single cloud platform data to determine the first numerical characteristic parameter T1. Then, select the maximum value and the minimum value from the associated several groups of data to determine the range, and the range = maximum value of data - minimum value of data. Label this range as the second numerical characteristic parameter T2. Then, perform variance processing on the associated several groups of data to determine the discrete value, and label this discrete value as the third numerical characteristic parameter T3. The specific method of discrete processing is: label several groups of data as SJ k , where k = 1, 2,..., n, and perform mean processing on several groups of data SJ k to determine the mean Jz, and use to determine the discrete value F, and this discrete value F is the third numerical characteristic parameter T3;

[0048] S22. Sort several groups of data associated within a single cloud platform data based on the timestamps inside the data. Then, based on the different data corresponding to different timestamps, determine the corresponding coordinate points in a two-dimensional coordinate system. The horizontal axis of the two-dimensional coordinate system is the time line, and the vertical axis is the data parameter. Determine the median line among several coordinate points. The median line is perpendicular to the vertical axis. Label the coordinate points above the median line as upper points, and label the coordinate points below the median line as lower points. Do not label the coordinate points that intersect the median line. Determine the sum of the perpendicular distances from several upper points to this median line as H1, and then determine the sum of the perpendicular distances from several lower points to this median line as H2. Move the median line up and down, and stop moving when H1 = H2. Determine the position of this median line, and the data parameter corresponding to this median line at this position (that is, the parameter corresponding to the vertical axis), and label this data parameter as the fourth numerical characteristic parameter T4. Specifically, the median line and the determined mean are two data characteristics, not the same data characteristic;

[0049] 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;

[0050] 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;

[0051] 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:

[0052] 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;

[0053] 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, and each group of measurement lines corresponds to a set of numerical characteristic parameters. If T1 i is 50, then the point position where the value 50 is located can be locked on the measurement line, and this point position is the determined measurement point position. Each measurement line is calibrated with a corresponding measurement point position. After connecting adjacent measurement point positions, the corresponding characteristic polygon can be determined;

[0054] S33. For the set of characteristic parameters corresponding to other cloud platform data, adopt the same processing method as in steps S31 - S32 to determine the corresponding characteristic polygon on several identical measurement lines;

[0055] S4. Based on the different characteristic polygons corresponding to the different cloud platform data in the same type of data, and based on the mutual intersection of the corresponding characteristic polygons, divide the cloud platform data with greater similarity into the same - characteristic data, and store the same - characteristic data in the same storage partition. Among them, the specific sub - steps for the division process are as follows:

[0056] S41. Based on the different characteristic polygons corresponding to different cloud platform data, randomly select two groups of characteristic polygons for similarity confirmation, identify the overlapping area of the two groups of characteristic polygons, and based on the area ratios ZB1 and ZB2 of the overlapping area in the corresponding characteristic polygons, perform an average value process on ZB1 and ZB2 to determine the average ratio ZJ. If ZJ≥Y2, then mark the two groups of characteristic polygons as the same - type characteristic polygons. If ZJ<Y2, it means that the two groups of characteristic polygons do not belong to the same - type characteristic polygons, where Y2 is a preset value, and its specific value is determined by the operator according to experience;

[0057] S42. Process any two groups of characteristic polygons one by one to lock the same - type characteristic polygons. If any two - by - two characteristic polygons are all the same - type characteristic polygons, then any two - by - two characteristic polygons are marked as the same - type characteristic polygons. Here, understand it in combination with an example: There are three polygons A, B, and C. After processing, A and B belong to the same - type characteristic polygons, B and C belong to the same - type characteristic polygons, and A and C also belong to the same - type characteristic polygons. Then A, B, and C are all the same - type characteristic polygons;

[0058] S43. Based on the determined same - type characteristic polygons, store the same - characteristic data corresponding to the corresponding characteristic polygons in the same storage partition.

[0059] Example 1 mainly focuses on the data feature processing of data from different cloud platforms, identifies the data features of data from different cloud platforms, and then based on the similarity between the data features, stores the cloud platform data belonging to the same type of feature boundary line in the same place to ensure the management effect of the data. At the same time, the features of several cloud platform data are fully classified to achieve a better data classification management effect.

[0060] Example 2

[0061] In the specific implementation process of this example, compared with the above example, this example mainly focuses on the encryption process of different cloud platform data within different storage partitions;

[0062] It also includes the following steps:

[0063] S5. Based on the different cloud platform data stored in each different storage partition, based on the different feature polygons corresponding to each different cloud platform data, lock the mean polygon of this storage partition, and then based on the numerical feature differences between the different feature polygons and the mean polygon, perform segmented processing on the different cloud platform data and perform relevant encryption on each different cloud platform data;

[0064] Among them, the specific sub-steps for segmented encryption processing are as follows:

[0065] S51. Based on the different feature polygons corresponding to the different cloud platform data, based on the measurement points corresponding to the different corner points of the different feature polygons, based on multiple groups of measurement points confirmed on the same measurement line, confirm their mean points, and then connect the adjacent mean points to determine the mean polygon;

[0066] S52. Determine the feature polygon corresponding to a set of cloud platform data, identify the area difference MC between the corresponding polygon areas of the feature polygon and the mean polygon located between adjacent measurement lines, and MC≥0. Then determine a set of measurement lines as the starting point, sort clockwise to the right, and perform ratio processing on the MC of the corresponding polygon areas in turn to determine a sequence of area difference ratios of several polygon areas;

[0067] S53. Based on the determined ratio sequence, segment the corresponding cloud platform data to confirm several data segments. The capacity ratio column of the data segments sorted before and after is consistent with the ratio sequence, and the data segments belonging to the same cloud platform data are all marked with the corresponding label i. If there are the same ratios in the ratio sequence, then there are also data segments with the same data capacity in the generated data segments. In order to distinguish before and after, the data segments in the front are marked with the specified label 1, and the data segments in the back are marked with the specified label 2. If there are three groups, perform label sorting of 1, 2, 3. The labels 1, 2, 3 are the corresponding digital labels, and the smaller the digital label, the earlier the corresponding data segment is;

[0068] S54. For other cloud platform data in this storage partition, the same method as steps S51 - S53 is adopted for segmented processing, and several data segments completed in this storage partition are randomly stored.

[0069] Specifically, when subsequently extracting the specified cloud platform data, the system performs the same processing method, that is, the method of steps S51 - S52, to determine its corresponding ratio sequence. Subsequently, data segments with the same marker i are extracted, and then, according to the ratio sequence, data segments with different data capacities are sorted. If there are data segments with the same capacity, they are sorted before and after according to the internal calibrated digital markers. After integrating the sorted data segments, the original cloud platform data is obtained.

[0070] A cloud platform data management system includes:

[0071] A same - type data division end, based on the identification features set on this platform, calibrates different cloud platform data with the same identification features, and divides several groups of calibrated cloud platform data into same - type data;

[0072] A characteristic parameter set confirmation end, performs characteristic analysis on several groups of different cloud platform data divided into same - type data, confirms the numerical characteristic parameters of several groups of different cloud platform data, and sorts the confirmed different numerical characteristic parameters to determine the characteristic parameter set corresponding to the cloud platform data;

[0073] A characteristic polygon confirmation end, based on the number of numerical characteristic parameters in the determined characteristic parameter set, constructs corresponding measurement lines, and the angles between each measurement line are equal. Based on the specific numerical characteristic parameters, specific points are determined on the measurement lines, and then the determined several specific points are connected to determine the characteristic polygon corresponding to the cloud platform data;

[0074] A data classification end, based on the different characteristic polygons corresponding to different cloud platform data in the same - type data, based on the mutual intersection situation of the corresponding characteristic polygons, divides cloud platform data with greater similarity into same - characteristic data, and stores the same - characteristic data in the same storage partition;

[0075] A segmented encryption processing end, based on the different cloud platform data stored in each different storage partition, based on the different characteristic 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 characteristic differences between the different characteristic polygons and the mean polygon.

[0076] Embodiment 3

[0077] In the specific implementation process of this embodiment, it includes all the implementation processes of the above two groups of embodiments.

[0078] Some of the data in the above formula are numerically calculated after removing their dimensions, and the content not described in detail in this specification belongs to the prior art well-known to those skilled in the art.

[0079] 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 of ordinary skill in the art should understand that the technical method of the present invention can be modified or equivalently replaced 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, corresponding points are determined on the measurement line, and then the determined corresponding points are connected to determine the characteristic polygon of the corresponding cloud platform data; S4, based on the different characteristic polygons corresponding to different cloud platform data in the same type of data, and based on the mutual intersection of the corresponding characteristic polygons, the cloud platform data with similarity are divided into the same characteristic data, and 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.

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 marked as upper points, and the coordinate points below the median line are marked as lower points. No calibration is performed on the coordinate points intersecting the median line, 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 them according to 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 1, 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.

6. The cloud platform data management method according to claim 5, 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.

7. The cloud platform data management method according to claim 6, 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.

8. A cloud platform data management system, which operates based on the cloud platform data management method according to any one of claims 1 to 7, 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, the corresponding points are determined on the measurement line, and then the determined corresponding points are connected to determine the feature polygon of the corresponding 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 intersection of the corresponding feature polygons, the cloud platform data with similarity are divided into data with the same feature, and the data with the same feature are 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.

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