Method and device for carrying out data clustering storage according to data genetic attributes

Abstract

The invention relates to the technical field of data processing, and discloses a method for carrying out data clustering storage according to data genetic attributes, which comprises the following steps: determining data attributes corresponding to data to be stored based on a genetic relationship standard, and extracting data elements with dimension attributes in the data attributes; sorting the data elements by using a hierarchical control algorithm based on the classification attributes to obtain compilation statistical values and strength statistical values corresponding to the sub-level attributes, and creating registers with the same dimension as the data elements; and performing statistics on the compilation statistical value and the strength statistical value in combination with a genetic algorithm and a clustering algorithm, and storing the to-be-stored data in a database according to the genetic relationship. By applying the method, the data acquisition speed can be increased by utilizing the existing defined data script, manual intervention is not needed, automatic reading and analysis can be realized only through statement definition setting, and executable data scripts are generated and interface data are pushed through a re-aggregated data structure. And the design of combining genetic recursion and clustering is realized.

Description

Technical Field

[0001] The present application relates to the field of data processing technology, and in particular to a method and device for clustering and storing data according to data genetic attributes. Background Art

[0002] In recent years, in some small and medium-sized application software, the database usually adopts relational database and central deployment mode. Usually, the core business data needs to be counted before the data can be stored. The current data statistics method is mainly achieved by writing specific statistical programs.

[0003] However, the current method of writing feature statistics programs to implement data statistics has the following main shortcomings: on the one hand, it can only meet the data statistics needs of specific application scenarios, so the business adaptability is poor and the program code cannot be reused; on the other hand, when the statistical rules change, the program code needs to be modified, so the time cycle is long, and sometimes the program needs to be reconstructed to meet the user's statistical needs; on the other hand, when the data volume reaches a certain scale, performance problems are prone to occur, which will affect the use of other business functions and make program debugging difficult; in addition, when it is necessary to provide data statistical results to the outside world, specific data interfaces need to be written.

[0004] In view of this, how to provide a data storage method to improve data acquisition speed and reduce costs has become a technical problem that urgently needs to be solved. Summary of the invention

[0005] The embodiments of the present application provide a method for clustering and storing data according to data genetic attributes, a device for clustering and storing data according to data genetic attributes, an electronic device, and a computer storage medium, which are used to solve the problems of time-consuming and high labor costs in current data storage.

[0006] In a first aspect of an embodiment of the present application, a method for clustering and storing data according to data genetic attributes is provided, comprising:

[0007] Based on the genetic relationship standard, the data attributes corresponding to the data to be stored are determined, and the data element with dimensional attributes in the data attributes is extracted, wherein the data attributes carry dimensional attributes and classification attributes; the dimensional attributes are parent attributes and child attributes with a genetic relationship, the child attributes are child attributes of the parent attributes, and each of the parent attributes corresponds to at least one child attribute; the classification attributes are attribute values ​​corresponding to each child attribute, including compilation attributes and strength attributes; the data element carries all parent attributes and child attributes corresponding to the parent attributes;

[0008] Based on the classification attributes, the data elements are sorted using a hierarchical control algorithm to obtain compilation statistics and strength statistics corresponding to each of the child attributes, and a register of the same dimension and size as the data element is created, wherein the compilation statistics carry the number and compilation value of each child attribute corresponding to each parent attribute; the strength statistics carry the number and strength value of each child attribute corresponding to each parent attribute;

[0009] The compilation statistics and the strength statistics are respectively counted by combining the genetic algorithm and the clustering algorithm, and the data to be stored is saved in a database according to the genetic relationship.

[0010] In a second aspect of an embodiment of the present application, a device for clustering and storing data according to data genetic attributes is provided, comprising:

[0011] The extraction module is configured to determine the data attributes corresponding to the data to be stored based on the genetic relationship standard, and extract the data element with dimensional attributes in the data attributes, wherein the data attributes carry dimensional attributes and classification attributes; the dimensional attributes are parent attributes and child attributes with a genetic relationship, the child attributes are child attributes of the parent attributes, and each of the parent attributes corresponds to at least one child attribute; the classification attributes are attribute values ​​corresponding to each child attribute, including compilation attributes and strength attributes; the data element carries all parent attributes and child attributes corresponding to the parent attributes;

[0012] The sorting module is configured to sort the data elements based on the classification attributes using a hierarchical control algorithm to obtain compilation statistics and strength statistics corresponding to each of the child attributes, and create a register with the same dimensional size as the data element, wherein the compilation statistics carry the number and compilation value of each child attribute corresponding to each parent attribute; the strength statistics carry the number and strength value of each child attribute corresponding to each parent attribute;

[0013] The statistical module is configured to combine the genetic algorithm and the clustering algorithm to respectively perform statistics on the compilation statistical value and the strength statistical value, and save the data to be stored into a database according to the genetic relationship.

[0014] According to a third aspect of an embodiment of the present application, a computing device is provided, including:

[0015] Memory and processor;

[0016] The memory is used to store computer executable instructions, and the processor is used to execute the computer executable instructions. When the computer executable instructions are executed by the processor, the steps of the method for clustering and storing data according to data genetic attributes are implemented.

[0017] According to a fourth aspect of an embodiment of the present application, a computer-readable storage medium is provided, which stores computer-executable instructions, and when the instructions are executed by a processor, the steps of the method for clustering and storing data according to data genetic attributes are implemented.

[0018] The present application provides a method for clustering and storing data according to data genetic attributes, comprising: first, based on the genetic relationship standard, determining the data attributes corresponding to the data to be stored, and extracting the data element with dimensional attributes from the data attributes, wherein the data attributes carry dimensional attributes and classification attributes; the dimensional attributes are parent attributes and child attributes with a genetic relationship, the child attributes are sub-attributes of the parent attributes, and each of the parent attributes corresponds to at least one child attribute; the classification attributes are attribute values ​​corresponding to each child attribute, including compilation attributes and strength attributes; the data element carries the attributes of all parent attributes and the parent attributes. The sub-level attributes corresponding to the attributes; then, based on the classification attributes, the data elements are sorted by using a hierarchical control algorithm to obtain the compilation statistics and strength statistics corresponding to each of the sub-level attributes, and a register with the same dimensional size as the data element is created, wherein the compilation statistics carry the numbers and compilation values ​​of each sub-level attribute corresponding to each parent attribute; the strength statistics carry the numbers and strength values ​​of each sub-level attribute corresponding to each parent attribute; finally, the compilation statistics and the strength statistics are respectively counted by combining a genetic algorithm with a clustering algorithm, and the data to be stored is saved in a database according to the genetic relationship.

[0019] By applying the method for clustering and storing data according to data genetic attributes provided in the embodiment of the present application and utilizing the existing defined data scripts, the speed of data acquisition can be greatly improved without manual intervention, and the manual maintenance cost can be greatly reduced. Automatic reading and analysis can be achieved simply through statement definition settings, and an executable data script can be generated through the re-aggregated data structure, and the interface data can be pushed to realize the design combining reverse recursion and clustering.

[0020] The above description is only an overview of the technical solution of the present application. In order to more clearly understand the technical means of the present application, which can be implemented in accordance with the contents of the specification, and in order to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the specific implementation methods of the present application are listed below. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] Various other advantages and benefits will become apparent to those of ordinary skill in the art by reading the detailed description of the preferred embodiments below. The accompanying drawings are only for the purpose of illustrating the preferred embodiments and are not to be considered as limiting the present application. Also, the same reference symbols are used throughout the accompanying drawings to represent the same components. In the accompanying drawings:

[0022] Figure 1 A flowchart of a method for clustering and storing data according to data genetic attributes provided in an embodiment of the present application;

[0023] Figure 2 A flowchart of another method for clustering and storing data according to data genetic attributes provided in an embodiment of the present application;

[0024] Figure 3 A schematic diagram of formatting a basic data table in a method for clustering and storing data according to data genetic attributes provided in an embodiment of the present application;

[0025] Figure 4 A schematic diagram of a basic data table after supplementing the data genetic relationship in a method for clustering and storing data according to data genetic attributes provided in an embodiment of the present application;

[0026] Figure 5 A schematic diagram of the fifth layer original data extraction table in a method for clustering and storing data according to data genetic attributes provided in an embodiment of the present application;

[0027] Figure 6 A schematic diagram of a process table for generating a result in layer 5 in a method for clustering and storing data according to data genetic attributes provided in an embodiment of the present application;

[0028] Figure 7 A schematic diagram of a table of the process of obtaining a transfer number at the fourth level in a method for clustering and storing data according to data genetic attributes provided in an embodiment of the present application;

[0029] Figure 8 A schematic diagram of a fourth-level original number extraction table in a method for clustering and storing data according to data genetic attributes provided in an embodiment of the present application;

[0030] Fig. 9 A schematic diagram of a result process table generated at the fourth layer in a method for clustering and storing data according to data genetic attributes provided in an embodiment of the present application;

[0031] Fig.10 A schematic diagram of a process table for obtaining a transfer number at level 3 in a method for clustering and storing data according to data genetic attributes provided in an embodiment of the present application;

[0032] Fig.11 A schematic diagram of a third-level original number extraction table in a method for clustering and storing data according to data genetic attributes provided in an embodiment of the present application;

[0033] Fig.12A schematic diagram of a process table for generating a result in layer 3 in a method for clustering and storing data according to data genetic attributes provided in an embodiment of the present application;

[0034] Fig.13 A schematic diagram of a process table for obtaining a transfer number in the second stage in a method for clustering and storing data according to data genetic attributes provided in an embodiment of the present application;

[0035] Fig.14 A schematic diagram of a second-level original number extraction table in a method for clustering and storing data according to data genetic attributes provided in an embodiment of the present application;

[0036] Fig.15 A schematic diagram of a second-layer generation result process table in a method for clustering and storing data according to data genetic attributes provided in an embodiment of the present application;

[0037] Fig.16 A schematic diagram of a table of the process of obtaining a transfer number at the first level in a method for clustering and storing data according to data genetic attributes provided in an embodiment of the present application;

[0038] Fig.17 A schematic diagram of a first-level original number extraction table in a method for clustering and storing data according to data genetic attributes provided in an embodiment of the present application;

[0039] Fig.18 A schematic diagram of a process table for generating a result in the first layer in a method for clustering and storing data according to data genetic attributes provided in an embodiment of the present application;

[0040] Fig.19 A schematic diagram of a top-level table of a process of obtaining a transfer number in a method for clustering and storing data according to data genetic attributes provided in an embodiment of the present application;

[0041] Fig. 20 A schematic diagram of a top-level original data extraction table in a method for clustering and storing data according to data genetic attributes provided in an embodiment of the present application;

[0042] Fig.21 A schematic diagram of a top-level generation result process table in a method for clustering and storing data according to data genetic attributes provided in an embodiment of the present application;

[0043] Fig. 22 A schematic diagram of a table of calculation process of an allocation rate in a method for clustering and storing data according to data genetic attributes provided in an embodiment of the present application;

[0044] Fig.23 A memory statistics diagram of a method for clustering and storing data according to data genetic attributes provided in an embodiment of the present application;

[0045] Fig.24 A schematic diagram of calculation time statistics of a method for clustering and storing data according to data genetic attributes provided in an embodiment of the present application;

[0046] Fig.25 A schematic diagram of the structure of a device for clustering and storing data according to data genetic attributes provided in an embodiment of the present application;

[0047] Fig.26 A structural block diagram of a computing device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0048] The exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. Although the exemplary embodiments of the present disclosure are shown in the accompanying drawings, it should be understood that the present disclosure can be implemented in various forms and should not be limited by the embodiments set forth herein. On the contrary, these embodiments are provided to enable a more thorough understanding of the present disclosure and to fully convey the scope of the present disclosure to those skilled in the art.

[0049] See also Figure 1 , Figure 1 The present invention provides a flow chart of a method for clustering and storing data according to data genetic attributes. Figure 1 As shown, the specific steps include the following steps.

[0050] Step S102: Based on the genetic relationship standard, determine the data attributes corresponding to the data to be stored, and extract the data element with dimensional attributes from the data attributes, wherein the data attributes carry dimensional attributes and classification attributes; the dimensional attributes are parent attributes and child attributes with a genetic relationship, the child attributes are child attributes of the parent attributes, and each of the parent attributes corresponds to at least one child attribute; the classification attributes are attribute values ​​corresponding to each child attribute, including compilation attributes and strength attributes; the data element carries all parent attributes and child attributes corresponding to the parent attributes.

[0051] Step S104: Based on the classification attributes, the data elements are sorted using a hierarchical control algorithm to obtain compilation statistics and strength statistics corresponding to each of the child attributes, and a register with the same dimensional size as the data element is created, wherein the compilation statistics carry the numbers and compilation values ​​of each child attribute corresponding to each parent attribute; the strength statistics carry the numbers and strength values ​​of each child attribute corresponding to each parent attribute.

[0052] Step S106: Combining the genetic algorithm with the clustering algorithm, respectively, the compilation statistics and the strength statistics are counted, and the data to be stored is saved in a database according to the genetic relationship.

[0053] It should be noted that the classification attributes here include genetic relationships and type attributes.

[0054] The dimension attributes here include the target object code and the object identifier. For example, the target object code can be a unit code or a device code, etc.; the object identifier can be an equipment leaf node identifier, a model calculation identifier, an equipment parent-child relationship identifier, etc.

[0055] In the embodiment of the present application, the statistical value is counted by combining the genetic algorithm and the clustering algorithm, and the data is stored according to the blood relationship, including: assigning the child attribute number corresponding to the parent attribute to be one, and judging the size of the child attribute number corresponding to the parent attribute and the number of child attributes; in response to the child attribute number of the parent attribute being not greater than the number of child attributes, judging whether the child attribute number corresponding to the parent attribute is counted by type; in response to the child attribute number of the parent attribute not being counted by type, judging whether the child attribute number corresponding to the parent attribute is a leaf node; in response to the child attribute number of the parent attribute not being a leaf node, setting the parent attribute number to the number of parent attributes, and judging whether the parent attribute number is equal to zero; in response to the parent attribute number not being equal to zero, taking the corresponding number of each child attribute The statistical value and register statistical value are added to the register; the parent attribute number is assigned a value of minus one, and it is determined whether the parent attribute number is equal to zero, until the parent attribute number is equal to zero, the child attribute number corresponding to the parent attribute is assigned a value plus one, and the size of the child attribute number corresponding to the parent attribute and the number of child attributes is determined; in response to the child attribute number corresponding to the parent attribute and the value of the number of child attributes being consistent, the classification statistical result with the parent attribute metadata as a set is obtained, and the completion number corresponding to the parent attribute metadata is set to one, and the size of the completion number corresponding to the parent attribute metadata and the number of parent attributes is determined; in response to the completion number corresponding to the parent attribute metadata and the value of the parent attribute number being consistent, the data stored in the storage area is saved to the database.

[0056] Specifically, the determination of whether the child attribute number corresponding to the parent attribute is counted in type also includes: in response to the child attribute number of the parent attribute being counted in type, setting the parent attribute number to the value of the number of parent attributes, and determining whether the parent attribute number is zero; in response to the parent attribute number being not zero, taking the statistical value corresponding to each child attribute, and writing the statistical value corresponding to the child attribute into a register; subtracting the parent attribute number by one, taking the statistical value corresponding to each child attribute, and writing the statistical value corresponding to the child attribute into a register; assigning the parent attribute number to be subtracted by one, and determining whether the parent attribute number is zero, and repeating the above operations until the parent attribute number is zero, assigning the child attribute number corresponding to the parent attribute to be increased by one, and determining the size of the child attribute number corresponding to the parent attribute and the number of child attributes.

[0057] Specifically, the determination of whether the child attribute number corresponding to the parent attribute is a leaf node includes: in response to the child attribute number of the parent attribute being a leaf node, setting the parent attribute number to the number of parent attributes, and determining whether the parent attribute number is equal to zero; in response to the case where the parent attribute number is not zero, subtracting the parent attribute number by one, taking the statistical value corresponding to each child attribute, and writing the statistical value corresponding to the child attribute into a register; subtracting the parent attribute number by one, and determining whether the parent attribute number is zero, and if the parent attribute number is zero, assigning the child attribute number corresponding to the parent attribute plus one, and determining the size of the child attribute number corresponding to the parent attribute and the number of child attributes.

[0058] Specifically, the determination of the size of the completion quantity and the number of parent attributes includes: in response to the inconsistency between the completion quantity and the value of the parent attribute number, setting the completion quantity of the child attribute corresponding to the parent attribute to one, and determining the size of the completion quantity of the child attribute and the number of child attributes corresponding to the parent attribute; in response to the completion quantity of the child attribute not being greater than the value of the number of child attributes corresponding to the parent attribute, obtaining the register statistical value, and calculating the statistical value difference of each child attribute corresponding to the parent attribute based on the register statistical value; using a compression algorithm, storing the statistical value corresponding to each child attribute corresponding to the parent attribute and the statistical value difference in the corresponding storage area; assigning a value of one to the completion quantity of the child attribute, and determining the value of the completion quantity of the child attribute and the number of child attributes corresponding to the parent attribute, and repeating the above steps until the completion quantity of the child attribute is consistent with the number of child attributes corresponding to the parent attribute, assigning a value of one to the completion quantity corresponding to the metadata of the parent attribute, and repeating the above steps until the completion quantity corresponding to the metadata of the parent attribute is greater than the number of the parent attribute, and then saving the data stored in the storage area to the database.

[0059] See also Figure 2 , Figure 2 A flowchart of another method for clustering and storing data according to data genetic attributes provided in an embodiment of the present application is shown below. Figure 2 As shown, the specific steps include the following steps. It should be noted that: Figure 2 Each step in Figure 1 Each step corresponds to each other.

[0060] Step S1: Acquire various attributes of the equipment data to be stored, and extract data elements with inherited attributes; the inherited attributes include parent attributes and child attributes, the child attributes are sub-attributes of the parent attributes, and one parent attribute corresponds to multiple child attributes; each data element has all parent attributes and child attributes corresponding to the parent attributes; the parent attributes include several classification attributes and several dimension attributes; based on the child attribute B(SL)ij and B(BZ) ij The attribute value of the data element is sorted by using the hierarchical control algorithm to obtain the sub-level attribute B(SL) ij and B(BZ) ij The corresponding statistical value B(SL,BZ) ij , where i is the number of the parent attribute, j is the number of the child attribute corresponding to the i-level attribute, 1<=i<=N i , 1<=j<=N j , N i is the number of child attributes under parent attribute i; let i = N i ; Create a register A(SL,BZ) ij .

[0061] It should be noted that step S1 corresponds to the above-mentioned step S102 and step S104.

[0062] Step S2: If j is greater than N j , obtain the classification statistics result with i-level metadata as the set, and go to step S8; otherwise, go to step S3.

[0063] Step S3: If calculated by type, assign i=N i , go to step S4, otherwise go to step S9.

[0064] Step S4: If i is equal to 0, go to step S7, otherwise go to step S5.

[0065] Step S5: Get the child attribute B (SL, BZ) ij The corresponding statistical value SUM(SL,BZ) ij , write the obtained statistical value into register A (SL, BZ) ij In. Take the child attribute B (SL, BZ) ij Perform i-1 statistics to obtain the parent attribute statistics SUM(SL,BZ) i-1,j , write the parent attribute statistics value into register A (SL, BZ) i-1,j , go to step 6.

[0066] Step S6: Assign i equal to i-1 and go to step S4.

[0067] Step S7: Assign j equal to j+1 and go to step S2.

[0068] Step S9: Not by type, if it is a leaf node, assign i=N i , go to step S10, otherwise assign i=N i , proceed to step S13.

[0069] Step S10: If i is equal to 0, go to step S7, otherwise go to step S11.

[0070] Step S11: Get the child attribute B (SL, BZ) ij Perform i-1 statistics to obtain the parent attribute statistics SUM(SL,BZ) i-1,j , write the parent attribute statistics value into register A (SL, BZ) i-1,j , go to step 12.

[0071] Step S12: Assign i equal to i-1 and go to step S10.

[0072] Step S13: If i is equal to 0, go to step S7, otherwise go to step S14.

[0073] Step S14: Get the child attribute B (SL, BZ) ij and register A(SL,BZ) ij The corresponding statistical value SUM(SL,BZ) ij , write the obtained statistical value into register A (SL, BZ) ij In. Take the child attribute B (SL, BZ) ij and register A(SL,BZ) ij Perform i-1 statistics to obtain the parent attribute statistics SUM(SL,BZ) i-1,j , write the parent attribute statistics value into register A (SL, BZ) i-1,j , go to step 15.

[0074] Step S15: Assign i equal to i-1 and go to step S13.

[0075] Step S8: Obtain the classification statistics result with the i-level metadata as a set, assign e equal to 1, and proceed to step S16.

[0076] Step S16: If e is greater than N i , go to step S21, otherwise assign f=1 and go to step S17.

[0077] Step S17: If f is greater than N j , go to step S20, otherwise go to step S18.

[0078] Step S18: Get register A (SL, BZ) ef Statistics SUM(SL,BZ) ef , do SUM(BZ) ef -SUM(SL) ef =SUM(CZ) ef , the data SUM(BZ) ef ,SUM(SL)ef ,SUM(CZ) ef Use compression algorithm to store in the corresponding storage area LEN (SL, BZ, CZ) ef , go to step S19.

[0079] Step S19: Assign f=f+1 and enter step S17.

[0080] Step S20: Assign e=e+1 and go to step S16.

[0081] Step S21: store LEN(SL, BZ, CZ) in the storage area ef The data is saved to the database and the method ends.

[0082] It should be noted that steps S2 to S21 correspond to the above-mentioned step S106.

[0083] For example, a unit has a large demand for statistics such as the allocation rate. Because the base number is very large, the statistics often take a long time. Usually, the data to be counted have the following characteristics: the compilation data statistics are divided into statistical classifications such as type compilation, category compilation, and cross-category compilation, and the number-by-number data statistics are divided into statistical classifications such as statistics by new and unavailable, statistics by in-service and unavailable, and statistics by time. Dimensional data is generally based on unit dimensions and equipment dimensions, and is expanded according to different business needs. These dimensional data usually have a depth of ten layers and nearly 20,000 data nodes.

[0084] In the first step, the serial data are organized into formatted data according to categories such as genetic relationships and type attributes.

[0085] (1) Format basic data results, see Figure 3 , Figure 3 A schematic diagram of formatting a basic data table in a method for clustering and storing data according to data genetic attributes provided in an embodiment of the present application.

[0086] (2) Refer to the hierarchical relationship and the arrangement relationship, and use the genetic algorithm to supplement the existing basic data. Figure 4 , Figure 4 A schematic diagram of a basic data table after supplementing data genetic relationships in a method for clustering and storing data according to data genetic attributes provided in an embodiment of the present application.

[0087] The second step is to perform data calculation on the fifth layer.

[0088] (1) Obtain the original number of the fifth layer. Figure 5 , Figure 5 A schematic diagram of the fifth layer original number extraction table in a method for clustering and storing data according to data genetic attributes provided in an embodiment of the present application.

[0089] (2) Perform calculations on the fifth layer of data. Figure 6 , Figure 6 A schematic diagram of a result process table generated at layer 5 in a method for clustering and storing data according to data genetic attributes provided in an embodiment of the present application.

[0090] The third step is to perform data calculation on the fourth layer.

[0091] (1) Calculate the 5th level and generate the transfer number of the 4th level. Figure 7 , Figure 7 A schematic diagram of a process table for obtaining transfer numbers at level 4 in a method for clustering and storing data according to data genetic attributes provided in an embodiment of the present application.

[0092] (2) Obtain the original number of the 4th layer. Figure 8 , Figure 8 A schematic diagram of a 4th level original number extraction table in a method for clustering and storing data according to data genetic attributes provided in an embodiment of the present application.

[0093] (3) Perform calculations on the fourth layer data. Fig. 9 , Fig. 9 A schematic diagram of a result process table generated at layer 4 in a method for clustering and storing data according to data genetic attributes provided in an embodiment of the present application.

[0094] The fourth step is to perform data calculation on the third layer.

[0095] (1) Calculate the 4th level and generate the transfer number of the 3rd level. Fig.10 , Fig.10 A schematic diagram of a process table for obtaining transfer numbers at level 3 in a method for clustering and storing data according to data genetic attributes provided in an embodiment of the present application.

[0096] (2) Obtain the original number of the third layer. Fig.11 , Fig.11 A schematic diagram of a third-level original number extraction table in a method for clustering and storing data according to data genetic attributes provided in an embodiment of the present application.

[0097] (3) Perform calculations on the third layer of data. Fig.12 , Fig.12 A schematic diagram of a process table for generating results at layer 3 in a method for clustering and storing data according to data genetic attributes provided in an embodiment of the present application.

[0098] The fifth step is to perform data calculation on the second layer.

[0099] (1) Calculate the third level and generate the transfer number of the second level. Fig.13 , Fig.13A schematic diagram of a process table for obtaining transfer numbers at the second level in a method for clustering and storing data according to data genetic attributes provided in an embodiment of the present application.

[0100] (2) Obtain the original number of the second layer. Fig.14 , Fig.14 A schematic diagram of a second-level original number extraction table in a method for clustering and storing data according to data genetic attributes provided in an embodiment of the present application.

[0101] (3) Perform calculations on the second layer of data. Fig.15 , Fig.15 A schematic diagram of a result process table generated at the second layer in a method for clustering and storing data according to data genetic attributes provided in an embodiment of the present application.

[0102] Step 6: The first layer performs data calculation.

[0103] (1) Calculate the second level and generate the transfer number of the first level. Fig.16 , Fig.16 A schematic diagram of a process table for obtaining a transfer number at the first level in a method for clustering and storing data according to data genetic attributes provided in an embodiment of the present application.

[0104] (2) Obtain the original number of the first layer. Fig.17 , Fig.17 A schematic diagram of a first-level original number extraction table in a method for clustering and storing data according to data genetic attributes provided in an embodiment of the present application.

[0105] (3) Perform calculations on the first layer of data. Fig.18 , Fig.18 A schematic diagram of a result process table generated at the first layer in a method for clustering and storing data according to data genetic attributes provided in an embodiment of the present application.

[0106] The seventh step is to perform data calculations at the top level.

[0107] (1) Calculate the first level and generate the top-level pass number. Fig.19 , Fig.19 A schematic diagram of a top-level table of a process for obtaining transfer numbers in a method for clustering and storing data according to data genetic attributes provided in an embodiment of the present application.

[0108] (2) Get the original number of the top layer. Fig. 20 , Fig. 20 A schematic diagram of a top-level original data extraction table in a method for clustering and storing data according to data genetic attributes provided in an embodiment of the present application.

[0109] (3) Perform calculations on the top-level data. Fig.21 , Fig.21 A schematic diagram of a top-level generation result process table in a method for clustering and storing data according to data genetic attributes provided in an embodiment of the present application.

[0110] Step 8: Calculation of the allocation rate data

[0111] (1) Calculate the global allocation ratio horizontally. Fig. 22 , Fig. 22 A schematic diagram of an allocation rate calculation process table in a method for clustering and storing data according to data genetic attributes provided in an embodiment of the present application.

[0112] According to the above steps, all statistical needs are completely exhausted, and the more complex the type-class relationship and the more discrete the dimensional data, the higher the efficiency.

[0113] Step 9: Comparison of calculation statistics.

[0114] First, the genetic characteristics-based orchestration algorithm calculates the quantity statistics table.

[0115] Table 1 Statistics of computational effort of the genetic-based orchestration algorithm

[0116] The third layer aggregate calculation Second layer aggregation calculation The first layer of aggregation calculation Total Calculation Unit Dimension 144 96 60 300 Device Dimensions 300 216 108 624

[0117] Then the aggregation algorithm is commonly used to calculate the quantity statistics table.

[0118] Table 2 Statistics of calculation amount of common aggregation algorithms

[0119] The third layer aggregate calculation Second layer aggregation calculation The first layer of aggregation calculation Total Calculation Unit Dimension 144 192 216 552 Device Dimensions 516 624 720 1860

[0120] According to the above statistical table, in terms of computational complexity, the reverse aggregation algorithm has a decreasing trend with more dimensions and more levels, while the commonly used recursive algorithm has an increasing trend with more dimensions and more levels. In the end, the computational complexity of the reverse aggregation algorithm is 1 / 3 of that of the commonly used recursive algorithm, and the more dimensions, the greater the difference in computational complexity.

[0121] In device data with a parent-child hierarchical relationship, each data value has multi-faceted attributes. In actual work, the orchestration relationship is often modified frequently due to different tasks and target objects, and calculating the orchestration rate is a very complicated process. Therefore, when faced with massive data, general calculation methods such as traversal are still used to aggregate and count data with missing orchestration relationships, and the time used will increase exponentially with the increase of statistical dimensions. The method provided in the embodiment of the present application can solve the above-mentioned data statistics problem.

[0122] In general computing, statistics of each dimension require the participation of all data, which results in the data memory usage doubling with each additional dimension. The genetic orchestration computing model adopts a bottom-up reverse recursive design, which makes the memory required for aggregation smaller and smaller. Fig.23 , Fig.23 A memory statistics diagram of a method for clustering and storing data according to data genetic attributes provided in an embodiment of the present application, such as Fig.23 As shown in the figure, memory usage increases significantly in 3 dimensions, reaches a peak in the 6th dimension, and starts to decrease significantly in the 6th dimension. Fig.24 , Fig.24 A statistical diagram of the computation time of a method for clustering and storing data according to data genetic attributes provided in an embodiment of the present application is shown in FIG. Fig.24 As shown in the figure, in terms of running time, the genetic orchestration computing model does not have much advantage before 4 dimensions, and is significantly lower than general computing after 4 dimensions. After 6 dimensions, the increase in running time is much smaller than that of general computing, showing a downward trend. This shows that the genetic orchestration computing model completely solves the memory overflow problem and the long running time problem of general computing in multi-dimensional statistics, and the more statistical dimensions, the more obvious the effect.

[0123] The present invention adopts the concept of combining three algorithms, namely genetic algorithm, reverse aggregation algorithm and convergence algorithm, and establishes a genetic orchestration calculation model. In the existing application environment, the business data element information is redefined and implemented in the business database by definition, generation and push. According to the user's statistical needs, the user business statistical model can be generated by defining the required data structure, and the business data in the database can be reclassified and stored in groups. The classification is generally divided according to the business and management objects, and the grouping is generally divided according to the business category and data scale to form a data block set for operation and processing, and according to the existing application data query interface, a data structure for data display is generated.

[0124] It should be noted that the method for clustering and storing data according to data genetic attributes provided by the embodiment of the present application is applicable to the design and development of relational database software, and is applicable to the following scenarios: 1. When data genetic relationships are missing, the data chain needs to be completed; 2. When statistical element information changes, data statistical elements need to be added; 3. When statistical dimensions change, statistics need to be added according to management levels; 4. When the data volume increases, the program query response time slows down, and the program needs to be modified. The business system software designed under the statistical model idea of ​​combining genetic algorithms with clustering has good adaptability and scalability for this scenario. When statistical requirements change, there is no need to modify the software program code, which reduces the workload of program code modification.

[0125] In addition, compared with counting business data through specific program codes, the embodiments of the present application utilize existing defined data scripts to greatly improve the speed of data acquisition, without the need for human intervention, and can significantly reduce manual maintenance costs. Automatic reading and analysis can be achieved simply through statement definition settings, and executable data scripts can be generated through re-aggregated data structures, and interface data can be pushed to achieve a design that combines reverse recursion with clustering.

[0126] By applying the method for clustering and storing data according to data genetic attributes provided in the embodiment of the present application and utilizing the existing defined data scripts, the speed of data acquisition can be greatly improved without manual intervention, and the manual maintenance cost can be greatly reduced. Automatic reading and analysis can be achieved simply through statement definition settings, and an executable data script can be generated through the re-aggregated data structure, and the interface data can be pushed to realize the design combining reverse recursion and clustering.

[0127] Corresponding to the above method embodiment, this specification also provides an embodiment of a device for clustering and storing data according to data genetic attributes. Fig.25 A schematic diagram of a device for clustering and storing data according to data genetic attributes provided in an embodiment of the present application. Fig.25 As shown, the device comprises:

[0128] The extraction module 502 is configured to determine the data attributes corresponding to the data to be stored based on the genetic relationship standard, and extract the data element with dimensional attributes in the data attributes, wherein the data attributes carry dimensional attributes and classification attributes; the dimensional attributes are parent attributes and child attributes with a genetic relationship, the child attributes are child attributes of the parent attributes, and each of the parent attributes corresponds to at least one child attribute; the classification attributes are attribute values ​​corresponding to each child attribute, including compilation attributes and strength attributes; the data element carries all parent attributes and child attributes corresponding to the parent attributes;

[0129] The sorting module 504 is configured to sort the data element based on the classification attribute by using a hierarchical control algorithm to obtain a compilation statistical value and a strength statistical value corresponding to each of the child attributes, and create a register of the same dimension size as the data element, wherein the compilation statistical value carries the number and compilation value of each child attribute corresponding to each parent attribute; the strength statistical value carries the number and strength value of each child attribute corresponding to each parent attribute;

[0130] The statistical module 506 is configured to combine the genetic algorithm and the clustering algorithm to respectively perform statistics on the compilation statistical value and the strength statistical value, and save the data to be stored into a database according to the genetic relationship.

[0131] In an optional embodiment, the statistics module 506 is further configured to:

[0132] Assign the sub-attribute number corresponding to the parent attribute to 1, and determine the size of the sub-attribute number corresponding to the parent attribute and the number of sub-attributes;

[0133] In response to the child attribute number of the parent attribute being not greater than the number of child attributes, determining whether the child attribute number corresponding to the parent attribute is counted by type;

[0134] In response to the child attribute number of the parent attribute not being counted, determining whether the child attribute number corresponding to the parent attribute is a leaf node;

[0135] In response to the child attribute number of the parent attribute not being a leaf node, setting the parent attribute number to the number of parent attributes, and determining whether the parent attribute number is equal to zero;

[0136] In response to the parent attribute number not being equal to zero, taking the statistical value and the register statistical value corresponding to each child attribute, and writing the register statistical value into the register;

[0137] Assign a value of minus one to the parent attribute number, and determine whether the parent attribute number is equal to zero. If the parent attribute number is equal to zero, assign a value of plus one to the child attribute number corresponding to the parent attribute, and determine the size of the child attribute number corresponding to the parent attribute and the number of child attributes.

[0138] In response to the sub-attribute number corresponding to the parent attribute being consistent with the value of the number of sub-attributes, a classification statistical result with the parent attribute metadata as a set is obtained, and the completion number corresponding to the parent attribute metadata is set to one, and the size of the completion number corresponding to the parent attribute metadata and the number of parent attributes is determined;

[0139] In response to the completion quantity corresponding to the parent attribute metadata being consistent with the value of the parent attribute number, the data stored in the storage area is saved in the database.

[0140] In an optional embodiment, the statistics module 506 is further configured to:

[0141] In response to the child attribute number of the parent attribute being counted by type, let the parent attribute number be the value of the number of parent attributes, and determine whether the parent attribute number is zero;

[0142] In response to the parent attribute number being not zero, obtaining the statistical value corresponding to each child attribute, and writing the statistical value corresponding to the child attribute into a register;

[0143] Subtract one from the parent attribute number, obtain the statistical value corresponding to each child attribute, and write the statistical value corresponding to the child attribute into the register;

[0144] Assign the parent attribute number minus one, and determine whether the parent attribute number is zero. Repeat the above operation until the parent attribute number is zero. Then assign the child attribute number corresponding to the parent attribute plus one, and determine the size of the child attribute number corresponding to the parent attribute and the number of child attributes.

[0145] In an optional embodiment, the storage module 508 is further configured to:

[0146] In response to the child attribute number of the parent attribute being a leaf node, the parent attribute number is set to the number of parent attributes, and it is determined whether the parent attribute number is equal to zero;

[0147] In response to the parent attribute number not being zero, the parent attribute number is reduced by one, the statistical value corresponding to each child attribute is obtained, and the statistical value corresponding to the child attribute is written into the register;

[0148] Subtract one from the parent attribute number and determine whether the parent attribute number is zero. If the parent attribute number is zero, assign the child attribute number corresponding to the parent attribute plus one, and determine the size of the child attribute number corresponding to the parent attribute and the number of child attributes.

[0149] In an optional embodiment, the statistics module 506 is further configured to:

[0150] In response to the inconsistency between the completion quantity and the number of the parent attribute, the completion quantity of the child attribute corresponding to the parent attribute is set to one, and the difference between the completion quantity of the child attribute and the number of the child attribute corresponding to the parent attribute is determined;

[0151] In response to the value that the number of completed child attributes is not greater than the number of child attributes corresponding to the parent attribute, a register statistical value is obtained, and a statistical value difference of each child attribute corresponding to the parent attribute is calculated based on the register statistical value;

[0152] Using a compression algorithm, the statistical values ​​corresponding to each child attribute corresponding to the parent attribute, as well as the statistical value differences, are stored in a corresponding storage area;

[0153] Assign a value to the sub-attribute completion quantity plus one, and determine the value of the sub-attribute completion quantity and the number of sub-attributes corresponding to the parent attribute. Repeat the above steps until the sub-attribute completion quantity is consistent with the number of sub-attributes corresponding to the parent attribute. Then, assign a value to the completion quantity corresponding to the parent attribute metadata plus one. Repeat the above steps until the completion quantity corresponding to the parent attribute metadata is greater than the parent attribute number. Then, save the data stored in the storage area into the database.

[0154] By using the device for clustering and storing data according to data genetic attributes provided by the embodiment of the present application, the speed of data acquisition can be greatly improved by utilizing the existing defined data scripts. Without the need for human intervention, the manual maintenance cost can be greatly reduced. Automatic reading and analysis can be achieved simply through statement definition settings. By re-aggregating the data structure, an executable data script can be generated, and the interface data can be pushed to realize a design combining reverse recursion and clustering.

[0155] Each embodiment in this specification is described in a progressive manner, and the same or similar parts between the embodiments can be referred to each other, and each embodiment focuses on the differences from other embodiments. In particular, for the device for clustering and storing data according to data genetic attributes, since it is basically similar to the method embodiment for clustering and storing data according to data genetic attributes, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiment for clustering and storing data according to data genetic attributes.

[0156] Fig.26 This is a block diagram of a computing device provided in an embodiment of the present application. The components of the computing device 600 include but are not limited to a memory 610 and a processor 620. The processor 620 is connected to the memory 610 via a bus 630, and the database 650 is used to store data.

[0157] The computing device 600 also includes an access device 640, which enables the computing device 600 to communicate via one or more networks 660. Examples of these networks include a public switched telephone network (PSTN), a local area network (LAN), a wide area network (WAN), a personal area network (PAN), or a combination of communication networks such as the Internet. The access device 640 may include one or more of any type of network interface (e.g., a network interface card (NIC)) of wired or wireless, such as an IEEE 802.11 wireless local area network (WLAN) wireless interface, a world-wide interoperability for microwave access (Wi-MAX) interface, an Ethernet interface, a universal serial bus (USB) interface, a cellular network interface, a Bluetooth interface, and a near field communication (NFC).

[0158] In one embodiment of the present specification, the above components of the computing device 600 and Fig.26 Other components not shown in the figure may also be connected to each other, for example, via a bus. It should be understood that Fig.26 The computing device structure block diagram shown is only for the purpose of illustration, and is not intended to limit the scope of this specification. Those skilled in the art can add or replace other components as needed.

[0159] The computing device 600 may be any type of stationary or mobile computing device, including a mobile computer or mobile computing device (e.g., a tablet computer, a personal digital assistant, a laptop computer, a notebook computer, a netbook, etc.), a mobile phone (e.g., a smart phone), a wearable computing device (e.g., a smart watch, smart glasses, etc.), or other types of mobile devices, or a stationary computing device such as a desktop computer or a personal computer (PC). The computing device 600 may also be a mobile or stationary server.

[0160] The processor 620 is used to execute the following computer executable instructions, which, when executed by the processor, implement the steps of the above-mentioned method for clustering and storing data according to data genetic attributes.

[0161] Each embodiment in this specification is described in a progressive manner, and the same or similar parts between the embodiments can be referred to each other, and each embodiment focuses on the differences from other embodiments. In particular, for the computing device embodiment, since it is basically similar to the method embodiment for clustering and storing data according to data genetic attributes, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiment for clustering and storing data according to data genetic attributes.

[0162] An embodiment of the present specification also provides a computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, implement the steps of the above-mentioned method for clustering and storing data according to data genetic attributes.

[0163] Each embodiment in this specification is described in a progressive manner, and the same or similar parts between the embodiments can be referred to each other, and each embodiment focuses on the differences from other embodiments. In particular, for the computer-readable storage medium embodiment, since it is basically similar to the method embodiment for clustering data storage according to data genetic attributes, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiment for clustering data storage according to data genetic attributes.

[0164] An embodiment of the present specification also provides a computer program, wherein when the computer program is executed in a computer, the computer is caused to execute the steps of the above-mentioned method for clustering and storing data according to data genetic attributes.

[0165] Each embodiment in this specification is described in a progressive manner, and the same or similar parts between the embodiments can be referred to each other, and each embodiment focuses on the differences from other embodiments. In particular, for the computer program embodiment, since it is basically similar to the method embodiment for clustering and storing data according to data genetic attributes, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiment for clustering and storing data according to data genetic attributes.

[0166] The above is a description of a specific embodiment of the specification. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recorded in the claims can be performed in an order different from that in the embodiments and still achieve the desired results. In addition, the processes depicted in the drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0167] The computer instructions include computer program codes, which may be in source code form, object code form, executable files or some intermediate forms, etc. The computer-readable medium may include: any entity or device capable of carrying the computer program code, recording medium, USB flash drive, mobile hard disk, magnetic disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), electric carrier signal, telecommunication signal and software distribution medium, etc. It should be noted that the content contained in the computer-readable medium may be appropriately increased or decreased according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, computer-readable media do not include electric carrier signals and telecommunication signals.

[0168] It should be noted that the above is a description of a specific embodiment of the present specification. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recorded in the claims can be performed in an order different from that in the embodiments and still achieve the desired results. In addition, the processes depicted in the accompanying drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous. Secondly, those skilled in the art should also be aware that the embodiments described in the specification are all preferred embodiments, and the actions and modules involved are not necessarily required by the embodiments of the present specification.

[0169] In the above embodiments, the description of each embodiment has its own emphasis. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0170] The preferred embodiments of this specification disclosed above are only used to help explain this specification. The optional embodiments do not describe all the details in detail, nor do they limit the invention to the specific implementation methods described. Obviously, many modifications and changes can be made according to the content of the embodiments of this specification. This specification selects and specifically describes these embodiments in order to better explain the principles and practical applications of the embodiments of this specification, so that technicians in the relevant technical field can understand and use this specification well. This specification is limited only by the claims and their full scope and equivalents.

Claims

1. A method for clustering and storing data based on data genetic attributes, characterized in that: include: Based on the genetic relationship standard, the data attributes corresponding to the data to be stored are determined, and the data element with dimensional attributes in the data attributes is extracted, wherein the data attributes carry dimensional attributes and classification attributes; the dimensional attributes are parent attributes and child attributes with a genetic relationship, the child attributes are child attributes of the parent attributes, and each of the parent attributes corresponds to at least one child attribute; the classification attributes are attribute values ​​corresponding to each child attribute, including compilation attributes and strength attributes; the data element carries all parent attributes and child attributes corresponding to the parent attributes; Based on the classification attributes, the data elements are sorted using a hierarchical control algorithm to obtain compilation statistics and strength statistics corresponding to each of the child attributes, and a register of the same dimension and size as the data element is created, wherein the compilation statistics carry the number and compilation value of each child attribute corresponding to each parent attribute; the strength statistics carry the number and strength value of each child attribute corresponding to each parent attribute; The compilation statistics and the strength statistics are respectively counted by combining the genetic algorithm and the clustering algorithm, and the data to be stored is saved in a database according to the genetic relationship.

2. The method according to claim 1, characterized in that The combining of the genetic algorithm and the clustering algorithm to respectively count the compilation statistics and the strength statistics, and saving the data to be stored in a database according to the genetic relationship, comprises: Assign the sub-attribute number corresponding to the parent attribute to 1, and determine the size of the sub-attribute number corresponding to the parent attribute and the number of sub-attributes; In response to the child attribute number of the parent attribute being not greater than the number of child attributes, determining whether the child attribute number corresponding to the parent attribute is counted by type; In response to the child attribute number of the parent attribute not being counted, determining whether the child attribute number corresponding to the parent attribute is a leaf node; In response to the child attribute number of the parent attribute not being a leaf node, setting the parent attribute number to the number of parent attributes, and determining whether the parent attribute number is equal to zero; In response to the parent attribute number not being equal to zero, taking the statistical value and the register statistical value corresponding to each child attribute, and writing the register statistical value into the register; Assign a value of minus one to the parent attribute number, and determine whether the parent attribute number is equal to zero. If the parent attribute number is equal to zero, assign a value of plus one to the child attribute number corresponding to the parent attribute, and determine the size of the child attribute number corresponding to the parent attribute and the number of child attributes. In response to the sub-attribute number corresponding to the parent attribute being consistent with the value of the number of sub-attributes, a classification statistical result with the parent attribute metadata as a set is obtained, and the completion number corresponding to the parent attribute metadata is set to one, and the size of the completion number corresponding to the parent attribute metadata and the number of parent attributes is determined; In response to the completion quantity corresponding to the parent attribute metadata being consistent with the value of the parent attribute number, the data stored in the storage area is saved in the database.

3. The method according to claim 2, characterized in that The step of determining whether the sub-level attribute number corresponding to the parent-level attribute is calculated by type further includes: In response to the child attribute number of the parent attribute being counted by type, let the parent attribute number be the value of the number of parent attributes, and determine whether the parent attribute number is zero; In response to the parent attribute number being not zero, obtaining the statistical value corresponding to each child attribute, and writing the statistical value corresponding to the child attribute into a register; Subtract one from the parent attribute number, obtain the statistical value corresponding to each child attribute, and write the statistical value corresponding to the child attribute into the register; Assign the parent attribute number minus one, and determine whether the parent attribute number is zero. Repeat the above operation until the parent attribute number is zero. Then assign the child attribute number corresponding to the parent attribute plus one, and determine the size of the child attribute number corresponding to the parent attribute and the number of child attributes.

4. The method according to claim 2, characterized in that: The step of judging whether the child attribute number corresponding to the parent attribute is a leaf node includes: In response to the child attribute number of the parent attribute being a leaf node, the parent attribute number is set to the number of parent attributes, and it is determined whether the parent attribute number is equal to zero; In response to the parent attribute number not being zero, the parent attribute number is reduced by one, the statistical value corresponding to each child attribute is obtained, and the statistical value corresponding to the child attribute is written into the register; Subtract one from the parent attribute number and determine whether the parent attribute number is zero. If the parent attribute number is zero, assign the child attribute number corresponding to the parent attribute plus one, and determine the size of the child attribute number corresponding to the parent attribute and the number of child attributes.

5. The method according to claim 2, characterized in that: The determining the size of the completion quantity and the number of parent attributes includes: In response to the inconsistency between the completion quantity and the number of the parent attribute, the completion quantity of the child attribute corresponding to the parent attribute is set to one, and the difference between the completion quantity of the child attribute and the number of the child attribute corresponding to the parent attribute is determined; In response to the value that the number of completed child attributes is not greater than the number of child attributes corresponding to the parent attribute, a register statistical value is obtained, and a statistical value difference of each child attribute corresponding to the parent attribute is calculated based on the register statistical value; Using a compression algorithm, the statistical values ​​corresponding to each child attribute corresponding to the parent attribute, as well as the statistical value differences, are stored in a corresponding storage area; Assign a value to the sub-attribute completion quantity plus one, and determine the value of the sub-attribute completion quantity and the number of sub-attributes corresponding to the parent attribute. Repeat the above steps until the sub-attribute completion quantity is consistent with the number of sub-attributes corresponding to the parent attribute. Then, assign a value to the completion quantity corresponding to the parent attribute metadata plus one. Repeat the above steps until the completion quantity corresponding to the parent attribute metadata is greater than the parent attribute number. Then, save the data stored in the storage area into the database.

6. A device for clustering and storing data according to data genetic attributes, characterized in that: include: The extraction module is configured to determine the data attributes corresponding to the data to be stored based on the genetic relationship standard, and extract the data element with dimensional attributes in the data attributes, wherein the data attributes carry dimensional attributes and classification attributes; the dimensional attributes are parent attributes and child attributes with a genetic relationship, the child attributes are child attributes of the parent attributes, and each of the parent attributes corresponds to at least one child attribute; the classification attributes are attribute values ​​corresponding to each child attribute, including compilation attributes and strength attributes; the data element carries all parent attributes and child attributes corresponding to the parent attributes; The sorting module is configured to sort the data elements based on the classification attributes using a hierarchical control algorithm to obtain compilation statistics and strength statistics corresponding to each of the child attributes, and create a register with the same dimensional size as the data element, wherein the compilation statistics carry the number and compilation value of each child attribute corresponding to each parent attribute; the strength statistics carry the number and strength value of each child attribute corresponding to each parent attribute; The statistical module is configured to combine the genetic algorithm and the clustering algorithm to respectively perform statistics on the compilation statistical value and the strength statistical value, and save the data to be stored into a database according to the genetic relationship.

7. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores an implementation program for information transmission, and when the program is executed by a processor, the steps of the method according to any one of claims 1 to 8 are implemented.

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