Data classification method, device, equipment, and computer-readable storage medium

Abstract

The embodiment of the present application discloses a data classification method and apparatus, device, and computer-readable storage medium. The method constructs a plurality of types of data element sets corresponding to the data elements to be classified, and obtains a plurality of data classification standard tables, wherein the data classification standard tables are used to classify the data elements to be classified; for each data classification standard table, the correlation value between the data classification standard table and the plurality of types of data element sets for the data elements is calculated; in this way, the data classification standard table is associated with the data element set through the data element, and the correlation value of each data classification standard table is calculated, that is, the correlation between the data classification standard table and the data element set is numerically quantified, so that the data classification standard table with the largest correlation value can be accurately selected from the plurality of data classification standard tables as the target data classification standard table, and the target data classification standard table is selected to classify the data elements to be classified, so that the classification process for the data elements to be classified is more accurate.

Description

Technical Field

[0001] The present application relates to the field of computers, and specifically to a data classification method and apparatus, equipment, and computer-readable storage medium. Background Art

[0002] In order to classify a large number of data elements to be classified, it is necessary to identify and classify the existing data classification standard tables and the newly created data classification standard tables, or to classify and verify them. However, due to the large number of related data classification standard tables, it is difficult to accurately select the target data classification standard table from multiple data classification standard tables to classify the data elements to be classified. Summary of the invention

[0003] To solve the above technical problems, the embodiments of the present application respectively provide a data classification method and apparatus, a device, and a computer-readable storage medium to accurately select a target data classification standard table from multiple data classification standard tables to classify the data elements to be classified.

[0004] Other features and advantages of the present application will become apparent from the following detailed description, or may be learned in part by the practice of the present application.

[0005] According to one aspect of an embodiment of the present application, a data classification method is provided, including: constructing a set of data elements of multiple types corresponding to the data elements to be classified, and obtaining multiple data classification standard tables, the data classification standard tables being used to classify the data elements to be classified; for each data classification standard table, calculating a correlation value between the data classification standard table and the set of data elements of the multiple types for the data elements; selecting a data classification standard table with the largest correlation value from the multiple data classification standard tables as a target data classification standard table; and classifying the data elements to be classified according to the target data classification standard table.

[0006] According to one aspect of an embodiment of the present application, a data classification device is provided, comprising: a construction module, configured to construct a set of data elements of multiple types corresponding to the data elements to be classified, and to obtain multiple data classification standard tables, wherein the data classification standard tables are used to classify the data elements to be classified; a calculation module, configured to calculate, for each data classification standard table, a correlation value between the data classification standard table and the sets of data elements of multiple types for the data elements; a selection module, configured to select a data classification standard table with the largest correlation value from the multiple data classification standard tables as a target data classification standard table; and a classification module, configured to classify the data elements to be classified according to the target data classification standard table.

[0007] In another embodiment, the calculation module includes: a first statistical unit, configured to count the number of data elements contained in multiple types of data element sets, respectively, to obtain first quantities corresponding to the multiple types of data element sets; a second statistical unit, configured to count the number of data elements in multiple types of data element sets that match the classification fields in the data classification standard table, respectively, to obtain second quantities corresponding to the multiple types of data element sets; a first correlation value calculation unit, configured to calculate the correlation value for the data element between the data classification standard table and the multiple types of data element sets based on the first quantity and the second quantity.

[0008] In another embodiment, the first correlation value calculation unit includes: a first average quantity calculation subunit, configured to perform an average operation on multiple first quantities to obtain a first average quantity; a second average quantity calculation subunit, configured to perform an average operation on multiple second quantities to obtain a second average quantity; and a correlation value calculation subunit, configured to calculate the correlation value for the data element between the data classification standard table and the multiple types of data element sets based on the first average quantity and the second average quantity.

[0009] In another embodiment, the correlation value calculation subunit includes: a first difference calculation module, configured to perform a difference operation on a first quantity corresponding to a data element set and the first average quantity for each type of data element set, so as to obtain first differences corresponding to multiple types of data element sets respectively; a second difference calculation module, configured to perform a difference operation on a second quantity corresponding to a data element set and the second average quantity, so as to obtain second differences corresponding to multiple types of data element sets respectively; and a correlation value calculation module, configured to calculate the correlation value for the data element between the data classification standard table and the multiple types of data element sets based on the first difference and the second difference.

[0010] In another embodiment, the correlation value calculation module includes: a sum operation submodule, configured to sum the absolute values ​​of multiple first difference values ​​to obtain a first sum value, and sum the absolute values ​​of multiple second difference values ​​to obtain a second sum value; a product operation submodule, configured to multiply the first sum value and the second sum value to obtain a first product; a traversal submodule, configured to traverse the data element set, take the traversed data element set as the target data element set, and take the first difference value corresponding to the target data element set as the target first difference value, and take the second difference value corresponding to the target data element set as the target second difference value; a sum of products submodule, configured to multiply the target first difference value and the target second difference value to obtain a target product, so as to obtain the sum of multiple products of the first difference value and the second difference value; a correlation value calculation submodule, configured to divide the sum of the multiple products by the first product to obtain the correlation value for the data element between the data classification standard table and the multiple types of data element sets.

[0011] In another embodiment, the calculation module includes: a second statistical unit, configured to count the number of data elements in multiple types of data element sets that match the classification fields in the data classification standard table, respectively, to obtain second quantities corresponding to the multiple types of data element sets; a second correlation value calculation unit, configured to sum the multiple second quantities, and use the sum obtained by the summation operation as the correlation value for the data element between the data classification standard table and the multiple types of data element sets.

[0012] In another embodiment, the data classification device also includes: a preprocessing module, configured to preprocess the fields contained in multiple initial data classification tables to obtain multiple preprocessed initial data classification tables, and use the multiple preprocessed initial data classification tables as the multiple data classification standard tables respectively; wherein the preprocessing includes at least one of the elimination of abnormal fields and the elimination of duplicate fields.

[0013] According to one aspect of an embodiment of the present application, an electronic device is provided, including: a controller; and a memory for storing one or more programs, which, when executed by the controller, executes the above-mentioned data classification method.

[0014] According to one aspect of an embodiment of the present application, a computer-readable storage medium is also provided, on which computer-readable instructions are stored. When the computer-readable instructions are executed by a processor of a computer, the computer executes the above-mentioned data classification method.

[0015] According to one aspect of the embodiments of the present application, a computer program product or a computer program is also provided, the computer program product or the computer program includes computer instructions, the computer instructions are stored in a computer-readable storage medium. The processor of the computer device reads the computer instructions from the computer-readable storage medium, and the processor executes the computer instructions, so that the computer device performs the above-mentioned data classification method.

[0016] In the technical solution provided in the embodiments of the present application, by constructing a plurality of types of data element sets corresponding to the data elements to be classified, and obtaining a plurality of data classification standard tables, the data classification standard tables are used to classify the data elements to be classified; for each data classification standard table, the correlation value between the data classification standard table and the plurality of types of data element sets for the data elements is calculated; in this way, the data classification standard table is associated with the data element set through the data element, and the correlation value of each data classification standard table is calculated, that is, the correlation between the data classification standard table and the data element set is numerically quantified, and the data classification standard table with the largest correlation value is selected from the plurality of data classification standard tables as the target data classification standard table, so that the target data classification standard table can be accurately selected from the plurality of data classification standard tables to classify the data elements to be classified, so that the classification process for the data elements to be classified is more accurate.

[0017] It is to be understood that the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] The drawings herein are incorporated into the specification and constitute a part of the specification, showing embodiments consistent with the present application, and together with the specification, are used to explain the principles of the present application. Obviously, the drawings described below are only some embodiments of the present application, and for those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative work. In the drawings:

[0019] Figure 1 is a flow chart of a data classification method shown in an exemplary embodiment of the present application;

[0020] Figure 2 is based on Figure 1 A flow chart of another data classification method proposed in the illustrated embodiment;

[0021] Figure 3 is based on Figure 2 A flow chart of another data classification method proposed in the illustrated embodiment;

[0022] Figure 4 is based on Figure 3 A flow chart of another data classification method proposed in the illustrated embodiment;

[0023] Figure 5 is based on Figure 4 A flow chart of another data classification method proposed in the illustrated embodiment;

[0024] Figure 6 is based on Figure 1 A flow chart of another data classification method proposed in the illustrated embodiment;

[0025] Figure 7 is based on Figures 1 to 6 A flowchart of another data classification method proposed in any of the embodiments shown in FIG.

[0026] Figure 8 It is a schematic diagram of an implementation environment involved in this application;

[0027] Fig. 9 is a structural schematic diagram of a data classification device shown in an exemplary embodiment of the present application;

[0028] Fig.10 An exemplary embodiment of the present application shows a schematic diagram of the structure of a computer system of an electronic device. DETAILED DESCRIPTION

[0029] Here, exemplary embodiments will be described in detail, examples of which are shown in the accompanying drawings. When the following description refers to the drawings, unless otherwise indicated, the same numbers in different drawings represent the same or similar elements. The implementations described in the following exemplary embodiments do not represent all implementations consistent with the present application. Instead, they are only examples of devices and methods consistent with some aspects of the present application as detailed in the attached claims.

[0030] The block diagrams shown in the accompanying drawings are merely functional entities and do not necessarily correspond to physically independent entities. That is, these functional entities may be implemented in software form, or in one or more hardware modules or integrated circuits, or in different networks and / or processor devices and / or microcontroller devices.

[0031] The flowcharts shown in the accompanying drawings are only exemplary and do not necessarily include all the contents and operations / steps, nor must they be executed in the order described. For example, some operations / steps can be decomposed, and some operations / steps can be combined or partially combined, so the actual execution order may change according to actual conditions.

[0032] The term "multiple" as used in this application refers to two or more than two. "And / or" describes the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can mean: A exists alone, A and B exist at the same time, and B exists alone. The character " / " generally indicates that the related objects are in an "or" relationship.

[0033] First see Figure 1 , Figure 1 FIG. 1 is a flow chart of a data classification method shown in an exemplary embodiment of the present application. Figure 1 As shown, the method at least includes S110 to S140, which are described in detail as follows:

[0034] S110: constructing a plurality of types of data element sets corresponding to the data elements to be classified, and obtaining a plurality of data classification standard tables, where the data classification standard tables are used to classify the data elements to be classified.

[0035] The data classification standard table is a preset template data table, which includes multiple fields, and the data elements to be classified can be classified according to the corresponding fields.

[0036] A data element set is a set obtained by dividing according to a certain data element classification standard. For example, according to a preset data element classification standard, the data elements to be classified are divided into n classification data element sets T (T1, T2...T n ), each data element set includes data elements of the corresponding category.

[0037] S120: For each data classification standard table, calculate a correlation value for a data element between the data classification standard table and a plurality of types of data element sets.

[0038] Exemplarily, the data classification standard tables include three data classification standard tables A, B, and C, including data element sets T1, T2, and T3; the correlation values ​​between the data classification standard table A and the data element sets T1, T2, and T3 are calculated for the data elements, and so on, the correlation values ​​between the data classification standard table B and the data element sets T1, T2, and T3 are calculated for the data elements, and the correlation values ​​between the data classification standard table C and the data element sets T1, T2, and T3 are calculated for the data elements.

[0039] The correlation value is a digital value that represents the correlation between each data classification standard table and multiple types of data element sets. For example, if there are 2 fields in the A data classification standard table that match the data elements in the T1 data element set (that is, the data elements in the T1 data element set match the 2 fields in the A data classification standard table), then the correlation value between the A data classification standard table and the T1 data element set is 2, thereby obtaining the number of matches between the A data classification standard table and the data elements in each data element set.

[0040] S130: Selecting a data classification standard table with the largest correlation value from the multiple data classification standard tables as a target data classification standard table.

[0041] For all data classification standard tables, the relevant values ​​corresponding to each data classification standard table are calculated, and the data classification standard table with the largest relevant value is used as the target data classification table. The fields in the target data classification standard table have a high degree of matching with the data elements in the data elements to be classified.

[0042] If there is at least one data classification standard table with the largest correlation value, it is necessary to further screen the multiple data classification standard tables with the largest correlation values. The screening can be further based on the number of specific fields, the order of fields, etc. For example, field A is a field with a high degree of matching with the data element to be classified. When the correlation values ​​of the data classification standard tables are the same, the more A fields there are (that is, the higher their proportion of the total matching fields), the higher the degree of matching between the data classification standard table and the data element to be classified.

[0043] S140: Classify the data elements to be classified according to the target data classification standard table.

[0044] Exemplarily, if the A data classification standard table is selected as the target data classification table, the classification standard in the A data classification standard table is obtained, and the data elements to be classified are classified according to the classification standard.

[0045] This embodiment constructs a plurality of types of data element sets corresponding to the data elements to be classified, and obtains a plurality of data classification standard tables, wherein the data classification standard tables are used to classify the data elements to be classified; for each data classification standard table, the correlation value between the data classification standard table and the plurality of types of data element sets for the data elements is calculated; in this way, the data classification standard table is associated with the data element set through the data element, and the correlation value of each data classification standard table is calculated, that is, the correlation between the data classification standard table and the data element set is numerically quantified, and the data classification standard table with the largest correlation value is selected from the plurality of data classification standard tables as the target data classification standard table, so that the target data classification standard table can be accurately selected from the plurality of data classification standard tables to classify the data elements to be classified, so that the classification process for the data elements to be classified is more accurate.

[0046] In another embodiment of the present application, further description is given on how to calculate the correlation value between the data classification standard table and the data element sets of multiple types for the data element. For details, please refer to Figure 2 , Figure 2 is based on Figure 1 The flowchart of another data classification method proposed in the embodiment shown is as follows. Figure 2As shown, the method in S120 at least includes S210 to S230, which are described in detail as follows:

[0047] S210: Count the numbers of data elements contained in multiple types of data element sets respectively to obtain first numbers corresponding to the multiple types of data element sets respectively.

[0048] Exemplarily, there are three types of data element sets, A, B, and C. Statistics show that the A data element set includes 10 data elements, then the first quantity corresponding to the A data element set is 10; the B data element set includes 11 data elements, then the first quantity corresponding to the B data element set is 11; the C data element set includes 13 data elements, then the first quantity corresponding to the C data element set is 13.

[0049] S220: Count the number of data elements in the data element sets of multiple types that match the classification fields in the data classification standard table, respectively, to obtain second numbers corresponding to the data element sets of multiple types.

[0050] Exemplarily, there are three types of data element sets, A, B, and C. For the data classification standard table a, the number of data elements in the data element set A that match the classification fields in the data classification standard table a is 5, so the second number is 5; the number of data elements in the data element set B that match the classification fields in the data classification standard table a is 4, so the second number is 4; the number of data elements in the data element set C that match the classification fields in the data classification standard table a is 8, so the second number is 8.

[0051] It should be noted that S210 and S220 in this embodiment may be performed simultaneously or in steps, and this embodiment does not limit the specific execution order of S210 and S220.

[0052] S230: Calculate, based on the first quantity and the second quantity, correlation values ​​for data elements between the data classification standard table and multiple types of data element sets.

[0053] Exemplarily, for each type of data element set, its second number is divided by its first number to obtain a corresponding quotient value, thereby obtaining quotient values ​​corresponding to all types of data element sets, adding the quotient values ​​corresponding to all types of data element sets, and using the sum obtained as the relevant value of the data classification standard table.

[0054] In another exemplary manner, for each data classification standard table, all the first quantities are summed up to obtain the sum of the first quantities; and all the second quantities are summed up to obtain the sum of the second quantities; the sum of the second quantities is divided by the sum of the first quantities, and the calculated quotient is used as the relevant value of the data classification standard table.

[0055] This embodiment further illustrates how to calculate the correlation value for a data element between a data classification standard table and a plurality of types of data element sets, and obtain the first quantities corresponding to the plurality of types of data element sets respectively by calculation; and count the number of data elements in the plurality of types of data element sets that match the classification fields in the data classification standard table, and obtain the second quantities corresponding to the plurality of types of data element sets respectively, and associate the data element with the classification fields in the data classification standard table, so that the correlation value obtained based on the first quantity and the second quantity is more strongly associated.

[0056] See also Figure 3 , Figure 3 is based on Figure 2 The flowchart of another data classification method proposed in the embodiment shown in FIG. Figure 2 The S230 shown also includes S310 to S330, which are described in detail below:

[0057] S310: performing an averaging operation on multiple first quantities to obtain a first average quantity.

[0058] Exemplarily, there are three types of data element sets, A, B, and C. Statistics show that the first number corresponding to the A data element set is 12, the first number corresponding to the B data element set is 11, and the first number corresponding to the C data element set is 13. Then the first average number = (12+11+13) / 3 = 12.

[0059] S320: Perform an averaging operation on the multiple second quantities to obtain a second average quantity.

[0060] Exemplarily, there are three types of data element sets, A, B, and C. For data classification standard table a, the second number of data elements in data element set A that match the classification fields in data classification standard table a is 5, the second number of data elements in data element set B that match the classification fields in data classification standard table a is 4, and the second number of data elements in data element set C that match the classification fields in data classification standard table a is 9. Then the second average number = (5+4+9) / 3 = 6.

[0061] S330: Based on the first average quantity and the second average quantity, calculate the correlation value for the data element between the data classification standard table and the data element sets of multiple types.

[0062] Exemplarily, the second average quantity corresponding to each data classification standard table is divided by the first average quantity to obtain the relevant value corresponding to the data classification standard table, thereby obtaining the relevant values ​​corresponding to all data classification standard tables.

[0063] This embodiment further illustrates that the correlation values ​​for data elements between the data classification standard table and multiple types of data element sets are calculated more accurately according to the first average quantity and the second average quantity.

[0064] See also Figure 4 , Figure 4 is based on Figure 3 The flowchart of another data classification method proposed in the embodiment shown in FIG. Figure 3 The S330 shown also includes S410 to S430, which are described in detail below:

[0065] S410: For each type of data element set, perform a difference operation on a first quantity corresponding to the data element set and a first average quantity to obtain first difference values ​​corresponding to multiple types of data element sets respectively.

[0066] Exemplarily, there are three types of data element sets, A, B, and C. Statistics show that the first number corresponding to the A data element set is 10, the first number corresponding to the B data element set is 10, and the first number corresponding to the C data element set is 13. Then the first average number = (10 + 10 + 13) / 3 = 11. The first difference corresponding to the A data element set is 10-11 = -1, the first difference corresponding to the B data element set is 10-11 = -1, and the first difference corresponding to the C data element set is 13-11 = 2.

[0067] S420: Perform a difference operation on the second quantity corresponding to the data element set and the second average quantity to obtain second difference values ​​corresponding to multiple types of data element sets respectively.

[0068] For example, there are three types of data element sets, A, B, and C. For the data classification standard table a, the second number of data elements in the data element set A that match the classification field in the data classification standard table a is 6, the second number of data elements in the data element set B that match the classification field in the data classification standard table a is 8, and the second number of data elements in the data element set C that match the classification field in the data classification standard table a is 10, then the second average number = (6+8+10) / 3 = 8. The second difference corresponding to the data element set A is 6-8 = -2, the second difference corresponding to the data element set B is 8-8 = 0, and the second difference corresponding to the data element set C is 10-8 = 2.

[0069] S430: Based on the first difference and the second difference, calculate and obtain the correlation value for the data element between the data classification standard table and the data element sets of multiple types.

[0070] Exemplarily, based on the above examples of S420 and S430, for the A data element set, its second difference is divided by the first difference = -2 / -1 = 2; for the B data element set, its second difference is divided by the first difference = 0 / -1 = 0; for the C data element set, its second difference is divided by the first difference = 2 / 2 = 1; the sum of the above differences (i.e., 2+0+1=3) is used as the correlation value for the data element between the a data classification standard table and multiple types of data element sets.

[0071] This embodiment further introduces a first difference between the first quantity and the first average quantity, and a second difference between the second quantity and the second average quantity, and based on the first difference and the second difference, calculates the correlation value for the data element between the data classification standard table and multiple types of data element sets, so that the correlation value is more accurate.

[0072] In order to better reflect the correlation between the data element and the fields in the data classification standard table, it is necessary to further operate on the first difference and the second difference, and calculate the relevant value of each data classification standard table according to the corresponding formula, so that the calculated relevant value is more accurate and can better reflect the strong correlation between the data element and the fields in the data classification standard table.

[0073] To this end, another data classification method is proposed in another embodiment of the present application. Figure 5 , Figure 5 is based on Figure 4 The flowchart of another data classification method proposed in the embodiment shown in FIG. Figure 4 The S430 shown includes S510 to S550, which are described in detail below:

[0074] S510: performing a sum operation on the absolute values ​​of the plurality of first differences to obtain a first sum value, and performing a sum operation on the absolute values ​​of the plurality of second differences to obtain a second sum value.

[0075] S520: Perform a product operation on the first sum value and the second sum value to obtain a first product.

[0076] S530: traverse the data element set, take the traversed data element set as the target data element set, take the first difference value corresponding to the target data element set as the target first difference value, and take the second difference value corresponding to the target data element set as the target second difference value.

[0077] S540: Perform a product operation on the target first difference and the target second difference to obtain a target product, so as to obtain a sum of multiple products of the first difference and the second difference.

[0078] S550: Divide the sum of the multiple products by the first product to obtain a correlation value for a data element between the data classification standard table and the multiple types of data element sets.

[0079] Exemplarily, the following matrix relationship is constructed according to the first quantity and the second quantity:

[0080]

[0081] Among them, x mn represents the second number of data elements in the m data classification standard table that match the data elements in the n data element set; n Indicates the first number of data elements in the set of n data elements.

[0082] For example, x 21 , x 22 , x ij ……x mn It indicates the second number of data elements in the second data classification standard table that match the first data element set, the second data element set, the i-th data element set, and the n-th data element set respectively.

[0083] The following formula is used to calculate the relevant value of each data classification standard table:

[0084]

[0085] Wherein, x represents a plurality of second quantities in the specified data classification standard table, Indicates the second average quantity of the specified data classification standard table, represents the first average quantity; represents the sum of multiple products of the first difference value and the second difference value in this embodiment; represents the second sum value in this embodiment; represents the first sum value in this embodiment; represents the first product in this embodiment.

[0086] This embodiment constructs a relationship matrix between the first quantity and the second quantity to facilitate the selection of multiple second quantities of the specified data classification standard table, calculate the second average quantity, and quickly obtain the first quantity to obtain the first average quantity, and accurately calculate the size of the relevant value of each data classification standard table according to the above formula, so that the calculated relevant value is more accurate and can better reflect the strong correlation between the data element and the field in the data classification standard table.

[0087] See also Figure 6 , Figure 6 is based on Figure 1The flowchart of another data classification method proposed in the embodiment shown in the figure. The method also includes S610 to S620, which are described in detail below:

[0088] S610: Count the number of data elements in multiple types of data element sets that match the classification fields in the data classification standard table, respectively, to obtain second numbers corresponding to the multiple types of data element sets.

[0089] Traverse each type of data element set and use the traversed data element set as the target data element set; count the number of data elements in the target data element set that match the classification field in the specified data classification standard table to obtain a second number corresponding to the target data element set, so as to obtain the second numbers corresponding to all types of data element sets respectively.

[0090] S620: performing a sum operation on the multiple second quantities, and using the sum value obtained by the sum operation as a correlation value for the data element between the data classification standard table and the multiple types of data element sets.

[0091] Exemplarily, there are three types of data element sets, A, B, and C. For the a data classification standard table, it is statistically obtained that the second number of data elements in the A data element set that match the classification field in the a data classification standard table is 4, the second number of data elements in the B data element set that match the classification field in the a data classification standard table is 3, and the second number of data elements in the C data element set that match the classification field in the a data classification standard table is 5; the second quantities corresponding to the three data element sets A, B, and C are summed up, and the sum is 4+3+5=12, and the sum (i.e., 12) is directly used as the relevant value of the a data classification standard table.

[0092] In this embodiment, for each data classification standard table, the sum of all the second quantities is directly used as its correlation value, so as to quickly determine the data classification standard table with the largest correlation value, saving time in the data classification process, and more efficiently determining the target data classification standard table from multiple data classification standard tables, so as to classify the data elements to be classified according to the target data classification standard table.

[0093] See also Figure 7 , Figure 7 is based on Figures 1 to 6 A flowchart of another data classification method proposed in any of the embodiments shown in the figure. The method, before S110, at least includes S710, which is described in detail below:

[0094] S710: Preprocess the fields contained in multiple initial data classification tables to obtain multiple preprocessed initial data classification tables, and use the multiple preprocessed initial data classification tables as multiple data classification standard tables respectively; wherein the preprocessing includes at least one of the removal of abnormal fields and the removal of duplicate fields.

[0095] Because there are invalid fields in the initial data classification table, that is, fields that cannot be matched with the data elements to be classified, and because the data elements to be classified are matched with multiple fields one by one, if there are invalid fields, it will affect the efficiency of data element matching. In order to improve the matching degree between the fields in the data classification table and the data elements to be classified, as well as to improve the matching efficiency, the initial data classification table needs to be preprocessed, such as removing abnormal fields and / or removing duplicate fields. On the premise that valid fields exist in the initial data classification table, invalid fields are removed, thereby reducing the number of fields in the matching process. While improving the matching accuracy between data elements and valid fields, the data element matching efficiency is also improved.

[0096] See also Figure 8 , Figure 8 8 is a schematic diagram of an implementation environment involved in the present application, including a server 810 and a database 820, wherein the database 820 stores a plurality of data classification standard tables for classifying data elements to be classified.

[0097] The server 810 may execute the data classification methods in the above-mentioned embodiments, as shown in the following examples:

[0098] The server 810 constructs a plurality of types of data element sets corresponding to the data elements to be classified, and obtains a plurality of data classification standard tables from the database 820; for each data classification standard table, calculates the correlation value between the data classification standard table and the plurality of types of data element sets for the data elements; selects the data classification standard table with the largest correlation value from the plurality of data classification standard tables as the target data classification standard table; and classifies the data elements to be classified according to the target data classification standard table.

[0099] Server 810 can be an independent physical server or a server cluster or distributed system composed of multiple physical servers, wherein multiple servers can form a blockchain, and the server is a node on the blockchain. Server 810 can also be a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, CDN (Content Delivery Network) and big data and artificial intelligence platforms; in particular, database 820 can be placed in server 810, or it can be a separate database placed in the cloud, and this is not restricted here.

[0100] Another aspect of the present application also provides a data classification device, such as Fig. 9 As shown, Fig. 9 : is a structural diagram of a data classification device shown in an exemplary embodiment of the present application. The data classification device includes:

[0101] The construction module 910 is configured to construct a set of data elements of multiple types corresponding to the data elements to be classified, and obtain multiple data classification standard tables, where the data classification standard tables are used to classify the data elements to be classified.

[0102] The calculation module 930 is configured to calculate, for each data classification standard table, a correlation value for a data element between the data classification standard table and a plurality of types of data element sets.

[0103] The selection module 950 is configured to select a data classification standard table with the largest correlation value from the multiple data classification standard tables as a target data classification standard table.

[0104] The classification module 970 is configured to classify the data elements to be classified according to the target data classification standard table.

[0105] In another embodiment, the calculation module 930 includes:

[0106] The first statistical unit is configured to respectively count the number of data elements contained in the data element sets of multiple types to obtain first numbers corresponding to the data element sets of multiple types respectively.

[0107] The second statistical unit is configured to count the number of data elements in the data element sets of multiple types that match the classification fields in the data classification standard table, respectively, to obtain second numbers corresponding to the data element sets of multiple types.

[0108] The first correlation value calculation unit is configured to calculate the correlation value for the data element between the data classification standard table and the data element sets of multiple types according to the first quantity and the second quantity.

[0109] In another embodiment, the first correlation value calculation unit includes:

[0110] The first average quantity calculation subunit is configured to perform an average operation on a plurality of first quantities to obtain a first average quantity.

[0111] The second average quantity calculation subunit is configured to perform an average operation on a plurality of second quantities to obtain a second average quantity.

[0112] The correlation value calculation subunit is configured to calculate the correlation value for the data element between the data classification standard table and the data element sets of multiple types based on the first average quantity and the second average quantity.

[0113] In another embodiment, the correlation value calculation subunit includes:

[0114] The first difference calculation module is configured to perform a difference operation on the first quantity corresponding to each type of data element set and the first average quantity to obtain first differences corresponding to multiple types of data element sets.

[0115] The second difference calculation module is configured to perform a difference operation on the second quantity corresponding to the data element set and the second average quantity to obtain second differences corresponding to multiple types of data element sets respectively.

[0116] The correlation value calculation module is configured to calculate the correlation value for the data element between the data classification standard table and the data element sets of multiple types based on the first difference and the second difference.

[0117] In another embodiment, the correlation value calculation module includes:

[0118] The sum operation submodule is configured to perform a sum operation on the absolute values ​​of the plurality of first differences to obtain a first sum value, and to perform a sum operation on the absolute values ​​of the plurality of second differences to obtain a second sum value.

[0119] The product operation submodule is configured to perform a product operation on the first sum value and the second sum value to obtain a first product.

[0120] The traversal sub-section is configured to traverse the data element set, take the traversed data element set as the target data element set, take the first difference corresponding to the target data element set as the target first difference, and take the second difference corresponding to the target data element set as the target second difference.

[0121] The product sum submodule is configured to perform a product operation on the target first difference and the target second difference to obtain a target product, so as to obtain the sum of multiple products of the first difference and the second difference.

[0122] The correlation value calculation submodule is configured to divide the sum of multiple products by the first product to obtain the correlation value for the data element between the data classification standard table and the data element sets of multiple types.

[0123] In another embodiment, the calculation module 930 includes:

[0124] The second statistical unit is configured to count the number of data elements in the data element sets of multiple types that match the classification fields in the data classification standard table, respectively, to obtain second numbers corresponding to the data element sets of multiple types.

[0125] The second correlation value calculation unit is configured to perform a sum operation on multiple second quantities, and use the sum value obtained by the sum operation as the correlation value for the data element between the data classification standard table and multiple types of data element sets.

[0126] In another embodiment, the data classification device further comprises:

[0127] The preprocessing module is configured to preprocess the fields contained in multiple initial data classification tables to obtain multiple preprocessed initial data classification tables, and use the multiple preprocessed initial data classification tables as multiple data classification standard tables respectively; wherein the preprocessing includes at least one of the elimination of abnormal fields and the elimination of duplicate fields.

[0128] It should be noted that the data classification device provided in the above embodiment and the data classification method provided in the above embodiment belong to the same concept, wherein the specific manner in which each module and unit performs operations has been described in detail in the method embodiment and will not be repeated here.

[0129] Another aspect of the present application provides an electronic device, including: a controller; and a memory for storing one or more programs, which, when executed by the controller, executes the above method.

[0130] See also Fig.10 , Fig.10 1 is a schematic diagram of the structure of a computer system of an electronic device shown in an exemplary embodiment of the present application, which shows a schematic diagram of the structure of a computer system of an electronic device suitable for implementing an embodiment of the present application.

[0131] It should be noted that Fig.10 The computer system 1000 of the electronic device shown is only an example and should not bring any limitation to the functions and scope of use of the embodiments of the present application.

[0132] like Fig.10As shown, the computer system 1000 includes a central processing unit (CPU) 1001, which can perform various appropriate actions and processes according to the program stored in the read-only memory (ROM) 1002 or the program loaded from the storage part 1008 to the random access memory (RAM) 1003, such as executing the method in the above embodiment. In the RAM 1003, various programs and data required for system operation are also stored. The CPU 1001, the ROM 1002 and the RAM 1003 are connected to each other through the bus 1004. The input / output (I / O) interface 1005 is also connected to the bus 1004.

[0133] The following components are connected to the I / O interface 1005: an input section 1006 including a keyboard, a mouse, etc.; an output section 1007 including a cathode ray tube (CRT), a liquid crystal display (LCD), etc., and a speaker, etc.; a storage section 1008 including a hard disk, etc.; and a communication section 1009 including a network interface card such as a LAN (Local Area Network) card, a modem, etc. The communication section 1009 performs communication processing via a network such as the Internet. A drive 1010 is also connected to the I / O interface 1005 as needed. A removable medium 1011, such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, etc., is installed on the drive 1010 as needed so that a computer program read therefrom is installed into the storage section 1008 as needed.

[0134] In particular, according to an embodiment of the present application, the process described above with reference to the flowchart can be implemented as a computer software program. For example, an embodiment of the present application includes a computer program product, which includes a computer program carried on a computer-readable medium, and the computer program includes a computer program for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from a network through a communication section 1009, and / or installed from a removable medium 1011. When the computer program is executed by a central processing unit (CPU) 1001, various functions defined in the system of the present application are executed.

[0135] It should be noted that the computer-readable medium shown in the embodiment of the present application can be a computer-readable signal medium or a computer-readable storage medium or any combination of the above two. The computer-readable storage medium can be, for example, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or device, or any combination of the above. More specific examples of computer-readable storage media can include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM), a flash memory, an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In the present application, a computer-readable storage medium can be any tangible medium containing or storing a program, which can be used by an instruction execution system, device or device or used in combination with it. In the present application, a computer-readable signal medium can include a data signal propagated in a baseband or as a part of a carrier wave, wherein a computer-readable computer program is carried. Such propagated data signals may take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. Computer-readable signal media may also be any computer-readable medium other than computer-readable storage media, which may send, propagate, or transmit programs for use by or in conjunction with an instruction execution system, apparatus, or device. The computer program contained on the computer-readable medium may be transmitted using any appropriate medium, including but not limited to: wireless, wired, etc., or any suitable combination of the above.

[0136] The flowchart and block diagram in the accompanying drawings illustrate the possible architecture, functions and operations of the system, method and computer program product according to various embodiments of the present application. Wherein, each box in the flowchart or block diagram can represent a module, a program segment, or a part of the code, and the above-mentioned module, program segment, or a part of the code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in a different order than the order marked in the accompanying drawings. For example, two boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram or flowchart, and the combination of boxes in the block diagram or flowchart can be implemented with a dedicated hardware-based system that performs a specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0137] The units involved in the embodiments described in this application may be implemented by software or hardware, and the units described may also be set in a processor. The names of these units do not, in some cases, constitute limitations on the units themselves.

[0138] Another aspect of the present application further provides a computer-readable storage medium on which a computer program is stored, and when the computer program is executed by a processor, the data classification method as described above is implemented. The computer-readable storage medium may be included in the electronic device described in the above embodiment, or may exist independently without being assembled into the electronic device.

[0139] Another aspect of the present application also provides a computer program product or a computer program, which includes a computer instruction stored in a computer-readable storage medium. A processor of a computer device reads the computer instruction from the computer-readable storage medium, and the processor executes the computer instruction, so that the computer device executes the data classification method provided in each of the above embodiments.

[0140] According to one aspect of an embodiment of the present application, a computer system is also provided, including a central processing unit (CPU), which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) or a program loaded from a storage portion into a random access memory (RAM), such as executing the method in the above embodiment. In RAM, various programs and data required for system operation are also stored. CPU, ROM and RAM are connected to each other via a bus. An input / output (I / O) interface is also connected to the bus.

[0141] The following components are connected to the I / O interface: an input part including a keyboard, a mouse, etc.; an output part including a cathode ray tube (CRT), a liquid crystal display (LCD), etc., and a speaker; a storage part including a hard disk, etc.; and a communication part including a network interface card such as a LAN (Local Area Network) card, a modem, etc. The communication part performs communication processing via a network such as the Internet. A drive is also connected to the I / O interface as needed. Removable media, such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, etc., are installed on the drive as needed so that the computer program read therefrom is installed into the storage part as needed.

[0142] The above content is only a preferred exemplary embodiment of the present application and is not intended to limit the implementation scheme of the present application. A person skilled in the art can easily make corresponding changes or modifications based on the main concept and spirit of the present application. Therefore, the scope of protection of the present application shall be based on the scope of protection required by the claims.

Claims

1. A data classification method, characterized in that: include: Constructing a plurality of types of data element sets corresponding to the data elements to be classified, and obtaining a plurality of data classification standard tables, wherein the data classification standard tables are used to classify the data elements to be classified; For each data classification standard table, calculating a correlation value for a data element between the data classification standard table and the data element sets of the plurality of types; Selecting the data classification standard table with the largest correlation value from the multiple data classification standard tables as the target data classification standard table; The data elements to be classified are classified according to the target data classification standard table.

2. The method according to claim 1, characterized in that The calculating the correlation value between the data classification standard table and the data element sets of the plurality of types for the data elements comprises: Counting the number of data elements contained in the data element sets of multiple types respectively to obtain first numbers corresponding to the data element sets of multiple types respectively; Counting the number of data elements in the data element sets of multiple types that match the classification fields in the data classification standard table respectively, to obtain second numbers corresponding to the data element sets of multiple types respectively; According to the first number and the second number, a correlation value for a data element between the data classification standard table and the data element sets of the multiple types is calculated.

3. The method according to claim 2, characterized in that The step of calculating, based on the first quantity and the second quantity, a correlation value for a data element between the data classification standard table and the data element sets of the plurality of types includes: Performing an averaging operation on the plurality of first quantities to obtain a first average quantity; Performing an averaging operation on the plurality of second quantities to obtain a second average quantity; Based on the first average number and the second average number, a correlation value for a data element between the data classification standard table and the data element sets of the multiple types is calculated.

4. The method according to claim 3, characterized in that: The step of calculating, based on the first average number and the second average number, a correlation value for a data element between the data classification standard table and the data element sets of the plurality of types includes: For each type of data element set, performing a difference operation on a first quantity corresponding to the data element set and the first average quantity to obtain first difference values ​​corresponding to the data element sets of multiple types respectively; and performing a difference operation on a second number corresponding to a data element set and the second average number to obtain second difference values ​​respectively corresponding to data element sets of multiple types; Based on the first difference value and the second difference value, a correlation value for a data element between the data classification standard table and the data element sets of the multiple types is calculated.

5. The method according to claim 4, characterized in that The step of calculating, based on the first difference and the second difference, a correlation value for a data element between the data classification standard table and the data element sets of the plurality of types includes: Performing a sum operation on the absolute values ​​of the plurality of first differences to obtain a first sum value, and performing a sum operation on the absolute values ​​of the plurality of second differences to obtain a second sum value; Performing a product operation on the first sum value and the second sum value to obtain a first product; Traversing the data element set, taking the traversed data element set as the target data element set, taking the first difference value corresponding to the target data element set as the target first difference value, and taking the second difference value corresponding to the target data element set as the target second difference value; Performing a product operation on the target first difference and the target second difference to obtain a target product, so as to obtain a sum of multiple products of the first difference and the second difference; The sum of the multiple products is divided by the first product to obtain a correlation value for a data element between the data classification standard table and the data element sets of the multiple types.

6. The method according to claim 1, characterized in that The calculating the correlation value between the data classification standard table and the data element sets of the plurality of types for the data elements comprises: Counting the number of data elements in the data element sets of multiple types that match the classification fields in the data classification standard table respectively, to obtain second numbers corresponding to the data element sets of multiple types respectively; A sum operation is performed on the multiple second quantities, and a sum value obtained by the sum operation is used as a correlation value for the data element between the data classification standard table and the multiple types of data element sets.

7. The method according to any one of claims 1 to 6, characterized in that Before constructing a plurality of types of data element sets corresponding to the data elements to be classified and obtaining a plurality of data classification standard tables, the method further includes: The fields contained in the multiple initial data classification tables are preprocessed to obtain the multiple preprocessed initial data classification tables, and the multiple preprocessed initial data classification tables are respectively used as the multiple data classification standard tables; wherein the preprocessing includes at least one of the elimination of abnormal fields and the elimination of duplicate fields.

8. A data classification device, characterized in that: include: A construction module is configured to construct a plurality of types of data element sets corresponding to the data elements to be classified, and obtain a plurality of data classification standard tables, wherein the data classification standard tables are used to classify the data elements to be classified; A calculation module configured to calculate, for each data classification standard table, a correlation value for a data element between the data classification standard table and the data element sets of the plurality of types; A selection module is configured to select a data classification standard table with a maximum correlation value from the multiple data classification standard tables as a target data classification standard table; The classification module is configured to classify the data elements to be classified according to the target data classification standard table.

9. An electronic device, characterized in that: include: Controller; A memory for storing one or more programs, which, when executed by the controller, enables the controller to implement the data classification method according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that: Computer-readable instructions are stored thereon, and when the computer-readable instructions are executed by a processor of a computer, the computer is caused to execute the data classification method according to any one of claims 1 to 7.

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