Data standard matching method and apparatus

Abstract

The embodiment of the application provides a data standard matching method and device, the method comprises the following steps: carrying out word disintegration on the field name of a data table, and matching the corresponding sample matrix in a self-learning library according to the disintegrated words, wherein the column vector of the sample matrix is a preset word, and the row vector of the sample matrix is a preset data standard; determining the correlation degree of the field name and each preset data standard in the sample matrix according to the correct association times of the disintegrated words in the sample matrix; and determining the preset data standard matched with the field name according to the correlation degree. The application can accurately match the correct data standard, and effectively improve the standard penetration rate.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of natural language processing, and can also be used in the field of finance, and particularly relates to a data standard matching method and device. BACKGROUND

[0002] In a large software development company or group, there are many different teams each responsible for different systems. In order to ensure the consistency of the underlying data of these systems, data standards (a rule stipulating the length, type, and whether to allow null of a certain field) need to be formulated to stipulate the length, type, etc. of a certain field (for example, the length of a mobile phone number is 11 digits, and the type is a number or a character). Through data standards, the cost of collaboration between different systems is reduced.

[0003] However, due to uneven personnel quality, poor data standard dissemination, and other problems, the corresponding data standards cannot be implemented in the data table design process, resulting in low standard implementation rate (the number of fields that have been associated with correct standards / the number of fields that should be implemented in the system. Used to reflect the implementation of data standards in data tables / systems), inconsistent data structures between systems, and high collaboration costs. SUMMARY

[0004] In view of the problems in the prior art, the present application provides a data standard matching method and device, which can accurately match correct data standards and effectively improve the standard implementation rate.

[0005] In order to solve at least one of the above problems, the present application provides the following technical solutions:

[0006] In a first aspect, the present application provides a data standard matching method, comprising:

[0007] performing word disassembly on the field name of a data table, and matching a corresponding sample matrix in a self-learning library according to the disassembled words, wherein the column vector of the sample matrix is a preset word, and the row vector of the sample matrix is a preset data standard;

[0008] determining the relevance of the field name and each preset data standard in the sample matrix according to the correct association times of the disassembled words in the sample matrix;

[0009] determining the preset data standard matched with the field name according to the relevance.

[0010] Further, before the matching of the corresponding sample matrix in the self-learning library according to the disassembled words, the method comprises:

[0011] The Chinese name, English name and table name of the field of the historical data table are word disassembled, the disassembled words are divided into three categories of Chinese name, English name and table name, and are taken as column vectors of a sample matrix together with a business field to which the data table belongs;

[0012] The preset data standard is taken as a row vector of the sample matrix;

[0013] The sample matrix is constructed according to the column vector and the row vector.

[0014] Further, the matching of the disassembled words with the corresponding sample matrix in the self-learning library comprises:

[0015] It is judged whether the disassembled words match the column vectors of each sample matrix in the self-learning library;

[0016] If matched, it is determined that the corresponding sample matrix matches the disassembled words.

[0017] Further, the determination of the relevance of the field name and each preset data standard in the sample matrix according to the correct association times of the disassembled words in the sample matrix comprises:

[0018] The column vector corresponding to the disassembled words in the sample matrix is determined;

[0019] The correct association times of the row vector corresponding to the column vector are obtained;

[0020] The relevance of the field name and each preset data standard in the sample matrix is determined according to the correct association times.

[0021] Further, the determination of the relevance of the field name and each preset data standard in the sample matrix according to the correct association times comprises:

[0022] The character similarity and sample similarity of the field name and each preset data standard in the sample matrix are determined according to the correct association times;

[0023] The numerical set of the character similarity and the sample similarity is normalized and averaged to obtain the relevance value of the field name and each preset data standard in the sample matrix.

[0024] Further, the determination of the preset data standard matching the field name according to the relevance comprises:

[0025] The preset data standard with the largest relevance is determined as the data standard matching the field name;

[0026] An association relationship between the field name and the data standard is established.

[0027] In a second aspect, the present application provides a data standard matching device, comprising:

[0028] a sample matrix matching module, configured to perform word disassembly on a field name of a data table, and match a corresponding sample matrix in a self-learning library according to the disassembled words, wherein a column vector of the sample matrix is a preset word, and a row vector of the sample matrix is a preset data standard;

[0029] a correlation degree determining module, configured to determine a correlation degree between the field name and each preset data standard in the sample matrix according to a correct association frequency of the disassembled words in the sample matrix;

[0030] a data standard matching module, configured to determine a preset data standard matched with the field name according to the correlation degree.

[0031] Further, the sample matrix matching module comprises:

[0032] a matrix column vector determining unit, configured to perform word disassembly on a Chinese name of a field, an English name of the field, and a table name to which the field belongs of a historical data table, divide the disassembled words into three categories of Chinese name, English name, and table name, and take the three categories of Chinese name, English name, and table name together with a business field to which the data table belongs as a column vector of a sample matrix;

[0033] a matrix row vector determining unit, configured to take a preset data standard as a row vector of the sample matrix;

[0034] a matrix constructing unit, configured to construct a sample matrix according to the column vector and the row vector.

[0035] Further, the sample matrix matching module further comprises:

[0036] a word matching unit, configured to determine whether the disassembled words match the column vector of each sample matrix in the self-learning library;

[0037] a matrix matching unit, configured to determine that the corresponding sample matrix matches the disassembled words if the disassembled words match.

[0038] Further, the correlation degree determining module comprises:

[0039] a corresponding column vector determining unit, configured to determine a column vector corresponding to the disassembled words in the sample matrix;

[0040] a corresponding row vector association frequency determining unit, configured to obtain a correct association frequency of a row vector corresponding to the column vector;

[0041] The correlation calculation unit is configured to determine the correlation of the field name with each preset data standard in the sample matrix according to the correct association times.

[0042] Further, the correlation calculation unit comprises:

[0043] The similarity calculation sub-unit is configured to determine the character similarity and sample similarity of the field name with each preset data standard in the sample matrix according to the correct association times.

[0044] The numerical processing sub-unit is configured to perform normalization processing and mean processing on the numerical set of the character similarity and the sample similarity to obtain the correlation numerical value of the field name with each preset data standard in the sample matrix.

[0045] Further, the data standard matching module comprises:

[0046] The optimal standard determination unit is configured to determine the preset data standard with the maximum correlation as the data standard matched with the field name.

[0047] The standard association unit is configured to establish the association relationship between the field name and the data standard.

[0048] In a third aspect, the present application provides an electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the steps of the data standard matching method.

[0049] In a fourth aspect, the present application provides a computer readable storage medium, which stores a computer program executable by a processor to implement the steps of the data standard matching method.

[0050] In a fifth aspect, the present application provides a computer program product, comprising a computer program / instruction executable by a processor to implement the steps of the data standard matching method.

[0051] From the above technical solution, the present application provides a data standard matching method and device, which determines the correlation of the field name with each preset data standard in the sample matrix according to the correct association times of the field name in the sample matrix, and determines the data standard most matched with the current field name, thereby accurately matching the correct data standard and effectively improving the standard penetration rate. BRIEF DESCRIPTION OF DRAWINGS

[0052] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the accompanying drawings needed to be used in the description of the embodiments or the prior art will be briefly introduced. Obviously, the accompanying drawings in the following description are some embodiments of the present application, and other drawings can be obtained by those skilled in the art without any creative effort based on these drawings.

[0053] Figure 1 One of the flowcharts of the data standard matching method in the embodiments of the present application;

[0054] Figure 2 The second flowchart of the data standard matching method in the embodiments of the present application;

[0055] Figure 3 The third flowchart of the data standard matching method in the embodiments of the present application;

[0056] Figure 4 The fourth flowchart of the data standard matching method in the embodiments of the present application;

[0057] Figure 5 The fifth flowchart of the data standard matching method in the embodiments of the present application;

[0058] Figure 6 The sixth flowchart of the data standard matching method in the embodiments of the present application;

[0059] Figure 7 One of the structural diagrams of the data standard matching device in the embodiments of the present application;

[0060] Figure 8 The second structural diagram of the data standard matching device in the embodiments of the present application;

[0061] Figure 9 The third structural diagram of the data standard matching device in the embodiments of the present application;

[0062] Figure 10 The fourth structural diagram of the data standard matching device in the embodiments of the present application;

[0063] Figure 11 The fifth structural diagram of the data standard matching device in the embodiments of the present application;

[0064] Figure 12 The sixth structural diagram of the data standard matching device in the embodiments of the present application;

[0065] Figure 13 The flowchart of the data standard matching in one of the embodiments of the present application;

[0066] Figure 14A structural schematic diagram of an electronic device in an embodiment of the present application. DETAILED DESCRIPTION

[0067] To make the objectives, technical solutions, and advantages of the embodiments of the present application clearer, the following will be combined with the accompanying drawings for the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are some but not all of the embodiments of the present application. Based on the embodiments in the present application, all other embodiments obtained by a person of ordinary skill in the art without creative work fall within the protection scope of the present application.

[0068] In the technical solutions of the present application, the acquisition, storage, use, processing, and the like of data comply with relevant provisions of national laws and regulations.

[0069] In view of the problem that in the prior art, corresponding data standards cannot be implemented in the data table design process, thereby leading to a low standard compliance rate, the present application provides a data standard matching method and device, which determines the relevance of a data table field name to each preset data standard by the correct association times of the data table field name in a sample matrix, and determines the data standard that is most matched with the current field name, thereby accurately matching the correct data standard and effectively improving the standard compliance rate.

[0070] In order to accurately match the correct data standard and effectively improve the standard compliance rate, an embodiment of a data standard matching method is provided in the present application, referring to Figure 1 The data standard matching method specifically includes the following contents:

[0071] Step S101: performing word disintegration on the field name of the data table, and matching the corresponding sample matrix in the self-learning library according to the disintegrated words, wherein the column vector of the sample matrix is a preset word, and the row vector of the sample matrix is a preset data standard.

[0072] Optionally, the data table can be a data table in which the research and development personnel have defined the field names in the process of designing the data table, but the data standard association has not been performed.

[0073] Optionally, the field name of the data table includes but is not limited to: field Chinese name, field English name, and field table name.

[0074] Optionally, the present application can perform word disintegration on the field name by using an existing word disintegration technology (for example, a word disassembler).

[0075] For example, a system in the field of public deposit needs to design a "bond float reconciliation statement", in which the Chinese name of a certain field is "bond issuer account", and the English name is "floaters_account". First, according to the word breaker, the Chinese name is broken down into bond, issuer, and account, the English name is broken down into floaters, account, and the table name is broken down into bond, float, and reconciliation.

[0076] Optionally, the application can pre-set a self-learning library containing a plurality of sample matrices according to historical data and / or preset data. The Chinese name of the field, the English name of the field, and the table name to which the field belongs of the historical data table are broken down into words, and the broken-down words are divided into three categories: Chinese name, English name, and table name. The business field to which the data table belongs is used as a column vector of the sample matrix, and the preset data standard is used as a row vector of the sample matrix. The column vector and the row vector are used to construct a sample matrix.

[0077] In some embodiments of the application, according to whether the row vector and the column vector in the historical data and / or the preset data are correctly associated (which can be determined by human judgment), a positive sample matrix and a negative sample matrix can be constructed respectively. In the positive sample matrix, if the row vector and the column vector are correctly associated, the corresponding association times are incremented by one. In the negative sample matrix, if the row vector and the column vector are incorrectly associated, the corresponding association times are incremented by one.

[0078] Optionally, since the column vector of the sample matrix is obtained by breaking down the field name, the application can determine the corresponding sample matrix in the self-learning library that matches the broken-down words by text matching. For example, by judging whether the broken-down words match the column vector of each sample matrix in the self-learning library, the sample matrix in the self-learning library that matches the field name of the application is determined.

[0079] Inherit the above example, find "field Chinese name word number * field English name word number * table name word number * 2 (positive matrix + negative matrix)" sample matrices from the self-learning library. In this example, 3*2*3*2=36 sample matrices are found.

[0080] Step S102: Determine the relevance of the field name to each preset data standard in the sample matrix according to the correct association times of the broken-down words in the sample matrix.

[0081] Optionally, the application can first determine the column vector in the sample matrix that corresponds to the broken-down words, such as "account number, account, reconciliation". Then, the correct association times of the row vector corresponding to the column vector are obtained, such as for the data standard "card number" and "issuer account", the correct association times are obtained. See Table 1 below:

[0082] Table 1 forward sample matrix

[0083]

[0084]

[0085] As can be seen from Table 1, 104 indicates that the "issuer account number" data standard is correctly associated 104 times in the "public deposit" field of all records in the self-learning library.

[0086] If it is a reverse sample matrix, it is as shown in Table 2 below:

[0087] Table 2 reverse sample matrix

[0088]

[0089] Then, the application can determine the relevance of the field name and each preset data standard in the sample matrix according to the correct association number, for example, determine the character similarity and sample similarity of the field name and each preset data standard in the sample matrix according to the correct association number, and then normalize and average the numerical set of the character similarity and the sample similarity to obtain the relevance value of the field name and each preset data standard in the sample matrix.

[0090] For example, for the "issuer account number" standard, P1=104, P2=10, P3=7, P4=10; S1=31, S2=3, S3=4, S4=5. Character similarity Sample similarity Since the values are all between 0 and 1, the normalization result is the data itself, and the relevance value R1≈0.42 can be obtained. The same processing is performed on the "card number" to obtain the relevance value R2≈0.37.

[0091] Step S103: determining a preset data standard matching the field name according to the relevance.

[0092] Optionally, in the above example, the data standard corresponding to R1 can be used as the preset data standard matching the "issuer account number" and the field name.

[0093] As can be seen from the above description, the data standard matching method provided by the embodiments of the application can determine the relevance of the data table field name and each preset data standard by the correct association number of the field name in the sample matrix, and determine the data standard most matching the current field name, thereby accurately matching the correct data standard and effectively improving the compliance rate.

[0094] In order to accurately construct the sample matrix for matching with the current field name, in an embodiment of the data standard matching method of the present application, referring to Figure 2 The step S101 can further include the following contents:

[0095] Step S201: disassemble the Chinese name, English name and table name of the field of the historical data table, divide the disassembled words into three categories of Chinese name, English name and table name, and use the business field to which the data table belongs as a column vector of the sample matrix.

[0096] Step S202: use the preset data standard as a row vector of the sample matrix.

[0097] Step S203: construct the sample matrix according to the column vector and the row vector.

[0098] Optionally, the present application can pre-set a self-learning library containing a plurality of sample matrices according to the historical data and / or the preset data, disassemble the Chinese name, English name and table name of the field of the historical data table, divide the disassembled words into three categories of Chinese name, English name and table name, and use the business field to which the data table belongs as a column vector of the sample matrix, use the preset data standard as a row vector of the sample matrix, and construct the sample matrix according to the column vector and the row vector.

[0099] In order to accurately determine the sample matrix matched with the field name, in an embodiment of the data standard matching method of the present application, referring to Figure 3 The step S101 can further include the following contents:

[0100] Step S301: determine whether the disassembled words match the column vectors of the sample matrices in the self-learning library.

[0101] Step S302: if matched, determine that the corresponding sample matrix matches the disassembled words.

[0102] Optionally, since the column vector of the sample matrix is obtained by disassembling the field name, the present application can determine the corresponding sample matrix in the self-learning library matched with the disassembled words of the present application by text matching, for example, by determining whether the disassembled words match the column vectors of the sample matrices in the self-learning library, to determine the sample matrix in the self-learning library matched with the field name of the present application.

[0103] In order to accurately determine the relevance of the field name and each preset data standard in the sample matrix, in an embodiment of the data standard matching method of the present application, referring to Figure 4 The step S102 can further include the following contents:

[0104] Step S401: determining a column vector in the sample matrix corresponding to the disassembled word.

[0105] Step S402: obtaining a correct association number of a row vector corresponding to the column vector.

[0106] Step S403: determining a relevance of the field name to each preset data standard in the sample matrix according to the correct association number.

[0107] Optionally, the application can first determine a column vector in the sample matrix corresponding to the disassembled word, such as the combination of "account number", "account", and "account checking", and then obtain a correct association number of a row vector corresponding to the column vector, such as the correct association number of "card number" and "issuer account number".

[0108] Then, the application can determine a relevance of the field name to each preset data standard in the sample matrix according to the correct association number, such as determining a character similarity and a sample similarity of the field name to each preset data standard in the sample matrix according to the correct association number, and then performing normalization processing and mean value processing on a numerical set of the character similarity and the sample similarity to obtain a relevance numerical value of the field name to each preset data standard in the sample matrix.

[0109] In order to accurately calculate the relevance, in an embodiment of the data standard matching method of the application, referring to Figure 5 The above step S403 can further include the following contents:

[0110] Step S501: determining a character similarity and a sample similarity of the field name to each preset data standard in the sample matrix according to the correct association number.

[0111] Step S502: performing normalization processing and mean value processing on a numerical set of the character similarity and the sample similarity to obtain a relevance numerical value of the field name to each preset data standard in the sample matrix.

[0112] For example, for the "issuer account number" standard, P1=104, P2=10, P3=7, and P4=10; S1=31, S2=3, S3=4, and S4=5. The character similarity The sample similarity Since the values are all between 0 and 1, the normalization processing result is the data itself, and the relevance numerical value R1≈0.42 can be obtained. The same processing is performed on "card number" to obtain the relevance numerical value R2≈0.37.

[0113] In order to determine the most matched data standard, in an embodiment of the data standard matching method of the present application, see Figure 6 The step S103 can further include the following contents:

[0114] Step S601: determining the preset data standard with the largest correlation degree as the data standard matched with the field name.

[0115] Step S602: establishing the association between the field name and the data standard.

[0116] In order to accurately match the correct data standard and effectively improve the standard penetration rate, the present application provides an embodiment of a data standard matching device for implementing all or part of the contents of the data standard matching method, see Figure 7 The data standard matching device specifically includes the following contents:

[0117] The sample matrix matching module 10 is configured to perform word disassembly on the field name of the data table, and match the corresponding sample matrix in the self-learning library according to the disassembled words, wherein the column vector of the sample matrix is a preset word, and the row vector of the sample matrix is a preset data standard.

[0118] The correlation degree determination module 20 is configured to determine the correlation degree between the field name and each preset data standard in the sample matrix according to the correct association times of the disassembled words in the sample matrix.

[0119] The data standard matching module 30 is configured to determine the preset data standard matched with the field name according to the correlation degree.

[0120] As can be seen from the above description, the data standard matching device provided by the embodiment of the present application can determine the correlation degree between the field name of the data table and each preset data standard according to the correct association times of the field name in the sample matrix, and determine the data standard most matched with the current field name, thereby accurately matching the correct data standard and effectively improving the standard penetration rate.

[0121] In order to accurately construct the sample matrix for matching with the current field name, in an embodiment of the data standard matching device of the present application, see Figure 8 The sample matrix matching module 10 includes:

[0122] The matrix column vector determination unit 11 is configured to perform word disassembly on the Chinese name, English name and table name of the field of the historical data table, divide the disassembled words into three categories of Chinese name, English name and table name, and take the business field of the data table together as the column vector of the sample matrix.

[0123] The matrix row vector determination unit 12 is configured to determine a preset data standard as a row vector of a sample matrix.

[0124] The matrix construction unit 13 is configured to construct the sample matrix according to the column vector and the row vector.

[0125] In order to accurately determine the sample matrix matching the field name, in an embodiment of the data standard matching device, referring to Figure 9 , the sample matrix matching module 10 further comprises:

[0126] The word matching unit 14 is configured to determine whether the disassembled word matches the column vector of each sample matrix in the self-learning library.

[0127] The matrix matching unit 15 is configured to determine that the corresponding sample matrix matches the disassembled word if the disassembled word matches the column vector.

[0128] In order to accurately determine the relevance of the field name and each preset data standard in the sample matrix, in an embodiment of the data standard matching device, referring to Figure 10 , the relevance determination module 20 comprises:

[0129] The corresponding column vector determination unit 21 is configured to determine the column vector in the sample matrix corresponding to the disassembled word.

[0130] The corresponding row vector association times determination unit 22 is configured to obtain the correct association times of the row vector corresponding to the column vector.

[0131] The relevance calculation unit 23 is configured to determine the relevance of the field name and each preset data standard in the sample matrix according to the correct association times.

[0132] In order to accurately calculate the relevance, in an embodiment of the data standard matching device, referring to Figure 11 , the relevance calculation unit 23 comprises:

[0133] The similarity calculation sub-unit 231 is configured to determine the character similarity and sample similarity of the field name and each preset data standard in the sample matrix according to the correct association times.

[0134] The numerical processing sub-unit 232 is configured to perform normalization processing and mean value processing on the numerical set of the character similarity and the sample similarity to obtain the relevance numerical value of the field name and each preset data standard in the sample matrix.

[0135] In order to determine the most matching data standard, in an embodiment of the data standard matching device, referring to Figure 12 , the data standard matching module 30 comprises:

[0136] The optimal standard determination unit 31 is configured to determine the preset data standard with the largest correlation degree as the data standard matched with the field name.

[0137] The standard association unit 32 is configured to establish an association between the field name and the data standard.

[0138] To further illustrate the present solution, the present application further provides a specific application example of a data standard matching method using the above data standard matching device, which is described in detail in the following Figure 13 , and specifically includes the following contents: a page, a word breaker module, an intelligent recommendation module, and a self-learning library.

[0139] First, when a software developer designs a table structure that needs to be associated with a data standard, the page transmits the field Chinese name, field English name, data table name, system name, and other attributes as parameters to the word breaker module. The word breaker module uses an open source program to break down all the parameters into words and transmits them to the intelligent recommendation module.

[0140] Then, the intelligent recommendation module receives the data transmitted by the word breaking module, reads the existing records from the self-learning library, uses an algorithm to calculate the data standard that is most likely needed to be associated with the field, and outputs the result to the page for display.

[0141] Next, after the developer associates the standard, the reviewer audits the result and transmits the result (pass / fail) to the self-learning database. The self-learning library records the result and updates the learning records, thereby continuously improving the accuracy of the model.

[0142] From the hardware aspect, in order to accurately match the correct data standard and effectively improve the standard penetration rate, the present application provides an embodiment of an electronic device for implementing all or part of the data standard matching method, which specifically includes the following contents:

[0143] A processor, a memory, a communications interface, and a bus; wherein the processor, the memory, and the communications interface complete mutual communication through the bus; the communications interface is configured to implement information transmission between the data standard matching device and the core business system, user terminal, and related database and other related devices; the logic controller can be a desktop computer, a tablet computer, a mobile terminal, and the like, and the present embodiment is not limited thereto. In the present embodiment, the logic controller can be implemented by referring to the embodiments of the data standard matching method and the embodiments of the data standard matching device, the contents of which are incorporated herein, and repeated descriptions are omitted.

[0144] It can be understood that the user terminal can include a smart phone, a tablet electronic device, a network set-top box, a portable computer, a desktop computer, a personal digital assistant (PDA), a vehicle-mounted device, a smart wearable device, etc. Among them, the smart wearable device can include smart glasses, a smart watch, a smart bracelet, etc.

[0145] In actual application, part of the data standard matching method can be executed on the electronic device as described above, or all operations can be completed in the client device. Specifically, it can be selected according to the processing capacity of the client device and the limitation of the user's use scene, etc. The present application does not limit this. If all operations are completed in the client device, the client device can also include a processor.

[0146] The above-mentioned client device can have a communication module (i.e. a communication unit) and can be in communication connection with a remote server to realize data transmission with the server. The server can include a server of the task scheduling center side, and in other implementation scenarios, it can also include a server of an intermediate platform, such as a server of a third-party server platform which is in communication link with the task scheduling center server. The server can include a single computer device, or a server cluster composed of multiple servers, or a server structure of a distributed device.

[0147] Figure 14 A schematic block diagram of the system structure of the electronic device 9600 of an embodiment of the present application is shown in FIG. 9. As shown in the figure, the electronic device 9600 can include a central processor 9100 and a memory 9140; the memory 9140 is coupled to the central processor 9100. It is worth noting that the structure shown in the figure is exemplary; other types of structures can also be used to supplement or replace the structure to realize telecommunication functions or other functions. Figure 14 Figure 14 The structure shown in the figure is exemplary; other types of structures can also be used to supplement or replace the structure to realize telecommunication functions or other functions.

[0148] In an embodiment, the data standard matching method function can be integrated into the central processor 9100. The central processor 9100 can be configured to perform the following control:

[0149] Step S101: performing word disintegration on the field name of the data table, and matching the corresponding sample matrix in the self-learning library according to the disintegrated words, wherein the column vector of the sample matrix is a preset word, and the row vector of the sample matrix is a preset data standard.

[0150] Step S102: determining the correlation degree of the field name and each preset data standard in the sample matrix according to the correct association times of the disintegrated words in the sample matrix.

[0151] ​Step S103: Determine a preset data standard that matches the field name based on the relevance.

[0152] As can be seen from the above description, the electronic device provided in this application embodiment determines the relevance of a data table field name to each preset data standard by the number of times the field name is correctly associated in the sample matrix, and determines the data standard that best matches the current field name. This enables accurate matching of the correct data standard and effectively improves the standardization rate.

[0153] In another embodiment, the data standard matching device can be configured separately from the central processing unit 9100. For example, the data standard matching device can be configured as a chip connected to the central processing unit 9100, and the data standard matching method function can be implemented through the control of the central processing unit.

[0154] like Figure 14 As shown, the electronic device 9600 may further include: a communication module 9110, an input unit 9120, an audio processor 9130, a display 9160, and a power supply 9170. It is worth noting that the electronic device 9600 does not necessarily need to include these components. Figure 14 All components shown; in addition, the electronic device 9600 may also include Figure 14 For components not shown, please refer to existing technologies.

[0155] like Figure 14 As shown, the central processing unit 9100, sometimes also referred to as a controller or operating control, may include a microprocessor or other processor device and / or logic device, which receives inputs and controls the operation of various components of the electronic device 9600.

[0156] The memory 9140 may be, for example, one or more of a cache, flash memory, hard drive, removable media, volatile memory, non-volatile memory, or other suitable devices. It may store the aforementioned failure-related information, and also store a program for executing that information. The central processing unit 9100 may execute the program stored in the memory 9140 to perform information storage or processing, etc.

[0157] Input unit 9120 provides input to central processing unit 9100. Input unit 9120 may be, for example, a keypad or touch input device. Power supply 9170 provides power to electronic device 9600. Display 9160 displays images and text. Display may be, for example, an LCD display, but is not limited thereto.

[0158] The memory 9140 can be a solid state memory such as a read only memory (ROM), a random access memory (RAM), a SIM card, or the like. It can also be a memory that retains information even when power is off, can be selectively erased, and is provided with more data, an example of which is sometimes referred to as an EPROM or the like. The memory 9140 can also be some other type of device. The memory 9140 includes a buffer memory 9141 (sometimes referred to as a buffer). The memory 9140 can include an application / function storage 9142 for storing application programs and function programs or for storing a flow for executing an operation of the electronic device 9600 by the central processing unit 9100.

[0159] The memory 9140 can also include a data storage 9143 for storing data such as contacts, digital data, pictures, sounds, and / or any other data used by the electronic device. A driver storage 9144 of the memory 9140 can include various drivers of the electronic device for a communication function and / or for performing other functions of the electronic device such as a messaging application, a phonebook application, and the like.

[0160] The communication module 9110 is a transmitter / receiver 9110 that transmits and receives signals via an antenna 9111. The communication module (transmitter / receiver) 9110 is coupled to the central processing unit 9100 to provide input signals and receive output signals, which can be the same as in the case of a conventional mobile communication terminal.

[0161] Based on different communication technologies, a plurality of communication modules 9110 such as a cellular network module, a Bluetooth module, and / or a wireless local area network module, and the like can be provided in the same electronic device. The communication module (transmitter / receiver) 9110 is also coupled to a speaker 9131 and a microphone 9132 via an audio processor 9130 to provide audio output via the speaker 9131 and receive audio input from the microphone 9132, thereby enabling a conventional telecommunication function. The audio processor 9130 can include any suitable buffer, decoder, amplifier, and the like. In addition, the audio processor 9130 is also coupled to the central processing unit 9100, thereby enabling recording on the local by the microphone 9132 and playing a sound stored on the local by the speaker 9131.

[0162] The embodiment of the present application also provides a computer readable storage medium capable of realizing all steps of the data standard matching method in the above embodiment, wherein the computer readable storage medium stores a computer program, and the computer program is executed by a processor to realize all steps of the data standard matching method in the above embodiment, for example, the computer program is executed by the processor to realize the following steps:

[0163] Step S101: words of a field name of a data table are disassembled, and corresponding sample matrices in a self-learning library are matched according to the disassembled words, wherein a column vector of the sample matrix is a preset word, and a row vector of the sample matrix is a preset data standard.

[0164] Step S102: a correlation degree of the field name and each preset data standard in the sample matrix is determined according to a correct association frequency of the disassembled words in the sample matrix.

[0165] Step S103: a preset data standard matched with the field name is determined according to the correlation degree.

[0166] As can be seen from the above description, the computer readable storage medium provided by the embodiment of the present application determines the correlation degree of the field name and each preset data standard through the correct association frequency of the field name in the sample matrix, and determines the data standard most matched with the current field name, so that the correct data standard can be accurately matched, and the data standard rate can be effectively improved.

[0167] The embodiment of the present application also provides a computer program product capable of realizing all steps of the data standard matching method in the above embodiment, wherein the computer program / instruction is executed by a processor to realize the steps of the data standard matching method, for example, the computer program / instruction realizes the following steps:

[0168] Step S101: words of a field name of a data table are disassembled, and corresponding sample matrices in a self-learning library are matched according to the disassembled words, wherein a column vector of the sample matrix is a preset word, and a row vector of the sample matrix is a preset data standard.

[0169] Step S102: a correlation degree of the field name and each preset data standard in the sample matrix is determined according to a correct association frequency of the disassembled words in the sample matrix.

[0170] Step S103: a preset data standard matched with the field name is determined according to the correlation degree.

[0171] From the above description, the computer program product provided by the embodiment of the present application determines the correlation degree of the data table field name with each preset data standard by the correct association times of the data table field name in the sample matrix, and determines the data standard that is most matched with the current field name, so that the correct data standard can be accurately matched, and the standard penetration rate is effectively improved.

[0172] Those skilled in the art will understand that embodiments of the present application can be provided as methods, apparatuses, or computer program products. Therefore, the present application can take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present application can take the form of a computer program product implemented on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROMs, optical storage, etc.) containing computer-usable program code.

[0173] The present application is described with reference to flowcharts and / or block diagrams of the methods, apparatuses (devices), and computer program products according to embodiments of the present application. It should be understood that each flow and / or block in the flowcharts and / or block diagrams, and the combination of flows and / or blocks in the flowcharts and / or block diagrams can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing apparatus to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing apparatus produce a device that implements the flow Figure 1 one or more flows and / or blocks Figure 1 an apparatus that performs the functions specified in one or more blocks or flows.

[0174] These computer program instructions can also be stored in a computer-readable memory that can direct the computer or other programmable data processing apparatus to work in a specific manner, so that the instructions stored in the computer-readable memory produce a manufactured product including instruction apparatus, which implements the flow Figure 1 one or more flows and / or blocks Figure 1 an apparatus that performs the functions specified in one or more blocks or flows.

[0175] These computer program instructions can also be loaded into a computer or other programmable data processing apparatus, so that a series of operation steps are performed on the computer or other programmable data processing apparatus to produce a computer-implemented process, so that the instructions executed on the computer or other programmable data processing apparatus provide a process for implementing the flow Figure 1 one or more flows and / or blocks Figure 1 an apparatus that performs the functions specified in one or more blocks or flows.

[0176] The principles and implementation manners of the present application are described by using specific examples, and the above examples are only used for helping to understand the method of the present application and its core idea; meanwhile, for the general technical personnel in the art, according to the idea of the present application, the specific implementation manners and application ranges will be changed, and the above description should not be understood as the limitation of the present application.

Claims

1. A data standard matching method, characterized in that, The method includes: The field names of the data table are decomposed into words, and the decomposed words are matched with the corresponding sample matrix in the self-learning library. The column vector of the sample matrix is ​​a preset word, and the row vector of the sample matrix is ​​a preset data standard. Based on the number of correct associations of the decomposed words in the sample matrix, the relevance of the field name to each preset data standard in the sample matrix is ​​determined. Based on the relevance, a preset data standard matching the field name is determined; Before matching the decomposed words to the corresponding sample matrix in the self-learning library, the following steps are included: A self-learning library containing multiple sample matrices is pre-configured based on historical data and / or preset data; The Chinese names, English names, and table names of the fields in the historical data table are decomposed into words. The decomposed words are divided into three categories: Chinese name, English name, and table name. These categories, along with the business domain to which the data table belongs, are used as column vectors of the sample matrix. Use the preset data standards as the row vectors of the sample matrix; Construct a sample matrix based on the column vectors and the row vectors.

2. The data standard matching method according to claim 1, characterized in that, The step of matching the decomposed words with the corresponding sample matrix in the self-learning database includes: Determine whether the decomposed words match the column vectors of each sample matrix in the self-learning database; If a match is found, the corresponding sample matrix is ​​determined to match the decomposed words.

3. The data standard matching method according to claim 1, characterized in that, The step of determining the relevance of the field name to each preset data standard in the sample matrix based on the correct association frequency of the decomposed words in the sample matrix includes: Determine the column vectors in the sample matrix that correspond to the decomposed words; Get the correct number of associations between the column vector and the corresponding row vector; The correlation between the field name and each preset data standard in the sample matrix is ​​determined based on the number of correct associations.

4. The data standard matching method according to claim 3, characterized in that, The step of determining the relevance between the field name and each preset data standard in the sample matrix based on the number of correct associations includes: The character similarity and sample similarity between the field name and each preset data standard in the sample matrix are determined based on the number of correct associations. The numerical sets of character similarity and sample similarity are normalized and mean-valued to obtain the correlation values ​​between the field name and each preset data standard in the sample matrix.

5. The data standard matching method according to claim 1, characterized in that, The step of determining the preset data standard that matches the field name based on the relevance includes: The preset data standard with the highest relevance is determined as the data standard that matches the field name; Establish the association between the field name and the data standard.

6. A data standard matching device, characterized in that, include: The sample matrix matching module is used to decompose the field names of the data table into words and match the decomposed words with the corresponding sample matrix in the self-learning library. The column vectors of the sample matrix are preset words, and the row vectors of the sample matrix are preset data standards. The relevance determination module is used to determine the relevance between the field name and each preset data standard in the sample matrix based on the number of times the decomposed words are correctly associated in the sample matrix. A data standard matching module is used to determine a preset data standard that matches the field name based on the relevance. Before matching the decomposed words to the corresponding sample matrix in the self-learning library, the following steps are included: A self-learning library containing multiple sample matrices is pre-set based on historical data and / or preset data; the Chinese names, English names, and table names of the fields in the historical data table are decomposed into words, and the decomposed words are divided into three categories: Chinese name, English name, and table name, and together with the business domain to which the data table belongs, they are used as column vectors of the sample matrix; preset data standards are used as row vectors of the sample matrix; and the sample matrix is ​​constructed based on the column vectors and the row vectors.

7. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the program, it implements the steps of the data standard matching method according to any one of claims 1 to 5.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that, When executed by a processor, the computer program implements the steps of the data standard matching method according to any one of claims 1 to 5.

9. A computer program product comprising a computer program / instructions, characterized in that, When the computer program / instruction is executed by the processor, it implements the steps of the data standard matching method according to any one of claims 1 to 5.

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