Method, device and computer equipment for determining classification information

Abstract

The present application relates to a method, device, computer equipment, storage medium and computer program product for determining hierarchical information, and relates to the field of information security technology. The method comprises: obtaining a newly added target data matching rule, and determining the similarity between the target data matching rule and each historical data matching rule, and determining the hierarchical information of the target data matching rule based on the similarity between the target data matching rule and each historical data matching rule, and the hierarchical information of each historical data matching rule. The use of this method can improve the efficiency of determining the hierarchical information of the newly added target data matching rule, and improve the accuracy of the hierarchical information of the target data matching rule.

Description

Technical Field

[0001] The present application relates to the field of information security technology, and in particular to a method, device, and computer equipment for determining hierarchical information. Background Art

[0002] With the increasing attention paid to data security, data needs to be divided, and the security levels corresponding to data with different classification information will be different. Among them, the same data has different corresponding classification information in databases corresponding to different business scenarios. For example, the classification and classification of address fields in personal systems and public systems are different.

[0003] In the related art, after a data matching rule is added, the classification information of the data matching rule is generally determined manually. Different classification information of the data matching rule applies to different databases.

[0004] However, as the number of data matching rules increases exponentially, the method of manually determining the hierarchical information of the data matching rules is very inefficient and has low accuracy. Summary of the invention

[0005] Based on this, it is necessary to provide a method, device, computer equipment, computer-readable storage medium and computer program product for determining hierarchical information that can improve efficiency in order to solve the above technical problems.

[0006] In a first aspect, the present application provides a method for determining classification information. The method comprises:

[0007] Get the newly added target data matching rules;

[0008] Determining the similarity between the target data matching rule and each historical data matching rule;

[0009] Based on the similarity between the target data matching rule and each of the historical data matching rules, and the classification information of each of the historical data matching rules, the classification information of the target data matching rule is determined.

[0010] In one embodiment, determining the similarity between the target data matching rule and each historical data matching rule includes:

[0011] Performing word segmentation on the target data matching rule to obtain a first word segmentation result;

[0012] For each historical data matching rule, the historical data matching rule is segmented to obtain a second segmentation result; the similarity between the first segmentation result and the second segmentation result is determined, and the similarity between the first segmentation result and the second segmentation result is used as the similarity between the target data matching rule and the historical data matching rule.

[0013] In one embodiment, determining the classification information of the target data matching rule based on the similarity between the target data matching rule and each historical data matching rule, and the classification information of each historical data matching rule, includes:

[0014] Based on the similarity between the target data matching rule and each historical data matching rule, the classification information of the historical data matching rule having the highest similarity to the target data matching rule is determined as the classification information of the target data matching rule.

[0015] In one embodiment, determining the classification information of the target data matching rule based on the similarity between the target data matching rule and each historical data matching rule, and the classification information of each historical data matching rule, includes:

[0016] According to a preset selection strategy and the similarity between the target data matching rule and each historical data matching rule, a plurality of first historical data matching rules whose similarities meet a similarity condition are selected from each historical data matching rule;

[0017] The hierarchical information with the highest proportion among the hierarchical information of the plurality of first historical data matching rules is determined as the hierarchical information of the target data matching rule.

[0018] In one embodiment, according to a preset selection strategy and the similarity between the target data matching rule and each historical data matching rule, a plurality of first historical data matching rules whose similarities satisfy a similarity condition are selected from each historical data matching rule, including:

[0019] Among the historical data matching rules, several first historical data matching rules having similarities higher than a preset threshold are selected.

[0020] In one embodiment, the selecting of the first several target historical data matching rules according to the preset selection strategy includes:

[0021] The similarities between the target data matching rule and each historical data matching rule are sorted from high to low; and the first several historical data matching rules are selected as the first historical data matching rule from each historical data matching rule arranged in ascending order of similarity.

[0022] In one of the embodiments, the classification information includes system identification, data classification, and data classification;

[0023] The determining the classification information of the target data matching rule based on the similarity between the target data matching rule and each historical data matching rule, and the classification information of each historical data matching rule, includes:

[0024] Determine a target system identifier of the target data matching rule based on the similarity between the target data matching rule and each historical data matching rule, and a system identifier corresponding to each historical data matching rule;

[0025] Determine the similarity between the second historical data matching rule corresponding to the target system identifier and the target data matching rule; based on the similarity between the target data matching rule and each of the second historical data matching rules, as well as the data classification and data classification of each of the second historical data matching rules, determine the target data classification and target data classification of the target data matching rule.

[0026] In a second aspect, the present application also provides a device for determining classification information. The device comprises:

[0027] An acquisition module is used to acquire newly added target data matching rules;

[0028] A first determination module, used to determine the similarity between the target data matching rule and each historical data matching rule;

[0029] The second determination module is used to determine the classification information of the target data matching rule based on the similarity between the target data matching rule and each of the historical data matching rules, and the classification information of each of the historical data matching rules.

[0030] In a third aspect, the present application further provides a computer device. The computer device includes a memory and a processor, the memory stores a computer program, and the processor implements the following steps when executing the computer program:

[0031] Get the newly added target data matching rules;

[0032] Determine the similarity between the target data matching rule and each historical data matching rule;

[0033] Based on the similarity between the target data matching rule and each of the historical data matching rules, and the classification information of each of the historical data matching rules, the classification information of the target data matching rule is determined.

[0034] In a fourth aspect, the present application further provides a computer-readable storage medium. The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the following steps are implemented:

[0035] Get the newly added target data matching rules;

[0036] Determine the similarity between the target data matching rule and each historical data matching rule;

[0037] Based on the similarity between the target data matching rule and each of the historical data matching rules, and the classification information of each of the historical data matching rules, the classification information of the target data matching rule is determined.

[0038] In a fifth aspect, the present application further provides a computer program product. The computer program product includes a computer program, and when the computer program is executed by a processor, the following steps are implemented:

[0039] Get the newly added target data matching rules;

[0040] Determine the similarity between the target data matching rule and each historical data matching rule;

[0041] Based on the similarity between the target data matching rule and each of the historical data matching rules, and the classification information of each of the historical data matching rules, the classification information of the target data matching rule is determined.

[0042] The above-mentioned method, device, computer equipment, storage medium and computer program product for determining the hierarchical information of the data matching rules determine the hierarchical information of the target data matching rules based on the similarity between the target data matching rules and each historical data matching rule, as well as the hierarchical information of each historical data matching rule, thereby improving the efficiency of determining the hierarchical information of the newly added target data matching rules and improving the accuracy of the hierarchical information of the target data matching rules. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] Figure 1 A schematic diagram of a flow chart of a method for determining classification information in an embodiment;

[0044] Figure 2 is a schematic diagram of the structure of a grading system in one embodiment;

[0045] Figure 3 is a structural block diagram of a device for determining classification information in one embodiment;

[0046] Figure 4 FIG. 4 is a diagram showing the internal structure of a computer device in one embodiment. DETAILED DESCRIPTION

[0047] In order to make the purpose, technical solution and advantages of the present application more clearly understood, the present application is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0048] Financial data is complex and diverse. If data is managed in a hierarchical manner, it can further clarify the data protection objects and help financial institutions to reasonably allocate data protection resources and costs. Therefore, it is necessary to divide the relevant financial data stored in the database, determine the classification information of each financial data, and implement different levels of security protection for different financial data.

[0049] Among them, the impact that may be caused by the destruction of the security of financial data (such as possible harm, loss or potential risks, etc.) is an important basis for determining the classification information of financial data, mainly considering the two factors of the affected object and the degree of impact. There are many business systems in financial enterprises, with hundreds of thousands of business systems and hundreds of millions of data. Therefore, the classification information of the same financial data in different business scenarios may be different. For example, the classification and classification of address fields in personal systems and public systems are different. The address field is classified as level 2 (more important) in personal systems and level 1 (unimportant) in public systems.

[0050] In the related art, each time a new data matching rule is added, the business personnel first determines the system to which the data matching rule belongs, and then determines the data classification and data grading of the financial data matched by the data matching rule, that is, determines the data classification and data grading of the data matching rule, and then forms a data matching rule with grading information.

[0051] On the one hand, the efficiency of business personnel in determining the classification information of new data matching rules is slow, and on the other hand, as the data of data matching rules increases, the accuracy of business personnel in determining the classification information of new data matching rules is low.

[0052] Based on this, the present application provides a method for determining the hierarchical information of data matching rules. A newly added target data matching rule is obtained, and the similarity between the target data matching rule and each historical data matching rule is determined, and the hierarchical information of the target data matching rule is determined based on the similarity between the target data matching rule and each historical data matching rule and the hierarchical information of each historical data matching rule.

[0053] The method for determining the hierarchical information of data matching rules provided in the present application improves, on the one hand, the efficiency of determining the hierarchical information of new data matching rules, and on the other hand, increases the accuracy of the hierarchical information of data matching rules.

[0054] The hierarchical information determination method of the data matching rules provided in the present application can be applied to a terminal or a server. The terminal can be, but is not limited to, various personal computers, laptops, smart phones, and tablet computers. The server can be implemented as an independent server or a server cluster consisting of multiple servers.

[0055] In one embodiment, Figure 1As shown, a method for determining hierarchical information is provided, comprising the following steps:

[0056] Step 101: Obtain newly added target data matching rules.

[0057] Among them, using the data matching rule, the data matching the data matching rule can be found in the database. For example, the data matching rule is: table note = (individual customer basic information end-of-day download file) && field note = (date of birth), then using the data matching rule, the data table with the table note of individual customer basic information end-of-day download file and the field note of date of birth can be found in the database. For another example, the data matching rule is: table note = (customer country of residence information modification detail table) && field note = (customer ID type), the data table with the table note of customer country of residence information modification detail table and the field note of customer ID type can be found in the database.

[0058] In one embodiment, after receiving a data matching rule input by a business person, the device determines that the input data matching rule is a newly added target matching rule. The device can also receive a request for a newly added data matching rule sent by other devices, and in response to the request, obtain the newly added target data matching rule.

[0059] In one embodiment, after receiving a new data matching rule, the device may first verify whether the format of the data matching rule is correct. After verifying that the format is correct, the device determines that the data matching rule is a newly added target data matching rule.

[0060] Step 103: Determine the similarity between the target data matching rule and each historical data matching rule.

[0061] The historical data matching rules are data matching rules for which hierarchical information has been determined. Before implementing the hierarchical information determination method for data matching rules of the present application, experienced business personnel may determine hierarchical information for several data matching rules to obtain several historical data matching rules. When the number of historical data matching rules corresponding to each hierarchical information reaches a certain number, for example, the number of historical data matching rules corresponding to each hierarchical information reaches 500.

[0062] As shown in Table 1, several data matching rules and corresponding classification information are given as examples in this application:

[0063] Table 1 Data matching rules and classification information

[0064]

[0065] In one embodiment, the device can respectively put the target data matching rule and the historical data matching rule into a preset encoder, obtain the target code of the target matching rule and the historical code of the historical data matching rule according to the output of the encoder, and then compare the target code and the historical code to obtain the similarity between the target data matching rule and the historical data matching rule. The device can also perform word segmentation on the target data matching rule and the historical data matching rule respectively, obtain the first word segmentation result of the target data matching rule and the second word segmentation result of the historical data matching rule, and then compare the similarity of the first word segmentation result with the second word segmentation result to obtain the similarity between the target data matching rule and the historical data matching rule. The device can also vectorize the target data matching rule and the historical data matching rule, and then input the vectorized target data matching rule and the historical data matching rule into the trained similarity comparison model, and determine the similarity between the target data matching rule and the historical data matching rule according to the result output by the similarity comparison model.

[0066] Step 105 : Determine the classification information of the target data matching rule based on the similarity between the target data matching rule and each historical data matching rule, and the classification information of each historical data matching rule.

[0067] Among them, the more similar the target data matching rule is to the historical data matching rule, the higher the overlap between the tables and columns involved in the target data matching rule and the historical data matching rule, so the security importance level is the same and the classification information should be the same.

[0068] In one embodiment, after the device determines the similarity between the target matching rule and each historical data matching rule, the device may determine the classification information of the historical data matching rule with the highest similarity to the target matching rule as the classification information of the target matching rule. The device may also select a number of first historical data matching rules from each historical data matching rule according to a preset selection strategy, and then determine the classification information with the highest proportion among the classification information of the first historical data matching rules as the classification information of the target data matching rule.

[0069] In this embodiment, on the one hand, the efficiency of determining the classification information of the new data matching rule is improved, and on the other hand, the accuracy of the classification information of the data matching rule is increased.

[0070] In one embodiment, the above step 103 specifically includes:

[0071] Step 1031: segment the target data according to the matching rule to obtain a first segmentation result.

[0072] In one embodiment, the device can remove the format content in the target data matching rule according to the requirements of the rule format, for example, the logical symbols required by the rule format, or the fixed fields required by the rule format, and then perform word segmentation based on the content of the format content removed from the target data matching rule to obtain the first word segmentation result. Taking the rule name as customer ID type and the rule content as table notes = (customer residence country information modification list) && field notes = (customer ID type) as an example, after removing the logical symbols "=, (,)" and removing the fixed fields "table notes, field notes", the content obtained is: customer ID type customer residence country information modification list customer ID type, and then perform word segmentation to obtain the first word segmentation result: customer, ID, type, customer, residence, country, information, modification, list, customer, ID, type.

[0073] In one embodiment, the device may also first perform semantic analysis on the target data matching rule, and sort out the nouns in the target data matching rule as the first word segmentation result. Taking the rule name as customer ID type and the rule content as table note = (customer country of residence information modification list) && field note = (customer ID type) as an example, the nouns in the rule include: customer, ID type, customer, country of residence, information, modification list, customer, ID type, etc.

[0074] Step 1033: For each historical data matching rule, perform word segmentation on the historical data matching rule to obtain a second word segmentation result.

[0075] In one embodiment, the device can remove the format content in the historical data matching rule according to the requirements of the rule format, for example, the logical symbols required by the rule format, or the fixed fields required by the rule format, and then perform word segmentation based on the content of the format content removed from the target data matching rule to obtain a second word segmentation result.

[0076] In one embodiment, the device may further perform semantic analysis on the historical data matching rule first, and sort out the nouns in the historical data matching rule as the second word segmentation result.

[0077] It should be noted that the word segmentation process for the target data matching rule is the same as the word segmentation process for the historical data matching rule, ensuring the consistency of the first word segmentation result and the second word segmentation result.

[0078] Step 1035: determine the similarity between the first word segmentation result and the second word segmentation result, and use the similarity between the first word segmentation result and the second word segmentation result as the similarity between the target data matching rule and the historical data matching rule.

[0079] In one embodiment, after the device obtains the first word segmentation result of the target data matching rule and the second word segmentation result of the historical data matching rule, it vectorizes the first word segmentation result and the second word segmentation result, calculates the cosine distance, Euclidean distance, Manhattan distance, etc. of the vectorized first word segmentation result and the second word segmentation result, and obtains the similarity between the first word segmentation result and the second word segmentation result.

[0080] Specifically, after the device obtains the first word segmentation result of the target data matching rule and the second word segmentation result of the historical data matching rule, it can combine the first word segmentation result and the second word segmentation result to obtain the word set union of the first word segmentation result and the second word segmentation result, and then calculate the word frequency of the first word segmentation result and the second word segmentation result respectively to obtain the first word segmentation vector corresponding to the first word segmentation result and the second word segmentation vector corresponding to the second word segmentation result. Then, the device obtains the similarity between the first word segmentation result and the second word segmentation result based on the cosine distance between the first word segmentation vector and the second analysis vector.

[0081] For example, the rule name of Rule 1 (target data matching rule) is: Customer ID type, and the rule content is: Table notes = (customer country of residence information modification details table) && Field notes = (customer ID type). The rule name of Rule 2 (historical data matching rule) is: English name, and the rule content is: Table notes = (customer name information modification details table) && Field notes = (English name). After removing the format content of Rule 1 and performing word segmentation, the first word segmentation result is: Customer / ID / Type / Residence / Country / Information / Modification / Details / Table, and after removing the format content of Rule 2 and performing word segmentation, the second word segmentation result is: Customer / Name / Information / Modification / Details / Table / English Name. The word segmentation results of Rule 1 and Rule 2 are combined to obtain the word set union: Customer / ID / Type / Residence / Country / Information / Modification / Details / Table / English Name / Name.

[0082] Calculate the word frequencies of rule 1 and rule 2 respectively, and obtain the first word segmentation vector corresponding to the first word segmentation result and the second word segmentation vector corresponding to the second word segmentation result:

[0083] First word vector: (3,2,2,1,1,1,1,1,1,0,0)

[0084] Second word vector: (1,0,0,0,0,1,1,1,1,2,1)

[0085] Substitute the first word segmentation vector and the second word segmentation vector into the cosine distance calculation formula:

[0086]

[0087] That is, the similarity between rule 1 and rule 2 is 0.46.

[0088] In this embodiment, the similarity between the first word segmentation result of the target data matching rule and the second word segmentation result of the historical data matching rule is calculated to obtain the similarity between the target data matching rule and the historical data matching rule.

[0089] In one embodiment, the above step 105 specifically includes:

[0090] Based on the similarity between the target data matching rule and each historical data matching rule, the classification information of the historical data matching rule having the highest similarity to the target data matching rule is determined as the classification information of the target data matching rule.

[0091] In one embodiment, after the device determines the similarity between the target data matching rule and each historical data matching rule, it determines the historical data matching rule with the highest similarity to the target data matching rule, and then determines the classification information of the historical data matching rule with the highest similarity to the target data matching rule as the classification information of the target data matching rule.

[0092] In one embodiment, after the device determines the similarity between the target data matching rule and each historical data matching rule, the number of historical data matching rules with the highest similarity to the target data matching rule is determined to be two or more. If the classification information of each historical data matching rule with the highest similarity to the target data matching rule is the same, the classification information of each historical data matching rule with the highest similarity to the target data matching rule is determined as the classification information of the target data matching rule. If the classification information of each historical data matching rule with the highest similarity determined by the device is different, the classification information with the highest proportion in the classification information of each historical data matching rule with the highest similarity is determined as the classification information of the target data matching rule.

[0093] In this embodiment, the ranking information of the historical data matching rule that is most similar to the target data matching rule is used as the ranking information of the target data matching rule.

[0094] In one embodiment, the above step 105 specifically includes:

[0095] Step 105A: According to a preset selection strategy and the similarity between the target data matching rule and each historical data matching rule, a plurality of first historical data matching rules whose similarity satisfies the selection strategy are selected from each historical data matching rule.

[0096] In one embodiment, the device obtains a preset selection strategy, and selects a plurality of first historical data matching rules whose similarities satisfy the selection strategy from the similarities between the target data matching rule and each historical data matching rule.

[0097] Specifically, the device may use a historical data matching rule with a similarity higher than a preset threshold as the first historical data matching rule, for example, a historical data matching rule with a similarity higher than 85% may be selected as the first historical data matching rule. The device may also sort the historical data matching rules from high to low according to the similarity, and select the first several historical data matching rules as the first historical data matching rule, for example, select the 20 historical data matching rules with the highest similarity as the first historical data matching rule.

[0098] Step 105B: Determine the hierarchical information with the highest proportion among the hierarchical information of the first historical data matching rules as the hierarchical information of the target data matching rule.

[0099] In one embodiment, the device selects several first historical data matching rules from each historical data matching rule according to a preset selection strategy, obtains the classification information of each first historical data matching rule, determines the classification information with the highest proportion among the classification information of these several first historical data matching rules, and uses the classification information with the highest proportion as the classification information of the target data matching rule.

[0100] In this embodiment, several historical data matching rules with the greatest similarity to the target data matching rule are selected, and then the classification information with the highest proportion among the selected classification information of the historical data matching rules is used as the classification information of the target data matching rule.

[0101] In one embodiment, the above step 105A specifically includes:

[0102] Among the historical data matching rules, several first historical data matching rules having similarities higher than a preset threshold are selected.

[0103] In one embodiment, the device can first obtain a preset threshold set for similarity, and after determining the similarity between each historical data matching rule and the target data matching rule, filter out historical data matching rules whose similarity is higher than the preset threshold, and use the filtered historical data matching rules as the first historical data matching rules.

[0104] In this embodiment, several historical data matching rules whose similarity with the target data matching rule is higher than a preset threshold are selected to ensure the reference credibility of the selected historical data matching rules.

[0105] In one embodiment, the above step 105A specifically includes:

[0106] Step A1: Sort the similarities between the target data matching rule and each historical data matching rule from high to low.

[0107] In one embodiment, after determining the similarity between the target data matching rule and each historical data matching rule, the device sorts the similarities between the target data matching rule and each historical data matching rule from high to low to obtain each historical data matching rule arranged in ascending order of similarity.

[0108] Step A2: Select the first several historical data matching rules from the historical data matching rules arranged in ascending order of similarity as the first historical data matching rule.

[0109] In one embodiment, after obtaining the historical data matching rules arranged in ascending order of similarity, the device selects first several historical data matching rules from the historical data matching rules arranged in ascending order of similarity as the first historical data matching rule.

[0110] In one embodiment, the above step 105A specifically includes:

[0111] Step B1: Sort the similarities between the target data matching rule and each historical data matching rule from low to high.

[0112] In one embodiment, after determining the similarity between the target data matching rule and each historical data matching rule, the device sorts the similarities between the target data matching rule and each historical data matching rule from low to high to obtain each historical data matching rule in descending order of similarity.

[0113] Step B2: Selecting the last several historical data matching rules from the historical data matching rules arranged in descending order of similarity as the first historical data matching rule.

[0114] In one embodiment, after obtaining the historical data matching rules arranged in descending order of similarity, the device selects first several historical data matching rules from the historical data matching rules arranged in descending order of similarity as the first historical data matching rule.

[0115] In this embodiment, several historical data matching rules with the greatest similarity to the target data matching rule are selected to ensure that the number of selected historical data matching rules is sufficient to serve as a reference for the target data matching rule.

[0116] In one embodiment, the classification information includes system identification, data classification, and data classification. In this case, the above step 105 specifically includes:

[0117] Step 1: Based on the similarity between the target data matching rule and each historical data matching rule, and the system identification corresponding to each historical data matching rule, determine the target system identification corresponding to the target data matching rule.

[0118] As shown in Table 2, several data matching rules and corresponding classification information are given as examples in this application, where the classification information includes system identification, data classification, and data classification.

[0119] Table 2 Data matching rules and classification information

[0120]

[0121]

[0122] In one embodiment, after determining the similarity between the target matching rule and each historical data matching rule, the device may determine the system identifier of the historical data matching rule with the highest similarity to the target matching rule as the target system identifier of the target matching rule.

[0123] In one embodiment, the device can also select several first historical data matching rules from each historical data matching rule according to a preset selection strategy, and then determine the system identifier with the highest proportion among the system identifiers of the several first historical data matching rules as the target system identifier of the target data matching rule.

[0124] Step 2: Determine the similarity between the second historical data matching rule corresponding to the target system identifier and the target data matching rule. Based on the similarity between the target data matching rule and each second historical data matching rule, and the data classification and data classification of each second historical data matching rule, determine the target data classification and target data classification of the target data matching rule.

[0125] In one embodiment, after the device determines the target system identifier of the target data matching rule, it determines each second historical data matching rule whose system identifier is the target system identifier, and obtains the data classification and data classification of the second historical data matching rule whose system identifier is the target system identifier. Then, the device can determine the data classification and data classification of the second historical data matching rule whose system identifier is the target system identifier as the target data classification and data classification of the target matching rule in the second historical data matching rule whose system identifier is the target system identifier. The device can also select several target second historical data matching rules according to a preset selection strategy in the second historical data matching rule whose system identifier is the target system identifier, and then determine the data classification and data classification with the highest proportion among the data classifications and data classifications of the several target second historical data matching rules as the target data classification and data classification of the target data matching rule.

[0126] In one embodiment, the device selects several historical data matching rules from each historical data matching rule according to a preset selection strategy, and determines the system identifier with the highest proportion among the system identifiers of the several historical data matching rules as the target system identifier of the target data matching rule. Then, the device determines the historical data matching rule in which the system identifier is the target system identifier among the several historical data matching rules, and sets the data classification and data grade with the highest proportion among the historical data matching rules in which the selected system identifier is the target system identifier as the target data classification and target data grade of the target data matching rule.

[0127] For example, the device sorts the similarities between each historical data matching rule and the target data matching rule, selects the K historical data matching rules with the greatest similarity, and determines the system identifiers of the K historical data matching rules. According to the principle of minority obeys majority, the target system identifier of the newly added target data matching rule is set to the system identifier with the highest proportion. Then, the device determines X historical data matching rules whose system identifiers are the target system identifiers among the K historical data matching rules, and determines the data classification and data classification of the X historical data matching rules. According to the principle of minority obeys majority, the target data classification and target data classification of the newly added target data matching rule are set to the data classification and data classification with the highest proportion.

[0128] In one embodiment, the device can also obtain the priority of each data matching rule, and divide the data in the database according to the data matching rules and classification information. If two data matching rules match the same data, the data is divided according to the data matching rule with a higher priority.

[0129] In addition, this application also provides a hierarchical system for data matching rules, such as Figure 2 As shown, the system includes a database, a database configuration module, a data acquisition module, a data matching rule matching module, a data matching rule grading module, and a rule base.

[0130] There are multiple databases in the database, each of which stores business data for different business scenarios.

[0131] The database configuration module is used to configure the connection configuration information of each database in the database, as well as the business system identifier to which each database belongs.

[0132] The data acquisition module is used to read data in the database and extract data feature information of each data table according to the connection configuration information of each database in the database configuration module.

[0133] The data matching rule matching module is used to match the data feature information extracted by the data acquisition module according to the data matching rules in the data matching rule library corresponding to each business system in the rule library, and determine the classification information of the matched data as the classification information of the data matching rule. The matching result is generally a report file or displayed on the page, including the data library name, table name, field name, classification, classification, matching rule name, etc.

[0134] The rule base is used to store the data matching rules corresponding to each business system.

[0135] The data matching rule classification module is used to receive newly added target data matching rules, automatically classify the newly added data matching rules into the rule base corresponding to each business system, and set corresponding data classification and data classification.

[0136] It should be understood that, although the various steps in the flowcharts involved in the above-mentioned embodiments are displayed in sequence according to the indication of the arrows, these steps are not necessarily executed in sequence according to the order indicated by the arrows. Unless there is a clear explanation in this article, the execution of these steps does not have a strict order restriction, and these steps can be executed in other orders. Moreover, at least a part of the steps in the flowcharts involved in the above-mentioned embodiments can include multiple steps or multiple stages, and these steps or stages are not necessarily executed at the same time, but can be executed at different times, and the execution order of these steps or stages is not necessarily to be carried out in sequence, but can be executed in turn or alternately with other steps or at least a part of the steps or stages in other steps.

[0137] Based on the same inventive concept, the embodiment of the present application also provides a hierarchical information determination device for implementing the hierarchical information determination method involved above. The implementation scheme for solving the problem provided by the device is similar to the implementation scheme recorded in the above method, so the specific limitations in one or more hierarchical information determination device embodiments provided below can refer to the limitations of the hierarchical information determination device method above, and will not be repeated here.

[0138] In one embodiment, Figure 3 As shown, a device for determining classification information is provided, comprising:

[0139] An acquisition module 301 is used to acquire a newly added target data matching rule;

[0140] A first determination module 303, used to determine the similarity between the target data matching rule and each historical data matching rule;

[0141] The second determination module 305 is used to determine the classification information of the target data matching rule based on the similarity between the target data matching rule and each of the historical data matching rules, and the classification information of each of the historical data matching rules.

[0142] In one embodiment, the second determining module 303 specifically includes:

[0143] A first word segmentation unit 303A (not shown in the figure), used to segment the target data matching rule to obtain a first word segmentation result;

[0144] The second word segmentation unit 303B (not shown in the figure) is used to segment each historical data matching rule to obtain a second word segmentation result;

[0145] The similarity comparison unit 303C (not shown in the figure) is used to determine the similarity between the first word segmentation result and the second word segmentation result, and use the similarity between the first word segmentation result and the second word segmentation result as the similarity between the target data matching rule and the historical data matching rule.

[0146] In one embodiment, the second determining module 305 is specifically used to:

[0147] Based on the similarity between the target data matching rule and each historical data matching rule, the classification information of the historical data matching rule having the highest similarity to the target data matching rule is determined as the classification information of the target data matching rule.

[0148] In one embodiment, the second determining module 305 specifically includes:

[0149] A selection unit 305A (not shown in the figure) is used to select a plurality of first historical data matching rules whose similarities meet the selection strategy from among the historical data matching rules according to a preset selection strategy and the similarities between the target data matching rule and the historical data matching rules;

[0150] The determination unit 305B (not shown in the figure) is used to determine the hierarchical information with the highest proportion among the hierarchical information of the plurality of first historical data matching rules as the hierarchical information of the target data matching rule.

[0151] In one embodiment, the selection unit 305A is specifically used for:

[0152] Among the historical data matching rules, several first historical data matching rules having similarities higher than a preset threshold are selected.

[0153] In one embodiment, the selection unit 305A specifically includes:

[0154] A sorting subunit A (not shown in the figure) is used to sort the similarities between the target data matching rule and each historical data matching rule from high to low;

[0155] The selection subunit B (not shown in the figure) is used to select the first several historical data matching rules from the historical data matching rules arranged in ascending order of similarity as the first historical data matching rule.

[0156] Each module in the above-mentioned classification information determination device can be implemented in whole or in part by software, hardware or a combination thereof. Each module can be embedded in or independent of a processor in a computer device in the form of hardware, or can be stored in a memory in a computer device in the form of software, so that the processor can call and execute the operations corresponding to each module.

[0157] In one embodiment, a computer device is provided. The computer device may be a server, and its internal structure diagram may be as follows: Figure 4 As shown. The computer device includes a processor, a memory and a network interface connected through a system bus. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the computer device is used to store historical data matching rule data. The network interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, a hierarchical information determination method is implemented.

[0158] In one embodiment, a computer device is provided. The computer device may be a terminal, and its internal structure diagram may be as follows: Figure 4 As shown. The computer device includes a processor, a memory, and a communication interface connected through a system bus. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The communication interface of the computer device is used to communicate with an external terminal in a wired or wireless manner, and the wireless manner can be implemented through WIFI, a mobile cellular network, NFC (near field communication) or other technologies. When the computer program is executed by the processor, a data classification determination method is implemented.

[0159] Those skilled in the art will understand that Figure 4The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0160] In one embodiment, a computer device is further provided, including a memory and a processor, wherein a computer program is stored in the memory, and the processor implements the steps in the above method embodiments when executing the computer program.

[0161] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the steps in the above-mentioned method embodiments are implemented.

[0162] In one embodiment, a computer program product is provided, including a computer program, which implements the steps in the above method embodiments when executed by a processor.

[0163] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties.

[0164] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to the memory, database or other medium used in the embodiments provided in the present application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetoresistive random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. As an illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM). The database involved in each embodiment provided in this application may include at least one of a relational database and a non-relational database. Non-relational databases may include distributed databases based on blockchains, etc., but are not limited to this. The processor involved in each embodiment provided in this application may be a general-purpose processor, a central processing unit, a graphics processor, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, etc., but are not limited to this.

[0165] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0166] The above-described embodiments only express several implementation methods of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the present application. It should be pointed out that, for a person of ordinary skill in the art, several variations and improvements can be made without departing from the concept of the present application, and these all belong to the protection scope of the present application. Therefore, the protection scope of the present application shall be subject to the attached claims.

Claims

1. A method for determining hierarchical information, characterized in that: The method comprises: Get the newly added target data matching rules; Determining the similarity between the target data matching rule and each historical data matching rule; Based on the similarity between the target data matching rule and each of the historical data matching rules, and the classification information of each of the historical data matching rules, the classification information of the target data matching rule is determined.

2. The method according to claim 1, characterized in that Determining the similarity between the target data matching rule and each historical data matching rule includes: Performing word segmentation on the target data matching rule to obtain a first word segmentation result; For each historical data matching rule, the historical data matching rule is segmented to obtain a second segmentation result; the similarity between the first segmentation result and the second segmentation result is determined, and the similarity between the first segmentation result and the second segmentation result is used as the similarity between the target data matching rule and the historical data matching rule.

3. The method according to claim 1, characterized in that The determining the classification information of the target data matching rule based on the similarity between the target data matching rule and each of the historical data matching rules, and the classification information of each of the historical data matching rules, includes: Based on the similarity between the target data matching rule and each historical data matching rule, the classification information of the historical data matching rule having the highest similarity to the target data matching rule is determined as the classification information of the target data matching rule.

4. The method according to claim 1, characterized in that: The determining the classification information of the target data matching rule based on the similarity between the target data matching rule and each of the historical data matching rules, and the classification information of each of the historical data matching rules, includes: According to a preset selection strategy and the similarity between the target data matching rule and each historical data matching rule, a plurality of first historical data matching rules whose similarity satisfies the selection strategy are selected from each historical data matching rule; The hierarchical information with the highest proportion among the hierarchical information of the plurality of first historical data matching rules is determined as the hierarchical information of the target data matching rule.

5. The method according to claim 4, characterized in that The method of selecting a plurality of first historical data matching rules whose similarities satisfy the selection strategy from among the historical data matching rules according to the preset selection strategy and the similarities between the target data matching rule and the historical data matching rules comprises: Among the historical data matching rules, several first historical data matching rules having similarities higher than a preset threshold are selected.

6. The method according to claim 4, characterized in that The method of selecting a plurality of first historical data matching rules whose similarities satisfy the selection strategy from among the historical data matching rules according to the preset selection strategy and the similarities between the target data matching rule and the historical data matching rules comprises: Sorting the similarities between the target data matching rule and each historical data matching rule from high to low; The first several historical data matching rules are selected from the historical data matching rules arranged in ascending order of similarity as the first historical data matching rule.

7. The method according to claim 1, characterized in that The classification information includes system identification, data classification, and data classification; The determining the classification information of the target data matching rule based on the similarity between the target data matching rule and each of the historical data matching rules, and the classification information of each of the historical data matching rules, includes: Determine a target system identifier of the target data matching rule based on the similarity between the target data matching rule and each historical data matching rule, and a system identifier corresponding to each historical data matching rule; Determine the similarity between the second historical data matching rule corresponding to the target system identifier and the target data matching rule; based on the similarity between the target data matching rule and each of the second historical data matching rules, as well as the data classification and data classification of each of the second historical data matching rules, determine the target data classification and target data classification of the target data matching rule.

8. A device for determining hierarchical information, characterized in that: The device comprises: An acquisition module is used to acquire newly added target data matching rules; A first determination module, used to determine the similarity between the target data matching rule and each historical data matching rule; The second determination module is used to determine the classification information of the target data matching rule based on the similarity between the target data matching rule and each of the historical data matching rules, and the classification information of each of the historical data matching rules.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 7 are implemented.

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