A basic data governance method and device

Abstract

The present application discloses a method and device for grass-roots data governance. The method includes: obtaining a data query request for a field to be queried; the field to be queried is related to the information of the behavior subject, and the behavior subject is related to users, enterprises, and social organizations; calling a query interface to retrieve the grass-roots associated data of the content of the field to be queried in the grass-roots data governance relationship graph; matching the field to be queried in the standard field dictionary to determine the association relationship of the field to be queried for the behavior subject; the standard field dictionary includes multiple standard field names of the grass-roots data governance relationship graph and the association relationship to which each standard field name belongs; the association relationship indicates the degree to which the field to be queried can identify the behavior subject; determining the confidence level of the grass-roots associated data according to the association relationship; the stronger the association relationship, the higher the confidence level of the grass-roots associated data; displaying the grass-roots associated data and the confidence level on the front-end user interface. The efficiency of grass-roots data governance is improved.

Description

Technical Field

[0001] This application relates to the technical field of data processing, and in particular to a grass-roots data governance method and device. Background Art

[0002] With the construction of grass-roots intelligent governance capabilities, there is a need for grass-roots data governance, expanding application scenarios and other requirements, such as grass-roots data governance in sub-districts, community neighborhood committees, etc.

[0003] Currently, in the process of grass-roots data governance, it is usually the way of sorting out reports for each grass-roots area to supervise the grass-roots area and promote grass-roots work. For example, the basic information of entities such as people, houses, vehicles, and organizations, and the ledgers of various types of businesses are stored in the form of table files in the computers of staff members and are repeatedly opened and queried. As a result, a large amount of manpower and time are repeatedly consumed, and complex grass-roots data cannot be efficiently utilized, and the informatization between the acting entities and grass-roots data cannot be achieved, resulting in low efficiency of the grass-roots data governance method. Summary of the Invention

[0004] Embodiments of this application provide a grass-roots data governance method and device for solving the problem of low efficiency of grass-roots data governance.

[0005] Embodiments of this application adopt the following technical solutions:

[0006] On the one hand, embodiments of this application provide a grass-roots data governance method, and the method includes: obtaining a data query request for a to-be-query field; wherein, the to-be-query field is related to the information of an acting entity, and the acting entity is related to users, enterprises, and social organizations; calling a query interface to retrieve grass-roots associated data corresponding to the content of the to-be-query field in a pre-constructed grass-roots data governance relationship graph; matching the to-be-query field in a preset standard field dictionary to determine the association relationship of the to-be-query field for the acting entity; wherein, the standard field dictionary includes multiple standard field names corresponding to the grass-roots data governance relationship graph, and the association relationship to which each standard field name belongs; the association relationship is used to represent the degree to which the to-be-query field can identify the acting entity; determining the confidence level of the grass-roots associated data according to the association relationship; the stronger the association relationship, the higher the confidence level of the grass-roots associated data; displaying the grass-roots associated data and the confidence level on a front-end user interface through a preset display layout.

[0007] In one example, before the method retrieves the underlying associated data corresponding to the content of the field to be queried in the pre-constructed underlying data governance relationship graph by invoking the query interface, the method further includes: constructing a basic information library, an extended information library, and a business theme library according to the underlying data set; where the basic information library includes a basic information table for each actor, the extended information library includes an extended information table for each actor, and the business theme library includes a business theme table for each actor; constructing a standard field dictionary according to the fields of the basic information table, the fields of the extended information table, and the fields of the business theme table; determining the association relationship to which each standard field belongs in the standard field dictionary; associating the basic table, the extended information table, and the business theme table corresponding to the same actor according to each standard field and the association relationship to which each standard field belongs, obtaining the underlying data governance relationship graph for each actor, and generating an association confidence level for the underlying data governance relationship graph of each actor. The higher the association confidence level, the higher the probability that the content in the underlying data governance relationship graph belongs to the same actor.

[0008] In one example, the step of associating the basic table, the extended information table, and the business theme table corresponding to the same actor according to each standard field and the association relationship to which each standard field belongs specifically includes: dividing the standard fields into identification fields and non-identification fields according to the association relationship to which each standard field belongs; the identification fields indicate that they can uniquely identify an actor, and the non-identification fields indicate that they cannot uniquely identify an actor; retrieving the fields of the basic information table of each actor to determine whether there is an identification field; if so, determining the field content of the identification field in the basic information table; in the extended information library, matching the identification field and the field content to determine whether there is an extended information table including the identification field and the field content; if so, in the business theme library, matching the identification field and the field content to determine whether there is a business theme table including the identification field and the field content; if so, associating the basic information table, the extended information table including the identification field and the field content, and the business theme table including the identification field and the field content, and generating the highest association confidence level for the underlying data governance relationship graph of each actor.

[0009] In one example, if the identification field is not available, in the fields of the basic information table of each entity, determine the non-identification field combination and the corresponding field content of the non-identification field combination in the basic information table; in the extended information library, match the non-identification field combination and the corresponding field content to determine whether there is an extended information table including the non-identification field combination and the corresponding field content; if so, in the business theme library, match the non-identification field combination and the corresponding field content to determine whether there is a business theme table including the non-identification field combination and the corresponding field content; if so, associate the basic information table, the extended information table including the non-identification field combination and the corresponding field content, and the business theme table including the non-identification field combination and the corresponding field content. The more non-identification fields included in the non-identification field combination, the higher the association confidence level generated for the grass-roots data governance relationship graph of each entity.

[0010] In one example, the identification field includes at least one of the certificate number, license plate number, mobile phone number, and low guarantee number; the non-identification field includes at least one of the name, community name, and household registration address.

[0011] In one example, constructing the standard field dictionary according to the grass-roots information table fields, extended information table fields, and business theme table fields specifically includes: standardizing the grass-roots information table fields, extended information table fields, and business theme table fields respectively to obtain standard fields; wherein, the grass-roots information table fields and extended information table fields are both included in the standard fields; determining the corresponding mapping relationships between the grass-roots information table fields, extended information table fields, and business theme table fields and the standard fields respectively; constructing the standard field dictionary according to the standard fields and the corresponding mapping relationships.

[0012] In one example, retrieving the fields of the basic information table of each entity to determine whether there is an identification field specifically includes: matching the identification field with the fields of the basic information table of each entity according to the mapping relationship between the identification field and the basic information table fields; if the match is successful, there is an identification field; if the match fails, there is no identification field.

[0013] In one example, retrieving the underlying associated data corresponding to the content of the field to be queried in the pre-constructed underlying data governance relationship graph specifically includes: determining that the field to be queried includes a target field and a condition field; judging whether the condition field is an identification field; if so, matching the content of the condition field in the underlying data governance relationship graph to determine the unique underlying relationship graph including the content of the condition field; and retrieving the content of the target field in the unique underlying relationship graph to generate the underlying associated data corresponding to the content of the field to be queried.

[0014] In one example, the method further includes: if the condition field is a non-identification field, matching the content of the condition field in the underlying data governance relationship graph to determine the underlying relationship graph including the content of the condition field; judging whether there are multiple underlying relationship graphs; if so, determining the confidence level of each underlying relationship graph; retrieving the content of the target field in each underlying relationship graph to generate the underlying associated data corresponding to the content of the field to be queried; and displaying the underlying associated data and the confidence level on the front-end user interface through a preset display layout, which specifically includes: determining the non-identification field combination related to the condition field in each underlying relationship graph; sorting the underlying associated data in order from high to low according to the confidence level of each underlying relationship graph; and displaying the underlying associated data, the confidence level, and the non-identification field combination on the front-end user interface through a preset display layout.

[0015] On the other hand, an embodiment of the present application provides an underlying data governance device, including: at least one processor; and a memory communicatively connected to the at least one processor; wherein, the memory stores instructions executable by the at least one processor, and when the instructions are executed by the at least one processor, the at least one processor is enabled to: obtain a data query request for the field to be queried; wherein, the field to be queried is related to the information of the behavioral entity, and the behavioral entity is related to users, enterprises, and social organizations; call a query interface to retrieve the underlying associated data corresponding to the content of the field to be queried in the pre-constructed underlying data governance relationship graph; match the field to be queried in a preset standard field dictionary to determine the association relationship of the field to be queried for the behavioral entity; wherein, the standard field dictionary includes multiple standard field names corresponding to the underlying data governance relationship graph and the association relationship to which each standard field name belongs; the association relationship is used to represent the degree to which the field to be queried can identify the behavioral entity; determine the confidence level of the underlying associated data according to the association relationship; the stronger the association relationship, the higher the confidence level of the underlying associated data; and display the underlying associated data and the confidence level on the front-end user interface through a preset display layout.

[0016] The above at least one technical solution adopted in the embodiments of the present application can achieve the following beneficial effects:

[0017] By pre - constructing a grass - roots data governance relationship graph, it is possible to efficiently utilize grass - roots data, enable users to retrieve the associated data of the behavioral entities, provide the confidence level of the associated data, improve the utilization efficiency of grass - roots data, improve the grass - roots work efficiency from the perspective of data application, and enable users to intuitively understand the confidence level of the associated data, effectively ensuring the accuracy of grass - roots work, thereby improving the grass - roots data governance efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] In order to more clearly illustrate the technical solutions of the present application, the following will, with reference to the drawings, elaborate on some embodiments of the present application in detail. In the drawings:

[0019] Figure 1 is a schematic flowchart of a grass - roots data governance method provided by an embodiment of the present application;

[0020] Figure 2 is a schematic diagram of a grass - roots data governance relationship graph of a behavioral entity provided by an embodiment of the present application;

[0021] Figure 3 is another grass - roots data governance relationship graph of a behavioral entity provided by an embodiment of the present application;

[0022] Figure 4 is a schematic diagram of a grass - roots data governance implementation plan provided by an embodiment of the present application;

[0023] Figure 5 is a schematic diagram of the structure of a grass - roots data governance device provided by an embodiment of the present application. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0024] To make the objectives, technical solutions, and advantages of the present application clearer, the following will clearly and completely describe the technical solutions of the present application in conjunction with specific embodiments and the corresponding drawings. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative efforts fall within the scope of protection of the present application.

[0025] The following will refer to the drawings to elaborate on some embodiments of the present application in detail.

[0026] Figure 1Schematic flowchart of a grass-roots data governance method provided by an embodiment of the present application. This method can be applied to different business fields, such as Internet finance business field, e-commerce business field, instant messaging business field, game business field, official business field, etc. Some input parameters or intermediate results in this process allow manual intervention and adjustment to help improve accuracy.

[0027] The implementation of the analysis method involved in the embodiments of the present application can be a terminal device or a server, and the present application does not make special restrictions on this. For the convenience of understanding and description, the following embodiments will be described in detail by taking the terminal device as an example.

[0028] It should be noted that the server can be a single device or a system composed of multiple devices, that is, a distributed server, and the present application does not make specific limitations on this.

[0029] Figure 1 The process in can include the following steps:

[0030] S101: Obtain a data query request for the field to be queried; wherein, the field to be queried is related to the information of the behavior subject, and the behavior subject is related to users, enterprises, and social organizations.

[0031] Among them, for example, the field to be queried is the grass-roots information corresponding to the ID number of the user, or the license plate number of user A.

[0032] S102: Invoke the query interface to retrieve the grass-roots associated data corresponding to the content of the field to be queried in the pre-constructed grass-roots data governance relationship graph.

[0033] It can be understood that through the query interface, the already associated grass-roots data governance relationship graph can be directly used to query the associated data. The number of associated data can be one or more, and if there is no result, it is empty.

[0034] In some embodiments of the present application, multiple grass-roots data governance relationship graphs corresponding to behavior subjects are pre-constructed, and each grass-roots information corresponding to each behavior subject is included in each behavior grass-roots data governance relationship graph.

[0035] When constructing, first construct a grass-roots governance database, then construct a standard field dictionary, then set the association relationship to which the standard field belongs, and then, according to the association relationship, construct a query logic to associate the grass-roots data, and finally, generate a query interface.

[0036] Specifically, first, obtain the grass-roots data set of the preset area, which can form a grass-roots data governance database. For example, the grass-roots data set comes from grass-roots areas such as streets, towns, community neighborhood committees, and village committees, and collects the basic information, extended information of the behavioral subjects such as users, enterprises, and social organizations in the grass-roots area, as well as various business theme data generated during the grass-roots work process. Among them, the basic information is used to represent the basic characteristics of the behavioral subject, and the extended information is used to supplement other characteristics of the behavioral subject.

[0037] Based on this, set the table structure and table fields required to represent the basic information. For example, the table structure includes a personnel information table, a housing information table, a building community information table, etc. Among them, the fields of the personnel information table include name, mobile phone number, gender, document type, document number, date of birth, household registration address, etc. The housing information table includes the name of the affiliated community, building number, unit number, room number, housing area, housing address, etc., and the fields of the building community information table include the affiliated street, affiliated community, community name, community type, construction year, property company, etc.

[0038] And set the table structure and table fields required to represent the extended information. Among them, the table structure includes a personnel extended information table and a vehicle information table. Among them, the fields of the personnel extended information table include name, mobile phone number, gender, document type, document number, education level, marital status, work unit, political status, etc., and the vehicle information table includes license plate number, license plate type, street name, community name, owner's name, owner's contact information, owner's document type, owner's document number, etc.

[0039] And set the table structure and table fields required to represent the business theme data. Among them, the table structure includes an elderly care information table, a longevity subsidy payment information table, a subsistence allowance handling table, and a disabled care information table. Among them, the fields of the elderly care information table include name, mobile phone number, gender, document type, age, residential address, disability level, personnel status, etc., the fields of the longevity subsidy payment information table include name, ID number, gender, age, payable months, payable standard, contact phone number, contact person's name, relationship with the person, etc., the fields of the subsistence allowance handling table include name, ID number, gender, age, education level, household number, household registration address, subsistence allowance certificate number, contact phone number, monthly subsidy amount, number of family members on subsistence allowance, residential address, etc., and the fields of the disabled care information table include name, gender, age, document type, document number, contact information, disabled person's certificate number, disability category, disability level, etc.

[0040] Based on this, through their respective corresponding table structures and table fields, a basic information library, an extended information library, and a business theme library will be constructed according to the grass-roots data set. For example, the business theme library includes a subsistence allowance theme library, a fertility theme library, an elderly theme library, etc.

[0041] It should be noted that the basic information library includes a unique personnel information table for each actor. That is to say, each actor has one and only one personnel information table. A unique identifier field must be included in the personnel information table of each actor. Note that this unique identifier field is not an inherent attribute of the subject, such as the ID card number of a person, but a unique identifier assigned to the subject by the system internally. The extended information library includes one or more tables of basic information types for each actor, and the business theme library includes one or more business subject tables for each actor. Both the basic information library and the extended information library are structured databases, such as My-SQL, etc.

[0042] Then, a standard field dictionary is constructed based on the fields of the grass-roots information table, the extended information table, and the business theme table.

[0043] Specifically, when constructing the standard field dictionary, the fields of the grass-roots information table, the extended information table, and the business theme table are standardized respectively to obtain standard fields; among them, the fields of the grass-roots information table and the extended information table are both included in the standard fields.

[0044] Then, the corresponding mapping relationships between the fields of the grass-roots information table, the extended information table, and the business theme table and the standard fields are determined respectively.

[0045] Then, a standard field dictionary is constructed according to the standard fields and the corresponding mapping relationships.

[0046] It should be noted that the field names of the fields used in grass-roots governance are standardized. And the mapping relationships between the standard fields and the fields of the basic information table, the extended information table, and the business theme table are established. For example, field names such as name, name, and given name are uniformly standardized as name. In the standard field dictionary, the name field is determined, and the mapping relationships between the name field, the name field, and the given name field and the standard field name are established respectively.

[0047] Among them, all fields in the basic information library and the extended information library must be correspondingly included in the standard field dictionary. For the tables in the business theme library added later, the fields of these tables need to be standardized first. If the standard fields of the fields of this table are not included in the standard field dictionary, the standard fields will be added to the standard field dictionary.

[0048] That is to say, those skilled in the art can understand that if the basic information and extended information of the actor change, mainly the changes in the field content, and for the business theme data, there are cases where new business themes are added. At this time, for the newly added business theme table, it is very likely that there are fields that have not appeared in the table fields. Therefore, the newly added fields need to be standardized first.

[0049] Further, in the standard field dictionary, determine the association relationships to which each standard field belongs. Among them, the association relationship is used to distinguish the probability that the standard field can uniquely determine the behavior subject information. The standard fields can be marked with strong association relationships and weak association relationships.

[0050] For example, when the standard fields are ID number and disabled person certificate number, the behavior subject can be uniquely determined. Therefore, the basic information of the behavior subject can be uniquely determined. Then, the ID number and the disabled person certificate number both correspond to strong association relationships.

[0051] For example, when the standard field is name, when the user queries only by name, since there may be duplicate names, the name cannot uniquely determine the behavior subject, and thus the basic information of the behavior subject cannot be uniquely determined. Then, the name field corresponds to a weak association relationship.

[0052] That is to say, the weak association relationship can be called a "clue", which can reflect that there is a probability correlation between the basic data, but it cannot be determined that there is a relationship.

[0053] Further, according to each standard field and the association relationship to which each standard field belongs, associate the basic table, extended information table, and business theme table corresponding to the same behavior subject to obtain the basic data governance relationship graph of each behavior subject, and generate an association confidence level for the basic data governance relationship graph of each behavior subject. The higher the association confidence level, the higher the probability that the content in the basic data governance relationship graph belongs to the same behavior subject.

[0054] Specifically, first, according to the association relationship to which each standard field belongs, divide the standard fields into identification fields and non-identification fields. The identification field means that it can uniquely identify the behavior subject, and the non-identification field means that it cannot uniquely identify the behavior subject. That is to say, the identification field corresponds to a strong association relationship, and the non-identification field corresponds to a weak association relationship. For example, the identification fields include ID number, license plate number, mobile phone number, and low guarantee number, and the non-identification fields include name, community name, and household registration address.

[0055] Then, retrieve the fields of the basic information table of each behavior subject to determine whether there is an identification field. For example, retrieve the building community information table, house information table, and personnel information table in the basic information database respectively to determine whether there is an identification field.

[0056] It should be noted that according to the mapping relationship between the identification field and the fields of the basic information table, match the identification field with the fields of the basic information table of each behavior subject. If the match is successful, there is an identification field; if the match fails, there is no identification field.

[0057] If so, determine the field content of the identification field in the basic information table, and in the extended information database, match the identification field and the field content to determine whether there is an extended information table including the identification field and the field content. For example, retrieve the extended information tables of personnel and vehicle information in the extended information database to find the extended information tables and vehicle information tables including the identification field.

[0058] If so, in the business theme database, match the identification field and the field content to determine whether there is a business theme table including the identification field and the field content. For example, retrieve the elderly care information table and the subsistence allowance application information table in the business theme database to find the elderly care information table and the subsistence allowance application information table including the identification field.

[0059] If so, associate the basic information table, the extended information table including the identification field and the field content, and the business theme table including the identification field and the field content to generate the highest association confidence level for the grass-roots data governance relationship graph of each actor.

[0060] Those skilled in the art can understand that the highest association confidence level indicates that the retrieved grass-roots associated data belongs to the same actor.

[0061] It should be noted that for some library tables that do not belong to the identification field, they can be assisted in association with the library table through other non-identification field contents in the identification field corresponding table.

[0062] More intuitively, Figure 2 This is a grass-roots data governance relationship graph of an actor provided by an embodiment of the present application.

[0063] In Figure 2 the identification field is the ID number. Through the ID number, all the grass-roots data corresponding to the actor to which the ID number content belongs are associated, so as to associate the tables with the same ID number field content.

[0064] It should be noted that the house information table, the building community information table, and the network information table that do not contain the ID number field can query the house address field in the house information table, the community name field in the building community information table, and the community field and grid number field in the network information table through the residential address field and household registration address field including the ID number in the personnel information table, so as to associate with the house information table, the building community information table, and the network information table corresponding to the corresponding actor.

[0065] Finally, in this figure, multi-table joins are implemented through views to achieve the effect of a single table. That is to say, with the identification number as the identifier, associations are made in each database table to form a single table for a certain entity. All basic data belonging to this entity can be obtained through the identification number.

[0066] In addition, when there is no identification field, for non-identification fields, for example, names are very likely to be the same. However, if the mobile phone number is combined with the name, the possibility that the associated data found through this combination belongs to the same new entity is increased.

[0067] Based on this idea, by using some combined identifiers to increase the confidence level to a certain extent, whether the data obtained is usable needs to be analyzed and determined in specific application scenarios. At this time, the combination of non-identification fields is equivalent to a clue and can be used as a supplementary solution when the identification field of the entity, such as a highly sensitive data like the ID number, is not available.

[0068] That is to say, application developers can use the associated data pulled by these clues according to the actual business needs. For example, when there is no ID number in the basic data governance database, how to determine that the content of field C in Table A is also the content of field D in another Table B, and then confirm that all the data in the two tables can be concatenated and used? The combination identifier "name + mobile phone number" can be used to match in the two tables to find users with the same name and the same mobile phone number. Then, there is a high probability in reality that the users in these two tables are the same person, and the data in Tables A and B can be concatenated and used. Then the relationship established by the combination "name + mobile phone number" can be considered a clue. The confidence level of the clue can be increased by increasing the complexity of the combination, but if it is increased too much, it will lose its practical effect because it is difficult for multiple tables to contain all the fields in the combination.

[0069] Based on this, if there is no identification field, in the fields of the basic information table of each entity, determine the combination of non-identification fields and the corresponding field content of the combination of non-identification fields in the basic information table.

[0070] In the extended information database, match the combination of non-identification fields and the corresponding field content to determine whether there is an extended information table that includes the combination of non-identification fields and the corresponding field content.

[0071] If so, in the business theme database, match the combination of non-identification fields and the corresponding field content to determine whether there is a business theme table that includes the combination of non-identification fields and the corresponding field content.

[0072] If so, associate the basic information table, the extended information table including non-identifying field combinations and corresponding field contents, and the business theme table including non-identifying field combinations and corresponding field contents. The more non-identifying fields included in the non-identifying field combination, the higher the association confidence level generated for the grass-roots data governance relationship graph of each actor.

[0073] Provide different clue solutions to each business scenario through non-identifying field combinations; when the quality of basic data collection is poor and identifying fields cannot be given, it is a method to make full use of grass-roots data.

[0074] More intuitively, Figure 3 This is another grass-roots data governance relationship graph of the actor provided by the embodiment of the present application.

[0075] In Figure 3 the non-identifying field combination is name plus mobile phone number. Among them, the standardized field names corresponding to the contact information fields of vehicle owners in the vehicle information table, the contact phone fields in the longevity subsidy payment information table, the contact information fields in the subsistence allowance handling information table, and the contact information fields in the disabled care information table are all mobile phone numbers. Thus, the tables with the same field contents are associated.

[0076] It should be noted that the housing information table, the building community information table, and the network information table that do not contain the name and mobile phone number fields can query the community name field, building number field, unit number field, and room number field in the housing information table, the affiliated community field and community name field in the building community information table, and the affiliated community field and grid name field in the network information table through the residential address field in the personnel information table, so as to associate the tables with the same residential address field contents. That is to say, the housing information table, the building community information table, and the network information table corresponding to the affiliated actors are associated.

[0077] Finally, generate a query interface so that the grass-roots data governance relationship graph can be queried. The usage methods of the identifying fields and non-identifying field combinations are provided in the query interface, and developers can make full use of clue pulling to associate data according to business requirements.

[0078] To sum up, with the actor as the main line, logically connect the basic information database, the extended information database, and each business theme database to construct a comprehensive graph of all activities of the actor at the grass-roots level, that is, construct the grass-roots data governance relationship graphs of multiple actors, that is, multiple grass-roots data governance relationship graphs, to provide grass-roots organizations with the overall and comprehensive data control ability of grass-roots situations.

[0079] In some embodiments of the present application, when retrieving the underlying associated data corresponding to the content of the field to be queried in the pre-constructed underlying data governance relationship graph, it is determined that the field to be queried includes a target field and a condition field. For example, the condition field is the ID number, and the target field is the license plate number. That is to say, the user wants to query the license plate number of the behavioral subject corresponding to the ID number.

[0080] Judge whether the condition field is an identification field. If so, match the content of the condition field in the underlying data governance relationship graph to determine the unique underlying relationship graph including the content of the condition field.

[0081] In the unique underlying relationship graph, retrieve the content of the target field to generate the underlying associated data corresponding to the content of the field to be queried.

[0082] Further, if the condition field is a non-identification field, match the content of the condition field in the underlying data governance relationship graph to determine the underlying relationship graph including the content of the condition field.

[0083] Judge whether there are multiple underlying relationship graphs. If so, determine the confidence level of each underlying relationship graph, and in each underlying relationship graph, retrieve the content of the target field to generate the underlying associated data corresponding to the content of the field to be queried.

[0084] Those skilled in the art can understand that if it is a non-identification field, since the behavioral subject cannot be uniquely determined, therefore, multiple underlying relationship graphs may be queried. For example, when the condition field is the name and the target field is the license plate number, if there are duplicate names, then the underlying relationship graphs with duplicate names may be queried.

[0085] If not, in one underlying relationship graph, retrieve the content of the target field to generate the underlying associated data corresponding to the content of the field to be queried.

[0086] S103: In the preset standard field dictionary, match the field to be queried to determine the association relationship of the field to be queried for the behavioral subject; wherein, the standard field dictionary includes multiple standard field names corresponding to the underlying data governance relationship graph, and the association relationship to which each standard field name belongs; the association relationship is used to represent the degree to which the field to be queried can identify the behavioral subject.

[0087] S104: According to the association relationship, determine the confidence level of the underlying associated data; the stronger the association relationship, the higher the confidence level of the underlying associated data.

[0088] S105: Through the preset display layout, display the underlying associated data and the confidence level on the front-end user interface.

[0089] Among them, when there are multiple grass-roots relationship graphs, in each grass-roots relationship graph, a non-identification field combination related to the conditional field is determined.

[0090] Then, in the order of the confidence levels of each grass-roots relationship graph from high to low, the grass-roots associated data is sorted in turn.

[0091] Finally, through a preset display layout, the grass-roots associated data, the confidence level, and the non-identification field combination are displayed on the front-end user interface. Thus, by displaying the non-identification field combination, it can enable the user to intuitively understand whether the grass-roots associated data belongs to the data to be queried. For example, in the display layout, multi-table connection is realized to achieve the effect of one table.

[0092] In some embodiments of the present application, developers can build application development through a query interface, so that the query interface can be called, and application developers can develop information services required for various grass-roots governance operations, helping to improve the work efficiency at the grass-roots level.

[0093] More intuitively, Figure 4 FIG. is a schematic diagram of a grass-roots data governance implementation solution provided by an embodiment of the present application.

[0094] In Figure 4 it includes a basic information library, an extended information library, and a business theme library. The field standardization of the table fields in the library is performed, and then according to the mapping relationship between the standard fields and the table fields in the library, as well as the identification fields (information in the figure) and non-identification field combinations (clues in the figure), the grass-roots data belonging to the same entity is associated to construct a grass-roots governance one-table model, that is, a grass-roots data governance relationship graph. Then, an external interface for merging / querying is provided, and application developers perform application development through the external interface. It should be noted that for information or clues, which confidence level data to use is entirely determined by application developers.

[0095] It should be noted that although the embodiments of the present application are described with reference to Figure 1 to introduce and illustrate steps S101 to S105 in sequence, this does not mean that steps S101 to S105 must be executed in a strict order. The reason why the embodiments of the present application introduce and illustrate steps S101 to S105 in the order shown in Figure 1 is to facilitate those skilled in the art to understand the technical solutions of the embodiments of the present application. In other words, in the embodiments of the present application, the order between steps S101 to S105 can be appropriately adjusted according to actual needs.

[0096] Through Figure 1The method can efficiently utilize grass-roots data by pre-constructing a grass-roots data governance relationship graph, enabling users to retrieve the associated data of the actor, providing the confidence level of the associated data, improving the utilization efficiency of grass-roots data, enhancing the grass-roots work efficiency from the perspective of data application, and allowing users to intuitively understand the confidence level of the associated data, effectively ensuring the accuracy of grass-roots work, thereby improving the grass-roots data governance efficiency.

[0097] Based on the same idea, some embodiments of the present application also provide the corresponding device and non-volatile computer storage medium for the above method.

[0098] Figure 5 The structure diagram of a grass-roots data governance device provided by an embodiment of the present application includes:

[0099] At least one processor; and,

[0100] A memory communicatively connected to the at least one processor; wherein,

[0101] The memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can:

[0102] Obtain a data query request for a query field to be queried; wherein, the query field to be queried is related to the information of the actor, and the actor is related to users, enterprises, and social organizations;

[0103] Call a query interface to retrieve the grass-roots associated data corresponding to the content of the query field to be queried in the pre-constructed grass-roots data governance relationship graph;

[0104] Match the query field to be queried in a preset standard field dictionary to determine the association relationship of the query field to be queried for the actor; wherein, the standard field dictionary includes multiple standard field names corresponding to the grass-roots data governance relationship graph, and the association relationship belonging to each standard field name; the association relationship is used to represent the degree to which the query field to be queried can identify the actor;

[0105] Determine the confidence level of the grass-roots associated data according to the association relationship; the stronger the association relationship, the higher the confidence level of the grass-roots associated data;

[0106] Display the grass-roots associated data and the confidence level on the front-end user interface through a preset display layout.

[0107] A grass-roots data governance non-volatile computer storage medium provided by some embodiments of the present application stores computer-executable instructions, and the computer-executable instructions are set as:

[0108] A data query request for obtaining a field to be queried; wherein, the field to be queried is related to the information of the behavior subject, and the behavior subject is related to users, enterprises, and social organizations;

[0109] Call the query interface to retrieve the underlying associated data corresponding to the content of the field to be queried in the pre-constructed underlying data governance relationship graph;

[0110] Match the field to be queried in the preset standard field dictionary to determine the association relationship of the field to be queried for the behavior subject; wherein, the standard field dictionary includes multiple standard field names corresponding to the underlying data governance relationship graph, and the association relationship belonging to each standard field name; the association relationship is used to represent the degree to which the field to be queried can identify the behavior subject;

[0111] Determine the confidence level of the underlying associated data according to the association relationship; the stronger the association relationship, the higher the confidence level of the underlying associated data;

[0112] Display the underlying associated data and the confidence level on the front-end user interface through a preset display layout.

[0113] Each embodiment in this application is described in a progressive manner. For the same or similar parts between each embodiment, reference can be made to each other. The key points described in each embodiment are the differences from other embodiments. In particular, for the device and medium embodiments, since they are basically similar to the method embodiments, the description is relatively simple, and the relevant parts can refer to the partial description of the method embodiments.

[0114] The devices and media provided by the embodiments of this application correspond one by one to the methods. Therefore, the devices and media also have beneficial technical effects similar to the corresponding methods. Since the beneficial technical effects of the methods have been described in detail above, the beneficial technical effects of the devices and media will not be elaborated here.

[0115] Those skilled in the art should understand that the embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present invention can adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk memories, CD-ROMs, optical memories, etc.) containing computer-usable program codes.

[0116] The present invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It should be understood that each flow and / or block in the flowchart illustrations and / or block diagrams, and combinations of flows and / or blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to the processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing device to produce a machine, such that the instructions executed by the processor of the computer or other programmable data processing device create means for implementing the functions specified in the flowchart Figure 1 one flow or a plurality of flows and / or blocks Figure 1 one block or a plurality of blocks.

[0117] These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means that implement the functions specified in the flowchart Figure 1 one flow or a plurality of flows and / or blocks Figure 1 one block or a plurality of blocks.

[0118] These computer program instructions may also be loaded onto a computer or other programmable data processing device, such that a series of operational steps are performed on the computer or other programmable device to produce a computer-implemented process, and thus the instructions executed on the computer or other programmable device provide steps for implementing the functions specified in the flowchart Figure 1 one flow or a plurality of flows and / or blocks Figure 1 one block or a plurality of blocks.

[0119] In a typical configuration, a computing device includes one or more processors (CPUs), an input / output interface, a network interface, and memory.

[0120] The memory may include non-permanent memory in the form of computer-readable media, random access memory (RAM), and / or non-volatile memory, such as read-only memory (ROM) or flash memory (flash RAM). The memory is an example of computer-readable media.

[0121] Computer readable media include permanent and non-permanent, removable and non-removable media that can be implemented by any method or technology to store information. Information can be computer readable instructions, data structures, program modules or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, compact disk read-only memory (CD-ROM), digital versatile disk (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices or any other non-transmission media that can be used to store information that can be accessed by a computing device. As defined herein, computer readable media does not include temporary computer readable media (transitory media), such as modulated data signals and carrier waves.

[0122] It should also be noted that the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, commodity or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, commodity or device. In the absence of more restrictions, the elements defined by the sentence "comprises a ..." do not exclude the existence of other identical elements in the process, method, commodity or device including the elements.

[0123] The above is only an embodiment of the present application and is not intended to limit the present application. For those skilled in the art, the present application may have various changes and variations. Any modification, equivalent replacement, improvement, etc. made within the technical principle of the present application should fall within the protection scope of the present application.

Claims

1. A method for grass-roots data governance, characterized in that, The method includes: Obtaining a data query request for the field to be queried; wherein, the field to be queried is related to the information of the behavioral entity, and the behavioral entity is related to users, enterprises, and social organizations; Invoking a query interface to retrieve the underlying associated data corresponding to the content of the field to be queried in a pre-constructed underlying data governance relationship graph; Matching the field to be queried in a preset standard field dictionary to determine the association relationship of the field to be queried for the behavioral entity; wherein, the standard field dictionary includes multiple standard field names corresponding to the underlying data governance relationship graph and the association relationships to which each standard field name belongs; the association relationship is used to indicate the degree to which the field to be queried can identify the behavioral entity; Determining the confidence level of the underlying associated data according to the association relationship; the stronger the association relationship, the higher the confidence level of the underlying associated data; Displaying the underlying associated data and the confidence level on the front-end user interface through a preset display layout; Before the step of invoking a query interface to retrieve the underlying associated data corresponding to the content of the field to be queried in a pre-constructed underlying data governance relationship graph, the method further includes: Constructing a basic information library, an extended information library, and a business theme library according to the underlying data set; wherein, the basic information library includes a basic information table for each behavioral entity, the extended information library includes an extended information table for each behavioral entity, and the business theme library includes a business theme table for each behavioral entity; Constructing a standard field dictionary according to the fields of the basic information table, the fields of the extended information table, and the fields of the business theme table; Determining the association relationship to which each standard field belongs in the standard field dictionary; Associating the basic table, the extended information table, and the business theme table corresponding to the same behavioral entity according to each standard field and the association relationship to which each standard field belongs to obtain the underlying data governance relationship graph of each behavioral entity, and generating an association confidence level for the underlying data governance relationship graph of each behavioral entity. The higher the association confidence level, the higher the probability that the content in the underlying data governance relationship graph belongs to the same behavioral entity.

2. The method according to claim 1, wherein The step of associating the basic table, the extended information table, and the business theme table corresponding to the same behavioral entity according to each standard field and the association relationship to which each standard field belongs specifically includes: Dividing the standard fields into identification fields and non-identification fields according to the association relationship to which each standard field belongs; the identification fields indicate that they can uniquely identify the behavioral entity, and the non-identification fields indicate that they cannot uniquely identify the behavioral entity; Retrieving the fields of the basic information table of each behavioral entity to determine whether there are identification fields; If so, determining the field content of the identification field in the basic information table; Matching the identification field and the field content in the extended information library to determine whether there is an extended information table including the identification field and the field content; If so, in the business theme library, match the identification field and the field content to determine whether there is a business theme table including the identification field and the field content; If so, associate the basic information table, the extended information table including the identification field and the field content, and the business theme table including the identification field and the field content, and generate the highest association confidence level for the grass-roots data governance relationship graph of each actor.

3. The method according to claim 2, wherein The method further includes: If the identification field is not available, in the fields of the basic information table of each actor, determine the non-identification field combination and the corresponding field content of the non-identification field combination in the basic information table; In the extended information library, match the non-identification field combination and the corresponding field content to determine whether there is an extended information table including the non-identification field combination and the corresponding field content; If so, in the business theme library, match the non-identification field combination and the corresponding field content to determine whether there is a business theme table including the non-identification field combination and the corresponding field content; If so, associate the basic information table, the extended information table including the non-identification field combination and the corresponding field content, and the business theme table including the non-identification field combination and the corresponding field content. The more non-identification fields included in the non-identification field combination, the higher the association confidence level generated for the grass-roots data governance relationship graph of each actor.

4. The method according to claim 2, wherein The identification field includes at least one of a certificate number, a license plate number, a mobile phone number, and a low guarantee number; the non-identification field includes at least one of a name, a community name, and a household registration address.

5. The method according to claim 1, wherein The construction of the standard field dictionary according to the fields of the grass-roots information table, the extended information table, and the business theme table specifically includes: Standardize the fields of the grass-roots information table, the extended information table, and the business theme table respectively to obtain standard fields; among them, the fields of the grass-roots information table and the extended information table are both included in the standard fields; Determine the corresponding mapping relationships between the fields of the grass-roots information table, the extended information table, and the business theme table and the standard fields respectively; Construct the standard field dictionary according to the standard fields and the corresponding mapping relationships.

6. The method according to claim 2, characterized in that, The retrieval of the fields of the basic information table of each actor to determine whether there is an identification field specifically includes: According to the mapping relationship between the identification field and the fields of the basic information table, match the identification field with the fields of the basic information table of each actor; If the match is successful, there is an identification field; If the match fails, there is no identification field.

7. The method according to claim 1, wherein The retrieval of the grass-roots association data corresponding to the content of the field to be queried in the pre-constructed grass-roots data governance relationship graph specifically includes: Determine that the field to be queried includes a target field and a condition field; Judge whether the condition field is an identification field; If so, match the content of the conditional field in the basic data governance relationship graph to determine the unique basic relationship graph including the content of the conditional field; In the unique basic relationship graph, retrieve the content of the target field to generate the basic associated data corresponding to the content of the field to be queried.

8. The method according to claim 7, wherein The method further includes: If the conditional field is a non-identification field, match the content of the conditional field in the basic data governance relationship graph to determine the basic relationship graph including the content of the conditional field; Determine whether the number of the basic relationship graphs is multiple; If so, determine the confidence level of each basic relationship graph; In each basic relationship graph, retrieve the content of the target field to generate the basic associated data corresponding to the content of the field to be queried; Through a preset display layout, display the basic associated data and the confidence level on the front-end user interface, specifically including: In each basic relationship graph, determine the combination of non-identification fields related to the conditional field; Sort the basic associated data in descending order of the confidence level of each basic relationship graph; Through a preset display layout, display the basic associated data, the confidence level, and the combination of non-identification fields on the front-end user interface.

9. A grass-roots data governance device, characterized in that, Including: At least one processor; And, A memory communicatively connected to the at least one processor; wherein, The memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute a basic data governance method according to any one of claims 1-8 above.

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