Urban credit foundation library construction method

Abstract

The invention discloses an urban credit basic library construction method, and relates to the technical field related to databases. Comprising the steps of 1, carding and planning an urban credit library structure: planning urban global data resources based on a business scene, and formulating corresponding table structures, fields and data types according to related data standards, and 2, collecting and converging data: collecting multi-source data through an integration tool or an exchange tool, and according to the planned urban credit library structure, collecting and converging the multi-source data according to the planned urban credit library structure. The data resources which are scattered in the business systems and are of different types and different sources are gathered in a database; and step 3, the integrated and gathered data are cleaned and stored in a database.

Description

Technical Field

[0001] The invention discloses a method for constructing a city credit basic database, and relates to the technical field related to databases. Background Art

[0002] With the implementation of the national big data strategy and the acceleration of digital construction, digital construction has become a new focus for implementing national network, smart society and other strategic actions, and building a data resource system is the only way to achieve digital construction. Promoting the integration and utilization of data resources has also become an inherent requirement for realizing a smart society. However, there is currently no corresponding resource database for credit data management, which is not conducive to accelerating the construction of a smart society. Summary of the invention

[0003] In view of the problems of the prior art, the present invention provides a method for constructing a city credit basic database, aiming to establish a credit database in the city basic database, so as to provide comprehensive data collection, governance, management, analysis and sharing and exchange capabilities for various data resources in the city, effectively promote cross-departmental and cross-level government data resource sharing and business collaboration, and promote the widespread application of data in government management, decision analysis and public service fields.

[0004] The specific scheme proposed by the present invention is:

[0005] The present invention provides a method for building a city credit base database, comprising:

[0006] Step 1: Organize and plan the structure of the city credit database: Based on the business scenario, plan the city's global data resources, and formulate the corresponding table structure, fields and data types according to relevant data standards.

[0007] Step 2: Collect and aggregate data: Collect multi-source data through integration tools or exchange tools. According to the planned urban credit database structure, aggregate data resources of different types and sources scattered in various business systems in the database.

[0008] Step 3: Clean, integrate and aggregate the data and store them in the database.

[0009] Furthermore, in step 1 of the method for building a city credit basic database, the type of the main object of credit in the business scenario is determined, the city's global data resources are planned according to the type of the main object, and the corresponding table structure, field and data type are formulated in accordance with relevant data standards.

[0010] Furthermore, in step 1 of the method for building a city credit base database, a corresponding table structure is formulated according to relevant data standards, wherein the fields in the table structure include asset mortgage registration information, third-party evaluation information report, certified enterprise list, taxpayer list, public utility negative information, judicial information, other credit evaluation information, administrative penalty information, and administrative licensing information.

[0011] Furthermore, in step 3 of the method for building a city credit basic database, the data after cleaning, integration and aggregation includes:

[0012] Remove duplicate data through data duplication cleaning service, obtain data correlation through data correlation cleaning service, verify data accuracy through data accuracy cleaning service, verify data integrity through data integrity cleaning service, verify data consistency through data consistency cleaning service, remove expired data through data timeliness cleaning service,

[0013] Standardize data according to a unified standard or format to achieve deep integration of records and dimensions.

[0014] The present invention also provides a city credit basic database construction device, including a combing and planning module, a collection and aggregation module and a cleaning and integration module.

[0015] The planning module plans and organizes the structure of the city credit database: based on business scenarios, it plans the city's global data resources and formulates the corresponding table structure, fields and data types in accordance with relevant data standards.

[0016] The collection and aggregation module collects and aggregates data: It collects multi-source data through integration tools or exchange tools, and aggregates data resources of different types and sources scattered in various business systems in the database according to the planned urban credit database structure.

[0017] The cleaning and integration module cleans and integrates the aggregated data and stores the data in the database.

[0018] Furthermore, the combing and planning module of the urban credit basic database construction device determines the type of the main object of credit in the business scenario, plans the city's global data resources according to the type of the main object, and formulates corresponding table structures, fields and data types in accordance with relevant data standards.

[0019] Furthermore, the combing and planning module of the urban credit basic database construction device formulates a corresponding table structure according to relevant data standards, wherein the fields in the table structure include asset mortgage registration information, third-party evaluation information report, certified enterprise list, taxpayer list, public utility negative information, judicial information, other credit evaluation information, administrative penalty information, and administrative licensing information.

[0020] Furthermore, the cleaning and integration module of the city credit basic database construction device cleans and integrates the aggregated data, including:

[0021] Remove duplicate data through data duplication cleaning service, obtain data correlation through data correlation cleaning service, verify data accuracy through data accuracy cleaning service, verify data integrity through data integrity cleaning service, verify data consistency through data consistency cleaning service, remove expired data through data timeliness cleaning service,

[0022] Standardize data according to a unified standard or format to achieve deep integration of records and dimensions.

[0023] The benefits of the present invention are:

[0024] Establish a credit database in the city's basic database to provide comprehensive data collection, governance, management, analysis, and sharing and exchange capabilities for various types of data resources in the city, effectively promote cross-departmental and cross-level government data resource sharing and business collaboration, and promote the widespread application of data in government management, decision-making analysis, and public services. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] Figure 1 It is a schematic flow chart of the method of the present invention. DETAILED DESCRIPTION

[0026] The present invention is further described below in conjunction with the accompanying drawings and specific embodiments so that those skilled in the art can better understand the present invention and implement it, but the embodiments are not intended to limit the present invention.

[0027] Example 1

[0028] The present invention provides a method for building a city credit base database, comprising:

[0029] Step 1: Sort out and plan the city credit database structure: Based on business scenarios, plan the city's global data resources, and formulate corresponding table structures, fields, and data types in accordance with relevant data standards.

[0030] In step 1, the type of the subject object of credit in the business scenario is also determined, and the city-wide data resources are planned according to the type of the subject object, and the corresponding table structure, field and data type are formulated according to the relevant data standards. The type of the subject object is generally a natural person or a legal person.

[0031] The corresponding table structure is formulated according to the relevant data standards. The fields in the table structure include asset mortgage registration information, third-party evaluation information report, certified enterprise list, taxpayer list, public utility bad information, judicial information, other credit evaluation information, administrative penalty information, and administrative license information. For asset mortgage registration information, please refer to Table 1.

[0032] Table 1

[0033]

[0034]

[0035] The third-party evaluation information report can be found in Table 2.

[0036] The list of certified companies in Table 2 can be found in Table 3.

[0037] TABLE_COMMENT COLUMN_NAME COLUMN_COMMENT Third-party evaluation information report XYZT Credit subject Third-party evaluation information report DM Code or ID number Third-party evaluation information report XYDJSY Credit Rating Explanation Third-party evaluation information report PJJG Name of assessment agency Third-party evaluation information report ID Internal Code Third-party evaluation information report DMLX Code or certificate type Third-party evaluation information report XYJ Credit rating Third-party evaluation information report JBJ Basic conclusion

[0038]

[0039]

[0040] Please refer to Table 4 for the list of taxpayers.

[0041] Table 4

[0042] For negative information on public utilities, please refer to Table 5.

[0043] Table 5

[0044] TABLE_COMMENT COLUMN_NAME COLUMN_COMMENT List of Grade A Taxpayers DM Code List of Grade A Taxpayers NSRZGSWJ Taxpayer's tax authority List of Grade A Taxpayers ID Internal Code List of Grade A Taxpayers DMLX Code Type List of Grade A Taxpayers XYJL Credit record code List of Grade A Taxpayers PJND Evaluation Year List of Grade A Taxpayers NSRMC Taxpayer Name

[0045] For judicial information, please refer to Table 6.

[0046] TABLE_COMMENT COLUMN_NAME COLUMN_COMMENT Other bad information about utilities FDDBRXM Name of legal representative Other bad information about utilities FDDBRZJHM Legal representative's ID number Other bad information about utilities RDG Certification Agency Other bad information about utilities BZ Remark Other bad information about utilities XYZT Credit subject Other bad information about utilities DM Code or ID number Other bad information about utilities FDDBRZJLX Legal representative certificate type Other bad information about utilities WGXXNR Illegal information content Other bad information about utilities RdR Date of recognition Other bad information about utilities ID Internal Code Other bad information about utilities DMLX Code or certificate type

[0047] Table 6

[0048] TABLE_COMMENT COLUMN_NAME COLUMN_COMMENT Other judicial information FDDBRZJLX Legal representative certificate type Other judicial information AH Case No. Other judicial information AY Cause of action Other judicial information PJF Sentencing Court Other judicial information ID Internal Code Other judicial information BZ Remark Other judicial information DMLX Code or certificate type Other judicial information FDDBRXM Name of legal representative Other judicial information FDDBRZJHM Legal representative's ID number Other judicial information LASJ Case filing time Other judicial information PJH Verdict Other judicial information QUR Date of judgment Other judicial information XYZT Credit subject Other judicial information DM Code or ID number

[0049] For other credit evaluation information, please refer to Table 7.

[0050] Table 7

[0051]

[0052]

[0053] For administrative penalty information, please refer to Table 8.

[0054] Table 8

[0055] TABLE_COMMENT COLUMN_NAME COLUMN_COMMENT Administrative penalty information BZ Remark Administrative penalty information FDDBRXM Name of legal representative Administrative penalty information FDDBRZJHM Legal representative's ID number Administrative penalty information CFMC Penalty Name Administrative penalty information CFSY Illegal Facts Administrative penalty information CFG Penalty Result Administrative penalty information XZV Administrative counterpart Administrative penalty information QUR Penalty validity period Administrative penalty information DM Code or ID number Administrative penalty information QUR Current Status Administrative penalty information FDDBRZJLX Legal representative certificate type Administrative penalty information XZCFJDSW Administrative penalty decision document number Administrative penalty information CFLB Penalty Type Administrative penalty information CfJ Basis of punishment Administrative penalty information ID Internal Code Administrative penalty information QDJ Date of penalty decision Administrative penalty information DMLX Code or certificate type Administrative penalty information CFBM Punishment Department

[0056] For administrative licensing information, please refer to Table 8.

[0057] Table 8

[0058]

[0059]

[0060] Step 2: Collect and aggregate data: Collect multi-source data through integration tools or exchange tools. According to the planned urban credit database structure, data resources of different types and sources scattered in various business systems will be aggregated in the database.

[0061] Step 3: Clean, integrate and aggregate the data and store them in the database.

[0062] Cleaning and integrating the aggregated data may further include:

[0063] Remove duplicate data through data duplication cleaning service, obtain data correlation through data correlation cleaning service, verify data accuracy through data accuracy cleaning service, verify data integrity through data integrity cleaning service, verify data consistency through data consistency cleaning service, remove expired data through data timeliness cleaning service,

[0064] Standardize data according to a unified standard or format to achieve deep integration of records and dimensions.

[0065] The method of the present invention provides comprehensive data collection, governance, management, analysis and sharing and exchange capabilities for various types of data resources in the city, effectively promotes cross-departmental and cross-level government data resource sharing and business collaboration, and promotes the widespread application of data in government management, decision-making analysis and public services.

[0066] Example 2

[0067] The present invention also provides a city credit basic database construction device, including a combing and planning module, a collection and aggregation module and a cleaning and integration module.

[0068] The planning module plans and organizes the structure of the city credit database: based on business scenarios, it plans the city's global data resources and formulates the corresponding table structure, fields and data types in accordance with relevant data standards.

[0069] The collection and aggregation module collects and aggregates data: It collects multi-source data through integration tools or exchange tools, and aggregates data resources of different types and sources scattered in various business systems in the database according to the planned urban credit database structure.

[0070] The cleaning and integration module cleans and integrates the aggregated data and stores the data in the database.

[0071] As the information interaction and execution process between the modules in the above-mentioned device are based on the same concept as the embodiment of the method of the present invention, the specific contents can be found in the description of the embodiment of the method of the present invention and will not be repeated here.

[0072] Likewise, the benefits of the device of the present invention are:

[0073] Establish a credit database in the city's basic database to provide comprehensive data collection, governance, management, analysis, and sharing and exchange capabilities for various types of data resources in the city, effectively promote cross-departmental and cross-level government data resource sharing and business collaboration, and promote the widespread application of data in government management, decision-making analysis, and public services.

[0074] It should be noted that not all steps and modules in the above-mentioned processes and device structures are necessary, and some steps or modules can be ignored according to actual needs. The execution order of each step is not fixed and can be adjusted as needed. The system structure described in the above-mentioned embodiments can be a physical structure or a logical structure, that is, some modules may be implemented by the same physical entity, or some modules may be implemented by multiple physical entities, or some components in multiple independent devices may be implemented together.

[0075] The above-described embodiments are only preferred embodiments for fully illustrating the present invention, and the protection scope of the present invention is not limited thereto. Equivalent substitutions or changes made by those skilled in the art based on the present invention are within the protection scope of the present invention. The protection scope of the present invention shall be subject to the claims.

Claims

1. A method for building a city credit base, characterized by: include: Step 1: Organize and plan the structure of the city credit database: Based on the business scenario, plan the city's global data resources, and formulate the corresponding table structure, fields and data types according to relevant data standards. Step 2: Collect and aggregate data: Collect multi-source data through integration tools or exchange tools. According to the planned urban credit database structure, aggregate data resources of different types and sources scattered in various business systems in the database. Step 3: Clean, integrate and aggregate the data and store them in the database.

2. According to the method for building a city credit basic database as described in claim 1, it is characterized in that the type of the main object of credit in the business scenario is determined in step 1, the city's global data resources are planned according to the type of the main object, and the corresponding table structure, field and data type are formulated in accordance with relevant data standards.

3. A method for building a city credit base according to claim 1, characterized in that In step 1, the corresponding table structure is formulated according to relevant data standards, where the fields in the table structure include asset mortgage registration information, third-party evaluation information report, certified enterprise list, taxpayer list, public utility adverse information, judicial information, other credit evaluation information, administrative penalty information, and administrative license information.

4. A method for building a city credit base according to claim 1, characterized in that In step 3, the data after cleaning and integration is collected, including: Remove duplicate data through data duplication cleaning service, obtain data correlation through data correlation cleaning service, verify data accuracy through data accuracy cleaning service, verify data integrity through data integrity cleaning service, verify data consistency through data consistency cleaning service, remove expired data through data timeliness cleaning service, Standardize data according to a unified standard or format to achieve deep integration of records and dimensions.

5. A device for building a city credit base, characterized by It includes combing and planning module, collection and aggregation module and cleaning and integration module. The planning module plans and organizes the structure of the city credit database: based on business scenarios, it plans the city's global data resources and formulates the corresponding table structure, fields and data types in accordance with relevant data standards. The collection and aggregation module collects and aggregates data: It collects multi-source data through integration tools or exchange tools, and aggregates data resources of different types and sources scattered in various business systems in the database according to the planned urban credit database structure. The cleaning and integration module cleans and integrates the aggregated data and stores the data in the database.

6. A device for building a city credit base according to claim 5, characterized in that The sorting and planning module determines the type of the main object of credit in the business scenario, plans the city's global data resources according to the type of the main object, and formulates the corresponding table structure, field and data type in accordance with relevant data standards.

7. The urban credit base construction device according to claim 5 is characterized in that The sorting and planning module formulates the corresponding table structure according to relevant data standards. The fields in the table structure include asset mortgage registration information, third-party evaluation information report, certified enterprise list, taxpayer list, public utility adverse information, judicial information, other credit evaluation information, administrative penalty information, and administrative licensing information.

8. The urban credit base construction device according to claim 5 is characterized by: The cleaning and integration module cleans and integrates the aggregated data, including: Remove duplicate data through data duplication cleaning service, obtain data correlation through data correlation cleaning service, verify data accuracy through data accuracy cleaning service, verify data integrity through data integrity cleaning service, verify data consistency through data consistency cleaning service, remove expired data through data timeliness cleaning service, Standardize data according to a unified standard or format to achieve deep integration of records and dimensions.

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