Community data integration and standardization method and tool

Abstract

The invention discloses a community data integration and standardization method and tool, and relates to the technical field of data governance, and the method comprises the steps: through Python programming, utilizing a configuration file and a database management tool, automatically identifying and connecting to a plurality of data sources, and scanning to obtain metadata information of the data sources; formulating a rule to define a corresponding relationship between the source data field and the target data field; processing and converting data by using a Pandas library and user-defined conversion logic to enable the data to conform to a unified standard format; automatically completing data extraction, conversion and loading processes by using an ETL tool, or compiling a Python script / executing complex data integration logic in a data warehouse by using an SQL script; defining a data quality verification rule, and compiling a corresponding python script or integrating the python script into an ETL process to execute verification; and storing the verified and integrated data into a data warehouse according to a pre-designed data model. The method is used for realizing integration and unified processing of cross-system community data.

Description

Technical Field

[0001] The present invention relates to the field of data governance technology, and in particular to a method and tool for integrating and standardizing community data. Background Art

[0002] With the rapid development of the Internet, the information age has been greatly strengthened, and relevant departments have promoted a series of policies to strengthen the construction of "smart communities". In the process of developing the government information system of "smart communities", it is often necessary to collect and process various huge community personnel and housing data from various units and departments. However, there are too many differences in community data between different business systems and departments. They vary greatly in format, encoding, semantics, etc., making it difficult to share and effectively use data. In this process, it is usually necessary to manually query various types of data in the database, and classify, summarize and govern various types of data. However, this method has many problems, such as low integration efficiency, insufficient standardization, and high time cost, which makes it difficult to meet the needs of modern enterprises for efficient integration and utilization of data. Summary of the invention

[0003] In response to the needs and shortcomings of current technological development, the present invention provides a community data integration and standardization method and tool, aiming to achieve cross-system and cross-platform community data integration and unified processing through efficient data integration technology and unified data standardization rules, improve data consistency and availability, and provide a high-quality data foundation for data analysis, decision-making and statistics.

[0004] In the first aspect, a method for integrating and standardizing community data of the present invention adopts the following technical solutions to solve the above technical problems:

[0005] A method for integrating and standardizing community data comprises the following steps:

[0006] S1. Data source identification and access: Through Python programming, using configuration files and database management tools, automatically identify and connect to multiple data sources, scan and obtain metadata information of data sources, and prepare for subsequent data access and conversion;

[0007] S2. Data conversion: formulate rules to define the correspondence between source data fields and target data fields; use the Pandas library and custom conversion logic to process and convert data to conform to a unified standard format;

[0008] S3. Data integration: Use ETL tools to automate the data extraction, transformation, and loading process, or write Python scripts / use SQL scripts to execute complex data integration logic in the data warehouse;

[0009] S4. Data verification: define data quality verification rules, write corresponding Python scripts or integrate them into the ETL process to perform verification;

[0010] S5. Data storage: During the data integration and verification process, the operation logs and verification results of each step are recorded, and the verified and integrated data are stored in the data warehouse according to the pre-designed data model; in the data warehouse, partitioning and indexing optimization techniques are used to improve data query efficiency.

[0011] Optionally, the data sources involved include a relational database, a non-relational database, and a file system;

[0012] For relational databases, use the SQLAlchemy library or the pymysql library to establish a database connection and execute SQL statements to query and extract data;

[0013] For non-relational databases, use the PyMongo library to connect to the database and perform query operations to obtain data;

[0014] For data in the file system, use Python's built-in file operation functions combined with the pandas library to read the file content and convert it into a DataFrame object.

[0015] Optionally, the configuration file involved is in YAML or JSON format, which is used to store the connection information of the data source; a program is developed by Python to read the data source connection information in the configuration file, and automatically establish a connection with the corresponding data source according to the data source connection information;

[0016] The database management tool involved is the Inspector class of the Python open source library SQLAlchemy, which is used to automatically scan the data source, obtain the metadata information of the data source, and prepare for subsequent data access and conversion.

[0017] Optionally, use the map(), replace(), and apply() functions provided by the Pandas library to process and transform data. map() is used to apply a function to each element of a Series, replace() is used to replace values ​​in a DataFrame or Series, and apply() is used to apply a function to each row or column of a DataFrame.

[0018] Write custom conversion logic according to specific needs to convert source data into standard data that conforms to the target data format. Custom conversion logic includes: unification of date formats, conversion of numeric types, and desensitization of identity information.

[0019] Optionally, the defined data quality verification rules include non-null verification, uniqueness verification, data type verification, range verification, format verification and logic verification;

[0020] According to the defined data quality verification rules, write the corresponding Python script to perform verification, or integrate the defined data quality verification rules into the ETL process so that verification can be automatically performed during data extraction and conversion.

[0021] In the second aspect, a community data integration and standardization tool of the present invention solves the above technical problems using the following technical solutions:

[0022] A community data integration and standardization tool that includes:

[0023] The identification and access module is used to automatically identify and connect to various data sources through Python programming, using configuration files and database management tools, and scan and obtain metadata information of data sources to prepare for subsequent data access and conversion;

[0024] The data conversion module is used to formulate rules to define the correspondence between source data fields and target data fields. It is also used to use the Pandas library and custom conversion logic to process and convert data to conform to a unified standard format;

[0025] Data integration module, which is used to automate the data extraction, transformation and loading process using ETL tools, or to write Python scripts / use SQL scripts to execute complex data integration logic in the data warehouse;

[0026] Data verification module, which is used to define data quality verification rules, write corresponding Python scripts or integrate them into the ETL process to perform verification;

[0027] The data storage module is used to record the operation logs and verification results of each step in the process of data integration and verification, and store the verified and integrated data in the data warehouse according to the pre-designed data model. The data warehouse uses partitioning and indexing optimization technology to improve data query efficiency.

[0028] Optionally, the data sources involved include a relational database, a non-relational database, and a file system;

[0029] For relational databases, the identification and access module uses the SQLAlchemy library or the pymysql library to establish a database connection and execute SQL statements to query and extract data;

[0030] For non-relational databases, the identification and access module uses the PyMongo library to connect to the database and perform query operations to obtain data;

[0031] For data in the file system, the identification and access module uses Python's built-in file operation functions combined with the pandas library to read file contents and convert them into DataFrame objects.

[0032] Optionally, the configuration file involved is in YAML or JSON format, which is used to store the connection information of the data source; the identification and access module develops a program through Python to read the data source connection information in the configuration file, and automatically establishes a connection with the corresponding data source according to the data source connection information;

[0033] The database management tool involved is the Inspector class of the Python open source library SQLAlchemy, which is used to automatically scan the data source, obtain the metadata information of the data source, and prepare for subsequent data access and conversion.

[0034] Optionally, the data conversion module uses the map(), replace() and apply() functions provided by the Pandas library to process and transform data, where map() is used to apply a function to each element of a Series, replace() is used to replace values ​​in a DataFrame or Series, and apply() is used to apply a function to each row or column of a DataFrame.

[0035] The data conversion module involved also writes custom conversion logic according to specific needs, so as to convert the source data into standard data that conforms to the target data format. The custom conversion logic includes: unification of date formats, conversion of numerical types, and desensitization of identity information.

[0036] Optionally, the data quality verification rules defined by the data verification module involved include non-null verification, uniqueness verification, data type verification, range verification, format verification and logic verification;

[0037] The data verification module involved writes a corresponding python script to perform verification according to the defined data quality verification rules, or the data verification module integrates the defined data quality verification rules into the ETL process so as to automatically perform verification during data extraction and conversion.

[0038] Compared with the prior art, the community data integration and standardization method and tool of the present invention have the following beneficial effects:

[0039] 1. The present invention can automatically identify and convert data from different sources and in different formats, realize rapid data integration and unified standardized processing, improve data consistency and availability, reduce the complexity and cost of data integration, and provide strong support for enterprise data governance and data analysis;

[0040] 2. This invention aims to achieve cross-system and cross-platform community data integration and unified processing through efficient data integration technology and unified data standardization rules, improve data consistency and availability, and provide a high-quality data foundation for data analysis, decision-making and statistics;

[0041] 3. The present invention can reduce manual intervention and error rates, reduce the complexity and cost of data integration, and ensure the efficiency and accuracy of data governance; the present invention can obtain high-quality data sets, provide a reliable foundation for subsequent data analysis and application, has high flexibility and scalability, and can adapt to the needs of different application scenarios. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] Attached Figure 1 is a flow chart of a method according to Embodiment 1 of the present invention;

[0043] Attached Figure 2 This is a module connection block diagram of the second embodiment of the present invention. DETAILED DESCRIPTION

[0044] In order to make the technical solution, the technical problem solved and the technical effect of the present invention more clearly understood, the technical solution of the present invention is clearly and completely described below in conjunction with specific embodiments.

[0045] Embodiment 1:

[0046] Combined with Figure 1 This embodiment proposes a method for integrating and standardizing community data, which includes the following steps:

[0047] S1. Data source identification and access: Through Python programming, using configuration files and database management tools, it can automatically identify and connect to various data sources, scan and obtain metadata information of data sources, and prepare for subsequent data access and conversion.

[0048] The various data sources involved in this embodiment include relational databases (such as MySQL, PostgreSQL), non-relational databases (such as MongoDB, Cassandra) and file systems (such as CSV, JSON files). For relational databases (such as MySQL, PostgreSQL), use the SQLAlchemy library or the pymysql library to establish a database connection, and execute SQL statements to query and extract data; for non-relational databases (such as MongoDB, Cassandra), use the PyMongo library to connect to the database, and execute query operations to obtain data; for data in the file system (such as CSV, JSON files), use Python's built-in file operation functions combined with the pandas library to read the file content and convert it into a DataFrame object.

[0049] The configuration file involved in this embodiment is in YAML or JSON format, which is used to store the connection information of the data source. In this step, a program is developed through Python to read the data source connection information in the configuration file, and automatically establish a connection with the corresponding data source according to the data source connection information. The database management tool is the Inspector class of the python open source library SQLAlchemy, which is used to automatically scan the data source, obtain the metadata information of the data source, and prepare for subsequent data access and conversion.

[0050] S2. Data conversion: formulate rules to define the correspondence between source data fields and target data fields; use the Pandas library and custom conversion logic to process and convert data to conform to a unified standard format.

[0051] This embodiment specifically uses the three functions map(), replace() and apply() provided by the Pandas library to process and transform data, where map() is used to apply a function to each element of a Series, replace() is used to replace values ​​in a DataFrame or Series, and apply() is used to apply a function to each row or column of a DataFrame.

[0052] This embodiment writes custom conversion logic according to specific needs, so as to convert source data into standard data that conforms to the target data format, wherein the custom conversion logic includes: unification of date formats, conversion of numerical types, and desensitization of identity information.

[0053] S3. Data integration: Use ETL tools to automate the data extraction, transformation, and loading process, or write Python scripts / use SQL scripts to execute complex data integration logic in the data warehouse.

[0054] S4. Data verification: Define data quality verification rules, write corresponding Python scripts or integrate them into the ETL process to perform verification.

[0055] The data quality verification rules defined in this embodiment include non-null verification, uniqueness verification, data type verification, range verification, format verification and logic verification.

[0056] In this embodiment, a corresponding python script is written to perform verification according to the defined data quality verification rules, or the defined data quality verification rules are integrated into the ETL process so as to automatically perform verification during data extraction and conversion.

[0057] S5. Data storage: During the data integration and verification process, the operation logs and verification results of each step are recorded, and the verified and integrated data are stored in the data warehouse according to the pre-designed data model; in the data warehouse, partitioning and indexing optimization techniques are used to improve data query efficiency.

[0058] Embodiment 2:

[0059] This embodiment proposes a community data integration and standardization tool, which includes:

[0060] The identification and access module is used to automatically identify and connect to various data sources through Python programming, using configuration files and database management tools, and scan and obtain metadata information of data sources to prepare for subsequent data access and conversion;

[0061] The data conversion module is used to formulate rules to define the correspondence between source data fields and target data fields. It is also used to use the Pandas library and custom conversion logic to process and convert data to conform to a unified standard format;

[0062] Data integration module, which is used to automate the data extraction, transformation and loading process using ETL tools, or to write Python scripts / use SQL scripts to execute complex data integration logic in the data warehouse;

[0063] Data verification module, which is used to define data quality verification rules, write corresponding Python scripts or integrate them into the ETL process to perform verification;

[0064] The data storage module is used to record the operation logs and verification results of each step in the process of data integration and verification, and store the verified and integrated data in the data warehouse according to the pre-designed data model. The data warehouse uses partitioning and indexing optimization technology to improve data query efficiency.

[0065] The various data sources mentioned in this embodiment include relational databases (such as MySQL, PostgreSQL), non-relational databases (such as MongoDB, Cassandra) and file systems (such as CSV, JSON files). For relational databases (such as MySQL, PostgreSQL), the identification and access module uses the SQLAlchemy library or the pymysql library to establish a database connection and execute SQL statements to query and extract data; for non-relational databases (such as MongoDB, Cassandra), the identification and access module uses the PyMongo library to connect to the database and execute query operations to obtain data; for data in the file system (such as CSV, JSON files), the identification and access module uses Python's built-in file operation functions combined with the pandas library to read the file content and convert it into a DataFrame object.

[0066] The configuration file mentioned in this embodiment is in YAML or JSON format and is used to store the connection information of the data source. The identification and access module develops a program in Python to read the data source connection information in the configuration file and automatically establish a connection with the corresponding data source according to the data source connection information.

[0067] The database management tool mentioned in this embodiment is the Inspector class of the Python open source library SQLAlchemy, which is used to automatically scan the data source and obtain the metadata information of the data source to prepare for subsequent data access and conversion.

[0068] The data conversion module mentioned in this embodiment uses the map(), replace() and apply() functions provided by the Pandas library to process and convert data, where map() is used to apply a function to each element of a Series, replace() is used to replace the value in a DataFrame or Series, and apply() is used to apply a function to each row or column of a DataFrame. The data conversion module also writes custom conversion logic according to specific needs to convert the source data into standard data that conforms to the target data format, where the custom conversion logic includes: unification of date formats, conversion of numerical types, and desensitization of identity information.

[0069] The data quality verification rules defined by the data verification module mentioned in this embodiment include non-empty verification, uniqueness verification, data type verification, range verification, format verification and logic verification. The data verification module writes corresponding python scripts to perform verification according to the defined data quality verification rules, or the data verification module integrates the defined data quality verification rules into the ETL process so as to automatically perform verification during data extraction and conversion.

[0070] In summary, the community data integration and standardization method and tool of the present invention can automatically identify and convert data from different sources and in different formats, realize rapid data integration and unified standardized processing, improve data consistency and availability, and reduce the complexity and cost of data integration.

[0071] The above specific examples are used to explain the principles and implementation methods of the present invention in detail. These examples are only used to help understand the core technical content of the present invention. Based on the above specific embodiments of the present invention, any improvements and modifications made by technicians in this technical field without departing from the principles of the present invention should fall within the scope of patent protection of the present invention.

Claims

1. A method for integrating and standardizing community data, characterized in that: The steps include: S1. Data source identification and access: Through Python programming, using configuration files and database management tools, automatically identify and connect to multiple data sources, scan and obtain metadata information of data sources, and prepare for subsequent data access and conversion; S2. Data conversion: formulate rules to define the correspondence between source data fields and target data fields; use the Pandas library and custom conversion logic to process and convert data to conform to a unified standard format; S3. Data integration: Use ETL tools to automate the data extraction, transformation, and loading process, or write Python scripts / use SQL scripts to execute complex data integration logic in the data warehouse; S4. Data verification: define data quality verification rules, write corresponding Python scripts or integrate them into the ETL process to perform verification; S5. Data storage: During the data integration and verification process, the operation log and verification results of each step are recorded, and the verified and integrated data are stored in the data warehouse according to the pre-designed data model; In the data warehouse, partitioning and indexing optimization techniques are used to improve data query efficiency.

2. A method for integrating and standardizing community data according to claim 1, characterized in that: The data sources include relational databases, non-relational databases and file systems; For relational databases, use the SQLAlchemy library or the pymysql library to establish a database connection and execute SQL statements to query and extract data; For non-relational databases, use the PyMongo library to connect to the database and perform query operations to obtain data; For data in the file system, use Python's built-in file operation functions combined with the pandas library to read the file content and convert it into a DataFrame object.

3. A method for integrating and standardizing community data according to claim 1, characterized in that: The configuration file is in YAML or JSON format and is used to store the connection information of the data source. A program is developed in Python to read the data source connection information in the configuration file and automatically establish a connection with the corresponding data source according to the data source connection information. The database management tool is the Inspector class of the Python open source library SQLAlchemy, which is used to automatically scan the data source, obtain the metadata information of the data source, and prepare for subsequent data access and conversion.

4. A method for integrating and standardizing community data according to claim 1, characterized in that: Use the map(), replace(), and apply() functions provided by the Pandas library to process and transform data. Map() is used to apply a function to each element of a Series, replace() is used to replace values ​​in a DataFrame or Series, and apply() is used to apply a function to each row or column of a DataFrame. Write custom conversion logic according to specific needs to convert source data into standard data that conforms to the target data format. Custom conversion logic includes: unification of date formats, conversion of numeric types, and desensitization of identity information.

5. A method for integrating and standardizing community data according to claim 1, characterized in that: The defined data quality verification rules include non-empty verification, uniqueness verification, data type verification, range verification, format verification and logic verification; According to the defined data quality verification rules, write the corresponding Python script to perform verification, or integrate the defined data quality verification rules into the ETL process so that verification can be automatically performed during data extraction and conversion.

6. A community data integration and standardization tool, characterized by: It includes: The identification and access module is used to automatically identify and connect to various data sources through Python programming, using configuration files and database management tools, and scan and obtain metadata information of data sources to prepare for subsequent data access and conversion; The data conversion module is used to formulate rules to define the correspondence between source data fields and target data fields. It is also used to use the Pandas library and custom conversion logic to process and convert data to conform to a unified standard format; Data integration module, which is used to automate the data extraction, transformation and loading process using ETL tools, or to write Python scripts / use SQL scripts to execute complex data integration logic in the data warehouse; Data verification module, which is used to define data quality verification rules, write corresponding Python scripts or integrate them into the ETL process to perform verification; The data storage module is used to record the operation logs and verification results of each step in the process of data integration and verification, and store the verified and integrated data in the data warehouse according to the pre-designed data model. The data warehouse uses partitioning and indexing optimization technology to improve data query efficiency.

7. A community data integration and standardization tool according to claim 6, characterized in that: The data sources include relational databases, non-relational databases and file systems; For relational databases, the identification and access module uses the SQLAlchemy library or the pymysql library to establish a database connection and execute SQL statements to query and extract data; For non-relational databases, the identification and access module uses the PyMongo library to connect to the database and perform query operations to obtain data; For data in the file system, the identification and access module uses Python's built-in file operation functions combined with the pandas library to read file contents and convert them into DataFrame objects.

8. A community data integration and standardization tool according to claim 6, characterized in that: The configuration file is in YAML or JSON format and is used to store the connection information of the data source; the identification and access module develops a program through Python to read the data source connection information in the configuration file and automatically establishes a connection with the corresponding data source according to the data source connection information; The database management tool is the Inspector class of the Python open source library SQLAlchemy, which is used to automatically scan the data source, obtain the metadata information of the data source, and prepare for subsequent data access and conversion.

9. A community data integration and standardization tool according to claim 6, characterized in that: The data conversion module uses the map(), replace() and apply() functions provided by the Pandas library to process and convert data, where map() is used to apply a function to each element of a Series, replace() is used to replace values ​​in a DataFrame or Series, and apply() is used to apply a function to each row or column of a DataFrame. The data conversion module also writes custom conversion logic according to specific needs, so as to convert the source data into standard data that conforms to the target data format, wherein the custom conversion logic includes: unification of date formats, conversion of numerical types, and desensitization of identity information.

10. A community data integration and standardization tool according to claim 6, characterized in that: The data quality verification rules defined by the data verification module include non-empty verification, uniqueness verification, data type verification, range verification, format verification and logic verification; The data verification module writes a corresponding python script to perform verification according to the defined data quality verification rules, or the data verification module integrates the defined data quality verification rules into the ETL process so as to automatically perform verification during data extraction and conversion.

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