Report generation and optimization method and system based on offline data warehouse

Abstract

The invention provides a report generation and optimization method and system based on an offline data warehouse. The system comprises a data extraction layer, a data loading layer, a data cleaning layer, a data integration layer, a model data layer, a report mart layer, a report generation layer and a report optimization layer. The method comprises the following steps: S1, extracting data; s2, data cleaning; s3, integrating the data; s4, ensuring data quality; s5, storing model data; s6, a report market is constructed; and step S7, report generation. A layered architecture is adopted, independent operation of each module of the system is ensured, and expansion and maintenance are facilitated. Data extraction, cleaning and integration processes are simplified by introducing an automatic tool, and redundant calculation is reduced by optimizing SQL and solidifying logic. Bottom logic is optimized for large-scale data volume and complex query, and report generation performance is improved. And monitoring and management functions of the whole life cycle of the data flow are provided, and data quality and report accuracy are ensured.

Description

Technical Field

[0001] The present invention relates to the field of computer information technology, and in particular to a report generation and optimization method and system based on an offline data warehouse. Background Art

[0002] In modern banking and other data-intensive industries, offline reporting is an important tool for decision support. Reports usually rely on data extracted from multiple data sources. After complex processing (such as cleaning, integration and modeling), these data are transformed into structured model data for subsequent analysis and report generation. However, traditional report generation methods face the following major challenges:

[0003] 1) Data is scattered in different systems (such as MySQL, Oracle, NoSQL, etc.), with complex data structure and low standardization;

[0004] 2) Data needs to go through multiple stages of processing (extraction, loading, cleaning, integration, modeling) to meet reporting requirements;

[0005] 3) With the increase of data volume and the complexity of report query, the computational efficiency of traditional methods cannot meet the requirements of real-time and high efficiency;

[0006] 4) The same logic is repeatedly calculated in multiple reports, wasting a lot of computing resources and time;

[0007] 5) The logic of data extraction and report generation is scattered, and there is a lack of unified optimization and management strategies, which makes maintenance difficult;

[0008] Therefore, there is an urgent need for an efficient, flexible and easy-to-maintain method and system to solve the above problems. Summary of the invention

[0009] In view of the technical problems existing in the prior art, the present invention provides a report generation and optimization method and system based on an offline data warehouse, so as to improve the efficiency, accuracy and scalability of offline report generation.

[0010] According to a first aspect of the present invention, the present invention provides a report generation and optimization method based on an offline data warehouse, comprising the following steps:

[0011] Step S1. Extracting raw data from multiple source systems in real time or on a scheduled basis;

[0012] Step S2. Cleaning the extracted data;

[0013] Step S3. Integrate the cleaned data according to business requirements to generate model data that meets business requirements;

[0014] Step S4. After cleaning and integration, check the consistency and integrity of the data;

[0015] Step S5. Store the integrated model data as the core data source for report generation, provide a standardized table structure, and support cross-departmental data reuse;

[0016] Step S6. Extract common data based on the model data to form a data mart for report generation, optimize the performance of the data mart, and support rapid response to complex queries;

[0017] Step S7: Generate offline reports using a reporting tool, provide configurable report templates, and support multiple output formats.

[0018] Based on the above technical solution, the present invention can also make the following improvements.

[0019] Optionally, extracting raw data from multiple source systems in real time or periodically includes:

[0020] Identify the required data from the source system and determine the strategy for extracting incremental data or full data;

[0021] Use ETL tools to configure data extraction tasks and field mapping relationships to ensure consistency between data fields and the target data warehouse;

[0022] Use scheduling tools to trigger extraction tasks regularly, monitor task execution status, and handle failed tasks in a timely manner.

[0023] Optionally, the data cleaning includes: performing data quality inspection and processing, including format standardization, missing value filling, outlier removal and introducing a rule engine to automatically clean data.

[0024] Optionally, the integration of the cleaned data according to business requirements includes:

[0025] 3.1. Use the rule engine to complete the integration;

[0026] 3.2. Based on business needs, define field mapping rules, convert original data fields into target model fields, and use SQL or scripts to complete multi-table joint mapping;

[0027] 3.3. Associate the data tables according to business logic, establish a master-slave relationship through the customer ID and transaction number foreign key fields, calculate the total transaction amount by customer ID, and count the average daily transaction volume by date;

[0028] 3.4. Check whether the integrated data is logically consistent with the source data to ensure that the total transaction amount is not deviated due to data integration.

[0029] Optionally, the use of a rule engine to complete the integration includes:

[0030] 3.1.1. Use Drools and Flink CEP rule engine tools to write data integration logic according to business rules;

[0031] 3.1.2. Define rules: including "IDs of the same customer in different systems must be uniformly mapped", "transaction amounts greater than a certain value are marked as high-value transactions", configure rule priorities, and ensure the correct execution order of conflicting rules;

[0032] 3.1.3. During data integration, the rule engine automatically processes data according to the rule base to ensure consistency of business logic.

[0033] Optionally, the consistency and integrity check of the data includes:

[0034] 4.1. Check the consistency and integrity of data through automated verification tools;

[0035] 4.2. Set quality monitoring indicators. When the indicators exceed the threshold, trigger an alarm and record the problem data;

[0036] 4.3. Generate a unique version number for each data processing and record all operation logs to facilitate quick backtracking when problems occur;

[0037] 4.4. For complex scenarios, generate data reports for review and conduct manual verification by business personnel.

[0038] Optionally, the quality monitoring indicators include missing rate and abnormality rate; and the consistency and integrity check of the data includes:

[0039] Check whether the data is within the allowed range and verify whether the total amount in the general ledger is equal to the sum of the amounts in the sub-ledgers.

[0040] Optionally, the stored integrated model data is used as the core data source for report generation, providing a standardized table structure and supporting cross-departmental data reuse, including:

[0041] Design a star or snowflake model of the data warehouse according to business requirements to ensure that data is easy to query and analyze, create materialized views, cache common query results, and improve query performance;

[0042] For large tables, set partitions based on time and geographic location dimensions, and add indexes for commonly used query fields;

[0043] Use permission management tools to limit the scope of data access for different users.

[0044] Optionally, the common data is extracted based on the model data to form a data mart for report generation, and the performance of the data mart is optimized to support rapid response to complex queries and support multiple output formats including:

[0045] Extract common dimensions and measures from model data to form a report mart, and use physical stratification to optimize data structure;

[0046] Use distributed computing tools to improve query efficiency and configure caching mechanisms to reduce the computing pressure of repeated queries.

[0047] According to a second aspect of the present invention, there is provided a report generation and optimization system based on an offline data warehouse, comprising:

[0048] The data extraction layer is used to extract raw data from multiple source systems in real time or on a scheduled basis. ETL tools are used to extract data. The extraction supports both incremental and full extraction modes.

[0049] The data loading layer is used to efficiently load the extracted data into the data warehouse, using batch loading to improve performance and supporting parallel loading of large amounts of data;

[0050] The data cleaning layer is used to check and process data quality, including format standardization, missing value filling, outlier removal, and the introduction of a rule engine to automatically clean data;

[0051] The data integration layer is used to integrate the cleaned data according to business requirements, generate model data that meets business requirements, and support association, aggregation, and conversion operations between data;

[0052] The model data layer is used to store the integrated model data. It serves as the core data source for report generation, provides a standardized table structure, and supports cross-departmental data reuse.

[0053] The report mart layer is used to extract common data based on model data to form a data mart for report generation, optimize the performance of the data mart, and support rapid response to complex queries;

[0054] The report generation layer is used to generate offline reports using reporting tools and provides configurable report templates;

[0055] Report optimization layer: used to optimize complex SQL query logic, improve performance through index introduction and query rewriting, and solidify commonly used calculation logic.

[0056] Technical effects and advantages of the present invention:

[0057] The present invention provides a report generation and optimization method and system based on an offline data warehouse. The system integrates data extraction, cleaning, integration, modeling, report generation and optimization through an integrated architecture design, which significantly improves the efficiency, accuracy and scalability of offline report generation. At the same time, a layered architecture is adopted to ensure that each module of the system runs independently, which is convenient for expansion and maintenance. The data extraction, cleaning and integration processes are simplified by introducing automated tools, and redundant calculations are reduced by optimizing SQL and solidified logic. The underlying logic is optimized for large-scale data volumes and complex queries to improve report generation performance. Monitoring and management functions for the entire life cycle of the data process are provided to ensure data quality and report accuracy. BRIEF DESCRIPTION OF THE DRAWINGS

[0058] Figure 1 It is a schematic diagram of a report generation and optimization system based on an offline data warehouse provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0059] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0060] It should be noted that the report generation and optimization method and system based on offline data warehouse provided in the embodiment of the present invention can be applied to:

[0061] Financial institutions: support offline reporting requirements of banking, insurance, securities and other industries;

[0062] Data analysis company: providing efficient report generation services;

[0063] Government agencies: Support the generation and optimization of large-scale data reports.

[0064] It is understandable that, based on the defects in the background technology, the embodiment of the present invention proposes a report generation and optimization method based on an offline data warehouse, and the method includes the following steps:

[0065] Step S1. Data extraction: extracting raw data from multiple source systems in real time or on a scheduled basis;

[0066] Specifically, during the data extraction process, ETL tools are used to extract data, and data extraction supports incremental extraction and full extraction modes.

[0067] The real-time or scheduled extraction of raw data from multiple source systems includes:

[0068] Data source identification: Identify the required data from the source system (such as MySQL, Oracle, NoSQL database, file storage, API interface, etc.) and determine the strategy for extracting incremental data or full data.

[0069] ETL tool configuration: Use ETL tools (such as Informatica, Talend, DataStage) to configure data extraction tasks and field mapping relationships to ensure the consistency of data fields with the target data warehouse.

[0070] Scheduling and monitoring: Use scheduling tools (such as Airflow) to trigger extraction tasks regularly, monitor the task execution status, and handle failed tasks in a timely manner.

[0071] Step S2. Cleaning the extracted data;

[0072] The data cleaning includes: performing data quality inspection and processing, including format standardization, missing value filling, outlier removal, and introducing a rule engine to automatically clean data;

[0073] Format standardization: Convert the formats of data from different sources (such as date format, encoding format) into a unified standard format, and use regular expressions or predefined rules to clean abnormal characters.

[0074] Missing value handling: fill in missing values ​​through business rules, such as:

[0075] Numeric fields: fill with mean, median, or default value.

[0076] Categorical fields: Fill with the mode or set to the "Unknown" category.

[0077] Missing values ​​were marked in sensitive cases for subsequent manual confirmation.

[0078] Outlier elimination: Use statistical methods (such as the 3-times standard deviation method) or machine learning models to detect data anomalies, and eliminate outlier records or store them separately for analysis.

[0079] Data deduplication: Use primary key or business rules to deduplicate duplicate data. If the primary key cannot be determined, keep the latest record by timestamp.

[0080] Step S3. Data integration: Integrate the cleaned data according to business requirements to generate model data that meets business requirements;

[0081] The generation of model data that meets business requirements must support association, aggregation and conversion operations between data.

[0082] The integration of the cleaned data according to business requirements includes:

[0083] 3.1. Use the rule engine to complete the integration:

[0084] 3.1.1. Rule engine tool selection: Use rule engine tools such as Drools and Flink CEP to write data integration logic based on business rules.

[0085] 3.1.2. Rule writing:

[0086] Define rules: such as "the IDs of the same customer in different systems must be uniformly mapped", "transaction amounts greater than a certain value are marked as high-value transactions", configure rule priorities, and ensure the correct execution order of conflicting rules.

[0087] 3.1.3. Real-time rule execution:

[0088] When integrating data, the rule engine automatically processes data according to the rule base to ensure consistency of business logic.

[0089] 3.2. Data mapping and conversion:

[0090] Based on business needs, define field mapping rules to convert original data fields into target model fields.

[0091] Use SQL or scripts to complete multi-table joint mapping.

[0092] 3.3. Association and aggregation:

[0093] Associate data tables according to business logic. For example, establish a master-slave relationship through foreign key fields such as customer ID and transaction number, and aggregate fields: such as calculating the total transaction amount by customer ID and calculating the average daily transaction volume by date.

[0094] 3.4.Consistency check:

[0095] Check whether the integrated data is logically consistent with the source data. For example, make sure that the total transaction amount is not deviated due to data integration.

[0096] Step S4. After cleaning and integration, check the consistency and integrity of the data;

[0097] In this embodiment, the consistency and integrity check of the data is performed by an automated verification tool, which specifically includes the following steps:

[0098] 4.1. Example validation rules:

[0099] Field range: Checks whether the data is within the allowed range (such as 0-120 for the age field).

[0100] Data consistency: Verify whether the total amount in the general ledger is equal to the sum of the amounts in the sub-ledgers.

[0101] 4.2. Abnormal monitoring and alarm:

[0102] Set quality monitoring indicators (such as missing rate, abnormality rate).

[0103] When an indicator exceeds a threshold, an alarm is triggered and the problem data is recorded.

[0104] 4.3. Version control and backtracking:

[0105] Generate a unique version number for each data processing and record all operation logs to facilitate quick backtracking when problems occur.

[0106] 4.4. Manual verification assistance:

[0107] For complex scenarios, data reports to be reviewed are generated and manually verified by business personnel.

[0108] Step S5. Store the integrated model data as the core data source for report generation, provide a standardized table structure, and support cross-departmental data reuse;

[0109] The stored integrated model data is used as the core data source for report generation, and provides a standardized table structure specifically divided into:

[0110] Storage Design:

[0111] Design the star or snowflake model of the data warehouse according to business requirements to ensure that the data is easy to query and analyze.

[0112] Create materialized views to cache frequently used query results and improve query performance.

[0113] Partitions and indexes:

[0114] For large tables, set partitions based on dimensions such as time and geographic location.

[0115] Add indexes (such as B-tree indexes and hash indexes) for commonly used query fields.

[0116] Access control:

[0117] Use permission management tools to limit the scope of data access for different users.

[0118] Step S6. Extract common data based on the model data to form a data mart for report generation, optimize the performance of the data mart, and support rapid response to complex queries;

[0119] Extracting common data based on model data to form a data mart for report generation includes:

[0120] Common data extraction:

[0121] Extract common dimensions and measures from model data to form an efficient report mart.

[0122] Use physical layering (such as separation of dimension tables and fact tables) to optimize data structure.

[0123] Market performance optimization:

[0124] Use distributed computing tools (such as Presto and Impala) to improve query efficiency.

[0125] Configure the cache mechanism to reduce the computing pressure of repeated queries.

[0126] Step S7. Report generation: Generate offline reports using a report tool, provide configurable report templates, and support multiple output formats.

[0127] Template design:

[0128] Design standardized report templates based on business requirements and define field layout, format, and style.

[0129] Templates support dynamic configuration to meet personalized needs.

[0130] Multi-format output:

[0131] Reports can be exported in multiple formats including PDF, Excel, HTML, etc. to suit different usage scenarios.

[0132] Finally, perform report optimization, including:

[0133] Complex SQL optimization:

[0134] By analyzing the query plan, optimize the SQL logical structure (such as avoiding Cartesian products and using partition pruning).

[0135] Introduce indexes or materialized views to reduce query time.

[0136] Logic solidification:

[0137] Encapsulate reusable logic as stored procedures or ETL components to reduce development workload.

[0138] Dynamic resource allocation:

[0139] Dynamically adjust computing resource allocation according to report generation requirements to avoid resource waste.

[0140] The embodiment of the present invention also proposes a report generation and optimization system based on an offline data warehouse, specifically Figure 1 As shown, the system adopts a layered architecture and is divided into the following modules:

[0141] The data extraction layer is used to extract raw data from multiple source systems (such as MySQL, Oracle, API interfaces, etc.) in real time or on a scheduled basis. It uses ETL tools to extract data and supports both incremental and full extraction modes.

[0142] The data loading layer is used to efficiently load the extracted data into the data warehouse, using batch loading to improve performance and supporting parallel loading of large amounts of data;

[0143] The data cleaning layer is used to check and process data quality, including format standardization, missing value filling, and outlier removal. The rule engine is introduced to automatically clean data to ensure consistency and accuracy.

[0144] The data integration layer is used to integrate the cleaned data according to business requirements, generate model data that meets business requirements, and support association, aggregation, and conversion operations between data;

[0145] The model data layer is used to store the integrated model data. It serves as the core data source for report generation, provides a standardized table structure, and supports cross-departmental data reuse.

[0146] The report mart layer is used to extract common data based on model data to form a data mart for report generation, optimize the performance of the data mart, and support rapid response to complex queries.

[0147] The report generation layer is used to generate offline reports using reporting tools (such as SmartBI, Tableau, etc.), providing configurable report templates and supporting multiple output formats (PDF, Excel, HTML, etc.);

[0148] Report optimization layer: used to optimize complex SQL query logic, improve performance through index introduction and query rewriting, solidify common calculation logic, and avoid repeated processing.

[0149] It can be understood that the report generation and optimization system based on offline data warehouse provided by the present invention corresponds to the report generation and optimization method based on offline data warehouse provided in the aforementioned embodiments. The relevant technical features of the report generation and optimization system based on offline data warehouse can refer to the relevant technical features of the report generation and optimization method based on offline data warehouse, which will not be repeated here.

[0150] In summary, the report generation and optimization method and system based on offline data warehouse provided by the embodiment of the present invention has the following technical effects and advantages:

[0151] Efficiency: Reduce report generation time by more than 50% through automation tools and optimization strategies;

[0152] Accuracy: Data cleaning and integration ensure high quality of report data;

[0153] Scalability: The modular design of the system facilitates expansion and supports dynamic adjustment of data processing strategies;

[0154] Low-cost maintenance: Integrated management reduces system maintenance costs and improves overall stability.

[0155] Through the description of the above implementation methods, those skilled in the art can clearly understand that each implementation method can be implemented by means of software plus a necessary general hardware platform, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solution is essentially or the part that contributes to the prior art can be embodied in the form of a software product, and the computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, a disk, an optical disk, etc., including a number of instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in each embodiment or some parts of the embodiments.

[0156] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A report generation and optimization method based on an offline data warehouse, characterized in that: The following steps are involved: Step S1. Extracting raw data from multiple source systems in real time or on a scheduled basis; Step S2. Cleaning the extracted data; Step S3. Integrate the cleaned data according to business requirements to generate model data that meets business requirements; Step S4. After cleaning and integration, check the consistency and integrity of the data; Step S5. Store the integrated model data as the core data source for report generation, provide a standardized table structure, and support cross-departmental data reuse; Step S6. Extract common data based on the model data to form a data mart for report generation, optimize the performance of the data mart, and support rapid response to complex queries; Step S7: Generate offline reports using a reporting tool, provide configurable report templates, and support multiple output formats.

2. The report generation and optimization method based on offline data warehouse according to claim 1 is characterized in that: The real-time or scheduled extraction of raw data from multiple source systems includes: Identify the required data from the source system and determine the strategy for extracting incremental data or full data; Use ETL tools to configure data extraction tasks and field mapping relationships to ensure consistency between data fields and the target data warehouse; Use scheduling tools to trigger extraction tasks regularly, monitor task execution status, and handle failed tasks in a timely manner.

3. The report generation and optimization method based on offline data warehouse according to claim 1 is characterized in that: The data cleaning includes: performing data quality inspection and processing, including format standardization, missing value filling, outlier removal and introducing a rule engine to automatically clean data.

4. The report generation and optimization method based on offline data warehouse according to claim 1 is characterized in that: The integration of the cleaned data according to business requirements includes: 3.

1. Use the rule engine to complete the integration; 3.

2. Based on business needs, define field mapping rules, convert original data fields into target model fields, and use SQL or scripts to complete multi-table joint mapping; 3.

3. Associate the data tables according to business logic, establish a master-slave relationship through the customer ID and transaction number foreign key fields, calculate the total transaction amount by customer ID, and count the average daily transaction volume by date; 3.

4. Check whether the integrated data is logically consistent with the source data to ensure that the total transaction amount is not deviated due to data integration.

5. The report generation and optimization method based on offline data warehouse according to claim 4 is characterized in that: The integration is completed using the rule engine include: 3.1.

1. Use Drools and Flink CEP rule engine tools to write data integration logic according to business rules; 3.1.

2. Define rules: including "the ID of the same customer in different systems must be uniformly mapped", "transaction amount greater than a certain value is marked as a high-value transaction", configure rule priority to ensure the correct execution order of conflicting rules; 3.1.

3. During data integration, the rule engine automatically processes data according to the rule base to ensure consistency of business logic.

6. The report generation and optimization method based on offline data warehouse according to claim 1 is characterized in that: The consistency and integrity check of the data includes: 4.

1. Check the consistency and integrity of data through automated verification tools; 4.

2. Set quality monitoring indicators. When the indicators exceed the threshold, trigger an alarm and record the problem data; 4.

3. Generate a unique version number for each data processing and record all operation logs to facilitate quick backtracking when problems occur; 4.

4. For complex scenarios, generate data reports for review and conduct manual verification by business personnel.

7. The report generation and optimization method based on offline data warehouse according to claim 6 is characterized in that: The quality monitoring indicators include missing rate and abnormality rate; the consistency and integrity check of data includes: Check whether the data is within the allowed range and verify whether the total amount in the general ledger is equal to the sum of the amounts in the sub-ledgers.

8. The report generation and optimization method based on offline data warehouse according to claim 1 is characterized in that: The stored integrated model data, as the core data source for report generation, provides a standardized table structure and supports cross-departmental data reuse, including: Design a star or snowflake model of the data warehouse according to business requirements to ensure that data is easy to query and analyze, create materialized views, cache common query results, and improve query performance; For large tables, set partitions based on time and geographic location dimensions, and add indexes for commonly used query fields; Use permission management tools to limit the scope of data access for different users.

9. The report generation and optimization method based on offline data warehouse according to claim 1 is characterized in that: The method extracts common data based on model data to form a data mart for report generation, optimizes the performance of the data mart, supports rapid response to complex queries, and supports multiple output formats including: Extract common dimensions and measures from model data to form a report mart, and use physical stratification to optimize data structure; Use distributed computing tools to improve query efficiency and configure caching mechanisms to reduce the computing pressure of repeated queries.

10. A report generation and optimization system based on an offline data warehouse, characterized in that: include: The data extraction layer is used to extract raw data from multiple source systems in real time or on a scheduled basis. ETL tools are used to extract data. The extraction supports both incremental and full extraction modes. The data loading layer is used to efficiently load the extracted data into the data warehouse, using batch loading to improve performance and supporting parallel loading of large amounts of data; The data cleaning layer is used to check and process data quality, including format standardization, missing value filling, outlier removal, and the introduction of a rule engine to automatically clean data; The data integration layer is used to integrate the cleaned data according to business requirements, generate model data that meets business requirements, and support association, aggregation, and conversion operations between data; The model data layer is used to store the integrated model data. It serves as the core data source for report generation, provides a standardized table structure, and supports cross-departmental data reuse. The report mart layer is used to extract common data based on model data to form a data mart for report generation, optimize the performance of the data mart, and support fast response to complex queries: The report generation layer is used to generate offline reports using reporting tools and provides configurable report templates; The report optimization layer is used to optimize complex SQL query logic, improve performance through index introduction and query rewriting, and solidify commonly used calculation logic.

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