Method and system for dynamically rendering report on basis of index model

Abstract

A method for dynamically rendering a report on the basis of an index model, relating to the technical field of service systems; the technical problems to be solved thereby are how to solve the problem of the inability to dynamically adjust and render a report during development of the report and how to ensure the consistency and accuracy of data. The method comprises the following steps: on the basis of an application scenario, processing acquired service data, and establishing in a backend a summary physical table, a dimensional physical table, and an association between the summary physical table and the dimensional physical table; on the basis of metadata information in the dimensional physical table, constructing a dimensional data model, on the basis of metadata information in the summary physical table, constructing an index data model, and integrating the dimensional data model, the index data model, and a full-dimensional data model constructed in the backend with a front-end interface; and, on the basis of a data request, loading the dimensional data model and the index data model on the front-end interface to form a service report, rendering the service report, and dynamically displaying same. Further disclosed is a system for dynamically rendering a report on the basis of an index model.

Description

Method and system for dynamically rendering reports based on indicator models Technical Field

[0001] The present invention relates to the technical field of business systems, and in particular to a method and system for dynamically rendering reports based on an indicator model. Background Art

[0002] In the big data era, report generation and presentation are crucial components of data processing and analysis. Currently, there are two traditional report generation methods in the industry: One is personalized static reporting, where the data and format remain fixed after generation and cannot be dynamically adjusted based on real-time data or specific needs. The other method involves using custom reporting tools. Implementers write SQL scripts within the reporting tools, which then render and display the report content based on the returned results. Currently, both report development methods have a high learning curve and are difficult for non-technical users to master. Furthermore, due to varying SQL proficiency levels among implementers, statistical caliber and granularity are inconsistent, failing to meet user requirements for data standardization, accuracy, and consistency.

[0003] How to solve the problem of being unable to dynamically adjust and render reports during development and ensure data consistency and accuracy is a technical problem that needs to be solved.

[0004] Summary of the Invention

[0005] The technical task of the present invention is to address the above shortcomings and provide a method and system for dynamically rendering reports based on indicator models to solve the problem of how to solve the problem of being unable to dynamically adjust and render reports during development, and to ensure data consistency and accuracy.

[0006] The present invention provides a method for dynamically rendering a report based on an indicator model, which is applied to a system including a front-end and a back-end, and comprises the following steps:

[0007] Data collection: For enterprises to be collected, the data of each business system within the enterprise is standardized and the global business data within the enterprise is collected through a unified data collection channel;

[0008] Data processing: Process the collected business data based on the application scenario, divide the business data into three layers: ODS, CDM, and ADS, and establish summary physical tables, dimension physical tables, and the relationship between summary physical tables and dimension physical tables in the background;

[0009] Data modeling: Build a dimensional data model based on the metadata information in the dimension physical table, and build an indicator data model based on the metadata information in the summary physical table. Associate the dimensional data model and the indicator data model with the primary key field to form a data model mapping relationship, and build a full dimensional data model in the form of a wide table. The association between the dimensional data model and the indicator data model is saved in the database, and the dimensional data model, indicator data model, and full dimensional data model built in the background are integrated with the front-end interface.

[0010] Front-end rendering: Based on data requests, the dimensional data model and indicator data model are loaded on the front-end interface to form business reports, and the business reports are rendered and dynamically displayed.

[0011] As a preference, the data collection channel supports data collection from a variety of structured, semi-structured and unstructured data sources, and supports batch, real-time, full or incremental synchronization methods, and supports offline data collection and real-time data collection methods.

[0012] Preferably, when constructing a dimensional data model based on metadata information in a dimension physical table, the metadata information of the dimension physical table in the database is read, the field names, field meanings, and field types of the dimension physical table are identified, and the dimensional data model is defined by normalizing the meanings and names of the physical table fields;

[0013] The dimensional data model includes the source physical table of the dimension field, the mapping relationship between the dimension field and the physical table field, the identification weight of the dimension field associated with the source physical table, the filtering conditions of the source physical table, the data dictionary name of the dimension field, and the business classification of the dimensional data model;

[0014] When building an indicator data model based on the metadata information in the summary physical table, read the metadata information of the summary physical table in the database, identify the field name, field meaning and field type of the summary physical table, and define the indicator data model by normalizing the meaning and name of the physical table field;

[0015] The indicator data model includes a variety of different data types, including numeric, text, and date types. Each indicator field in the indicator data model is set with a set of indicator functions, including sum, total number of entries, average, and variance. The indicator data model also includes the source physical table weight of the dimension field, the filtering conditions of the source physical table, the exclusion of dimensions not supported by the indicator, and the indicator statistical unit.

[0016] Preferably, after the dimensional data model, the indicator data model, and the full dimensional data model are integrated with the front-end interface, the front-end interface and the back-end cooperate to perform the following:

[0017] Select different data items and filter conditions through the front-end interface. Data items include data sources, dimensions, and indicators. Filter conditions include query conditions and sorting conditions.

[0018] Obtain the dimensions, indicators, query conditions, and sorting conditions selected on the front-end interface, and find the actual physical table and field information based on the data model mapping relationship;

[0019] Based on the relationship between the dimensional data model and the indicator data model, assemble the SQL scripts required to query different data according to the indicators and query conditions;

[0020] All SQL scripts are executed concurrently by scheduling the thread pool to obtain query results;

[0021] The query results are spliced ​​and packaged, and the data is returned to the front-end interface after packaging.

[0022] Preferably, the front-end rendering includes the following steps:

[0023] Encapsulate the page components used by the dimensional data model and indicator data model built in the background as universal application tags;

[0024] Drag and drop data models on the front-end interface, and build business reports by dragging dimensions and indicators on the front-end interface;

[0025] Dynamically update the calculation method and data source of the indicator model based on real-time data, and render business reports in real time;

[0026] Dynamically adjust the layout and visibility of business reports based on user interactions.

[0027] In a second aspect, the present invention provides a system for dynamically rendering reports based on an indicator model, which is applied between a front-end interface and a back-end interface, and is used to render business reports using a method for dynamically rendering reports based on an indicator model as described in any one of the first aspects. The system includes a data acquisition module, a data processing module, a data modeling module, and a front-end rendering module.

[0028] The data collection module is used to perform the following: for the enterprise to be collected, standardize the data of each business system in the enterprise, and collect the global business data within the enterprise through a unified data collection channel;

[0029] The data processing module is used to perform the following: process the collected business data based on the application scenario, divide the business data into three layers: ODS, CDM and ADS, and establish summary physical tables, dimension physical tables, and the association relationship between summary physical tables and dimension physical tables in the background;

[0030] The data modeling module is used to perform the following: construct a dimensional data model based on the metadata information in the dimension physical table, and construct an indicator data model based on the metadata information in the summary physical table, associate the dimensional data model and the indicator data model with the primary key field to form a data model mapping relationship, and construct a full dimensional data model in the form of a wide table, save the association relationship between the dimensional data model and the indicator data model in the database, and integrate the dimensional data model, indicator data model and full dimensional data model constructed in the background with the front-end interface;

[0031] The front-end rendering module is used to perform the following: based on the data request, the dimension data model and the indicator data model are loaded on the front-end interface to form a business report, and the business report is rendered and dynamically displayed.

[0032] As a preference, the data collection channel supports data collection from a variety of structured, semi-structured and unstructured data sources, and supports batch, real-time, full or incremental synchronization methods, and supports offline data collection and real-time data collection methods.

[0033] Preferably, when constructing a dimensional data model based on metadata information in a dimension physical table, the data modeling module is configured to perform the following: reading metadata information of the dimension physical table in a database, identifying field names, field meanings, and field types of the dimension physical table, and defining the dimensional data model by normalizing the meanings and names of the physical table fields;

[0034] The dimensional data model includes the source physical table of the dimension field, the mapping relationship between the dimension field and the physical table field, the identification weight of the dimension field associated with the source physical table, the filtering condition of the source physical table, the data dictionary name of the dimension field, and the business classification of the dimensional data model;

[0035] When constructing an indicator data model based on the metadata information in the aggregated physical table, the data modeling module is used to perform the following: read the metadata information of the aggregated physical table in the database, identify the field names, field meanings, and field types of the aggregated physical table, and define the indicator data model by normalizing the meanings and names of the physical table fields;

[0036] The indicator data model includes a variety of different data types, including numeric, text, and date types. Each indicator field in the indicator data model is set with a set of indicator functions, including sum, total number of items, average, and variance. The indicator data model also includes the source physical table weight of the dimension field, the filtering conditions of the source physical table, the exclusion of dimensions not supported by the indicator, and the indicator statistical unit.

[0037] Preferably, after the dimensional data model, the indicator data model, and the full dimensional data model are integrated with the front-end interface, the front-end interface and the back-end cooperate to perform the following:

[0038] Select different data items and filter conditions through the front-end interface. Data items include data sources, dimensions, and indicators. Filter conditions include query conditions and sorting conditions.

[0039] Obtain the dimensions, indicators, query conditions, and sorting conditions selected on the front-end interface, and find the actual physical table and field information based on the data model mapping relationship;

[0040] Based on the relationship between the dimensional data model and the indicator data model, assemble the SQL scripts required to query different data according to the indicators and query conditions;

[0041] All SQL scripts are executed concurrently by scheduling the thread pool to obtain query results;

[0042] The query results are spliced ​​and packaged, and the data is returned to the front-end interface after packaging.

[0043] Preferably, the front-end rendering module is used to perform the following operations:

[0044] Encapsulate the page components used by the dimensional data model and indicator data model built in the background as universal application tags;

[0045] Drag and drop data models on the front-end interface, and build business reports by dragging dimensions and indicators on the front-end interface;

[0046] Dynamically update the calculation method and data source of the indicator model based on real-time data, and render business reports in real time;

[0047] Dynamically adjust the layout and visibility of business reports based on user interactions.

[0048] The method and system for dynamically rendering reports based on indicator models of the present invention have the following advantages: users can flexibly and dynamically generate reports by selecting different data items on the front end and performing operations such as filtering and querying the data. Users and implementers who do not have program development capabilities can dynamically generate reports by selecting dimensions and indicators, thereby ensuring data quality and consistency while meeting the need to quickly respond to changes in business needs. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the embodiments or descriptions of the prior art. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0050] The present invention will be further described below with reference to the accompanying drawings.

[0051] FIG1 is a flowchart of a method for dynamically rendering a report based on an indicator model according to Example 1;

[0052] FIG2 is a flowchart of front-end rendering in a method for dynamically rendering reports based on an indicator model in Example 1. DETAILED DESCRIPTION

[0053] The present invention will be further described below with reference to the accompanying drawings and specific embodiments so that those skilled in the art can better understand the present invention and implement it. However, the embodiments given are not intended to limit the present invention. Unless there is a conflict, the embodiments of the present invention and the technical features in the embodiments may be combined with each other.

[0054] The embodiments of the present invention provide a method and system for dynamically rendering reports based on an indicator model, which are used to solve the problem of being unable to dynamically adjust and render reports during report development and to ensure the consistency and accuracy of data.

[0055] Example 1:

[0056] The present invention provides a method for dynamically rendering reports based on an indicator model, which is applied to a system including a front-end and a back-end. As shown in FIG1 , the method includes four steps: data collection, data processing, data modeling, and front-end rendering.

[0057] Step S100: Data collection: For the enterprise to be collected, the data of each business system in the enterprise is standardized, and the global business data within the enterprise is collected through a unified data collection channel.

[0058] In this embodiment, the data acquisition channel supports data acquisition from multiple data sources such as structured, semi-structured and unstructured, and supports batch, real-time, full or incremental synchronization modes, and supports offline data acquisition and real-time data acquisition.

[0059] The implementation of this embodiment requires the user to provide a data processing suite for normalizing, standardizing, lightly summarizing, and personalizing data processing of the original data. The main data computing resources used include basic resources such as big data computing and high-performance analytical databases.

[0060] When this embodiment collects data, it uses the unified data collection channel provided by the data processing suite, and the architecture must support multiple data sources such as structured, semi-structured, and unstructured data, and support batch or real-time, full or incremental synchronization methods to achieve the collection of massive offline data and real-time data. Before data collection, the data of each business system in the enterprise needs to be standardized and normalized according to the business segment and data domain. Data collection will strictly follow the data standards to complete the collection of internal and external data sources of the enterprise. The collected data content includes the enterprise's internal full-domain business data. The data elements of each business system in the big data computing library must be uniformly defined with standard field coding to ensure that each business department in the enterprise can understand the business dimensions and metrics, and can correctly use the relevant dimensions and metrics.

[0061] Step S200: Data processing: Process the collected business data based on the application scenario, divide the business data into three layers: ODS, CDM and ADS, and establish summary physical tables, dimension physical tables, and the association relationship between summary physical tables and dimension physical tables in the background.

[0062] As a specific implementation of data processing, based on the data collection into the big data computing database, based on different data application scenarios, the data is processed from "raw materials" into "semi-finished products" or "finished products" through various methods to support the data consumption needs of different scenarios.

[0063] Data processing follows industry best practices and is divided into three tiers: ODS, CDM, and ADS. Based on the data processing suite and clear business relationships, fact tables, dimension tables, and their interlinking relationships are established to enable lightweight processing and aggregation of general summary data. These lightweight summary tables must be versatile, meeting data modeling requirements while also supporting in-depth processing for personalized services.

[0064] Step S300 data modeling: construct a dimensional data model based on the metadata information in the dimension physical table, and construct an indicator data model based on the metadata information in the summary physical table, associate the dimensional data model and the indicator data model with the primary key fields to form a data model mapping relationship, and build a full dimensional data model in the form of a wide table. The association relationship between the dimensional data model and the indicator data model is saved in the database, and the dimensional data model, indicator data model and full dimensional data model built in the background are integrated with the front-end interface.

[0065] The data modeling in this embodiment relies on the data summary table and dimension table after data processing, and constructs a data model for the summary data table and dimension data for use in data reports. It specifically includes four steps: constructing a dimensional data model, constructing an indicator data model, constructing model association relationships, and data integration application.

[0066] Building a dimensional data model: When building a dimensional data model based on the metadata information in the dimension physical table, read the dimension physical table metadata information in the database, identify the dimension physical table field names, field meanings, and field types, and define the dimensional data model by normalizing the physical table field meanings and names.

[0067] Among them, the dimensional data model includes the source physical table of the dimension field, the mapping relationship between the dimension field and the physical table field, the identification weight of the dimension field associated with the source physical table, the filtering condition of the source physical table, the data dictionary name of the dimension field and the business classification of the dimensional data model.

[0068] Constructing an indicator data model: When constructing an indicator data model based on the metadata information in the aggregated physical table, read the metadata information of the aggregated physical table in the database, identify the field names, field meanings, and field types of the aggregated physical table, and define the indicator data model by normalizing the meanings and names of the physical table fields;

[0069] The indicator data model includes a variety of different data types, including numeric, text, and date types. Each indicator field in the indicator data model is set with a set of indicator functions, including sum, total number of items, average, and variance. The indicator data model also includes the source physical table weight of the dimension field, the filtering conditions of the source physical table, the exclusion of dimensions not supported by the indicator, and the indicator statistical unit.

[0070] Build model association relationships: Associate the dimensional data model and the indicator data model with the primary key fields to build a full-dimensional data model in the form of a large wide table. Save the association relationships of the data model to the business database to facilitate the assembly and processing of data query SQL when subsequent users request data.

[0071] Data model integrated application: After the backend data model is technically integrated with the front-end application, users can select different data items and filtering conditions through components such as selection boxes and drop-down lists on the interface. Based on the selected relevant dimensions and indicators, the results are dynamically displayed on the visual interface, and the required report results can be obtained by adjusting different parameters.

[0072] After integrating the dimensional data model, indicator data model, and full-dimensional data model with the front-end interface, the front-end interface and the back-end work together to perform the following:

[0073] (1) Select different data items and filtering conditions through the front-end interface. Data items include data sources, dimensions, and indicators. Filtering conditions include query conditions and sorting conditions.

[0074] (2) Obtain the dimensions, indicators, query conditions, and sorting conditions selected on the front-end interface, and search for actual physical tables and field information based on the data model mapping relationship;

[0075] (3) Based on the relationship between the dimensional data model and the indicator data model, assemble the SQL scripts for different data queries according to the indicators and query conditions;

[0076] (4) All SQL scripts are executed concurrently by scheduling the thread pool to obtain query results;

[0077] (5) The query results are spliced ​​and packaged, and the data is returned to the front-end interface after packaging.

[0078] Step S400: Front-end rendering: Based on the data request, the dimension data model and the indicator data model are loaded into the front-end interface to form a business report, and the business report is rendered and dynamically displayed.

[0079] In this embodiment, front-end rendering includes the following steps:

[0080] (1) Encapsulate the page components used by the dimensional data model and indicator data model built in the background into universal application tags;

[0081] (2) Drag and drop data models through the front-end interface, and drag and drop dimensions and indicators to build business reports through the front-end interface;

[0082] (3) Dynamically update the calculation method and data source of the indicator model based on real-time data, and render business reports in real time;

[0083] (4) Dynamically adjust the layout and visibility of business reports based on user interactions.

[0084] As a specific implementation of front-end rendering, the front-end development program incorporates forward-thinking and popular frameworks such as Node.js, Vue, React, and ElementUI. These common frameworks are then repackaged, and the page components used by the dimension and indicator models built in the backend are encapsulated into universal application tags. Users can drag and drop data models in the main functional interface and assemble business reports by dragging and dropping dimensions and indicators. The front-end interface dynamically adjusts the report data and presentation based on real-time data and the indicators and dimensions selected by user operations. Specifically, the calculation method and data source of the indicator model are dynamically updated based on real-time data, and the report is rendered in real time. At the same time, the layout and style of the report are dynamically adjusted based on user interaction to meet the needs of different scenarios.

[0085] As shown in Figure 2, when the report is rendered, the front end selects the dimension and indicator request, parses the dimensions and indicators in the request, and identifies the indicator calculation formula. According to the mapping relationship, the actual physical table and field information are found for the data model mapping relationship. According to the relationship between the dimension data model and the indicator data model, the SQL scripts that need to query data are spliced ​​according to the indicators and query conditions. All SQL scripts are executed in batches through the scheduling thread pool. After the execution is completed, the SQL return results are collected, and the indicator calculation is performed to generate subtotals. The calculated columns are processed based on the subtotals to obtain the final query results. The query results are spliced ​​and packaged. After the packaging is completed, the data is returned to the front end interface.

[0086] Example 2:

[0087] The present invention provides a system for dynamically rendering reports based on an indicator model, which is applied between the front-end interface and the back-end, and is used to render business reports through the method disclosed in Example 1. The system includes a data acquisition module, a data processing module, a data modeling module and a front-end rendering module.

[0088] The data collection module is used to perform the following: for the enterprise to be collected, the data of each business system within the enterprise is standardized, and the global business data within the enterprise is collected through a unified data collection channel.

[0089] In this embodiment, the data acquisition channel supports data acquisition from multiple data sources such as structured, semi-structured and unstructured, and supports batch, real-time, full or incremental synchronization modes, and supports offline data acquisition and real-time data acquisition.

[0090] The implementation of this embodiment requires the user to provide a data processing suite for normalizing, standardizing, lightly summarizing, and personalizing data processing of the original data. The main data computing resources used include basic resources such as big data computing and high-performance analytical databases.

[0091] When this embodiment collects data, it uses the unified data collection channel provided by the data processing suite, and the architecture must support multiple data sources such as structured, semi-structured, and unstructured data, and support batch or real-time, full or incremental synchronization methods to achieve the collection of massive offline data and real-time data. Before data collection, the data of each business system in the enterprise needs to be standardized and normalized according to the business segment and data domain. Data collection will strictly follow the data standards to complete the collection of internal and external data sources of the enterprise. The collected data content includes the enterprise's internal full-domain business data. The data elements of each business system in the big data computing library must be uniformly defined with standard field coding to ensure that each business department in the enterprise can understand the business dimensions and metrics, and can correctly use the relevant dimensions and metrics.

[0092] The data processing module is used to perform the following: process the collected business data based on the application scenario, divide the business data into three layers: ODS, CDM and ADS, and establish summary physical tables, dimension physical tables, and the association relationship between summary physical tables and dimension physical tables in the background.

[0093] As a specific implementation of data processing, based on the data collection into the big data computing database, based on different data application scenarios, the data is processed from "raw materials" into "semi-finished products" or "finished products" through various methods to support the data consumption needs of different scenarios.

[0094] Data processing follows industry best practices and is divided into three tiers: ODS, CDM, and ADS. Based on the data processing suite and clear business relationships, fact tables, dimension tables, and their interlinking relationships are established to enable lightweight processing and aggregation of general summary data. These lightweight summary tables must be versatile, meeting data modeling requirements while also supporting in-depth processing for personalized services.

[0095] The data modeling module is used to perform the following: build a dimensional data model based on the metadata information in the dimension physical table, and build an indicator data model based on the metadata information in the summary physical table, associate the dimensional data model and the indicator data model with the primary key fields to form a data model mapping relationship, and build a full dimensional data model in the form of a wide table. The association relationship between the dimensional data model and the indicator data model is saved in the database, and the dimensional data model, indicator data model and full dimensional data model built in the background are integrated with the front-end interface.

[0096] The data modeling module in this embodiment relies on data summary tables and dimension tables after data processing. The data modeling module in this embodiment is used to perform four operations: constructing a dimensional data model, constructing an indicator data model, constructing model associations, and applying data integration.

[0097] When building a dimensional data model, the data modeling module is used to perform the following operations: when building a dimensional data model based on the metadata information in the dimension physical table, read the dimension physical table metadata information in the database, identify the dimension physical table field name, field meaning and field type, and define the dimensional data model by normalizing the physical table field meaning and name.

[0098] Among them, the dimensional data model includes the source physical table of the dimension field, the mapping relationship between the dimension field and the physical table field, the identification weight of the dimension field associated with the source physical table, the filtering condition of the source physical table, the data dictionary name of the dimension field and the business classification of the dimensional data model.

[0099] When building an indicator data model, the data modeling module is used to perform the following operations: when building an indicator data model based on the metadata information in the summary physical table, read the metadata information of the summary physical table in the database, identify the field name, field meaning and field type of the summary physical table, and define the indicator data model by normalizing the meaning and name of the physical table field.

[0100] The indicator data model includes a variety of different data types, including numeric, text, and date types. Each indicator field in the indicator data model is set with a set of indicator functions, including sum, total number of items, average, and variance. The indicator data model also includes the source physical table weight of the dimension field, the filtering conditions of the source physical table, the exclusion of dimensions not supported by the indicator, and the indicator statistical unit.

[0101] When building model association relationships, the data modeling module is used to perform the following operations: associate the dimensional data model and the indicator data model with the primary key fields, build a full-dimensional data model in the form of a large wide table, and save the association relationships of the data model to the business database to facilitate the assembly and processing of data query SQL when subsequent users request data.

[0102] After technically integrating the backend data model with the front-end application through the data modeling module, users can select different data items and filtering conditions through components such as selection boxes and drop-down lists on the interface, and dynamically display the results on the visual interface based on the selected relevant dimensions and indicators. By adjusting different parameters, the required report results can be obtained.

[0103] After integrating the dimensional data model, indicator data model, and full-dimensional data model with the front-end interface, the front-end interface and the back-end work together to perform the following:

[0104] (1) Select different data items and filtering conditions through the front-end interface. Data items include data sources, dimensions, and indicators. Filtering conditions include query conditions and sorting conditions.

[0105] (2) Obtain the dimensions, indicators, query conditions, and sorting conditions selected on the front-end interface, and search for actual physical tables and field information based on the data model mapping relationship;

[0106] (3) Based on the relationship between the dimensional data model and the indicator data model, assemble the SQL scripts for different data queries according to the indicators and query conditions;

[0107] (4) All SQL scripts are executed concurrently by scheduling the thread pool to obtain query results;

[0108] (5) The query results are spliced ​​and packaged, and the data is returned to the front-end interface after packaging.

[0109] The front-end rendering module is used to perform the following: based on data requests, the dimension data model and the indicator data model are loaded on the front-end interface to form a business report, and the business report is rendered and dynamically displayed.

[0110] In this embodiment, the front-end rendering module is used to perform the following operations:

[0111] (1) Encapsulate the page components used by the dimensional data model and indicator data model built in the background into universal application tags;

[0112] (2) Drag and drop data models through the front-end interface, and drag and drop dimensions and indicators to build business reports through the front-end interface;

[0113] (3) Dynamically update the calculation method and data source of the indicator model based on real-time data, and render business reports in real time;

[0114] (4) Dynamically adjust the layout and visibility of business reports based on user interactions.

[0115] As a specific implementation of front-end rendering, the front-end development program incorporates forward-thinking and popular frameworks such as Node.js, Vue, React, and ElementUI. These common frameworks are then repackaged, and the page components used by the dimension and indicator models built in the backend are encapsulated into universal application tags. Users can drag and drop data models in the main functional interface and assemble business reports by dragging and dropping dimensions and indicators. The front-end interface dynamically adjusts the report data and presentation based on real-time data and the indicators and dimensions selected by user operations. Specifically, the calculation method and data source of the indicator model are dynamically updated based on real-time data, and the report is rendered in real time. At the same time, the layout and style of the report are dynamically adjusted based on user interaction to meet the needs of different scenarios.

[0116] When rendering a report, the front-end selects dimension and indicator requests, parses the dimensions and indicators in the request, and identifies the indicator calculation formula. Based on the mapping relationship, the actual physical table and field information are found for the data model mapping relationship. Based on the relationship between the dimension data model and the indicator data model, the SQL scripts for different data queries are spliced ​​according to the indicators and query conditions. All SQL scripts are executed in batches through the scheduling thread pool. After the execution is completed, the SQL return results are collected, and the indicator calculation is performed to generate subtotals. The calculated columns are processed based on the subtotals to obtain the final query results. The query results are spliced ​​and packaged. After the packaging is completed, the data is returned to the front-end interface.

[0117] The present invention has been shown and described in detail above through the accompanying drawings and preferred embodiments. However, the present invention is not limited to these disclosed embodiments. Based on the above multiple embodiments, those skilled in the art can know that the means in the above different embodiments can be combined to obtain more embodiments of the present invention, and these embodiments are also within the scope of protection of the present invention.

Claims

1. A method for dynamically rendering reports based on an index model, characterized in that, Applied to a system including a front end and a back end, the method includes the following steps: Data collection: For the enterprise to be collected, standardize the data of each business system within the enterprise, and collect the global business data within the enterprise through a unified data collection channel; Data processing: Process the collected business data based on the application scenario, divide the business data into three layers of ODS, CDM, and ADS, and establish the association relationships between the summary physical table, the dimension physical table, and between the summary physical table and the dimension physical table in the back end; Data modeling: Build a dimension data model based on the metadata information in the dimension physical table, and build an indicator data model based on the metadata information in the summary physical table. Associate the dimension data model and the indicator data model through the primary key field to form a data model mapping relationship, and build a full-dimension data model in the form of a wide table. Save the association relationship between the dimension data model and the indicator data model to the database, and integrate the dimension data model, the indicator data model, and the full-dimension data model built in the back end with the front-end interface; Front-end rendering: Load the dimension data model and the indicator data model to form a business report on the front-end interface based on the data request, and render and dynamically display the business report.

2. The method for dynamically rendering a report based on an index model according to claim 1, wherein The data collection channel supports data collection from various data sources such as structured, semi-structured, and unstructured, supports multiple synchronization methods such as batch, real-time, full volume, or incremental, and supports the collection methods of offline data collection and real-time data collection.

3. The method for dynamically rendering a report based on an index model according to claim 1 or 2, characterized in that, When building a dimension data model based on the metadata information in the dimension physical table, read the metadata information of the dimension physical table in the database, identify the field name, field meaning, and field type of the dimension physical table, and define the dimension data model by standardizing the meaning and name of the physical table fields; The dimension data model includes the source physical table of the dimension field, the mapping relationship between the dimension field and the physical table field, the identification weight of the dimension field associated with the source physical table, the filtering conditions of the source physical table, the data dictionary name of the dimension field and the business classification of the dimension data model; When building an indicator data model based on the metadata information in the summary physical table, read the metadata information of the summary physical table in the database, identify the field name, field meaning, and field type of the summary physical table, and define the indicator data model by standardizing the meaning and name of the physical table fields; The indicator data model includes multiple different data types, including numeric, text, and date types. Each indicator field of the indicator data model is set with a group of indicator functions, including sum, total number, average value, and variance, and the indicator data model includes the source physical table weight of the dimension field, the filtering conditions of the source physical table, excluding dimensions not supported by the indicator, and the indicator statistical unit.

4. The method for dynamically rendering a report based on an index model according to claim 3, wherein After integrating the dimension data model, the indicator data model, and the full-dimension data model with the front-end interface, the front-end interface and the back end cooperate to perform the following: Select different data items and filtering conditions through the front-end interface. The data items include data sources, dimensions, and indicators, and the filtering conditions include query conditions and sorting conditions; Obtain the dimensions, metrics, query conditions, and sorting conditions selected on the front-end interface, and find the actual physical tables and field information according to the data model mapping relationship; According to the relationship between the dimension data model and the metric data model, splice the SQL scripts for querying different data required according to the metrics and query conditions; Execute all SQL scripts concurrently through the scheduling thread pool to obtain the query results; Splice and encapsulate the query results, and return the data to the front-end interface after the encapsulation is completed.

5. The method for dynamically rendering a report based on an indicator model according to claim 4, wherein The front-end rendering includes the following steps: Encapsulate the page components used in the dimension data model and metric data model constructed in the background as General application tags; Drag and select the data model through the front-end interface, and construct a business report by dragging dimensions and metrics through the front-end interface; Dynamically update the calculation method and data source of the metric model according to real-time data, and render the business report in real time; Dynamically adjust the layout and perspective of the business report according to user interaction.

6. A system for dynamically rendering reports based on an index model, characterized in that, Applied between the front-end interface and the background, used for business report rendering through a method of dynamically rendering reports based on a metric model as described in any one of claims 1-5, the system includes a data collection module, a data processing module, a data modeling module, and a front-end rendering module; The data collection module is used to perform the following: for the enterprise to be collected, standardize the data of each business system within the enterprise, and collect the global business data within the enterprise through a unified data collection channel; The data processing module is used to perform the following: process the collected business data based on the application scenario, divide the business data into three layers: ODS, CDM, and ADS, and establish the association relationship between the summary physical table, dimension physical table, and the summary physical table and dimension physical table in the background; The data modeling module is used to perform the following: construct a dimension data model based on the metadata information in the dimension physical table, construct a metric data model based on the metadata information in the summary, associate the dimension data model and the metric data model through the primary key field to form a data model mapping relationship, and construct a full-dimension data model in the form of a wide table. The association relationship between the dimension data model and the metric data model is saved in the database, and the dimension data model, metric data model, and full-dimension data model constructed in the background are integrated with the front-end interface; The front-end rendering module is used to perform the following: load the dimension data model and the metric data model on the front-end interface based on the data request to form a business report, and render and dynamically display the business report Show.

7. The system for dynamically rendering reports based on an index model according to claim 6, wherein The data collection channel supports data collection from multiple data sources such as structured, semi-structured, and unstructured, supports multiple synchronization methods such as batch, real-time, full-volume, or incremental, and supports the collection methods of offline data collection and real-time data collection.

8. The system for dynamically rendering reports based on an indicator model according to claim 6 or 7, characterized in that, When constructing a dimension data model based on the metadata information in the dimension physical table, the data modeling module is used to perform the following: read the metadata information of the dimension physical table in the database, identify the field name, field meaning, and field type of the dimension physical table, and define the dimension data model by standardizing the field meaning and name of the physical table; Among them, the dimension data model includes the source physical table of dimension fields, the mapping relationship between dimension fields and physical table fields, the recognition weight of dimension fields associated with the source physical table, the filtering conditions of the source physical table, the data dictionary name of dimension fields, and the business classification of the dimension data model; When constructing the indicator data model based on the metadata information in the summary physical data, the data modeling module is used to perform the following: read the metadata information of the summary physical table in the database, identify the field names, field meanings, and field types of the summary physical table, and define the indicator data model by normalizing the meanings and names of the physical table fields; Among them, the indicator data model includes multiple different data types, including numeric type, text type, and date type. Each indicator field in the indicator data model is set with a group of indicator functions, including sum, total number, average value, and variance. And the indicator data model includes the source physical table weight of dimension fields, the filtering conditions of the source physical table, excluding dimensions not supported by indicators, and the indicator statistical unit.

9. The system for dynamically rendering a report based on an index model according to claim 8, wherein After integrating the dimension data model, the indicator data model, and the full-dimension data model with the front-end interface, the front-end interface and the background cooperate to perform the following: Select different data items and filtering conditions through the front-end interface. The data items include data sources, dimensions, and indicators, and the filtering conditions include query conditions and sorting conditions; Obtain the dimensions, indicators, query conditions, and sorting conditions selected by the front-end interface, and find the actual physical table and field information according to the data model mapping relationship; According to the relationship between the dimension data model and the indicator data model, splice the SQL scripts for querying different required data according to the indicators and query conditions; Concurrently execute all SQL scripts through the scheduling thread pool to obtain the query results; Splice and encapsulate the query results, and return the data to the front-end interface after the encapsulation is completed.

10. The system for dynamically rendering reports based on an indicator model according to claim 9, wherein The front-end rendering module is used to perform the following operations: Encapsulate the page components used in the dimension data model and the indicator data model constructed by the background into common application tags; Drag and select the data model through the front-end interface, and construct a business report by dragging dimensions and indicators through the front-end interface; Dynamically update the calculation method and data source of the indicator model according to real-time data, and render the business report in real time; Dynamically adjust the layout and perspective of the business report according to user interaction.

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