CN117251482A - Data analysis processing method, device, equipment and storage medium - Google Patents

Abstract

This application provides a data analysis and processing method, device, equipment and storage medium, relating to the field of data processing technology. This method obtains a pre-stored business model or measurement model as a data model. After obtaining the data model determined by the user, it obtains the user-configured measurement element creation including the creation type indicating the creation type of the measurement element and the creation details indicating the creation details of the measurement element configuration parameters. information, and create metric elements used to indicate the configuration rules for obtaining target data based on the metric element creation information, and store the metric elements set by the user in the metric metadata database, so that it can conveniently and agilely help different types of business personnel perform operations based on database metrics. For data analysis, the target data is obtained through metric elements in the data model and displayed on the front-end page. This avoids repeated configuration of metric elements with the same rules and reduces configuration time.

Description

Data analysis and processing methods, devices, equipment and storage media

Technical field

This application relates to the field of data processing technology, and specifically to a data analysis and processing method, device, equipment and storage medium.

Background technique

In a cloud-native environment, various business applications have been moved to the cloud through models such as microservices. As the running time of business applications becomes longer, more and more business data are obtained by various business systems, and the measurement requirements for business processes are getting higher and higher. It is getting higher and higher, that is, the requirements for data analysis are getting higher and higher, and some quantifiable dimensions or indicators and data analysis results need to be quantitatively displayed to promote the improvement of process management and final results.

In existing data analysis solutions, actual business personnel are usually not familiar with the specific measurements of the database, and technical business personnel who are familiar with the specific measurements of the database do not understand the feature data that needs to be extracted in actual business operations. Therefore, the existing The data analysis solution cannot conduct data analysis according to different business types, which reduces the decision-making accuracy of business personnel when conducting data analysis. Moreover, one or more measurement items in the existing technical solution can only be directly bound to the corresponding on the chart, so the configuration steps need to be repeated each time the corresponding chart is generated, and the configuration time cost is high.

When the existing technology performs data analysis for different business types, it cannot conveniently and agilely help different types of business personnel conduct data analysis based on database metrics, resulting in a reduction in decision-making accuracy and the configuration of chart results when data analysis is generated. There are still some shortcomings in the aspect of high time cost.

Contents of the invention

This application provides a data analysis and processing method, device, equipment and storage medium to solve the problem that the existing technology cannot conveniently and agilely help different types of business personnel perform data analysis based on database metrics when conducting data analysis for different business types. Data analysis, low decision-making accuracy and high configuration time cost.

In the first aspect, this application provides a data analysis and processing method, including:

Obtain a pre-stored data model, wherein the data model is generated according to business data, the data model includes a business model and a measurement model, the business model is used to indicate configuration of the business data, and the measurement model Used to instruct the configuration of the business data after association processing;

Obtain the user's metric creation information, where the metric creation information includes a creation type and creation details, and create a metric based on the metric creation information, where the creation type is used to indicate the creation method of the metric, so The creation details are used to indicate the configuration parameters of the metric;

In the data model, the target data is obtained through the metric element, and the target data is displayed on the front-end page, where the metric element is used to indicate the configuration rule for obtaining the target data.

In a possible design, before obtaining the pre-stored data model, the steps include:

Obtain the user's model configuration parameters, which include business configuration parameters and measurement configuration parameters;

Obtain target service data from the service data according to the service configuration parameters, store the target service data in the target service database, and obtain the service model according to the target service data, wherein the service configuration parameters Used to indicate the scope of obtaining target business data in the business data, and the target business database stores multiple different business models;

Correlate multiple data tables in the business data according to the measurement configuration parameters, obtain the measurement model based on the multiple data tables after correlation processing, and obtain the measurement model based on the business model and the measurement model. Describe the data model and store it.

In a possible design, the correlation processing of multiple data tables in the business data according to the measurement configuration parameters includes:

Obtain the correlation target information in the measurement configuration parameters, where the correlation target information is used to indicate the name of the data table that needs to be correlated;

Multiple target data tables are determined in the business data according to the associated target information, and the multiple target data tables are associated and stored in a metric database, where the metric database stores multiple different metric models.

In a possible design, obtaining the user's metric element creation information includes:

Obtain the creation type in the metric creation information, and obtain the corresponding creation details according to the creation type, where the creation type includes business-driven creation or custom statement creation;

If the creation type is the business-driven creation, the business configuration information is obtained through the business configuration unit, and the business configuration information is used as the creation details. The business configuration information includes field configuration information, condition constraint information and sorting. Information, wherein the field configuration information is used to indicate the operation function of the field, the condition constraint information is used to indicate the constraint query condition of the field, and the sorting information is used to indicate the sorting method of the field;

If the creation type is the custom statement creation, obtain the SQL control statement through the statement configuration unit, and use the SQL control statement as the creation details;

Create a metric based on the metric creation information.

In a possible design, creating a metric unit according to the metric unit creation information includes:

Obtain basic configuration information, confirm the information data consisting of the basic configuration information, the creation type and the creation details as the metric element, and associate and store it in the metric element database;

Wherein, the basic configuration information includes the name of the metric element and the collection frequency. The collection frequency is used to indicate the triggering frequency of the metric element. A plurality of different metric elements are stored in the metric element database.

In a possible design, before creating the metric element according to the metric element creation information, the method further includes:

Obtain a target usage model, and load the corresponding data model according to the target usage model, where the target usage model is a business model or a measurement model;

If the target usage model is the business model, search the same identification ID in the target business database according to the identification ID of the target usage model, and confirm the business model with the same identification ID as the calling model;

If the target usage model is the measurement model, retrieve the same identification identification in the measurement database according to the identification identification of the target usage model, and confirm the measurement model with the same identification identification as the calling model;

In the calling model, target data is obtained through the metric element, and the target data is displayed on the front-end page.

In one possible design, obtaining target data through the metric element in the calling model, and displaying the target data to the front-end page includes:

Obtain target data in the calling model according to the creation details of the metric element;

Choose the method of nesting page functions through links to realize the construction of page framework, and bind and combine the icon components in the page with the field configuration information of the metric unit, so that the target data obtained according to the field configuration information can be automatically generate charts;

The field configuration information is bound to the query condition variable, so that the corresponding target data can be returned through the metric element according to the query conditions input by the user, and the chart can be updated according to the corresponding target data.

In a second aspect, this application provides a data analysis and processing device, including:

An acquisition module, used to acquire a pre-stored data model, wherein the data model is generated according to business data, the data model includes a business model and a measurement model, and the business model is used to indicate configuring the business data. , the measurement model is used to indicate the configuration of the business data after correlation processing;

A processing module, configured to obtain the user's metric element creation information, where the metric element creation information includes a creation type and creation details, and obtain the metric element according to the metric element creation information, wherein the creation type is used to indicate the metric element The creation method, the creation details are used to indicate the configuration parameters of the metric unit;

An execution module, configured to obtain target data according to the metric element and the data model, and display the target data to a front-end page, where the metric element is used to indicate the configuration rules for obtaining the target data.

In a third aspect, this application provides an electronic device, including: a processor, and a memory communicatively connected to the processor;

The memory stores computer execution instructions;

The processor executes computer execution instructions stored in the memory to implement the data analysis and processing method.

In a fourth aspect, the present application provides a computer-readable storage medium in which computer-executable instructions are stored, and when executed by a processor, the computer-executable instructions are used to implement a data analysis and processing method.

The data analysis and processing method, device, equipment and storage medium provided by this application obtain a pre-stored business model or measurement model as a data model, wherein the business model is generated based on the configuration based on the original business data, and the business model after correlation processing is generated. Configure the data to generate a measurement model. After obtaining the data model determined by the user, obtain the measurement element creation information configured by the user including the creation type indicating the creation type of the measurement element and the creation details indicating the creation details of the measurement element configuration parameters, and create it based on the measurement element creation information. It is used to indicate the metric element for obtaining the target data configuration rules, and stores the metric element set by the user in the metric metadata database, so that it can conveniently and agilely help different types of business personnel conduct data analysis based on database metrics, and use metrics in the data model. Elements obtain target data and display the target data to the front-end page, avoiding repeated configuration of measurement elements with the same rules and reducing configuration time.

Description of drawings

In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following will briefly introduce the drawings that need to be used in the description of the embodiments or the prior art. Obviously, the drawings in the following description These are some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without exerting creative efforts.

Figure 1 is a schematic flow chart 1 of the data analysis and processing method provided by the embodiment of the present application;

Figure 2 is a schematic flow chart 2 of the data analysis and processing method provided by the embodiment of the present application;

Figure 3 is an interface diagram of a new business model provided by the embodiment of this application;

Figure 4 is an interface diagram for editing a measurement model provided by an embodiment of the present application;

Figure 5 is a metric element management interface diagram provided by the embodiment of the present application;

Figure 6 is a service configuration interface diagram provided by the embodiment of this application;

Figure 7 is a customized configuration interface diagram provided by the embodiment of this application;

Figure 8 is an interface diagram for configuring basic information provided by the embodiment of this application;

Figure 9 is a schematic structural diagram of a data analysis and processing device provided by an embodiment of the present application;

FIG. 10 is a schematic structural diagram of an electronic device provided by an embodiment of the present application.

Detailed ways

Exemplary embodiments will be described in detail herein, examples of which are illustrated in the accompanying drawings. When the following description refers to the drawings, the same numbers in different drawings refer to the same or similar elements unless otherwise indicated. The implementations described in the following exemplary embodiments do not represent all implementations consistent with this application. Rather, they are merely examples, and not exhaustive embodiments, of apparatus and methods consistent with certain aspects of the application as detailed in the appended claims. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without making creative efforts fall within the scope of protection of the present invention.

In the process of analyzing business data on the cloud, for example, business personnel accumulate user trajectories for platform applications, conduct unified data cleaning, and quantitatively display some quantifiable dimensions or indicators to promote process management and final results. When improving, business personnel use database-based measurement items to express the definition of requirements for each measurement unit of various data in the data analysis process, express the presentation of various measurement result data through charts or reports, and express user needs through measurement configuration. Different measurement units are freely combined to form a measurement set, and the measurement data of business personnel's data analysis is output through measurement reports or charts.

In existing data analysis solutions, the calculation formulas for measurement items are usually bound to the corresponding chart or report, that is, the indicator interface. Business personnel need to repeatedly set the configuration parameters every time they create a new report or chart. That is, the reusability is low, so the operational requirements for business personnel are large, and the configuration time cost is high. Moreover, because the measurement items are directly bound to charts or reports, business personnel cannot control the range of data displayed in chart reports, and currently In the actual business operation process, the business personnel who understand the actual scenario do not understand the database technology, and the technical personnel who understand the database technology do not understand the characteristic data that the business personnel need to extract in the actual scenario, or the business of indicator formulation. Therefore, existing data analysis solutions cannot conduct specific data analysis based on the implementation of database technology by different operators, making it impossible to ensure the accuracy of business personnel's decision-making when performing data analysis.

Therefore, this application provides a data analysis and processing method by obtaining a pre-stored business model or measurement model as a data model, wherein the business model is configured and generated based on the original business data, and the business model is configured and generated based on the associated processed business data. The measurement model, after obtaining the data model determined by the user, obtains the measurement element creation information configured by the user including the creation type indicating the creation type of the measurement element and the creation details indicating the creation details of the measurement element configuration parameters, and creates based on the measurement element creation information to indicate the acquisition The target data configures the metric elements of the rules, and stores the metric elements set by the user in the metric metadata database, which can conveniently and agilely help different types of business personnel conduct data analysis based on database metrics, and obtain the target data through the metric elements in the data model. , and displays the target data to the front-end page, avoiding repeated configuration of measurement elements with the same rules and reducing configuration time.

The following uses specific examples to describe in detail the technical solution of the present application and how the technical solution of the present application solves the above technical problems. The following specific embodiments can be combined with each other, and the same or similar concepts or processes may not be described again in some embodiments. The embodiments of the present application will be described below with reference to the accompanying drawings.

Embodiment 1

Figure 1 is a schematic flow chart of the data analysis and processing method provided by the embodiment of the present application. As shown in Figure 1, the method includes:

S101. Obtain a pre-stored data model, where the data model is generated based on business data. The data model includes a business model and a measurement model. The business model is used to indicate configuration of the business data. The measurement model is used to instruct the configuration of the business data after correlation processing;

Specifically, the data model selected by the user is obtained as a business model or a measurement model. The business model and measurement model are pre-configured and generated based on their respective corresponding business data, and are stored in each corresponding data model library. For business data sources Carry out real-time collection and synchronize the collected business data to the business data warehouse. The data types in the collected business data warehouse include relational, non-relational, files and logs, and of course can also include other types of data;

Furthermore, when collecting business data, real-time monitoring and early warning of the amount of collected business data is performed. When it is detected that the amount of data is greater than the data threshold, the collection nodes are expanded through cluster or container deployment to ensure that there will not be a large amount of data squeeze. Blockage situation, resulting in data delay, and configure the consumption order according to the logs in the business data, or the information file itself to ensure that the logs randomly distributed to each node will not be collected out of order, so that the business data can be stably and orderly collected in real time. Added to the specified location in the business data warehouse.

S102. Obtain the user's metric creation information. The metric creation information includes a creation type and creation details. Create a metric based on the metric creation information. The creation type is used to indicate the creation method of the metric. , the creation details are used to indicate the configuration parameters of the metric element;

Specifically, after obtaining the data model selected by the user, the metric element is configured according to the metric element creation information determined by the user, so that the data or fields in the data model can implement corresponding function operations based on the creation details in the metric element, and The operation results are saved to the corresponding operation result library or cache, and users such as actual business operators can set the creation details by selecting the creation type, that is, they can choose different ways to input and configure the configuration parameters of the metric unit. .

S103. Obtain target data in the data model through the metric element, and display the target data to the front-end page, where the metric element is used to indicate the configuration rule for obtaining the target data.

Specifically, after obtaining the data model selected by the user and completing the creation of the metric unit, the data or fields in the business model or metric model selected by the user are processed according to the configuration parameters in the metric unit, that is, the operation rules. The calculation is performed, and the calculation results are displayed in the chart on the front-end page. The chart on the front-end page is combined with the measurement elements to achieve the final chart display or chart printing.

This embodiment provides a data analysis and processing method. After obtaining a pre-stored business model or metric model as a data model, the user-configured metric creation information including the creation type and configuration parameters indicating the metric creation type is obtained, and the metric creation information is obtained according to the metric. The meta-creation information creates metric elements used to instruct the acquisition of target data configuration rules, and stores the metric elements set by the user in the metric metadata database, so that it can conveniently and agilely help different types of business personnel conduct data analysis based on database metrics. In the data model Obtain target data through metric elements and display the target data to the front-end page, thus reducing the configuration time when data analysis generates chart results.

A specific embodiment is used below to describe in detail the data analysis and processing method of this application.

Embodiment 2

Figure 2 is a schematic flowchart 2 of the data analysis and processing method provided by the embodiment of the present application. As shown in Figure 2, the method includes:

S201. Obtain the user's model configuration parameters, where the model configuration parameters include business configuration parameters and measurement configuration parameters;

Specifically, before obtaining the pre-stored data model, the data model needs to be pre-generated according to the user's model configuration parameters and stored in the corresponding model database for subsequent users to determine the required data model. Among them, the business configuration parameters are In order to indicate the scope of the target business data in the business data warehouse, and obtain the business model based on the business data within the scope, the measurement configuration parameters indicate the light aggregation of the original business data, that is, correlation processing, and obtain the measurement based on the business data after correlation processing. Model.

S202. Obtain target business data from the business data according to the business configuration parameters, store the target business data in the target business database, and acquire the business model according to the target business data;

Specifically, after obtaining the user's model configuration parameters, obtain the target business data in the original business data according to the business configuration parameters in the model configuration parameters, store the target business data in the target business database, and obtain the target business data according to the target business data. The business model, where the granularity of the business model is consistent with the business data source, that is, the data or fields in the business model are data or fields in the ODS (Operational Data Store) layer and DWD (Data Warehouse Detai) layer, and the target The business database stores multiple different business models that were previously stored;

Further, as shown in Figure 3, a new business model is added on the interface through the visual interface, and the business data extracted through ETL (Extract-Transform-Load) is imported into the specified business model, that is, the business model is obtained based on the target business data. , that is, generate the ODS layer, or upload manually cleaned business data to the business model through the interface, supporting various documents such as csv, excel, etc., that is, executing the create statement through the table structure created by the interface user, and uploading data is executed in batches insert statement into the specified table.

S203. Obtain the associated target information in the measurement configuration parameters, determine multiple target data tables in the business data according to the associated target information, and associate and store the multiple target data tables in the measurement database. Correlate multiple processed data tables to obtain the measurement model;

Specifically, as shown in Figure 4, multiple data tables in the business data are associated according to the model configuration parameters. The association target information is used to indicate the name of the data table that needs to be associated, and the association is found in the business data. The name of the data table indicated by the target information, and the multiple found data tables are associated and stored in the measurement database as the corresponding measurement model, that is, the data of the measurement model is DWM (DataWarehouse) that is lightly aggregated based on basic business data. Middle) layer and the data or fields in the DWS (Data Warehouse Servce) used to generate business subject data, and the measurement database stores multiple different measurement models that were previously stored, that is, business-side subject data (DWS) It is an intermediate table after light association. For SQL statements, the association operation and operation preview association are performed through the visual interface provided by the interface to generate the associated measurement model.

S204. Obtain the target usage model, and load the corresponding data model according to the target usage model;

Specifically, after pre-generating and obtaining the corresponding data model according to the user's model configuration parameters, the pre-stored data model determined by the user is obtained, that is, the target usage model is obtained, where the type of the target usage model is a business model or a measurement model. , so that subsequent operations and calculations on data or fields can be based on the data in the business model or measurement model, and the data model that needs to be called is determined in the corresponding model database according to the type of the target usage model.

S205. If the target usage model is the business model, retrieve the same identification in the target business database according to the identification of the target usage model, and confirm the business model with the same identification as the calling model;

Specifically, if it is detected that the type of the data model determined by the user is a business model, then based on the name of the data model determined by the user, that is, the name of the target usage model, a business model with the same name is retrieved in the target business database, and the The business model with the same name is confirmed as the data model that needs to be called, that is, the calling model.

S206. If the target usage model is the measurement model, retrieve the same identification identification in the measurement database according to the identification identification of the target usage model, and confirm the measurement model with the same identification identification as the calling model;

Specifically, if it is detected that the type of the data model determined by the user is a measurement model, the measurement model with the same name will be retrieved in the measurement database according to the name of the target use model, and the measurement model with the same name will be confirmed as the data that needs to be called. Model, that is, calling the model.

S207. Obtain the creation type in the metric creation information, where the creation type includes business-driven creation or custom statement creation;

Specifically, as shown in Figure 5, after confirming the data model that needs to be called, enter the metric management interface and obtain the corresponding creation details according to the creation type selected by the user. Among them, when the user selects the business-driven creation method to configure the creation details ;, the creation details can be configured according to the page operation instructions. When the user selects the custom statement creation method, that is, the custom SQL method to configure the creation details, the creation details can be configured directly based on the input SOL statement. .

S208. If the creation type is the service-driven creation, obtain the service configuration information through the service configuration unit, and use the service configuration information as the creation details;

Specifically, when the creation type selected by the user is the business-driven creation method, the business configuration information is obtained through the business configuration unit, where the business configuration information includes field configuration information, condition constraint information and sorting information, and the field configuration information is used to indicate the fields. The operation function of The input field configuration information, conditional constraint information and sorting information generate SOL statements;

Further, as shown in Figure 6, the field configuration information includes the original name, alias, indicator/dimension and aggregate function item of the field. The dimension refers to the grouping situation in the visual display, that is, the grouped content in SQL, the measurement item It refers to the field information that needs to be displayed during visual display, that is, the content after selection in the SQL action. Among them, the aggregation function can only be edited when the selected field is an indicator, that is, a measurement item. If the selected field is a dimension, it will be empty. Condition The constraint information includes selection fields, constraint methods, AND/OR items and target condition items, and the sorting information includes selection fields and sorting methods.

S209. If the creation type is the custom statement creation, obtain the SQL control statement through the statement configuration unit, and use the SQL control statement as the creation details;

Specifically, as shown in Figure 7, when the creation type selected by the user is the custom statement creation method, the SQL control statement input by the user is obtained through the statement configuration unit, and the input SQL control statement is used as the creation details, that is, the SQL control statement in the SQL All kinds of required data information are abstracted into input methods of interface forms, allowing users to configure data analysis rules and generate corresponding data results through selection.

S210. Obtain the basic configuration information, confirm the information data consisting of the basic configuration information, the creation type and the creation details as the metric element, and associate and store it in the metric element database;

Specifically, as shown in Figure 8, after obtaining the corresponding creation details according to the creation type selected by the user, the metric name, encoding and description information, as well as the collection frequency, enablement information, dimensions and units of the metric are obtained, The collection frequency indicates the configuration basic information of computing timeliness methods such as real-time, scheduled tasks and API interface trigger frequency. Among them, the basic configuration information is also determined by the user, and the information determined by the user includes basic configuration information, creation type and creation details. The data is confirmed as a metric element, and the basic configuration information, creation type and creation details are associated and stored in the metric element database to realize the storage of user-configured metric elements.

S211. Obtain target data through the metric element in the calling model, and display the target data to the front-end page;

Specifically, after completing the acquisition of the calling model and the configuration of the metric element, the target data is obtained in the calling model according to the creation details of the metric element, that is, the calculation data result is obtained. Among them, the metric element is in the business model or metric according to the creation details. The data or fields in the model are grouped, deduplicated, sorted, and functional operations such as averaging, summing, aggregation, and date conversion operations are performed, and low-code capabilities are used to combine charts and metrics, and user input is required The form is constructed and bound to variables, and finally the H5 page address is output, allowing users to embed the address through iframe to achieve the final chart display or chart printing. Of course, the chart display page can also support data drill-down analysis, from external interfaces, external data Source obtains display data;

Furthermore, for displaying the target data to the front-end page, we choose to nest the page functions through links to build the page framework, and bind and combine the icon components in the page with the field configuration information of the metric to be able to configure according to the fields. The target data obtained from the information automatically generates a chart, and binds the field configuration information to the query condition variables, so that the corresponding target data can be returned through the metric element according to the query conditions input by the user, and the chart can be updated based on the corresponding target data;

Furthermore, when the operation aging mode in the configuration basic information is set to real-time mode, the SQL operation rules in the measurement element are triggered once, and the returned operation results are updated to the chart interface. When the operation aging mode is set to timing mode, , then according to the API interface trigger frequency in the basic configuration information, the corresponding data results are obtained in the data calculation database or cache, thereby reducing the calculation time.

This embodiment provides a data analysis and processing method by obtaining a pre-stored business model or measurement model as a data model, wherein the business model is configured and generated based on the original business data, and the business model is configured and generated based on the associated business data. The measurement model, after obtaining the data model determined by the user, obtains the measurement element creation information configured by the user including the creation type indicating the creation type of the measurement element and the creation details indicating the creation details of the measurement element configuration parameters, and creates based on the measurement element creation information to indicate the acquisition The target data configures the metric elements of the rules, and stores the metric elements set by the user in the metric metadata database, which can conveniently and agilely help different types of business personnel conduct data analysis based on database metrics, and obtain the target data through the metric elements in the data model. , and displays the target data to the front-end page, avoiding repeated configuration of measurement elements with the same rules and reducing configuration time.

Embodiments of the present invention can divide the electronic device or the main control device into functional modules according to the above method examples. For example, each functional module can be divided corresponding to each function, or two or more functions can be integrated into one processing unit. . The above integrated units can be implemented in the form of hardware or software function modules. It should be noted that the division of modules in the embodiment of the present invention is schematic and is only a logical function division. In actual implementation, there may be other division methods.

Figure 9 is a schematic structural diagram of a data analysis and processing device provided by an embodiment of the present application. As shown in Figure 4, the device 90 includes:

Acquisition module 901 is used to acquire a pre-stored data model, where the data model is generated according to business data, the data model includes a business model and a measurement model, and the business model is used to indicate performing operations on the business data. Configuration, the measurement model is used to instruct the configuration of the business data after correlation processing;

Processing module 902, configured to obtain the user's metric element creation information, where the metric element creation information includes a creation type and creation details, and obtain the metric element according to the metric element creation information, where the creation type is used to indicate the metric The creation method of the element, and the creation details are used to indicate the configuration parameters of the measurement element;

Execution module 903 is configured to obtain target data according to the metric element and the data model, and display the target data to the front-end page, where the metric element is used to indicate the configuration rule for obtaining the target data.

Further, the acquisition module 901 is specifically used to obtain the user's model configuration parameters, where the model configuration parameters include business configuration parameters and measurement configuration parameters;

Obtain target service data from the service data according to the service configuration parameters, store the target service data in the target service database, and obtain the service model according to the target service data, wherein the service configuration parameters Used to indicate the scope of obtaining target business data in the business data, and the target business database stores multiple different business models;

Perform correlation processing on multiple data tables in the business data according to the measurement configuration parameters, and obtain the measurement model based on the multiple data tables after correlation processing;

According to the business model and the measurement model, the data model is obtained and stored.

Further, the acquisition module 901 is specifically used to obtain the correlation target information in the measurement configuration parameters, and the correlation target information is used to indicate the name of the data table that needs to be correlated;

Multiple target data tables are determined in the business data according to the associated target information, and the multiple target data tables are associated and stored in a metric database, where the metric database stores multiple different metric models.

Further, the processing module 902 is specifically configured to obtain the creation type in the metric creation information, and obtain the corresponding creation details according to the creation type, where the creation type includes business-driven creation or custom statements. create;

If the creation type is the business-driven creation, the business configuration information is obtained through the business configuration unit, and the business configuration information is used as the creation details. The business configuration information includes field configuration information, condition constraint information and sorting. Information, wherein the field configuration information is used to indicate the operation function of the field, the condition constraint information is used to indicate the constraint query condition of the field, and the sorting information is used to indicate the sorting method of the field;

If the creation type is the custom statement creation, obtain the SQL control statement through the statement configuration unit, use the SQL control statement as the creation details, and create the metric element according to the metric element creation information.

Further, the processing module 902 is specifically used to obtain basic configuration information, confirm the information data composed of the basic configuration information, the creation type and the creation details as the metric element, and associate and store it in the metric element database. ;

Wherein, the basic configuration information includes the name of the metric element and the collection frequency. The collection frequency is used to indicate the triggering frequency of the metric element. A plurality of different metric elements are stored in the metric element database.

Further, the processing module 902 is also used to obtain the target usage model, and load the corresponding data model according to the target usage model, where the target usage model is a business model or a measurement model;

If the target usage model is the business model, search the same identification ID in the target business database according to the identification ID of the target usage model, and confirm the business model with the same identification ID as the calling model;

If the target usage model is the measurement model, retrieve the same identification identification in the measurement database according to the identification identification of the target usage model, and confirm the measurement model with the same identification identification as the calling model;

In the calling model, target data is obtained through the metric element, and the target data is displayed on the front-end page.

Further, execution module 903 is specifically configured to obtain target data in the calling model according to the creation details of the metric element;

Choose the method of nesting page functions through links to realize the construction of page framework, and bind and combine the icon components in the page with the field configuration information of the metric unit, so that the target data obtained according to the field configuration information can be automatically generate charts;

The field configuration information is bound to the query condition variable, so that the corresponding target data can be returned through the metric element according to the query conditions input by the user, and the chart can be updated according to the corresponding target data.

The data analysis and processing device provided in this embodiment can execute the data analysis and processing method of the above embodiment. Its implementation principles and technical effects are similar, and will not be described again in this embodiment.

In the specific implementation of the aforementioned data analysis and processing device, each module can be implemented as a processor, and the processor can execute computer execution instructions stored in the memory, so that the processor executes the above-mentioned data analysis and processing method.

FIG. 10 is a schematic structural diagram of an electronic device provided by an embodiment of the present application. As shown in FIG. 10 , the electronic device 100 includes: at least one processor 110 and a memory 120 . The electronic device 10 also includes a communication component 130 . Among them, the processor 110, the memory 120 and the communication component 130 are connected through the bus 140.

In a specific implementation process, at least one processor 110 executes computer execution instructions stored in the memory 120, so that at least one processor 110 executes the above data analysis and processing method executed on the electronic device side.

The specific implementation process of the processor 110 can be found in the above method embodiments. The implementation principles and technical effects are similar and will not be described again in this embodiment.

In the above embodiments, it should be understood that the processor may be a central processing unit (English: Central Processing Unit, referred to as: CPU), or other general-purpose processor, digital signal processor (English: Digital Signal Processor, referred to as: DSP), Application Specific Integrated Circuit (English: Application Specific Integrated Circuit, abbreviation: ASIC), etc. A general-purpose processor may be a microprocessor or the processor may be any conventional processor, etc. The steps of the method disclosed in conjunction with the invention can be directly embodied and executed by a hardware processor, or executed by a combination of hardware and software modules in the processor.

The memory may include high-speed RAM memory and may also include non-volatile storage NVM, such as at least one disk memory.

The bus may be an Industry Standard Architecture (Industry Standard Architecture, ISA) bus, a Peripheral Component Interconnect (PCI) bus, or an Extended Industry Standard Architecture (Extended Industry Standard Architecture, EISA) bus, etc. The bus can be divided into address bus, data bus, control bus, etc. For ease of presentation, the bus in the drawings of this application is not limited to only one bus or one type of bus.

The above describes the solution provided by the embodiment of the present invention with respect to the functions implemented by the electronic device and the main control device. It can be understood that, in order to realize the above functions, the electronic device or the main control device includes corresponding hardware structures and/or software modules for executing each function. With reference to the units and algorithm steps of each example described in the embodiments disclosed in the embodiments of the present invention, the embodiments of the present invention can be implemented in the form of hardware or a combination of hardware and computer software. Whether a function is performed by hardware or computer software driving the hardware depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered to go beyond the scope of the technical solutions of the embodiments of the present invention.

This application also provides a computer-readable storage medium, which stores computer-executable instructions. When the processor executes the computer-executed instructions, the above data analysis and processing method is implemented.

The above-mentioned computer-readable storage medium, the above-mentioned readable storage medium can be implemented by any type of volatile or non-volatile storage device or their combination, such as static random access memory (SRAM), electrically erasable memory. Programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, magnetic or optical disk. Readable storage media can be any available media that can be accessed by a general purpose or special purpose computer.

An exemplary readable storage medium is coupled to the processor such that the processor can read information from the readable storage medium and write information to the readable storage medium. Of course, the readable storage medium may also be an integral part of the processor. The processor and the readable storage medium may be located in Application Specific Integrated Circuits (ASIC). Of course, the processor and the readable storage medium may also exist as discrete components in the electronic device or the host control device.

This application also provides a computer program product. The computer program product includes: a computer program. The computer program is stored in a readable storage medium. At least one processor of the electronic device can read the computer program from the readable storage medium. At least one processor The computer program is executed by the computer so that the electronic device executes the solution provided by any of the above embodiments.

Persons of ordinary skill in the art can understand that all or part of the steps to implement the above method embodiments can be completed by hardware related to program instructions. The aforementioned program can be stored in a computer-readable storage medium. When the program is executed, the steps including the above-mentioned method embodiments are executed; and the aforementioned storage media include: ROM, RAM, magnetic disks, optical disks and other media that can store program codes.

Finally, it should be noted that the above embodiments are only used to illustrate the technical solution of the present invention, but not to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that: The technical solutions described in the foregoing embodiments can still be modified, or some or all of the technical features can be equivalently replaced; and these modifications or substitutions do not deviate from the essence of the corresponding technical solutions from the technical solutions of the embodiments of the present invention. scope.

Claims (10)

1. A method of data analysis processing, the method comprising:

acquiring a pre-stored data model, wherein the data model is generated according to service data, the data model comprises a service model and a measurement model, the service model is used for indicating to configure the service data, and the measurement model is used for indicating to configure the service data after the association processing;

the method comprises the steps of obtaining measurement element creation information of a user, wherein the measurement element creation information comprises creation types and creation details, and the measurement elements are created according to the measurement element creation information, wherein the creation types are used for indicating the creation modes of the measurement elements, and the creation details are used for indicating the configuration parameters of the measurement elements;

and acquiring target data in the data model through the metric element, and displaying the target data to a front-end page, wherein the metric element is used for indicating a configuration rule for acquiring the target data.

2. The method of claim 1, wherein prior to the acquiring the pre-stored data model, the method further comprises:

obtaining model configuration parameters of a user, wherein the model configuration parameters comprise service configuration parameters and measurement configuration parameters;

Acquiring target service data from the service data according to the service configuration parameters, storing the target service data in a target service database, and acquiring the service model according to the target service data, wherein the service configuration parameters are used for indicating the range of acquiring the target service data from the service data, and the target service database stores a plurality of different service models;

performing association processing on a plurality of data tables in the service data according to the measurement configuration parameters, and acquiring the measurement model according to the plurality of data tables after association processing;

and acquiring and storing the data model according to the service model and the measurement model.

3. The method according to claim 2, wherein said associating a plurality of data tables in said service data according to said metric configuration parameters comprises:

acquiring associated target information in the measurement configuration parameters, wherein the associated target information is used for indicating a data table name needing to be subjected to association processing;

and determining a plurality of target data tables in the service data according to the associated target information, and storing the plurality of target data tables in a measurement database in an associated mode, wherein the measurement database stores a plurality of different measurement models.

4. The method of claim 1, wherein the obtaining metric meta-creation information for a user comprises:

acquiring the creation type in the metric meta creation information, and acquiring corresponding creation details according to the creation type, wherein the creation type comprises service driving creation or custom statement creation;

if the creation type is the creation of the service driver, acquiring service configuration information through a service configuration unit, and taking the service configuration information as creation details, wherein the service configuration information comprises field configuration information, condition constraint information and ordering information, the field configuration information is used for indicating an operation function of a field, the condition constraint information is used for indicating constraint query conditions of the field, and the ordering information is used for indicating an ordering mode of the field;

if the creation type is the creation of the custom statement, acquiring an SQL control statement through a statement configuration unit, and taking the SQL control statement as the creation details;

and creating a metric element according to the metric element creation information.

5. The method of claim 4, wherein said creating a metric element from said metric element creation information comprises;

Acquiring configuration basic information, confirming information data composed of the configuration basic information, the creation type and the creation details as the metric element, and storing the metric element in a metric element database in an associated manner;

the configuration basic information comprises a metric element name and acquisition frequency, wherein the acquisition frequency is used for indicating the triggering frequency of the metric element, and a plurality of different metric elements are stored in a metric element database.

6. The method of claim 5, further comprising, prior to said creating a metric from said metric creation information:

acquiring a target use model, and loading the corresponding data model according to the target use model, wherein the target use model is a business model or a measurement model;

if the target usage model is the service model, searching the same identification mark in a target service database according to the identification mark of the target usage model, and confirming the service model with the same identification mark as a calling model;

if the target usage model is the measurement model, retrieving the same identification mark in a measurement database according to the identification mark of the target usage model, and confirming the measurement model with the same identification mark as a calling model;

And acquiring target data through the metric element in the call model, and displaying the target data to a front-end page.

7. The method of claim 6, wherein the obtaining the target data through the metrics in the call model and exposing the target data to a front page comprises:

acquiring target data in the call model according to the creation details of the metric elements;

selecting a mode of nesting page functions through links, constructing a page frame, binding and combining an icon assembly in the page with field configuration information of the metric element so as to automatically generate a chart according to target data acquired by the field configuration information;

and binding the field configuration information with the query condition variable, so that corresponding target data can be returned through the metric element according to the query condition input by the user, and updating the chart according to the corresponding target data.

8. A data analysis processing apparatus, comprising:

the system comprises an acquisition module, a correlation processing module and a correlation processing module, wherein the acquisition module is used for acquiring a pre-stored data model, the data model is generated according to service data, the data model comprises a service model and a measurement model, the service model is used for indicating to configure the service data, and the measurement model is used for indicating to configure the service data after the correlation processing;

The processing module is used for acquiring the measurement element creation information of the user, wherein the measurement element creation information comprises creation types and creation details, and the measurement elements are acquired according to the measurement element creation information, wherein the creation types are used for indicating the creation modes of the measurement elements, and the creation details are used for indicating the configuration parameters of the measurement elements;

and the execution module is used for acquiring target data according to the metric element and the data model and displaying the target data to a front-end page, wherein the metric element is used for indicating a configuration rule for acquiring the target data.

9. An electronic device, comprising: a processor, and a memory communicatively coupled to the processor;

the memory stores computer-executable instructions;

the processor executes computer-executable instructions stored in the memory to implement the method of any one of claims 1 to 7.

10. A computer readable storage medium having stored therein computer executable instructions which when executed by a processor are adapted to carry out the method of any one of claims 1 to 7.