Business data analysis method and device, equipment and storage medium

Abstract

The invention provides a business data analysis method, device and equipment and a storage medium, and relates to the technical field of data processing, and the method comprises the steps: obtaining data set configuration information input by a user on a front-end interface; the data set configuration information comprises definitions of a data source and a data column, the data source supports an OLAP database and comprises a newly added first data set, and the definition of the data column supports a filtering condition and an analysis function; generating a query SQL (Structured Query Language) according to the definition of the data column, querying in a back-end analytical database according to the query SQL, and determining target business data corresponding to the first data set and a data analysis result corresponding to the target business data; all business data are stored in the analysis type database; generating a display interface on a front-end interface; the display interface comprises a data display area and / or a query UI, and the data analysis result is displayed in the data display area. By adopting the technical scheme of the invention, the data analysis and interpretation efficiency can be improved.

Description

Technical Field

[0001] The present invention relates to the field of data processing technology, and in particular to a business data analysis method, device, equipment and storage medium. Background Art

[0002] With the continuous development of Internet technology, the business data generated by enterprises in Internet applications has also increased. The analysis and interpretation of business data is very important to enterprises. Therefore, it is particularly important to analyze and interpret massive business data in real time, accurately and quickly.

[0003] In related technologies, when analyzing and interpreting massive amounts of business data, a microservice vertical database architecture is usually established, that is, an independent database is established for each microservice, and then the data in each database is analyzed and interpreted separately.

[0004] However, the above technologies have the problem of low efficiency in data analysis and interpretation. Summary of the invention

[0005] The present invention provides a business data analysis method, device, equipment and storage medium, which are used to solve the defect in the prior art that each microservice establishes its own database and analyzes and interprets the data in each database separately, resulting in low efficiency of data analysis and interpretation. It is realized that the required data and the corresponding data analysis results are queried and displayed in the analytical database through user-defined data sets and query SQL, so that there is no need to analyze and interpret the data in the database of each microservice, thereby improving the efficiency of data analysis and interpretation.

[0006] The present invention provides a business data analysis method, comprising: Acquire the data set configuration information input by the user on the front-end interface; the data set configuration information includes the definition of the data source and the data column, the data source supports the online analytical processing OLAP database, including the newly added first data set, and the definition of the data column supports filtering conditions and analytical functions; Generate a query structured query language SQL according to the definition of the data column, and query in the back-end analytical database according to the query SQL to determine the target business data corresponding to the first data set and the data analysis result corresponding to the target business data; the above analytical database stores all the business data; A display interface is generated on the front-end interface; the display interface includes a data display area and / or a query user interface UI, and the data display area displays the data analysis results.

[0007] According to a business data analysis method provided by the present invention, the query is performed in the back-end analytical database according to the query SQL to determine the target business data corresponding to the first data set and the data analysis result corresponding to the target business data, including: Perform a query in the backend analytical database according to the query SQL to determine the target business data corresponding to the first data set; Generates materialized view creation SQL based on target business data, and controls the analytical database to execute the materialized view creation SQL to generate the target materialized view corresponding to the target business data; Perform a selection operation on the target materialized view to determine the data analysis results corresponding to the target business data.

[0008] According to a business data analysis method provided by the present invention, the data set configuration information further includes a materialization type, and the materialization type includes synchronous materialization or asynchronous materialization. The method further includes: If the materialization type is asynchronous materialization, the refresh interval and bucket fields entered by the user on the front-end interface are obtained; The updated data corresponding to the bucketed fields in the first data set are periodically queried in the analytical database according to the refresh interval, and the target materialized view is updated according to the updated data.

[0009] According to a business data analysis method provided by the present invention, the data set configuration information also includes a materialization type, and the materialized view creation SQL generated according to the target business data includes: According to the target business data and the materialization type, a materialized view creation SQL matching the materialization type is generated; different materialization types correspond to different materialized view creation SQLs.

[0010] According to a business data analysis method provided by the present invention, the method further includes: Acquire deletion information input by the user on the front-end interface; the deletion information includes a second data set selected by the user from a data set list on the front-end interface and a deletion operation to be performed on the second data set, wherein the data set list includes at least one data set; Detecting whether the second data set has been used; the above-mentioned use includes that the second data set has generated an analysis list or an analysis chart, or the second data set is generating an analysis list or an analysis chart; If the second data set has been used, a first prompt message is output; the first prompt message is used to indicate that the second data set cannot be deleted.

[0011] According to a business data analysis method provided by the present invention, the method further includes: If the second data set is not used, delete the second data set from the data set list and execute the materialized view deletion SQL.

[0012] According to a business data analysis method provided by the present invention, the method further includes: Receive a chart setting operation input by a user, and in response to the chart setting operation, display a chart setting interface; the chart setting interface includes a first tab interface and a second tab interface; the first tab interface is a data setting interface required for creating a chart, and the second tab interface is a style setting interface required for creating a chart; Acquire data setting information input by the user on the first tab interface and style setting information input on the second tab interface; the data setting information includes at least one of the third data set selected in the data set list, the selected fields for the X-axis and the Y-axis, and the set specific parameters of the chart; and the style setting information includes attribute information of the chart; An analysis chart corresponding to the third data set is generated according to the data setting information and the style setting information.

[0013] According to a business data analysis method provided by the present invention, the method further includes: Execute data migration commands and use the data integration tool Flink CDC 3.0 to obtain data tables from different source databases in real time; In the analytical database, a data table with the same table structure as the data table in each source database is established, and each data table in each source database is synchronously migrated to the data table in the corresponding table structure in the analytical database.

[0014] The present invention also provides a business data analysis device, comprising the following modules: The configuration information acquisition module is used to acquire the data set configuration information input by the user on the front-end interface; the data set configuration information includes the definition of the data source and the data column, the data source supports the OLAP database, including the newly added first data set, and the definition of the data column supports filtering conditions and analysis functions; A query module, which is used to generate a query SQL according to the definition of the data column, and perform a query in the back-end analytical database according to the query SQL to determine the target business data corresponding to the first data set and the data analysis result corresponding to the target business data; the above analytical database stores all the business data; The analysis result display module is used to generate a display interface on the front-end interface; the above display interface includes a data display area and / or a query UI, and the data display area displays the data analysis results.

[0015] The present invention also provides an electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, the business data analysis method as described above is implemented.

[0016] The present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements any of the business data analysis methods described above.

[0017] The present invention also provides a computer program product, comprising a computer program, wherein when the computer program is executed by a processor, the business data analysis method as described above is implemented.

[0018] The business data analysis method, device, equipment and storage medium provided by the present invention obtain the data set configuration information input by the user on the front-end interface, generate query SQL according to the definition of the data column in the data set configuration information, and query in the back-end analytical database according to the query SQL, determine the target business data corresponding to the first data set newly added in the data source included in the data set configuration information and the data analysis result corresponding to the target business data, and generate a display interface on the front-end interface, the display interface includes a data display area and / or a query UI, and the data display area displays the data analysis result; wherein, all business data are stored in the analytical database, the data set configuration information includes the definition of the data source and the data column, the data source supports the OLAP database, and the definition of the data column supports the filtering condition and the analysis function. In this method, after the required data is queried in the analytical database through the user-defined data set and query SQL, the data analysis is performed to obtain the corresponding data analysis results and display them, thereby realizing the rapid analysis and interpretation of all business data through the analytical database and query SQL, etc., without the need to analyze and interpret the data in the database of each microservice, thereby improving the efficiency of data analysis and interpretation. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] In order to more clearly illustrate the technical solutions in the present invention or the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0020] Figure 1 It is a flow chart of the business data analysis method provided by the present invention.

[0021] Figure 2 This is an ELT mode diagram based on Flink CDC 3.0 and StarRocks provided by the present invention.

[0022] Figure 3 It is a schematic diagram of the diverse visualization integration provided by the present invention.

[0023] Figure 4 It is a structural schematic diagram of the business data analysis device provided by the present invention.

[0024] Figure 5 It is a structural schematic diagram of the electronic device provided by the present invention. DETAILED DESCRIPTION

[0025] In order to make the purpose, technical solution and advantages of the present invention clearer, the technical solution of the present invention will be clearly and completely described below in conjunction with the drawings of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0026] At present, the importance of business data is becoming more and more significant. Faced with rapidly changing market demands, enterprises are in urgent need of real-time, fast and accurate analysis and interpretation of massive data. This pursuit of real-time performance is not only to respond to market dynamics in a timely manner, but also to provide a solid data foundation for decision-making and business optimization. Especially in the context of the increasing volume of data, enterprises must be able to process and analyze large-scale data sets in order to gain in-depth insights and quickly identify potential opportunities or risks. This reliance on real-time data analysis enables enterprises to maintain agility in a highly competitive market, thereby achieving higher efficiency and accuracy in business decision-making. In enterprise applications that adopt a microservice vertical sub-library architecture, the sub-library strategy can improve the scalability and flexibility of the system, while also posing new challenges and opportunities for data statistical analysis. With the rapid development of enterprise business and the iteration of personalized software functions, data management, integration and analysis have become particularly important. However, the microservice sub-library architecture generally has the following defects in terms of real-time and large-volume data analysis: 1. Isolated data storage: Each microservice has an independent database, which makes it difficult to easily integrate data and form data islands, which makes cross-service business analysis complicated. For example, data from different services need to be manually aggregated, which is error-prone and time-consuming. 2. Complex ETL (Extract, Transform, Load) process: In order to conduct comprehensive data analysis, it is necessary to establish a complex ETL process, which not only increases the workload of development and maintenance, but also may cause data delays. 3. Delay problem: In the microservice architecture, the transmission and conversion of data between different services may cause delays, affecting the ability of real-time analysis. Especially in the event-driven architecture, the processing, transmission and aggregation of events usually take time, which may cause decision delays. 4. Difficulty in real-time monitoring: In a rapidly changing business environment, real-time monitoring and analysis are crucial; however, the decentralized data storage and processing architecture makes it difficult to capture and analyze real-time data, affecting the ability to respond quickly to business dynamics. 5. Performance bottleneck: When processing large amounts of data, distributed databases often require complex queries and aggregations, which may lead to performance bottlenecks. In the microservice architecture, multiple microservices access their respective databases for complex analysis at the same time, which may result in a large number of I / O (Input / Output) operations, thereby affecting the overall system performance. 6. Data storage and query efficiency: After database sharding, the data in each database is unevenly distributed, and the amount of data for some services may surge. For the analysis of such large amounts of data, it is usually necessary to optimize the storage and query methods. However, in an independent database environment, this optimization work becomes complicated and difficult to manage uniformly.

[0027] Based on this, an embodiment of the present invention provides a business data analysis method, device, equipment and storage medium, which can solve this technical problem.

[0028] Combine the following Figure 1-Figure 3 A business data analysis method according to an embodiment of the present invention is described.

[0029] It should be noted that the execution subject of the embodiments of the present invention may be a business data analysis device, an electronic device, or other devices or equipment. The following embodiments will take the electronic device as an example of the execution subject for explanation.

[0030] Figure 1 It is a flow chart of the business data analysis method provided by the present invention, such as Figure 1 As shown, the method comprises the following steps: S102, obtaining data set configuration information input by the user on the front-end interface; the data set configuration information includes the definition of data source and data column, the data source supports online analytical processing OLAP database, including the newly added first data set, and the definition of data column supports filtering conditions and analysis functions.

[0031] Among them, a dynamic data set management module can be provided in the electronic device for managing the data set. The dynamic data set management module can be provided with a front-end interface, which can provide an entry for users to input information. The front-end interface is an interface for the front end of the dynamic data set management module to interact with the back end / server.

[0032] In addition, the data source can be displayed on the front-end interface. The data source can be displayed in the form of a data set list. The data set list can display one or more data sets and related information of each data set. The related information of each data set can include data set name, creation time, data set status and operation information. The functions of the dynamic data set management module can include data set list management, data set editing, adding and deleting functions. The data set list management mainly manages the data sets in the data set list displayed on the front-end interface.

[0033] When the user needs to customize the data source / data set, he can click the "Add Data Set" button on the front-end interface to enter the data set addition interface / page. On the data set addition interface / page, the user can enter the data set configuration information, which can include the data source to be configured and the definition of the data column. Among them, the data source can include the data set to be configured and its related information. The data set to be configured can include a newly added data set, which is recorded as the first data set. After entering the above data set configuration information, the relevant information of the first data set (such as the view name, data set name, data set description information, etc. of the first data set) can be obtained. At this time, the first data set is empty, and the data required by the first data set needs to be queried through the following steps. In addition, after obtaining the newly added first data set, the first data set can be added to the data set list and the displayed data set list can be updated. In addition, the data source supports online analytical processing OLAP (Online Analytical Processing) database, where the OLAP database is a database used for data analysis and decision support, mainly used for query analysis of multidimensional data, allowing users to perform complex queries and analyses through various dimensions (such as time, region, product, etc.), suitable for processing large-scale data aggregation calculations. The OLAP databases here may include ClickHouse, BigQuery, StarRocks, etc.

[0034] The definition of the data columns in the above data set configuration information supports filtering conditions and analysis functions, where the filtering conditions refer to the conditions for filtering the columns of the data table in the database, and the specific content of the filtering conditions can be defined. The analysis function refers to the function for analyzing the columns of the data table in the database, such as sum analysis, difference analysis, weighted average processing analysis and other related analysis functions.

[0035] S104, generate a query structured query language SQL according to the definition of the data column, and query in the back-end analytical database according to the query SQL to determine the target business data corresponding to the first data set and the data analysis results corresponding to the target business data; all business data are stored in the above analytical database.

[0036] After obtaining the definition of the data column in the data set configuration information, a matching query SQL can be generated according to the filtering conditions and analysis functions in the data column definition. The query SQL here refers to the SQL for the data required to be queried for the data source (such as the first data set) according to the filtering conditions and analysis functions in the data column definition. For example, the query SQL can be used to filter out certain data in the database or analyze certain data. SQL refers to the Structured Query Language, through which the corresponding data can be queried to construct a data set.

[0037] After the query SQL is generated, the query can be performed in the back-end analytical database according to the query SQL to obtain the required data analysis results. As an optional embodiment, when querying, the query can be performed in the back-end analytical database according to the query SQL to determine the target business data corresponding to the first data set; generate a materialized view creation SQL according to the target business data, and control the analytical database to execute the materialized view creation SQL to generate a target materialized view corresponding to the target business data; perform a selection operation on the target materialized view to determine the data analysis results corresponding to the target business data.

[0038] Specifically, when querying the data required by the data source (such as the first data set), the query may be performed in an analytical database (such as a StarRocks database) storing all the business data. Taking querying business data in the StarRocks database as an example, before querying business data in the StarRocks database, the business data needs to be synchronized to the StarRocks database. The synchronization method of the business data stored in the StarRocks database may optionally include: Execute the data migration command and use the data integration tool Flink CDC 3.0 to obtain the data tables in different source databases in real time; create a data table in the analytical database (such as StarRocks database) with the same table structure as the data table in each source database, and synchronously migrate each data table in each source database to the data table in the corresponding table structure in the analytical database (such as StarRocks database).

[0039] For example, take querying business data in the StarRocks database as an example. Figure 2The ELT mode diagram based on Flink CDC3.0 and StarRocks is shown. The business data was originally stored in different source MySQL / SQL databases (MySQL is used for illustration below). In this embodiment, the data processing module in the electronic device can be used to synchronously migrate all business data stored in different source MySQL databases to the same StarRocks. The StarRocks here is a high-performance analytical database designed for online analytical processing scenarios. It can process large-scale data sets and support real-time data query and analysis. In addition, the data processing module here uses a real-time data acquisition tool based on FlinkCDC 3.0 when synchronously migrating data. At the same time, combined with StarRocks' ELT (Extract, Load, Transform) data processing method, real-time and efficient data synchronization can be achieved.

[0040] The specific synchronization and migration of business data can be divided into two parts: one is the synchronization of library and table structures, and the other is the real-time synchronization of data.

[0041] For synchronizing the library and table structures, the data processing module can use the data migration tool "**StarRocks Migration Tool(SMT)**" (also known as StarRocks-migrate-tools). The specific migration process is: according to the configuration information of the source MySQL and StarRocks, execute the command ". / starrocks-migrate-tool" (that is, the command to start the above data migration tool, used to migrate the database to StarRocks), read the library and table structure to be synchronized from the source MySQL, and generate an SQL file (starrocks-create.all.sql). This SQL file is used to create the corresponding target library and table in the StarRocks analysis library, thereby ensuring the consistency of the table structure in the source database and the analytical database. After executing the command to start the above data migration tool, the data processing module can connect to StarRocks and execute the data migration command: copycodesql mysql -h<fe_host> -P<fe_query_port> -u user2 -p <password><starrocks-create.all.sql>. Among them, the meanings of each command in this data migration command are as follows: copy codesql: Indicates to use the copy command to copy each SQL text to StarRocks. mysql: Specifies that the transmission method is the MySQL protocol, which is usually used for connection to StarRocks. -h <fe_host>: Specifies the hostname or IP (Internet Protocol) address of the Frontend (FE) node. -P <fe_query_port>: Specifies the query port of the Frontend node, usually 9030 (may vary according to the configuration). -u user2: Specifies the username used to connect to StarRocks. -p <password>:Specify the password of the user. Note that there is no space before the password. <starrocks-create.all.sql>: Specify the SQL script file to be executed, which contains SQL statements for creating databases, tables, and related objects. By repeatedly executing the above data migration commands, the table structures in the source MySQL databases of different microservices that need to be synchronized can be synchronized to the analytical database StarRocks.

[0042] For real-time data synchronization, Flink CDC 3.0 provides real-time data capture capabilities. The data processing module can use the Flink CDC Connector (the connection component of the Flink data collection tool) to connect to the source data tables (Source table) in the source MySQL, read the binary log file Binlog of the source MySQL in real time, and monitor the change events (such as insert, update, delete, etc.) of the data tables in the source MySQL. Whenever the data in the source MySQL changes, Flink CDC 3.0 can immediately detect it and generate corresponding pipeline / stream data for the changed data. Then, the SinkConnector component of Flink (the component for writing data to external systems) can be used to insert the generated pipeline / stream data into the corresponding target data tables (Sink table) in StarRocks through high concurrency. StarRocks and the Sink table are connected through the starrocks flink-connector (the connection component of the starrocks data collection tool). In addition, the data processing module can configure / add their respective Flink Jobs (i.e., data collection tasks) for the source MySQL of different microservices, and by executing the Flink Jobs of each source MySQL respectively, realize the real-time parallel monitoring of data changes in each source MySQL, and update the updated data in each MySQL to StarRocks in real time in parallel, so as to support the real-time synchronization of data from multiple data sources / multiple source databases after vertical database sharding of microservices.

[0043] Through the data synchronization of the above-mentioned data processing module, the data in different source databases can be synchronously migrated to the same analytical database (such as StarRocks database), that is, all business data are stored in the analytical database. Then, after the user enters the data set configuration information on the front-end interface to obtain the name of the first data set, he can click the "parse and run" button on the front-end interface, so that the electronic device backend can know that it is necessary to perform data analysis on the first data set. At this time, the query SQL generated above can be executed in the analytical database. If the query SQL is successfully executed, the business data corresponding to the first data set can be obtained and recorded as the target business data. If the query SQL fails to execute or is abnormal, the specific error information can be displayed in the data preview area (or it can also be a data display area), such as the specific reason for the abnormal execution of the query SQL, such as the query SQL configuration error, so that the user can quickly obtain the reason for the abnormal execution of the query SQL and make changes in time to achieve the successful execution of the query SQL.

[0044] After obtaining the target business data required for data analysis of the first data set in the analytical database, data analysis can be performed on the target business data. The data analysis here specifically includes processing and analyzing the materialized view of the target business data. When processing and analyzing the materialized view, the materialized view creation SQL can be generated first, and then the materialized view creation SQL is executed in the analytical database, that is, the materialized view is attempted to be created through the target business data in the analytical database. If the materialized view creation SQL of the target business data is executed successfully, it means that the corresponding materialized view can be generated, and the generated materialized view of the target business data can be recorded as the target materialized view.

[0045] After the materialized view of the target business data is successfully created, the SELECT operation of the materialized view can be performed, that is, the analysis results of the target materialized view are interpreted to obtain the corresponding data analysis results, that is, the data analysis results corresponding to the target business data are obtained.

[0046] S106, generating a display interface on the front-end interface; the display interface includes a data display area and / or a query user interface UI, and the data display area displays the data analysis results.

[0047] In this step, after obtaining the data query results corresponding to the target business data, a display interface can be automatically generated in the front-end interface, which can include a data display area, a query UI interface, etc., and of course, other areas. The data display area can display the data analysis results obtained after the above data query and analysis, and the query UI interface can display the query controls / buttons provided to the user for the user to query.

[0048] The positions of the data display area and the query UI interface on the front-end interface can be set according to actual conditions and are not specifically limited here.

[0049] In addition, when displaying the data analysis results of the target business data in the data display area, it can be displayed in the form of a paging list, which makes it convenient for users to check the data analysis results of different data sets.

[0050] Furthermore, the above-mentioned analysis results of the target materialized view are interpreted to obtain data analysis results of the target business data. Specifically, the target materialized view can be disassembled, such as the meaning of the horizontal and vertical axes of the curves included in the target materialized view, the range of the horizontal and vertical axes, the meaning of the curves or graphics included in the target materialized view, etc. This is easier for users to understand, thereby improving the efficiency of data analysis.

[0051] In this embodiment, by obtaining the data set configuration information input by the user on the front-end interface, generating a query SQL according to the definition of the data column in the data set configuration information, and querying in the back-end analytical database according to the query SQL, determining the target business data corresponding to the first data set newly added in the data source included in the data set configuration information and the data analysis results corresponding to the target business data, and generating a display interface on the front-end interface, the display interface includes a data display area and / or a query UI, and the data display area displays the data analysis results; wherein, all business data are stored in the analytical database, the data set configuration information includes the definition of the data source and the data column, the data source supports the OLAP database, and the definition of the data column supports filtering conditions and analysis functions. In this method, after the required data is queried in the analytical database through the user-defined data set and query SQL, the corresponding data analysis results are obtained and displayed, thereby realizing the rapid analysis and interpretation of all business data through the analytical database and query SQL, without the need to analyze and interpret the data in the database of each microservice, thereby improving the efficiency of data analysis and interpretation.

[0052] The following embodiment describes a process of updating a target materialized view when the data set configuration information further includes a materialization type, and the materialization type includes synchronous materialization or asynchronous materialization.

[0053] In some embodiments, the above method may further include the following steps: Step A1: If the materialization type is asynchronous materialization, the refresh interval and bucket fields entered by the user on the front-end interface are obtained.

[0054] When users input data set configuration information, they can also select the required materialization type in the materialization type option on the front-end interface. The materialization type refers to the synchronization type when generating materialized views. Materialization types generally include synchronous materialization and asynchronous materialization. Synchronous materialization refers to the real-time generation of materialized views based on the business data in the analytical database (such as the StarRocks database). That is, when the business data in the analytical database changes, the materialized views generated based on the business data in the analytical database will also be updated synchronously and in real time. Asynchronous materialization means that when the business data in the analytical database changes, the materialized views generated based on the business data in the analytical database will only be updated when certain conditions are met, rather than in real time. This ensures that the materialized views currently being used by users are not affected, improving the user experience.

[0055] This embodiment mainly describes the update process of the materialized view of the asynchronous materialization type. When the user selects the asynchronous materialization type, the front-end interface will display / output an interface or option for the user to enter the refresh interval and bucket fields, etc. The user can enter the required refresh interval and the bucket fields to be queried in the interface or option. The refresh interval here refers to the update interval or update time of the generated materialized view, such as 5 minutes, 10 minutes, etc.; the bucket fields refer to which fields in the target business data corresponding to the first data set in the analytical database are used for data query.

[0056] Step A2: query the analytical database for updated data corresponding to the bucketed fields in the first data set at a regular interval according to the refresh interval, and update the target materialized view according to the updated data.

[0057] In this step, after obtaining the refresh interval and bucket field input by the user, it is possible to monitor whether the time interval between the current time and the last generated / updated target materialized view reaches the refresh interval. If the refresh interval is reached, the corresponding update data in the first data set can be found in the analytical database according to the bucket field, and the target materialized view generated / updated last time is updated using the update data to obtain the target materialized view after the current update. Here, querying based on the bucket field can avoid full table query, thereby improving query efficiency.

[0058] In addition, as an optional embodiment, after the user inputs the materialization type, the above-mentioned "generating materialized view creation SQL according to target business data" may include: generating materialized view creation SQL matching the materialization type according to the target business data and the materialization type; wherein different materialization types correspond to different materialized view creation SQLs. That is, if the user inputs the materialization type, the corresponding materialized view creation SQL may be generated according to the materialization type input by the user, which can improve the accuracy of the generated materialized view creation SQL and the accuracy and success rate of materialized view generation.

[0059] In this embodiment, when the user selects the asynchronous materialization type, the corresponding update data is obtained in the analytical database through the bucket field when the refresh interval is reached according to the refresh interval and bucket field input by the user to update the target materialized view generated / updated last time, so that the materialized view generated last time can be used normally within a certain time range, thereby improving the user experience. In addition, the corresponding materialized view creation SQL is generated according to the materialization type input by the user, so that the accuracy of the generated materialized view creation SQL and the accuracy and success rate of the final materialized view generation can be improved.

[0060] The following embodiment describes a process in which a dataset list can be displayed in a front-end interface, and the dataset list can include relevant information of one or more datasets, and whether a dataset in the dataset list can be successfully deleted when a user needs to delete the dataset.

[0061] In some embodiments, the above method may further include the following steps: Step B1, obtaining deletion information input by the user on the front-end interface; the deletion information includes a second data set selected by the user in a data set list on the front-end interface and a deletion operation to be performed on the second data set, and the data set list includes at least one data set.

[0062] Among them, as explained above, the functions of the dynamic dataset management module include a dataset deletion function, that is, the present embodiment provides a function of deleting datasets in a dataset list, and the dataset list may include one or more datasets, which may be datasets added by users through new functions, or datasets automatically generated from data in an analytical database, or may also include datasets obtained in other ways.

[0063] When the user needs to delete a data set in the data set list, the user can enter deletion information on the front-end interface. The deletion information here can be, for example, that the user selects the data set to be deleted in the data set list, which is recorded as the second data set, and then performs a trigger operation such as clicking on the selected data set. The front-end interface can display the operations that can be performed on the data set, and then the user can select the desired deletion operation from these operations, so that the electronic device can obtain the deletion operation that the user wants to delete the second data set.

[0064] Step B2, detecting whether the second data set has been used; the above-mentioned use includes that the second data set has generated an analysis list or an analysis chart, or the second data set is generating an analysis list or an analysis chart.

[0065] In this step, after the user selects the second data set to be deleted, it is possible to detect whether the second data set is being generated or has generated a corresponding analysis list or analysis chart, that is, to determine whether the second data set and its corresponding data have been used, and obtain corresponding detection results or judgment results.

[0066] Step B3: if the second data set has been used, output a first prompt message; the first prompt message is used to indicate that the second data set cannot be deleted.

[0067] In this step, if the second data set is being generated or has generated a corresponding analysis list or analysis chart, it means that the second data set has been used in the subsequent chart / list analysis process. To ensure that the user can still use the generated analysis chart or analysis list normally, the second data set is not allowed to be deleted, and the first prompt information can be output to prompt the user that the second data set cannot be deleted. Of course, the reason why the second data set cannot be deleted can also be output so that the user can know the reason why it cannot be deleted in time to ensure user experience.

[0068] In addition, as an optional embodiment, if the second data set is not used, the second data set is deleted from the data set list and the materialized view deletion SQL is executed. That is, if the second data set does not generate a corresponding analysis list or analysis chart, it means that the second data set is not used by the subsequent chart / list analysis process, then the second data set and its related information can be directly deleted from the data set list, and the materialized view deletion SQL (DROPMATERIALIZED VIEW SQL) can be executed to delete the materialized view generated by the second data set, so as to avoid errors in subsequent query data and save storage space.

[0069] In this embodiment, when a user needs to delete a data set, it is first determined whether the data set has been used, and if it has been used, a prompt message is output to remind the user that the data set cannot be deleted, so that the user can still use the analysis chart or analysis list generated by the data set normally, thereby improving the external user experience. In addition, if the data set to be deleted is not used, the data set and its corresponding materialized view can be deleted, so that errors in subsequent query data can be avoided and storage space can be saved.

[0070] In order to facilitate better analysis and presentation of the data in the data set list, the electronic device of the embodiment of the present invention also includes a visualization display module, which can provide a complete front-end and candidate / server interaction interface, realize the definition function of analysis list and chart pair, and support various visualization displays. The specific functions provided mainly include chart list management, chart creation, preview, design, edit, copy, delete and publish functions. The following embodiment describes the chart or list design / creation process of the visualization display module.

[0071] In some embodiments, the above method may further include the following steps: Step C1, receiving a chart setting operation input by a user, and displaying a chart setting interface in response to the chart setting operation; the chart setting interface includes a first tab interface and a second tab interface; the first tab interface is a data setting interface required for creating a chart, and the second tab interface is a style setting interface required for creating a chart.

[0072] In this step, the chart list management function of the visualization display module is first explained. The chart list uses card-tiled thumbnails to display all created visualization charts in the front-end interface, and can obtain relevant data of the chart or list from the back-end through the API (Application Programming Interface), display the name and thumbnail of the chart or list, and provide operation controls (such as operation buttons) such as preview, design, edit, delete and copy.

[0073] Secondly, for the new chart function, after the user clicks the icon to create the related control of the new function, a chart configuration page / interface can pop up. The user can enter the chart name, sorting, remarks and other information on the chart configuration page / interface and submit the input data. After submission, the input data can be sent to the backend through the API interface. The candidate can save the configuration information related to the chart and return a success status. The chart list can automatically refresh and display the newly added chart.

[0074] For the chart design function, users can click on the relevant controls of the chart design on the front-end interface to enter the chart design interface. The left side of the chart design interface can display a variety of commonly used graphic reports (such as tables, line charts, bar charts, pie charts, scatter charts, water wave charts, funnel charts, etc.). Users can select the required graphics from the various commonly used graphic reports and drag them to the design area on the right side of the chart design interface for subsequent processing. It should be noted that it supports dragging and displaying multiple charts in the same chart design interface. These multiple charts can be the same chart or different charts, which can increase the number of chart analyses, thereby improving the efficiency of chart analysis and decision-making efficiency.

[0075] After one or more chart graphics are displayed in the right design area of ​​the above-mentioned chart design interface, taking a chart graphic as an example, the user can click on the chart graphic in the right design area of ​​the chart design interface. The click operation represents the setting operation of the chart, so that the electronic device can obtain the chart setting operation input by the user. After that, the electronic device can respond to the chart setting operation and pop up a chart setting interface in the right area. The chart setting interface includes two tabs. Different tabs correspond to different setting interfaces. The setting interfaces corresponding to the two tabs can be respectively referred to as the first tab interface and the second tab interface. The first tab interface is the data setting interface required to create a chart, and the second tab interface is the style setting interface required to create a chart.

[0076] Step C2, obtaining data setting information input by the user on the first tab interface and style setting information input on the second tab interface; the above data setting information includes a third data set selected in the data set list, selected fields for the X-axis and the Y-axis, and at least one of the specific parameters of the set chart, and the above style setting information includes attribute information of the chart.

[0077] In this step, the first tab interface includes a variety of data setting options, including data set setting, data configuration, and parameter configuration options. In the data set setting option, the user can select a data set from the existing data sets in the data set list as the data source of the analysis chart. For example, the selected data set can be recorded as the third data set. In the data configuration option, the user can select the fields of the X-axis and Y-axis of the analysis chart, that is, the meaning fields represented by the X-axis and Y-axis respectively; in addition, the stacked grouping field and the Chinese interpretation of the table field can be set. In the parameter configuration option, the user can set specific parameters of the chart, which can include search conditions, fixed conditions, etc.

[0078] The second tab interface includes information for setting the style of the chart. The style setting related information here mainly includes the basic attribute information of the chart, such as the title text, font, color, etc. of the chart; at the same time, different chart types can provide their own unique setting options. For example, for a pie chart, you can set the fill color of the pie chart, and for a line chart, you can set the line color of the line chart, etc.

[0079] The above-mentioned first tab page and second tab interface can be jumped to different tab interfaces by the user clicking different tabs on the chart setting interface to perform corresponding data setting and style setting. After the user sets the data and style on the corresponding interface, the electronic device can obtain the corresponding data setting information and style setting information of the chart.

[0080] In addition, when the user sets the relevant data and style of the chart on the corresponding interface, the preview area can update the displayed chart in real time to ensure that the user can see the modified chart effect in a timely and rapid manner.

[0081] Step C3, generating an analysis chart corresponding to the third data set according to the data setting information and the style setting information.

[0082] In this step, after obtaining the data setting information and style setting information corresponding to the chart at the front end of the electronic device, the data setting information and style setting information corresponding to the chart can be sent to the back end through the API interface. The back end saves the data setting information and style setting information corresponding to the chart, and synchronously generates a chart thumbnail corresponding to the third data set, which is recorded as an analysis chart.

[0083] Furthermore, users can view the visualization of the generated analysis chart in a new window through the "Preview" button on the front-end interface to ensure that the configuration is correct. At the same time, you can click the "Edit" button on the front-end interface to modify the information of the analysis chart, and click the "Copy" button to copy all the configurations of the current analysis chart to the new chart to achieve the purpose of supporting independent design. In addition, you can also click the "Delete" button to delete the generated analysis chart. Here, a confirmation box can pop up when deleting the analysis chart. The confirmation box may include information reminding the user that the deletion operation is irreversible. If the user confirms the deletion, the front-end can call the API interface to perform the deletion operation. After the deletion operation is successful, the chart will be removed from the chart list, and the chart list and the display content of the front-end interface can be updated at the same time.

[0084] For example, see Figure 3 The various visualization integrated diagrams shown in the figure may include a variety of different styles of charts, and the charts may display the meaning of the horizontal and vertical axes of the charts, the colors of the charts, and other information, so that users can understand the meaning behind the charts and improve decision-making efficiency and accuracy. Figure 3 It is only an example, and the specific content displayed therein (such as specific text or specific numbers, etc.) does not affect the technical essence of the embodiments of the present invention.

[0085] In this embodiment, a rich variety of chart options are provided to users through the visualization display module, which makes it easy for users to customize the required chart information according to their needs and generate corresponding analysis charts, thereby realizing the user's personalized chart generation needs; at the same time, multiple different setting options can be provided for users to choose from, which makes it easy for users to quickly understand the information behind the data and improve the work efficiency and satisfaction of analysts.

[0086] It can be seen from the description of the above embodiments that in the embodiments of the present invention, real-time data capture and synchronization are achieved based on Flink CDC 3.0, which can improve the timeliness of data analysis. At the same time, based on the column storage and optimized query engine of analytical databases (such as StarRocks database), the use of synchronous materialization and asynchronous materialization can solve the problem of efficiency in massive data analysis queries. At the same time, it supports users to customize dynamic data sets according to their needs and flexibly respond to different analysis scenarios. And it can provide a wealth of chart options to provide users with more visualization forms, so that users can quickly understand the information behind the data.

[0087] The technical solution of the embodiment of the present invention has the following technical effects: 1. Improve decision-making efficiency: Through real-time data analysis and visualization, the system enables enterprises to quickly obtain market insights, thereby optimizing the decision-making process, reducing decision-making time, and improving the accuracy and effectiveness of decisions.

[0088] 2. Enhance data integration capabilities: Effectively integrate data from different microservices, reduce data silos, make cross-departmental and cross-business analysis simpler and more efficient, and provide enterprises with a comprehensive business perspective.

[0089] 3. Optimize user experience: The user-friendly front-end interactive interface and flexible data set management functions enable users to easily create and manage data sets and visualization charts, improving the work efficiency and satisfaction of analysts.

[0090] 4. Improve analysis accuracy: By using the high-performance query engine and materialized views of analytical databases, errors caused by query complexity in large-scale data analysis can be reduced, ensuring data accuracy and consistency, thereby enhancing the credibility of the results.

[0091] 5. Reduce operating costs: Through optimized ETL processes and real-time data processing capabilities, the system reduces the manpower and time costs required for data processing and maintenance, while improving resource utilization efficiency and reducing overall operating costs for the enterprise.

[0092] 6. Support diverse data display: Provide a variety of visual chart forms so that users with different backgrounds and needs can quickly understand and apply data analysis results, and promote the spread of data-driven decision-making culture within the enterprise.

[0093] The business data analysis device provided by the present invention is described below. The business data analysis device described below and the business data analysis method described above can be referenced to each other.

[0094] Figure 4 is a schematic diagram of the structure of the business data analysis device provided by the present invention, see Figure 4 As shown, the device may include: Configuration information acquisition module 410, used to acquire data set configuration information input by the user on the front-end interface; the data set configuration information includes the definition of data source and data column, the data source supports OLAP database, including the newly added first data set, and the definition of data column supports filtering conditions and analysis functions; The query module 420 is used to generate a query SQL according to the definition of the data column, and perform a query in the back-end analytical database according to the query SQL to determine the target business data corresponding to the first data set and the data analysis result corresponding to the target business data; the above analytical database stores all the business data; The analysis result display module 430 is used to generate a display interface on the front-end interface; the above display interface includes a data display area and / or a query UI, and the data display area displays the data analysis results.

[0095] In some embodiments, the query module 420 is specifically used to query in the back-end analytical database according to the query SQL to determine the target business data corresponding to the first data set; generate a materialized view creation SQL according to the target business data, and control the analytical database to execute the materialized view creation SQL to generate a target materialized view corresponding to the target business data; perform a selection operation on the target materialized view to determine the data analysis results corresponding to the target business data.

[0096] In some embodiments, the data set configuration information further includes a materialization type, and the materialization type includes synchronous materialization or asynchronous materialization. The apparatus further includes: The interval and field acquisition module is used to obtain the refresh interval and bucket fields entered by the user on the front-end interface if the materialization type is asynchronous materialization; The update module is used to query the update data corresponding to the bucket field in the first data set in the analytical database at a regular interval according to the refresh interval, and update the target materialized view according to the update data.

[0097] Optionally, the data set configuration information also includes a materialization type. The query module 420 is specifically used to generate a materialized view creation SQL matching the materialization type according to the target business data and the materialization type. Different materialization types correspond to different materialized view creation SQLs.

[0098] In some embodiments, the above apparatus further comprises: A deletion information acquisition module, used to acquire deletion information input by a user on the front-end interface; the deletion information includes a second data set selected by the user from a data set list on the front-end interface and a deletion operation to be performed on the second data set, wherein the data set list includes at least one data set; A detection module, used to detect whether the second data set has been used; the above-mentioned use includes that the second data set has generated an analysis list or an analysis chart, or the second data set is generating an analysis list or an analysis chart; The output module is used to output a first prompt message if the second data set has been used; the first prompt message is used to indicate that the second data set cannot be deleted.

[0099] Optionally, the apparatus further comprises a deletion module, configured to delete the second data set from the data set list and execute a materialized view deletion SQL if the second data set is not used.

[0100] In some embodiments, the above apparatus further comprises: A display module, used to receive a chart setting operation input by a user, and in response to the chart setting operation, display a chart setting interface; the chart setting interface includes a first tab interface and a second tab interface; the first tab interface is a data setting interface required for creating a chart, and the second tab interface is a style setting interface required for creating a chart; A setting information acquisition module is used to acquire data setting information input by the user on the first tab interface and style setting information input on the second tab interface; the data setting information includes at least one of the third data set selected in the data set list, the selected fields for the X-axis and the Y-axis, and the set specific parameters of the chart, and the style setting information includes attribute information of the chart; The chart generation module is used to generate an analysis chart corresponding to the third data set according to the data setting information and the style setting information.

[0101] In some embodiments, the above apparatus further comprises: The synchronous migration module is used to execute data migration commands and use the data integration tool Flink CDC 3.0 to obtain data tables in different source databases in real time; establish data tables in the analytical database with the same table structure as the data tables in each source database, and synchronously migrate each data table in each source database to the data table in the corresponding table structure in the analytical database.

[0102] It should be noted here that the above-mentioned device provided in the embodiment of the present invention can implement all the method steps implemented in the above-mentioned method embodiment, and can achieve the same technical effect. The parts and beneficial effects that are the same as the method embodiment in this embodiment will not be described in detail here.

[0103] Figure 5 An example of a physical structure diagram of an electronic device is shown in FIG. Figure 5 As shown, the electronic device may include: a processor 510, a communication interface 520, a memory 530 and a communication bus 540, wherein the processor 510, the communication interface 520 and the memory 530 communicate with each other through the communication bus 540. The processor 510 may call the logic instructions in the memory 530 to execute the business data analysis method, which includes: obtaining the data set configuration information input by the user on the front-end interface; the data set configuration information includes the definition of the data source and the data column, the data source supports the OLAP database, including the newly added first data set, and the definition of the data column supports the filtering condition and the analysis function; generating a query SQL according to the definition of the data column, and querying in the analytical database at the back end according to the query SQL to determine the target business data corresponding to the first data set and the data analysis result corresponding to the target business data; the analytical database stores all the business data; generating a display interface on the front-end interface; the display interface includes a data display area and / or a query user interface UI, and the data display area displays the data analysis result.

[0104] In addition, the logic instructions in the above-mentioned memory 530 can be implemented in the form of a software functional unit and can be stored in a computer-readable storage medium when it is sold or used as an independent product. Based on such an understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art or the part of the technical solution, can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including a number of instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk, etc. Various media that can store program codes.

[0105] On the other hand, the present invention also provides a computer program product, which includes a computer program, which can be stored on a non-transitory computer-readable storage medium. When the computer program is executed by a processor, the computer can execute the business data analysis method provided by the above-mentioned methods, which includes: obtaining data set configuration information input by a user on a front-end interface; the above-mentioned data set configuration information includes the definition of a data source and a data column, the data source supports an OLAP database, including a newly added first data set, and the definition of a data column supports filtering conditions and analysis functions; generating a query SQL according to the definition of the data column, and querying the back-end analytical database according to the query SQL to determine the target business data corresponding to the first data set and the data analysis results corresponding to the target business data; the above-mentioned analytical database stores all business data; generating a display interface on the front-end interface; the above-mentioned display interface includes a data display area and / or a query user interface UI, and the data display area displays the data analysis results.

[0106] On the other hand, the present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, is implemented to execute the business data analysis method provided by the above-mentioned methods, the method comprising: obtaining data set configuration information input by a user on a front-end interface; the above-mentioned data set configuration information includes the definition of a data source and a data column, the data source supports an OLAP database, including a newly added first data set, and the definition of a data column supports filtering conditions and analysis functions; generating a query SQL according to the definition of the data column, and querying in a back-end analytical database according to the query SQL to determine the target business data corresponding to the first data set and the data analysis results corresponding to the target business data; the above-mentioned analytical database stores all business data; generating a display interface on the front-end interface; the above-mentioned display interface includes a data display area and / or a query user interface UI, and the data display area displays the data analysis results.

[0107] The device embodiments described above are merely illustrative, wherein the units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the scheme of this embodiment. Ordinary technicians in this field can understand and implement it without paying creative labor.

[0108] Through the description of the above implementation methods, those skilled in the art can clearly understand that each implementation method can be implemented by means of software plus a necessary general hardware platform, and of course, can also be implemented by hardware. Based on this understanding, the above technical solution is essentially or the part that contributes to the prior art can be embodied in the form of a software product, and the computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, a disk, an optical disk, etc., including a number of instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in each embodiment or some parts of the embodiments.

[0109] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.< / password> < / password>

Claims

1. A business data analysis method, characterized in that: include: Obtain the data set configuration information entered by the user on the front-end interface; The data set configuration information includes the definition of data source and data column, the data source supports online analytical processing (OLAP) database, including a newly added first data set, and the definition of data column supports filtering conditions and analysis functions; Generate a query SQL according to the definition of the data column, and query in a backend analytical database according to the query SQL to determine the target business data corresponding to the first data set and the data analysis result corresponding to the target business data; the analytical database stores all business data; Generate a display interface on the front-end interface; The display interface includes a data display area and / or a query user interface UI, and the data display area displays the data analysis result.

2. The business data analysis method according to claim 1, characterized in that: The querying in the backend analytical database according to the query SQL to determine the target business data corresponding to the first data set and the data analysis result corresponding to the target business data includes: Performing a query in a backend analytical database according to the query SQL to determine target business data corresponding to the first data set; Generate a materialized view creation SQL according to the target business data, and control the analytical database to execute the materialized view creation SQL to generate a target materialized view corresponding to the target business data; A selection operation is performed on the target materialized view to determine a data analysis result corresponding to the target business data.

3. The business data analysis method according to claim 2, characterized in that: The data set configuration information also includes a materialization type, and the materialization type includes synchronous materialization or asynchronous materialization. The method further includes: If the materialization type is asynchronous materialization, then obtaining the refresh interval and bucket fields entered by the user on the front-end interface; The updated data corresponding to the bucketed field in the first data set is periodically queried in the analytical database according to the refresh interval, and the target materialized view is updated according to the updated data.

4. The business data analysis method according to claim 2, characterized in that: The data set configuration information also includes a materialization type. The step of generating a materialized view creation SQL according to the target business data includes: According to the target business data and the materialization type, a materialized view creation SQL matching the materialization type is generated; wherein different materialization types correspond to different materialized view creation SQLs.

5. The business data analysis method according to claim 1, characterized in that: The method further comprises: Acquire deletion information input by the user on the front-end interface; the deletion information includes a second data set selected by the user from a data set list on the front-end interface and a deletion operation to be performed on the second data set, wherein the data set list includes at least one data set; Detecting whether the second data set has been used; the use includes that the second data set has generated an analysis list or an analysis chart, or the second data set is generating an analysis list or an analysis chart; If the second data set has been used, a first prompt message is output; the first prompt message is used to indicate that the second data set cannot be deleted.

6. The business data analysis method according to claim 1, characterized in that: The method further comprises: Receive a chart setting operation input by a user, and in response to the chart setting operation, display a chart setting interface; the chart setting interface includes a first tab interface and a second tab interface, the first tab interface is a data setting interface required for creating a chart, and the second tab interface is a style setting interface required for creating a chart; Acquire data setting information input by the user on the first tab interface and style setting information input on the second tab interface; the data setting information includes at least one of the third data set selected in the data set list, the selected fields for the X-axis and the Y-axis, and the set specific parameters of the chart; and the style setting information includes attribute information of the chart; An analysis chart corresponding to the third data set is generated according to the data setting information and the style setting information.

7. The business data analysis method according to claim 1, characterized in that: The method further comprises: Execute data migration commands and use the data integration tool Flink CDC 3.0 to obtain data tables from different source databases in real time; A data table having the same table structure as the data table in each of the source databases is established in the analytical database, and each data table in each of the source databases is synchronously migrated to a data table in the corresponding table structure in the analytical database.

8. A business data analysis device, characterized in that: include: The configuration information acquisition module is used to obtain the data set configuration information entered by the user on the front-end interface; The data set configuration information includes the definition of data source and data column, the data source supports online analytical processing (OLAP) database, including a newly added first data set, and the definition of data column supports filtering conditions and analysis functions; A query module, used to generate a query SQL according to the definition of the data column, and query in a back-end analytical database according to the query SQL to determine the target business data corresponding to the first data set and the data analysis results corresponding to the target business data; the analytical database stores all business data; An analysis result display module, used to generate a display interface on the front-end interface; The display interface includes a data display area and / or a user query UI area, and the data display area displays the data analysis results.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the computer program, the business data analysis method according to any one of claims 1 to 7 is implemented.

10. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the business data analysis method according to any one of claims 1 to 7 is implemented.

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