Data processing method, device, electronic device, storage medium and program product

Abstract

The present disclosure provides a data processing method, apparatus, electronic device, storage medium, and program product, relating to the field of big data technology, particularly to technical fields such as data statistics, analysis, experimentation, and computer technology. A specific implementation scheme comprises: utilizing multiple query code blocks to process source data separately, obtaining multiple data tables, each of which has different data indicators, and the source data includes interactive behavior data. An analysis code block is utilized to analyze a target data table to obtain analysis results. The target data table is obtained by linking multiple data tables.

Description

Technical Field

[0001] The present disclosure relates to the field of big data technology, in particular to data statistics, analysis, experimentation, computer technology, and other technical fields, and specifically to data processing methods, devices, electronic devices, storage media, and program products. Background Art

[0002] With the rapid development of computers, reconstructing and deeply analyzing massive amounts of data is no longer an insurmountable challenge. By combining data analysis and visualization using computer data processing software or platforms, real-time data analysis and intuitive presentation are possible, enabling integrated analysis of massive amounts of data and providing a basis for business decision-making and informed business strategies. However, how to conduct this analysis quickly and efficiently has become a research priority. Summary of the Invention

[0003] The present disclosure provides a data processing method, apparatus, electronic device, storage medium, and program product.

[0004] According to one aspect of the present disclosure, a data processing method is provided, comprising: utilizing a plurality of different query code blocks to process source data respectively to obtain a plurality of data tables, wherein the data indicators of the plurality of the above-mentioned data tables are different, and the above-mentioned source data include interactive behavior data; and utilizing an analysis code block to analyze a target data table to obtain an analysis result, wherein the above-mentioned target data table is obtained by associating the plurality of the above-mentioned data tables.

[0005] According to another aspect of the present disclosure, a data processing device is provided, including: a processing module for using different multiple query code blocks to process source data respectively to obtain multiple data tables, wherein the multiple data tables have different data indicators, and the above-mentioned source data include interactive behavior data; and an analysis module for using the analysis code block to analyze the target data table to obtain an analysis result, wherein the above-mentioned target data table is obtained by associating the multiple data tables.

[0006] According to another aspect of the present disclosure, an electronic device is provided, comprising: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute the method as disclosed in the present disclosure.

[0007] According to another aspect of the present disclosure, a non-transitory computer-readable storage medium storing computer instructions is provided, wherein the computer instructions are used to enable the computer to execute the method of the present disclosure.

[0008] According to another aspect of the present disclosure, a computer program product is provided, comprising a computer program, wherein the computer program implements the method of the present disclosure when executed by a processor.

[0009] It should be understood that the contents described in this section are not intended to identify the key or important features of the embodiments of the present disclosure, nor are they intended to limit the scope of the present disclosure. Other features of the present disclosure will become readily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0010] The accompanying drawings are provided to facilitate a better understanding of the present invention and do not constitute a limitation of the present disclosure.

[0011] Figure 1 Schematically illustrates an exemplary system architecture to which the data processing method and apparatus according to an embodiment of the present disclosure may be applied;

[0012] Figure 2 The following schematically shows a flow chart of a data processing method according to an embodiment of the present disclosure;

[0013] Figure 3 Schematically shows a flow diagram of multiple data tables according to relevant embodiments;

[0014] Figure 4A The following schematically shows a flow chart of a data processing method according to an embodiment of the present disclosure;

[0015] Figure 4B A schematic diagram of a flow chart of a data processing method according to a relevant embodiment is shown;

[0016] Figure 5 A schematic diagram schematically illustrates a new analysis result table according to an embodiment of the present disclosure;

[0017] Figure 6 A block diagram schematically shows a data processing device according to an embodiment of the present disclosure; and

[0018] Figure 7 A block diagram schematically shows an electronic device suitable for implementing a data processing method according to an embodiment of the present disclosure. DETAILED DESCRIPTION

[0019] The following description of exemplary embodiments of the present disclosure is made in conjunction with the accompanying drawings, including various details of the embodiments of the present disclosure to facilitate understanding. These details should be considered as merely exemplary. Therefore, those skilled in the art will recognize that various changes and modifications may be made to the embodiments described herein without departing from the scope and spirit of the present disclosure. Similarly, for the sake of clarity and conciseness, descriptions of well-known functions and structures are omitted in the following description.

[0020] Experimental data monitoring and analysis refers to the process of collecting, observing, analyzing, and providing early warnings for experimental data indicators in real time or on a regular basis during the experiment. Its purpose is to ensure that the experiment proceeds as expected, detect abnormalities in the experiment in a timely manner, and provide data support for experimental decision-making.

[0021] The online e-commerce user base is vast, with diverse needs and behaviors. Users of different ages, genders, regions, and consumption habits react differently to e-commerce platforms' page design, product recommendations, and promotions. For example, younger users may prefer to learn about products through short videos, while older users may prefer detailed text descriptions. A / B experiments can help e-commerce platforms test different approaches to find the strategy that best meets the needs of diverse user groups.

[0022] E-commerce platforms frequently launch new technologies and features, such as virtual fitting mirrors and livestream shopping. A / B experiments can be used to validate the effectiveness and user acceptance of these new features. For example, in a livestream shopping experiment, users were divided into two groups: Group A could watch the livestream shopping, while Group B could not. By comparing metrics such as user engagement (e.g., viewing time) and purchase conversion rates between the two groups, the effectiveness of the livestream shopping feature on sales could be assessed, providing a basis for considering a full rollout.

[0023] In e-commerce operations, poor decisions can lead to wasted resources and user churn. AB experiments offer a data-driven approach to decision-making, using data collected from experiments to evaluate the pros and cons of different options, rather than relying solely on experience or intuition. For example, when deciding whether to conduct a large-scale rebranding of a platform, AB experiments can be used to compare user brand awareness, loyalty, and other metrics before and after the rebranding (Group A and Group B). This reduces risk and ensures scientific and accurate decision-making.

[0024] As we've seen above, continuous monitoring and analysis of experimental data can help you identify anomalies during the experiment in a timely manner. Monitoring and analyzing experimental data helps you identify experimental trends in advance, thereby ensuring the scientificity and accuracy of operational decisions.

[0025] Taking the e-commerce live streaming recommendation scenario as an example, there are many different data sources, including business monitoring data, performance monitoring data, and hourly granularity data.

[0026] (1) Business monitoring data may include traffic data, user path analysis data, and search data, etc.

[0027] Traffic data can refer to monitoring the number of visits to a website or application, including daily and monthly unique visitors (UV), page views (PV), etc., to understand the traffic distribution of different channels and different pages, such as the number and proportion of users entering the e-commerce platform through search engines, social media, and directly entering the URL, so as to evaluate the traffic diversion effect of each channel.

[0028] User path analysis data can refer to recording the user's operational behavior path on the platform, such as the conversion rate of browsing product pages, adding to shopping carts, submitting orders, etc., analyzing the user's stay time on different pages, bounce rate, etc., so as to discover user behavioral preferences and possible problem links, such as which pages have a high bounce rate, whether there are factors such as cumbersome processes that lead to user loss.

[0029] Search data can refer to the keywords users enter into the platform's search box, search frequency, and click-through rate of search results, etc., to understand users' search needs and intentions, as well as the effectiveness of search functions. For example, if the search volume for a certain product category suddenly increases, but the corresponding purchase conversion rate is low, it may be necessary to optimize the display or recommendation strategy for this product.

[0030] (2) Performance monitoring data may include system performance data and application performance data.

[0031] System performance data: Monitor the e-commerce platform's server response time, page loading speed, system throughput, number of concurrent users, and other performance indicators to ensure the stability and smoothness of the experimental environment and avoid performance issues that affect user experience and the accuracy of experimental results.

[0032] Application performance data: For e-commerce apps or other applications, monitor their startup time, crash rate, freeze rate, memory usage and other performance data to promptly identify and resolve application performance issues and improve user satisfaction and loyalty during use.

[0033] (3) Hourly granularity data

[0034] During e-commerce experiments, data collection, analysis, and feedback are carried out almost simultaneously. This monitoring method can capture data changes in a very short period of time (typically within seconds to minutes). Real-time monitoring of traffic data is crucial for large-scale promotional events on e-commerce platforms, such as "Double 11" and "618." In livestreaming sales scenarios, real-time monitoring of product click-through rates, add-to-cart rates, and conversion rates can help hosts and operations teams quickly adjust their livestreaming strategies. If a product's click-through rate is low, the host can immediately adjust their introduction or emphasize the product's selling points to increase its appeal.

[0035] The technical problems with the data tables collected through the above data sources include at least the following: there are many data tables, many overlapping scenarios, but they are scattered in different data tables, and multi-system data fusion is required. Live broadcast and video data scenarios overlap, and different data tables have different observation focuses. As the complexity of the business increases, the data tables become cumbersome and difficult to understand. In addition, as the amount of data grows, the original SQL (Structured Query Language) code may become inefficient. For example, in a large e-commerce source data, if the original query involves joining multiple data tables and the index is not used reasonably, the query speed may be very slow. In addition, maintainability is difficult because there are data intersections between multiple data tables, resulting in strong coupling between SQL code blocks, which is difficult to modify and expand.

[0036] In view of this, an embodiment of the present disclosure provides a data processing method, comprising: utilizing multiple query code blocks to process source data separately to obtain multiple data tables, wherein the multiple data tables each have different data indicators, and the source data includes interaction behavior data; utilizing an analysis code block to analyze a target data table to obtain an analysis result; and obtaining the target data table by associating the multiple data tables.

[0037] By having different data indicators in multiple data tables, the multiple data tables are independent of each other, and thus the multiple query code blocks built based on the multiple data tables are independent of each other and decoupled from each other, thereby reducing the difficulty of modifying and maintaining a single query code block, reducing the amount of redundant data in the target data table, and improving the processing efficiency of the analysis result table.

[0038] In the technical solution disclosed herein, the collection, storage, use, processing, transmission, provision, disclosure and application of user personal information involved comply with the provisions of relevant laws and regulations, take necessary confidentiality measures, and do not violate public order and good morals.

[0039] In the technical solution disclosed herein, the user's authorization or consent is obtained before obtaining or collecting the user's personal information.

[0040] Figure 1 An exemplary system architecture to which the data processing method and apparatus according to an embodiment of the present disclosure can be applied is schematically shown.

[0041] It should be noted that Figure 1The examples shown are merely examples of system architectures to which the embodiments of the present disclosure may be applied, to help those skilled in the art understand the technical content of the present disclosure. This does not mean that the embodiments of the present disclosure cannot be applied to other devices, systems, environments, or scenarios. For example, in another embodiment, an exemplary system architecture to which the data processing method and apparatus may be applied may include a terminal device, but the terminal device may implement the data processing method and apparatus provided by the embodiments of the present disclosure without interacting with a server.

[0042] like Figure 1 As shown, the system architecture 100 according to this embodiment may include terminal devices 101, 102, 103, a network 104, and a server 105. The network 104 is used as a medium for providing communication links between the terminal devices 101, 102, 103 and the server 105. The network 104 may include various connection types, such as wired and / or wireless communication links, etc.

[0043] Users can use terminal devices 101, 102, and 103 to interact with server 105 via network 104 to receive or send messages, etc. Various communication client applications can be installed on terminal devices 101, 102, and 103, such as knowledge reading applications, web browser applications, search applications, instant messaging tools, email clients, and / or social platform software (for example only).

[0044] The terminal devices 101 , 102 , and 103 may be various electronic devices having a display screen and supporting web browsing, including but not limited to smart phones, tablet computers, laptop computers, and desktop computers.

[0045] Server 105 may be a server that provides various services, such as a background management server (for example only) that supports content browsed by users using terminal devices 101, 102, and 103. The background management server may analyze and process received data such as user requests, and feed back processing results (e.g., web pages, information, or data obtained or generated based on user requests) to the terminal device.

[0046] It should be noted that the data processing method provided in the embodiment of the present disclosure can generally be executed by the terminal device 101, 102, or 103. Accordingly, the data processing apparatus provided in the embodiment of the present disclosure can also be provided in the terminal device 101, 102, or 103.

[0047] Alternatively, the data processing method provided in the embodiments of the present disclosure may also be generally executed by the server 105. Accordingly, the data processing apparatus provided in the embodiments of the present disclosure may generally be provided in the server 105. The data processing method provided in the embodiments of the present disclosure may also be performed by a server or server cluster that is different from the server 105 and that is capable of communicating with the terminal devices 101, 102, 103 and / or the server 105. Accordingly, the data processing apparatus provided in the embodiments of the present disclosure may also be provided in a server or server cluster that is different from the server 105 and that is capable of communicating with the terminal devices 101, 102, 103 and / or the server 105.

[0048] For example, the terminal devices 101, 102, and 103 may send the acquired source data to the server 105, which processes the source data to obtain an analysis result table. Alternatively, a server or server cluster capable of communicating with the terminal devices 101, 102, and 103 and / or the server 105 may analyze the source data and ultimately obtain an analysis result table.

[0049] It should be understood that Figure 1 The number of terminal devices, networks and servers in the embodiment is merely illustrative. Any number of terminal devices, networks and servers may be provided as required.

[0050] It should be noted that the sequence numbers of the operations in the following method are only used to indicate the operation for the purpose of description, and should not be regarded as indicating the order in which the operations should be performed. Unless explicitly stated, the method does not need to be performed in the order shown.

[0051] In addition, it should be noted that users are aware of and agree to the acquisition and use of source data involved in the following plans, and they comply with relevant laws and regulations and do not violate public order and good morals.

[0052] Figure 2 The flowchart of the data processing method according to the embodiment of the present disclosure is schematically shown.

[0053] like Figure 2 As shown, the method includes operations S210 to S220.

[0054] In operation S210, source data is processed using different query code blocks to obtain multiple data tables, each of which has different data indices.

[0055] In operation S220, the target data table is analyzed using the analysis code block to obtain an analysis result. The target data table is obtained by associating multiple data tables.

[0056] A query code block may refer to a structured query statement type code block used to automatically process source data to generate a data table. Multiple data tables each have different data indicators, and therefore, the multiple data tables are independent of each other. The indicator values ​​for the data indicators in each data table can be directly extracted from the source data, but this is not limited to this. Indicator values ​​may also be obtained by processing the data extracted from the source data. Any indicator value for the corresponding data indicator obtained by processing the source data using the query code block is sufficient.

[0057] Source data includes interactive behavior data. For example, interactive behavior data may include interactive behavior data of the subject on the short video, such as likes, favorites, and other behavioral data. The interactive behavior data is analyzed, and the analysis results obtained can represent the degree of interest in the short video. However, it is not limited to this. It can also include interactive behavior data for AB experiments. By analyzing this data, a tendency opinion for the AB experiment can be obtained. Source data may include

[0058] A single query code block can process source data to generate a data table, and a mapping relationship can be established between the query code block and the data table. However, this is not limited to this. Alternatively, multiple query code blocks can jointly process source data to generate a data table. As long as the multiple data tables are independent of each other, the running code in the query code blocks can be decoupled from each other. This allows query code blocks to independently change as the data table changes with business dimensions, improving update configuration efficiency.

[0059] Multiple independent data tables obtained can be associated, for example, concatenated, to obtain a target data table.

[0060] The target data table is analyzed using the analysis code block to obtain an analysis result table. The analysis code block may include an aggregation code statement, which processes the indicator values ​​of the indicators to be analyzed in the target data table through the aggregation code to obtain the indicator values ​​of the result indicators in the analysis result table.

[0061] Aggregate code statements are used to perform aggregate processing, including but not limited to calculations, comparisons, logical operations, and joins. Calculations include, for example, addition, subtraction, multiplication, and division. Comparisons include equals, greater than, and less than. Logical operations include AND, OR, and NOT. Joins involve concatenating multiple data points.

[0062] The target data table is analyzed using the analysis code block to obtain analysis results. These results clearly display the statistical analysis results, improving the evaluation of the interactive behavior data of the source data. Furthermore, the analysis code block is separated from multiple query code blocks to reduce the coupling between the multiple code blocks and improve the flexible maintenance of the independent code blocks.

[0063] According to the embodiments of the present disclosure, good maintainability is crucial in the case of constantly changing business needs. By designing a code structure that decouples multiple query code blocks and decouples query code blocks from analysis code blocks, the code structure is made reasonable, which makes it easy to modify and expand subsequent code blocks. For example, if you need to add a new filtering condition or count a new result indicator in the result analysis, you can easily find the appropriate code location in the analysis code block to modify it without affecting the running logic of other code blocks, such as the query code block.

[0064] The above describes the data processing method of the embodiment of the present disclosure in general. The following describes how to obtain the data table in detail.

[0065] According to the embodiments of the present disclosure, Figure 2 Operation S210, in which source data is processed using multiple query code blocks to obtain multiple data tables, may include determining a target query code block from the multiple query code blocks that matches a data index in the data table. A mapping relationship exists between the query code blocks and the data indexes in the data table. The target query code block is used to process the source data to obtain the data table.

[0066] For example, the multiple data tables include data table A, data table B, and data table C. A mapping relationship exists between the data tables and the query code blocks. A mapping relationship table can be established based on the table subject, table identifier, or data indicator of the data table and the query code block. For example, the mapping relationship table includes a mapping relationship between data table A and query code block A, a mapping relationship between data table B and query code block B, and a mapping relationship between data table C and query code block C.

[0067] Based on the mapping relationship table, the target query code block for obtaining data table A, namely query code block A, can be determined from query code block A, query code block C, and query code block D. Query code block A is used to process the source data to obtain data table A. Similarly, query code block B, which matches data table B, can be used to process the source data to obtain data table B. Query code block C, which matches data table C, can be used to process the source data to obtain data table C.

[0068] According to the embodiments of the present disclosure, there is a mapping relationship between the query code block and the data indicators of the data table. The target query code block matching the data table is used to process the source data. While decoupling multiple query code blocks, the screening speed and accuracy of the target query code block can be improved through the mapping relationship, thereby improving data processing efficiency.

[0069] The above describes in detail how to obtain multiple data tables. The following describes how to generate a target data table using multiple data tables.

[0070] In one example, multiple data tables can be concatenated to obtain a target data table. For example, association codes can be used to aggregate data from multiple data tables, such as by concatenating multiple data tables to obtain a target data table. Alternatively, data from multiple data tables can be entered into a predetermined data table to obtain a target data table. Any method that can associate data from multiple data tables to obtain a target data table is sufficient.

[0071] The following two examples illustrate how to determine multiple data tables.

[0072] In one example, based on the result indicators in the analysis result table, an indicator to be analyzed for generating the indicator value of the result indicator can be determined. Based on the indicator to be analyzed, a data indicator for generating a target data table can be determined. Based on the data indicators in the target data table, multiple data tables for concatenating the target data table can be determined. Multiple query code blocks are used to process the source data to obtain multiple data tables.

[0073] By using this method to obtain the target data table, the indicators to be analyzed in the target data table are simple and effective, invalid data indicators are avoided, and the efficiency of subsequent analysis is improved.

[0074] In another example, data indicators of a target data table may be predetermined. Based on the predetermined data indicators of the target data table, multiple data tables for concatenating the target data table are determined. The source data is processed using multiple query code blocks to obtain multiple data tables.

[0075] Compared with the method of generating a target data table by analyzing the result indicators in the result table, the method of generating a target data table by using the data indicators of a predetermined target data table can provide comprehensive and rich data indicators. When the analysis dimensions or analysis indicators are updated, the modification of the query code block is avoided, thereby reducing subsequent maintenance costs.

[0076] Using the data processing method provided by the embodiments of the present disclosure, the data indicators of the first, second, and third data tables are different. The first data table lists relevant content of the product theme, such as product category, product introduction content, and product price. The second data table lists sales information, such as sales volume, unit price, marketing strategy, etc. The third data table lists sales channels, such as live broadcast, video, and other push methods.

[0077] The data indicators of multiple data tables are different, and the multiple data tables are independent of each other. Therefore, when modifying any data table, only the query code block of the corresponding data table needs to be modified, without coupling other query code blocks. This improves the structural clarity of the query code block.

[0078] Figure 3 A flowchart of multiple data tables according to relevant embodiments is schematically shown.

[0079] like Figure 3 As shown, the first data table 301 and the second data table 302 each have cross items, for example, they both have the same data indicators such as average price per unit and fast sliding rate, see Figure 3 The contents of the box.

[0080] and Figure 3 Compared with the target data table obtained by using the multiple data tables shown in the figure, the target data table obtained by using the multiple data tables provided by the embodiment of the present disclosure can simplify the redundant data in the target data table, facilitate subsequent analysis, and improve the processing time and processing accuracy of the analysis results obtained by analyzing the target data table using the analysis code block.

[0081] The above describes how to determine multiple data tables and generate a target data table using the multiple data tables. The following describes how to generate a query code block for obtaining a data table.

[0082] According to an embodiment of the present disclosure, the source data includes a plurality of source data sub-tables.

[0083] According to an embodiment of the present disclosure, when executing Figure 2 Before the operation S210 shown, the data processing method may further include the following operation: generating a query code block.

[0084] According to an embodiment of the present disclosure, generating a query code block may include: determining an aggregation relationship between a data indicator in a data table and target data in source data; determining an indexing method for each of the multiple source data sub-tables based on the data dimensions of each of the multiple source data sub-tables; and generating a query code block that has a mapping relationship with the data indicator in the data table based on the indexing method and aggregation relationship of each of the multiple source data sub-tables.

[0085] The target data includes data to be analyzed for obtaining indicator values ​​of data indicators.

[0086] The query code block may include query code statements for extracting data to be analyzed from source data and aggregation code statements for performing aggregation processing.

[0087] Based on the data dimensions of the multiple source data sub-tables, the indexing methods of the multiple source data sub-tables can be determined. Based on the indexing methods of the multiple source data sub-tables, query code statements can be determined.

[0088] An aggregation processing method for performing aggregation processing on the target data may be determined based on the aggregation relationship between the data indicator and the target data, and an aggregation code statement may be determined based on the aggregation processing method.

[0089] Based on the query code statements and aggregation code statements, a query code block is generated that has a mapping relationship with the data indicators in the data table.

[0090] Using this approach to generate query code blocks that match the data indicators in a data table can improve the efficiency of query code block generation while also enhancing its effectiveness and comprehensiveness. Furthermore, query code blocks include query code statements that read data and aggregation code statements that aggregate data, thereby increasing the comprehensiveness of the query code blocks and, in turn, their ability to automatically process data.

[0091] When writing query code blocks, you can follow unified code writing standards, such as unified naming rules (table names, column names, variable names, etc.), indentation format, comment standards, etc. This can improve code readability.

[0092] According to an embodiment of the present disclosure, the data processing method may further include: testing and verifying the query code block. If the test and verification result indicates that the query code block is correct, using the query code block to process the source data. If the test and verification result indicates that the query code block contains errors, optimizing the query code block.

[0093] In addition, the query code block can be tested and verified to ensure the functional accuracy of the query code block.

[0094] By testing and verifying the query code block, you can identify problems such as data type mismatch, foreign key association errors, or omissions in complex conditional judgments, and thus ensure the functional correctness of the code through corrections.

[0095] According to an embodiment of the present disclosure, the operation of testing and verifying a query code block may include: performing data processing on test source data using the query code block to be tested to obtain a test data table. Based on the test data table and a reference data table, determining a test result for the query code block to be tested. The reference data table is a result obtained by processing the test source data using a predetermined code set, wherein the predetermined code set includes code for analyzing the source data to obtain an analysis result.

[0096] The query code block to be tested includes at least one of the multiple query code blocks. However, this is not limited to this. The query code block to be tested may also include an analysis code block. Any code block that can be tested using the above method will suffice.

[0097] The data type of the test source data may be the same as the data type of the source data. For example, during the test process, historical source data that has been manually evaluated for accuracy may be used as the test source data.

[0098] In one example, the predetermined code set may be a code set with processing logic different from the query code block and the analysis code block, as long as the code can process the test source data to obtain the reference data table and process the source data to obtain the analysis result.

[0099] The function of the query code block to be tested can be determined, and a predetermined code set with the same function can be determined from multiple predetermined code sets. The same function can be understood as the same data indicators used to obtain.

[0100] Determining the test result of the query code block to be tested based on the test data table and the reference data table may include: using the parameter data table as the correct data table; using the test data table obtained using the query code to be tested as the test table to be evaluated; if the results of the reference data table and the test data table are consistent, determining that the test result indicates that the query code block to be tested is correct; if the results of the reference data table and the test data table are inconsistent, determining that the test result indicates that the query code block to be tested has an error, and correction and optimization are required.

[0101] According to the embodiments of the present disclosure, by testing and verifying the query code block, the accuracy of the query code block can be improved, and the accuracy and validity of the data table obtained based on the query code block can be improved.

[0102] In another example, the predetermined code set may include codes that directly process the source data to obtain the analysis results without constructing multiple independent data tables. Figure 4A and Figure 4B The introduction is used to distinguish the processing logic between the predetermined code set and the query code block and the analysis code block.

[0103] Figure 4A The flowchart of the data processing method according to the embodiment of the present disclosure is schematically shown.

[0104] like Figure 4AAs shown, the source data includes multiple source data sub-tables. A first source data sub-table 401, a second source data sub-table 402, and a third source data sub-table 403. The first query code block M401 can be used to process the first source data sub-table 401 and the second source data sub-table 402 to obtain a first data table 410. The second query code block M402 can be used to process the third source data sub-table 403 to obtain a second data table 420. The first data table 410 and the second data table 420 are concatenated to obtain a target data table 430. The target data table 430 is analyzed using the analysis code block M403 to obtain an analysis result table 440.

[0105] Figure 4B The flowchart of the data processing method according to the relevant embodiment is schematically shown.

[0106] like Figure 4B As shown, the source data includes multiple source data sub-tables: a first source data sub-table 401, a second source data sub-table 402, and a third source data sub-table 403. Data processing and analysis can be performed on the first source data sub-table 401, the second source data sub-table 402, and the third source data sub-table 403 using a predetermined code set M404 to obtain an analysis result table 440'.

[0107] pass Figure 4A and Figure 4B From the comparison, we can see that compared with using a predetermined code set, using the processing logic of querying code blocks and analyzing code blocks can decouple the code blocks and improve the structural clarity of the code blocks. When dealing with errors in code blocks, the problem can be located more quickly because its logical structure is clearer and the function of each part is clear.

[0108] According to an embodiment of the present disclosure, the analysis code block may include multiple analysis code blocks. The analysis result may include an analysis result table.

[0109] According to the embodiments of the present disclosure, Figure 2 The operation S220 shown, using the analysis code block to analyze the target data table to obtain the analysis result, may include: determining a target analysis code block that matches the result indicator in the analysis result table from multiple analysis code blocks. A mapping relationship exists between the analysis code block and the result indicator in the analysis result table. The target analysis code block is used to aggregate the indicator values ​​of at least one indicator to be analyzed in the target data table to obtain the indicator value of the result indicator. According to the predetermined dimension of the analysis result table, the indicator value of the result indicator is filled into the predetermined indicator field of the analysis result table.

[0110] Multiple analysis code blocks can be set. Each analysis code block is used to obtain the indicator value of at least one result indicator.

[0111] A mapping relationship between the analysis code block and the result indicator can be established, for example, by establishing a mapping relationship table. Based on the result indicator in the analysis result table, a target analysis code block is determined from the mapping relationship table. Using the aggregation code prediction in the target analysis code block, the indicator value of at least one indicator to be analyzed in the target data table is aggregated to obtain an indicator value for the result indicator. The indicator value of the result indicator can be automatically populated into a predetermined indicator field in the analysis result table, thereby obtaining an analysis result table having the indicator value.

[0112] By setting up multiple analysis code blocks, decoupling between multiple code blocks can be achieved, and the independence of multiple code blocks can be improved. This improves the ease of maintenance and flexibility of modification of code blocks, while at the same time improving the analysis capability and scalability of analysis through multiple analysis code blocks.

[0113] The following is a detailed description of the internal code statements of the analysis code block.

[0114] According to an embodiment of the present disclosure, aggregating the indicator values ​​of at least one indicator to be analyzed in a target data table using a target analysis code block to obtain an indicator value of a result indicator may include: extracting the indicator value of at least one indicator to be analyzed from the target data table using a query code statement in the target analysis code block; and aggregating the indicator values ​​of at least one indicator to be analyzed using an aggregation code statement in the target analysis code block to obtain an indicator value of the result indicator.

[0115] The query code statement is determined according to the indexing method of the target data table.

[0116] The aggregation code statement is determined based on the aggregation relationship between at least one indicator to be analyzed and the result indicator. For example, based on the aggregation relationship between the at least one indicator to be analyzed and the result indicator, an aggregation processing method between the at least one indicator to be analyzed and the result indicator is determined, and the aggregation code statement is determined based on the aggregation processing method.

[0117] The target analysis code block includes query code statements with data reading capabilities and aggregation code statements with aggregation capabilities, thereby improving the comprehensiveness of the analysis code block and further improving the ability to automatically process data.

[0118] The previous section explains how to perform data analysis using the analysis code block. The following section explains how to add a new analysis code block when adding a new analysis dimension.

[0119] Figure 5 The figure schematically shows a new analysis result table according to an embodiment of the present disclosure.

[0120] like Figure 5As shown, in the original analysis result table 510, the result indicators include an effect result indicator 511. In response to the request to add a new analysis dimension, a confidence evaluation indicator 512 is added to the original analysis result table.

[0121] It should be noted that adding confidence evaluation indicators to the analysis result table can achieve the following effects. For example, (1) Enhance the reliability of evaluation data: Confidence testing can help determine whether the experimental data is reliable. During the experiment, the data may be interfered with by various factors, such as measurement error, sample bias, changes in the experimental environment, etc. Through confidence testing, the credibility of the data can be quantified. For example, in e-commerce, whether different colored fonts will affect the user's click conversion, the data can be tested for confidence to determine whether the data truly reflects the user conversion fluctuations or the deviation caused by unreasonable sample selection or inaccurate measurement. (2) Determine the validity of the experimental results: It can determine whether the experimental results are statistically significant. In scientific research and experiments, it is not enough to simply observe the difference in data. It is also necessary to determine whether the difference is caused by experimental factors or just random fluctuations. Confidence testing can calculate the probability that the experimental results are caused by random factors, thereby helping to determine whether the experiment has successfully revealed the true relationship between variables. For example, in a new advertising strategy experiment, the confidence of product sales data before and after the advertising is tested. If the test results show that the difference is statistically significant, the new advertising strategy can be considered effective; otherwise, it may be ineffective or require further experiments. (3) Accuracy of business decisions: In the business field, accurate decisions are crucial to the success of a company. Conducting confidence tests on experimental data such as market research, product development, and marketing strategies can prevent companies from making wrong decisions based on unreliable data. For example, a catering company conducts market testing before launching a new dish. If it does not conduct a confidence test, it may misjudge the popularity of the new dish, resulting in a poor market response after a large amount of investment. For example, in an experiment to optimize the user experience of an e-commerce platform, conducting a confidence test on user satisfaction data can help the platform determine truly effective optimization measures and avoid blindly investing resources.

[0122] According to an embodiment of the present disclosure, to adapt to adding a new analysis dimension to the analysis result table, the data processing method may further include an added analysis code block.

[0123] According to an embodiment of the present disclosure, adding an analysis code block may include: in response to a request for adding an analysis dimension to an analysis result, determining a target result indicator for the added analysis dimension and an indicator to be analyzed associated with the target result indicator. The indicator to be analyzed is an indicator to be analyzed in a target data table. Based on the target result indicator, the indicator to be analyzed, and the aggregation relationship between the target result indicator and the indicator to be analyzed, an analysis code block matching the target result indicator is generated.

[0124] Exemplarily, the added analysis dimension is the confidence test dimension, and the target result indicator is the confidence test result indicator.

[0125] The indicator to be analyzed associated with the target result indicator may refer to an indicator used to generate the indicator value of the target result indicator.

[0126] An aggregation method may be determined based on the aggregation relationship between the target result indicator and the indicator to be analyzed. An aggregation code statement for obtaining the target result indicator may be determined based on the aggregation method, the target result indicator, and the indicator to be analyzed.

[0127] Based on the aggregation code statement, an analysis code block matching the target result indicator is generated. However, this is not limited to this. Analysis code blocks matching the target result indicator can also be generated based on the aggregation code statement and the query code statement. The query code statement is used to determine the code statement for the indicator to be analyzed from the target data table.

[0128] According to an embodiment of the present disclosure, analysis code blocks and result indicators are mapped so that multiple analysis code blocks are independent of each other. When new result indicators are added, they can respond quickly and construct new analysis code blocks to match the new result indicators, thereby improving the analysis capability while avoiding affecting other result indicators.

[0129] Figure 6 The block diagram schematically shows a data processing device according to an embodiment of the present disclosure.

[0130] like Figure 6 As shown, the data processing device 600 includes: a processing module 610 and an analysis module 620.

[0131] The processing module 610 is configured to process the source data using different query code blocks to obtain multiple data tables. The multiple data tables each have different data indicators. The source data includes interaction behavior data.

[0132] The analysis module 620 is used to analyze the target data table using the analysis code block to obtain the analysis result. The target data table is obtained by associating multiple data tables.

[0133] According to an embodiment of the present disclosure, the processing module 610 includes: a query matching submodule and a processing submodule.

[0134] The query matching submodule is used to determine a target query code block that matches the data indicators in the data table from multiple query code blocks, wherein a mapping relationship exists between the query code block and the data indicators in the data table.

[0135] The processing submodule is used to process the source data using the target query code block to obtain a data table.

[0136] According to an embodiment of the present disclosure, the source data includes a plurality of source data sub-tables.

[0137] According to an embodiment of the present disclosure, the data processing apparatus further includes: an aggregation determination module, an index determination module, and a generation module.

[0138] The aggregation determination module is used to determine the aggregation relationship between the data indicators in the data table and the target data in the source data. The target data includes the data to be analyzed for obtaining the indicator value of the data indicator.

[0139] The index determination module is used to determine the index mode of each of the multiple source data sub-tables based on the data dimensions of each of the multiple source data sub-tables.

[0140] The generation module is used to generate a query code block that has a mapping relationship with the data indicators in the data table based on the indexing methods and aggregation relationships of multiple source data sub-tables.

[0141] According to an embodiment of the present disclosure, the data processing device further includes: a testing module and a verification module.

[0142] The test module is used to process the test source data using the query code block to be tested to obtain a test data table. The query code block to be tested includes at least one of the multiple query code blocks.

[0143] The verification module is used to determine the test result of the query code block to be tested based on the test data table and the reference data table. The reference data table is the result of processing the test source data using a predetermined code set, which includes the code used to analyze the source data to obtain the analysis result.

[0144] According to an embodiment of the present disclosure, the analysis code block includes multiple.

[0145] According to an embodiment of the present disclosure, the analysis module includes: an analysis and matching submodule, an aggregation submodule, and a filling submodule.

[0146] The analysis matching submodule is used to determine a target analysis code block that matches the result indicators in the analysis result table from the multiple analysis code blocks. There is a mapping relationship between the analysis code blocks and the result indicators in the analysis result table.

[0147] The aggregation submodule is used to aggregate the indicator values ​​of at least one indicator to be analyzed in the target data table using the target analysis code block to obtain the indicator value of the result indicator.

[0148] The filling submodule is used to fill the indicator value of the result indicator into the predetermined indicator field of the analysis result table according to the predetermined dimension of the analysis result table.

[0149] According to an embodiment of the present disclosure, the aggregation submodule includes: an extraction unit and an aggregation unit.

[0150] The extraction unit is used to extract the indicator value of at least one indicator to be analyzed from the target data table using the query code statement in the target analysis code block. The query code statement is determined according to the index mode of the target data table.

[0151] The aggregation unit is used to aggregate the indicator values ​​of at least one indicator to be analyzed using the aggregation code statement in the target analysis code block to obtain the indicator value of the result indicator.

[0152] According to an embodiment of the present disclosure, the data processing device further includes: a response module and a new module.

[0153] The response module is configured to respond to a request for adding an analysis dimension to the analysis result table and determine a target result indicator of the added analysis dimension and an indicator to be analyzed associated with the target result indicator. The indicator to be analyzed is an indicator to be analyzed in the target data table.

[0154] A new module is added to generate an analysis code block that matches the target result indicator based on the target result indicator, the indicator to be analyzed, and the aggregation relationship between the target result indicator and the indicator to be analyzed.

[0155] According to an embodiment of the present disclosure, the present disclosure also provides an electronic device, a readable storage medium, and a computer program product.

[0156] According to an embodiment of the present disclosure, an electronic device includes: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to execute a method as in the embodiment of the present disclosure.

[0157] According to an embodiment of the present disclosure, a non-transitory computer-readable storage medium storing computer instructions is provided, wherein the computer instructions are used to cause a computer to execute a method according to an embodiment of the present disclosure.

[0158] According to an embodiment of the present disclosure, a computer program product includes a computer program. When the computer program is executed by a processor, the method according to the embodiment of the present disclosure is implemented.

[0159] Figure 7 A schematic block diagram of an example electronic device 700 that can be used to implement embodiments of the present disclosure is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital assistants, cellular phones, smartphones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present disclosure described and / or claimed herein.

[0160] like Figure 7 As shown, device 700 includes a computing unit 701, which can perform various appropriate actions and processes according to a computer program stored in a read-only memory (ROM) 702 or a computer program loaded from a storage unit 708 into a random access memory (RAM) 703. RAM 703 may also store various programs and data required for the operation of device 700. Computing unit 701, ROM 702, and RAM 703 are connected to each other via a bus 704. An input / output (I / O) interface 705 is also connected to bus 704.

[0161] Various components in device 700 are connected to an input / output (I / O) interface 705, including an input unit 706, such as a keyboard and mouse; an output unit 707, such as various types of displays and speakers; a storage unit 708, such as a magnetic disk and optical disk; and a communication unit 709, such as a network card, a modem, a wireless communication transceiver, etc. Communication unit 709 allows device 700 to exchange information / data with other devices via a computer network such as the Internet and / or various telecommunication networks.

[0162] The computing unit 701 can be any general-purpose and / or specialized processing component with processing and computing capabilities. Some examples of the computing unit 701 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various specialized artificial intelligence (AI) computing chips, various computing units running machine learning model algorithms, a digital signal processor (DSP), and any suitable processor, controller, microcontroller, etc. The computing unit 701 performs the various methods and processes described above, such as the data processing method. For example, in some embodiments, the data processing method may be implemented as a computer software program tangibly embodied in a machine-readable medium, such as the storage unit 708. In some embodiments, part or all of the computer program may be loaded and / or installed onto the device 700 via the ROM 702 and / or the communication unit 709. When the computer program is loaded into the RAM 703 and executed by the computing unit 701, one or more steps of the data processing method described above may be performed. Alternatively, in other embodiments, the computing unit 701 may be configured to perform the data processing method by any other suitable means (e.g., via firmware).

[0163] Various embodiments of the systems and techniques described above can be implemented in digital electronic circuit systems, integrated circuit systems, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), system-on-a-chip systems (SOCs), complex programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments can include being implemented in one or more computer programs that are executable and / or interpreted on a programmable system that includes at least one programmable processor, which can be a special purpose or general purpose programmable processor that can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.

[0164] The program code for implementing the method of the present disclosure can be written in any combination of one or more programming languages. These program codes can be provided to a processor or controller of a general-purpose computer, a special-purpose computer, or other programmable data processing device so that when the program code is executed by the processor or controller, the functions / operations specified in the flow chart and / or block diagram are implemented. The program code can be executed entirely on the machine, partially on the machine, as a stand-alone software package, partially on the machine and partially on a remote machine, or entirely on a remote machine or server.

[0165] In the context of the present disclosure, a machine-readable medium may be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, apparatus, or device. A machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. More specific examples of machine-readable storage media may include an electrical connection based on one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), optical fibers, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0166] To provide interaction with a user, the systems and techniques described herein can be implemented on a computer having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user can provide input to the computer. Other types of devices can also be used to provide interaction with the user; for example, the feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including acoustic input, voice input, or tactile input).

[0167] The systems and techniques described herein can be implemented in a computing system that includes back-end components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes front-end components (e.g., a user computer with a graphical user interface or a web browser through which a user can interact with implementations of the systems and techniques described herein), or a computing system that includes any combination of such back-end components, middleware components, or front-end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include a local area network (LAN), a wide area network (WAN), and the Internet.

[0168] A computer system may include a client and a server. The client and server are generally remote from each other and typically interact through a communication network. The client-server relationship arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other. The server may be a cloud server, a server in a distributed system, or a server integrated with a blockchain.

[0169] It should be understood that the various forms of the processes shown above can be used to reorder, add, or delete steps. For example, the steps described in this disclosure can be performed in parallel, sequentially, or in a different order, as long as the desired results of the technical solutions disclosed in this disclosure can be achieved. This is not a limitation herein.

[0170] The above specific embodiments do not constitute a limitation on the scope of protection of this disclosure. Those skilled in the art will appreciate that various modifications, combinations, sub-combinations, and substitutions may be made based on design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this disclosure shall be included within the scope of protection of this disclosure.

Claims

1. A data processing method, comprising: Using multiple query code blocks to process source data respectively to obtain multiple data tables, wherein the multiple data tables have different data indicators, and the source data includes interactive behavior data; and Analyzing a target data table using an analysis code block to obtain an analysis result, wherein the target data table is obtained by associating multiple data tables; The query code block is generated in the following manner: Determining an aggregation relationship between a data indicator in the data table and target data in the source data, wherein the target data includes data to be analyzed for obtaining an indicator value of the data indicator; Determining an indexing method for each of the plurality of source data sub-tables based on respective data dimensions of the plurality of source data sub-tables, wherein the source data includes the plurality of source data sub-tables; and Based on the respective indexing modes of the plurality of source data sub-tables and the aggregation relationship, a query code block having a mapping relationship with the data indicators in the data table is generated.

2. The method according to claim 1, wherein The source data is processed separately using multiple query code blocks to obtain multiple data tables, including: Determining a target query code block that matches the data indicator in the data table from the plurality of query code blocks, wherein a mapping relationship exists between the query code block and the data indicator in the data table; and The target query code block is used to perform data processing on the source data to obtain the data table.

3. The method according to claim 1, further comprising: Processing the test source data using the query code block to be tested to obtain a test data table, wherein the query code block to be tested includes at least one of the plurality of query code blocks; and Based on the test data table and the reference data table, a test result of the query code block to be tested is determined, wherein the reference data table is a result obtained by processing the test source data using a predetermined code set, and the predetermined code set includes a code for analyzing the source data to obtain the analysis result.

4. The method according to any one of claims 1 to 3, wherein The analysis code blocks include a plurality of blocks; the analysis results include an analysis result table; The target data table is analyzed using the analysis code block to obtain analysis results, including: Determining a target analysis code block that matches the result indicator in the analysis result table from the plurality of analysis code blocks, wherein a mapping relationship exists between the analysis code blocks and the result indicator in the analysis result table; Using the target analysis code block, aggregate the indicator values ​​of at least one indicator to be analyzed in the target data table to obtain the indicator value of the result indicator; and According to the predetermined dimension of the analysis result table, the indicator value of the result indicator is filled into the predetermined indicator field of the analysis result table.

5. The method according to claim 4, wherein The step of using the target analysis code block to aggregate the indicator values ​​of at least one indicator to be analyzed in the target data table to obtain the indicator value of the result indicator includes: Extracting the indicator value of at least one of the indicators to be analyzed from the target data table using a query code statement in the target analysis code block, wherein the query code statement is determined according to an indexing method of the target data table; and The aggregation code statement in the target analysis code block is used to perform aggregation processing on the indicator value of at least one of the indicators to be analyzed to obtain the indicator value of the result indicator.

6. The method according to any one of claims 1 to 3, further comprising: In response to a request for adding an analysis dimension to the analysis result, determining a target result indicator of the added analysis dimension and an indicator to be analyzed associated with the target result indicator, wherein the indicator to be analyzed is an indicator to be analyzed in the target data table; and Based on the target result indicator, the indicator to be analyzed, and the aggregation relationship between the target result indicator and the indicator to be analyzed, an analysis code block matching the target result indicator is generated.

7. A data processing device comprising: a processing module, configured to process source data using different query code blocks to obtain multiple data tables, wherein the data indicators of the multiple data tables are different, and the source data includes interaction behavior data; and An analysis module, configured to analyze a target data table using an analysis code block to obtain an analysis result, wherein the target data table is obtained by associating a plurality of the data tables; Wherein, the source data includes multiple source data sub-tables; The query code block is generated by the following modules of the device: an aggregation determination module, configured to determine an aggregation relationship between a data indicator in the data table and target data in the source data, wherein the target data includes data to be analyzed for obtaining an indicator value of the data indicator; an index determination module, configured to determine an index mode for each of the plurality of source data sub-tables based on the data dimensions of each of the plurality of source data sub-tables; and A generation module is used to generate a query code block that has a mapping relationship with the data indicators in the data table based on the respective indexing methods of the multiple source data sub-tables and the aggregation relationship.

8. The device according to claim 7, wherein The processing module includes: a query matching submodule, configured to determine a target query code block that matches a data indicator in the data table from the plurality of query code blocks, wherein a mapping relationship exists between the query code blocks and the data indicators in the data table; and The processing submodule is used to process the source data using the target query code block to obtain the data table.

9. The apparatus according to claim 7, further comprising: a testing module, configured to process test source data using a query code block to be tested to obtain a test data table, wherein the query code block to be tested includes at least one of the plurality of query code blocks; and A verification module is used to determine the test result of the query code block to be tested based on the test data table and the reference data table, wherein the reference data table is the result obtained by processing the test source data using a predetermined code set, and the predetermined code set includes a code for analyzing the source data to obtain the analysis result.

10. The device according to any one of claims 7 to 9, wherein The analysis code blocks include a plurality of blocks; the analysis results include an analysis result table; The analysis module includes: an analysis matching submodule, configured to determine, from the plurality of analysis code blocks, a target analysis code block that matches a result indicator in the analysis result table, wherein a mapping relationship exists between the analysis code blocks and the result indicators in the analysis result table; an aggregation submodule, configured to aggregate the indicator values ​​of at least one indicator to be analyzed in the target data table using the target analysis code block to obtain an indicator value of a result indicator; and The filling submodule is used to fill the indicator value of the result indicator into the predetermined indicator field of the analysis result table according to the predetermined dimension of the analysis result table.

11. The device according to claim 10, wherein The aggregation submodule includes: an extraction unit, configured to extract the indicator value of at least one of the indicators to be analyzed from the target data table using a query code statement in the target analysis code block, wherein the query code statement is determined according to an indexing method of the target data table; and The aggregation unit is used to aggregate the indicator value of at least one of the indicators to be analyzed using the aggregation code statement in the target analysis code block to obtain the indicator value of the result indicator.

12. The apparatus according to any one of claims 7 to 9, further comprising: a response module, configured to, in response to a request for adding an analysis dimension to the analysis result table, determine a target result indicator of the added analysis dimension and an indicator to be analyzed associated with the target result indicator, wherein the indicator to be analyzed is an indicator to be analyzed in the target data table; and A new module is added, which is used to generate an analysis code block matching the target result indicator based on the target result indicator, the indicator to be analyzed, and the aggregation relationship between the target result indicator and the indicator to be analyzed.

13. An electronic device comprising: at least one processor; as well as a memory communicatively connected to the at least one processor; wherein, The memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to perform the method according to any one of claims 1 to 6.

14. A non-transitory computer-readable storage medium storing computer instructions, wherein: The computer instructions are used to cause the computer to execute the method according to any one of claims 1 to 6.

15. A computer program product comprising a computer program, which, when executed by a processor, implements the method according to any one of claims 1 to 6.

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