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

Abstract

The invention provides a data processing method and device, electronic equipment, a storage medium and a program product, and relates to the technical field of big data, in particular to the technical fields of data statistics, analysis, experiments, computer technologies and the like. According to the specific implementation scheme, a plurality of different query code blocks are used for processing source data, a plurality of data tables are obtained, data indexes of the data tables are different, and the source data comprise interaction behavior data. And analyzing the target data table by utilizing the analysis code block to obtain an analysis result. The target data table is obtained by associating a plurality of data tables.

Description

Technical Field

[0001] The present disclosure relates to the field of big data technology, in particular to data statistics, analysis, experiment, 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, it is no longer an insurmountable difficulty to reconstruct and deeply analyze massive amounts of data. By using computer data processing software or platforms, data analysis and visualization processing are combined to analyze data in real time and display it intuitively, thereby realizing the integration and analysis of massive data and providing a basis for corporate decision-making so as to make wise business strategies. How to analyze quickly and effectively has become a research focus. Summary of the invention

[0003] The present disclosure provides a data processing method, device, 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 plurality of data tables have different data indicators, and the 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 target data table is obtained by associating the plurality of data tables.

[0005] According to another aspect of the present disclosure, a data processing device is provided, including: a processing module, used to use 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 source data includes interactive behavior data; and an analysis module, used to use the analysis code block to analyze the target data table to obtain an analysis result, wherein the 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 a 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, including a computer program, and when the computer program is executed by a processor, the method of the present disclosure is implemented.

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

[0010] The accompanying drawings are used to better understand the present solution and do not constitute a limitation of the present disclosure.

[0011] Figure 1 An exemplary system architecture to which the data processing method and device according to an embodiment of the present disclosure can be applied is schematically shown;

[0012] Figure 2 A flowchart schematically shows a data processing method according to an embodiment of the present disclosure;

[0013] Figure 3 A schematic diagram of a process flow of multiple data tables according to a related embodiment is schematically shown;

[0014] Figure 4A The flowchart of the data processing method according to the embodiment of the present disclosure is schematically shown;

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

[0016] Figure 5 A schematic diagram schematically shows 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 of an electronic device suitable for implementing a data processing method according to an embodiment of the present disclosure is schematically shown. DETAILED DESCRIPTION

[0019] The following is a description of exemplary embodiments of the present disclosure in conjunction with the accompanying drawings, including various details of the embodiments of the present disclosure to facilitate understanding, which should be considered as merely exemplary. Therefore, it should be recognized by those of ordinary skill in the art 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 real-time or regular collection, observation, analysis and early warning of experimental data indicators during the experiment. Its purpose is to ensure that the experiment is carried out as expected, to detect abnormal situations in the experiment in a timely manner, and to provide data support for experimental decision-making.

[0021] The user base of Internet e-commerce is huge and has diverse needs and behavior patterns. Users of different ages, genders, regions, and consumption habits react differently to the page design, product recommendations, and promotions of e-commerce platforms. For example, young users may prefer to learn about products through short videos, while older users may prefer detailed text descriptions. AB experiments can help e-commerce platforms test different solutions to find the strategy that best meets the needs of various users.

[0022] E-commerce platforms often launch new technologies and features, such as virtual fitting mirrors and live shopping. AB experiments can be used to verify the effectiveness and user acceptance of these new features. For example, in the live shopping experiment, users are divided into two groups, Group A can watch live shopping, and Group B cannot watch. By comparing the engagement (such as viewing time) and purchase conversion rate of the two groups of users, the role of the live shopping function in promoting sales is evaluated, providing a basis for whether to fully promote the function.

[0023] In e-commerce operations, wrong decisions may lead to waste of resources and loss of users. AB experiments provide a data-driven decision-making method, which uses the data collected by experiments to evaluate the pros and cons of different solutions, rather than making decisions based on experience or intuition alone. For example, when deciding whether to reshape the brand image of a platform on a large scale, AB experiments can be used to compare the brand awareness, loyalty and other indicators of users before and after the reshaping (Group A and Group B) to reduce decision-making risks and ensure the scientificity and accuracy of decisions.

[0024] From the above introduction, we can know that by continuously monitoring and analyzing the experimental data, abnormal situations in the experimental process can be discovered in time. Monitoring and analyzing experimental data helps to discover experimental trends in advance, thereby better ensuring the scientificity and accuracy of operational decisions.

[0025] Taking the e-commerce live broadcast recommendation scenario as an example, there are many 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 understanding 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 drainage 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 the user's behavioral preferences and possible problems, such as which pages have a high bounce rate, whether there are cumbersome processes and other factors that lead to user loss.

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

[0030] (ii) 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 discover and resolve application performance issues and improve user satisfaction and loyalty during use.

[0033] 3. Hourly granularity data

[0034] During the e-commerce experiment, data collection, analysis and feedback are almost carried out simultaneously. This monitoring method can capture data changes in a very short time (usually within a few seconds to a few minutes). For large-scale promotional activities on e-commerce platforms, such as "Double 11" and "618", real-time monitoring of traffic data is crucial. In the live broadcasting scene, real-time monitoring of the click-through rate, add-to-cart rate and conversion rate of products can help anchors and operation teams quickly adjust live broadcast strategies. If the click-through rate of a certain product is low, the anchor can immediately change the introduction method or emphasize the product selling points to increase its appeal.

[0035] The technical problems with the data tables collected through the above data sources include at least: there are many data tables, many cross-scenario scenarios but they are scattered in different data tables, and multi-system data fusion is required. The 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 the connection of 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 cross-items between multiple data tables, resulting in strong coupling between SQL code blocks, which are difficult to modify and expand.

[0036] In view of this, an embodiment of the present disclosure provides a data processing method, including: 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 source data includes interactive behavior data. Using an analysis code block to analyze a target data table to obtain an analysis result. The target data table is obtained by associating multiple data tables.

[0037] By having different data indicators in multiple data tables, the multiple data tables are made independent of each other, so that 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 of the present disclosure, 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 of the present disclosure, 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 only examples of system architectures to which the embodiments of the present disclosure can be applied, in order to help those skilled in the art understand the technical content of the present disclosure, but do not mean that the embodiments of the present disclosure cannot be used in other devices, systems, environments or scenarios. For example, in another embodiment, the exemplary system architecture to which the data processing method and apparatus can 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 the 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 to provide a medium for 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, 103 to interact with server 105 through network 104 to receive or send messages, etc. Various communication client applications can be installed on terminal devices 101, 102, 103, such as knowledge reading applications, web browser applications, search applications, instant messaging tools, email clients and / or social platform software, etc. (only as examples).

[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] The server 105 may be a server that provides various services, such as a background management server (only an example) that provides support for the content browsed by the user using the terminal devices 101, 102, and 103. The background management server may analyze and process the received data such as user requests, and feed back the processing results (such as web pages, information, or data obtained or generated according to the user request) 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 set in the terminal device 101, 102, or 103.

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

[0048] For example, the terminal devices 101, 102, 103 may send the acquired source data to the server 105, and the server 105 processes the source data to obtain an analysis result table. Alternatively, a server or server cluster that can communicate with the terminal devices 101, 102, 103 and / or the server 105 analyzes the source data and finally obtains an analysis result table.

[0049] It should be understood that Figure 1 The number of terminal devices, networks and servers in the embodiment is only for illustration. Any number of terminal devices, networks and servers may be provided according to implementation requirements.

[0050] It should be noted that the sequence numbers of the operations in the following method are only used as representations of the operations for the purpose of description, and should not be regarded as representing the execution order of the operations. Unless explicitly stated, the method does not need to be executed completely 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 are in compliance 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 respectively using a plurality of different query code blocks to obtain a plurality of data tables, wherein the data indexes of the plurality of data tables are different.

[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] The query code block may refer to a code block of a structured query statement type, which is used to automatically process source data to obtain a data table. The data indicators of the multiple data tables are different, so the multiple data tables are independent of each other. The index value of the data index in each data table can be data directly extracted from the source data, but it is not limited to this, and can also be an index value obtained by processing the data extracted from the source data. As long as the index value of the corresponding data index is obtained by processing the source data using the query code block, it will be fine.

[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 behavior data. The interactive behavior data is analyzed to obtain the analysis results, which 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] It is possible to use a query code block to process the source data to obtain a data table, and establish a mapping relationship between the query code block and the data table. However, it is not limited to this. It is also possible that multiple query code blocks jointly process the source data to obtain a data table. As long as the multiple data tables are independent of each other, the running codes in the query code blocks are decoupled from each other. Therefore, while the data table changes with the business dimension, the query code block can also be changed independently, thereby improving the efficiency of updating the configuration.

[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 by using the analysis code block to obtain the analysis result table. The analysis code block may include an aggregation code statement, and the index value of the to-be-analyzed index in the target data table is processed by the aggregation code to obtain the index value of the result index in the analysis result table.

[0061] Aggregate code statements are used to perform aggregate processing, which includes but is not limited to: calculation processing, comparison processing, logic processing, and connection processing. Calculation processing includes, for example, addition, subtraction, multiplication, and division. Comparison processing includes equal, greater than, less than, and other processing. Logic processing includes and, or, not, and other processing. Connection processing includes processing to splice multiple data.

[0062] The target data table is analyzed using the analysis code block to obtain the analysis results, which can clearly display the results of the statistical analysis and improve the evaluation of the interactive behavior data of the source data. In addition, the analysis code block and multiple query code blocks are split from each other to reduce the coupling between multiple code blocks, thereby improving the flexible maintainability of 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 reasonable, which makes it easy to modify and expand subsequent code blocks. For example, if you need to add a new screening condition or count a new result indicator in the result analysis, you can easily find a suitable code location in the analysis code block to modify it without affecting the running logic of other parts of the 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, and the following describes how to obtain the data table in detail.

[0065] According to the embodiments of the present disclosure, Figure 2 The operation S210 shown in FIG. 210 : using different multiple query code blocks to process the source data respectively to obtain multiple data tables may include: determining a target query code block that matches the data index in the data table from the multiple query code blocks. There is a mapping relationship between the query code block and the data index in the data table. Using the target query code block to process the source data to obtain the data table.

[0066] For example, the plurality of data tables include data table A, data table B, and data table C. There is a mapping relationship between the data table and the query code block. 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, i.e., query code block A, can be determined from query code block A, query code block C, and query code block D. The source data is processed using query code block A to obtain data table A. Similarly, the source data can be processed using query code block B that matches data table B to obtain data table B. The source data is processed using query code block C that matches data table C 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 may be joined to obtain a target data table. For example, data in multiple data tables may be summarized using association codes, such as joining multiple data tables to obtain a target data table. Data in multiple data tables may also be filled into a predetermined data table to obtain a target data table. Any method that can associate data in multiple data tables to obtain a target data table may be used.

[0071] The following two examples will explain in detail how to determine multiple data tables.

[0072] In one example, based on the result indicator in the analysis result table, the indicator to be analyzed for generating the indicator value of the result indicator can be determined. Based on the indicator to be analyzed, the data indicator for generating the target data table can be determined. Based on the data indicator of the target data table, multiple data tables for splicing the target data table can be determined. The source data is processed using multiple query code blocks 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, a data index of a target data table may be predetermined. Based on the predetermined data index of the target data table, multiple data tables for splicing 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 can be avoided, thereby reducing subsequent maintenance costs.

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

[0077] The data indicators of the 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, and there is no need to couple 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 Box contents.

[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 explains how to determine multiple data tables and generate a target data table using the multiple data tables. The following will explain 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. Based on the data dimensions of each of the multiple source data sub-tables, determining an indexing method for each of the multiple source data sub-tables. Based on the indexing methods and aggregation relationships of each of the multiple source data sub-tables, generating a query code block that has a mapping relationship with the data indicator in the data table.

[0085] The target data includes data to be analyzed for obtaining an indicator value of a data indicator.

[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, the query code statements can be determined.

[0088] Based on the aggregation relationship between the data indicator and the target data, an aggregation processing method for performing aggregation processing on the target data can be determined. Based on the aggregation processing method, an aggregation code statement is determined.

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

[0090] By using the above method to generate a query code block that matches the data indicators in the data table, the generation efficiency of the query code block can be improved while improving the effectiveness and comprehensiveness of the query code block. In addition, the query code block includes a query code statement with data reading and an aggregation code statement with aggregation capabilities, thereby improving the comprehensiveness of the query code block and further improving the 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 formats, comment standards, etc. This can improve the readability of the code.

[0092] According to an embodiment of the present disclosure, the data processing method may further include: testing and verifying the query code block. When the test and verification result indicates that the query code block is correct, the query code block is used to process the source data. When the test and verification result indicates that the query code block has an error, the query code block is optimized.

[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 ensure the functional correctness of the code through corrections.

[0095] According to an embodiment of the present disclosure, the operation of testing and verifying the query code block may include: using the query code block to be tested to perform data processing on the test source data to obtain a test data table. Based on the test data table and the reference data table, the test result of the query code block to be tested is determined. 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 an analysis result.

[0096] The query code block to be tested includes at least one of the multiple query code blocks. However, it is not limited thereto. 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 may be sufficient.

[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 judged to be accurate or not may be used as the test source data.

[0098] In one example, the predetermined code set may be a code set of 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] Based on the test data table and the reference data table, determining the test result of the query code block to be tested may include: the parameter data table may be used as the correct data table. The test data table obtained by using the query code to be tested is used as the test table to be judged. When the results of the reference data table and the test data table are consistent, it is determined that the test result indicates that the query code block to be tested is correct. When the results of the reference data table and the test data table are inconsistent, it is determined that the test result indicates that there is an error in the query code block to be tested. 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 code that directly processes the source data to obtain the analysis result 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 is 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 spliced ​​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 A flowchart of a data processing method according to a 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. The first source data sub-table 401, the second source data sub-table 402 and the third source data sub-table 403 can be processed and analyzed 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 code blocks and improve the structural clarity of 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 a plurality of 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. There is a mapping relationship 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 may be set, each of which is used to obtain an 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, a mapping relationship table is established. Based on the result indicator in the analysis result table, the target analysis code block is determined from the mapping relationship table. The aggregation code in the target analysis code block is used to predict the index value of at least one indicator to be analyzed in the target data table, and the index value of the result indicator is obtained. The index value of the result indicator can be automatically filled into the predetermined index field of the analysis result table to obtain the analysis result table with the index value.

[0112] By setting up multiple analysis code blocks, decoupling between the multiple code blocks can be achieved, and the independence of the multiple code blocks can be improved. This improves the ease of maintenance and flexibility of modification of the code blocks, while at the same time improving the analysis capability and scalability of the 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, using a target analysis code block to aggregate the indicator values ​​of at least one indicator to be analyzed in a target data table to obtain the indicator value of a result indicator may include: using a query code statement in the target analysis code block to extract the indicator value of at least one indicator to be analyzed from the target data table. Using an aggregation code statement in the target analysis code block to aggregate the indicator values ​​of at least one indicator to be analyzed to obtain the indicator value of the result indicator.

[0115] The query code statement is determined according to the index 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 at least one indicator to be analyzed and the result indicator, an aggregation processing method between 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 above describes how to perform data analysis in the analysis code block. The following describes how to add a new analysis code block when a new analysis dimension is added.

[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 a 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 tests can help determine whether experimental data is reliable. During the experiment, data may be interfered by various factors, such as measurement errors, sample bias, changes in the experimental environment, etc. Through confidence tests, the credibility of data can be quantified. For example, in e-commerce, whether different colored fonts will affect user click conversions, the data can be tested for confidence to determine whether these data truly reflect user conversion fluctuations or are caused by unreasonable sample selection or inaccurate measurement. (2) Determine the validity of experimental results: It can determine whether the experimental results are statistically significant. In scientific research and experiments, it is not enough to simply observe differences in data. It is also necessary to determine whether such differences are caused by experimental factors or are just random fluctuations. Confidence tests 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 advertisement 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 in 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, the added analysis code block may include: in response to a request for adding an analysis dimension of an analysis result, determining 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 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 a confidence test dimension, and the target result indicator is a 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] The aggregation method can be determined based on the aggregation relationship between the target result indicator and the indicator to be analyzed. Based on the aggregation method, the target result indicator and the indicator to be analyzed, an aggregation code statement for obtaining the target result indicator is determined.

[0127] Based on the aggregation code statement, an analysis code block matching the target result indicator is generated. However, it is not limited to this. An analysis code block 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 of 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 build 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 used to process the source data using different query code blocks to obtain multiple data tables. The data indicators of the multiple data tables are different. The source data includes interactive 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 a data indicator in a data table from a plurality of query code blocks, wherein a mapping relationship exists between the query code block and the data indicator 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 device 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 index in the data table and the target data in the source data. The target data includes the data to be analyzed for obtaining the index value of the data index.

[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 respective 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 obtained by processing the test source data using a predetermined code set, and the predetermined code set includes codes for analyzing the source data to obtain analysis results.

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

[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 index in the analysis result table from the multiple analysis code blocks. There is a mapping relationship between the analysis code block and the result index in the analysis result table.

[0147] The aggregation submodule is used to use 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.

[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 index value of at least one to-be-analyzed index 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 used to respond to the request for adding an analysis dimension to the analysis result table, determine the target result indicator of the added analysis dimension and the indicator to be analyzed associated with the target result indicator. The indicator to be analyzed is the 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 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 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 as described in an embodiment of the present disclosure.

[0158] According to an embodiment of the present disclosure, a computer program product includes a computer program, and 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 an embodiment 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 processing, cellular phones, smart phones, 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 required herein.

[0160] like Figure 7 As shown, the 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. In the RAM 703, various programs and data required for the operation of the device 700 can also be stored. The computing unit 701, the ROM 702, and the RAM 703 are connected to each other via a bus 704. An input / output (I / O) interface 705 is also connected to the bus 704.

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

[0162] The computing unit 701 may be a variety of general and / or special processing components 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 dedicated artificial intelligence (AI) computing chips, various computing units running machine learning model algorithms, digital signal processors (DSPs), and any appropriate processors, controllers, microcontrollers, etc. The computing unit 701 performs the various methods and processes described above, such as data processing methods. For example, in some embodiments, the data processing method may be implemented as a computer software program, which is tangibly contained in a machine-readable medium, such as a storage unit 708. In some embodiments, part or all of the computer program may be loaded and / or installed on 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 in any other appropriate manner (e.g., by means of firmware).

[0163] Various implementations of the systems and techniques described above herein 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), systems on chips (SOCs), complex programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various implementations can include: being implemented in one or more computer programs that can be executed and / or interpreted on a programmable system including 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 may be written in any combination of one or more programming languages. These program codes may be provided to a processor or controller of a general-purpose computer, a special-purpose computer, or other programmable data processing device, so that the program code, when executed by the processor or controller, enables the functions / operations specified in the flow chart and / or block diagram to be implemented. The program code may be executed entirely on the machine, partially on the machine, partially on the machine and partially on a remote machine as a stand-alone software package, 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, device, or equipment. 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, device, or device, or any suitable combination of the foregoing. A more specific example of a machine-readable storage medium may include an electrical connection based on one or more lines, 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), an optical fiber, 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 may 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 may 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 the server are generally remote from each other and typically interact through a communication network. The relationship of client and server arises through computer programs running on 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 combined with a blockchain.

[0169] It should be understood that the various forms of processes shown above can be used to reorder, add or delete steps. For example, the steps recorded in this disclosure can be executed in parallel, sequentially or in different orders, as long as the desired results of the technical solutions disclosed in this disclosure can be achieved, and this document does not limit this.

[0170] The above specific implementations do not constitute a limitation on the protection scope of the present disclosure. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions can be made according to design requirements and other factors. Any modification, equivalent substitution and improvement made within the spirit and principle of the present disclosure shall be included in the protection scope of the present disclosure.

Claims

1. A data processing method, comprising: Using different query code blocks to process source data respectively to obtain multiple data tables, wherein the data indicators of the multiple data tables are different, and the source data includes interactive behavior data; and The target data table is analyzed by using the analysis code block to obtain an analysis result, wherein the target data table is obtained by associating a plurality of the data tables.

2. The method according to claim 1, wherein: The source data is processed respectively by using different query code blocks to obtain multiple data tables, including: Determine 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 or 2, wherein: The source data includes a plurality of source data sub-tables; The method further comprises: 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; Determining an indexing method 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; 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.

4. The method according to any one of claims 1 to 3, 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, the test result of the query code block to be tested is determined, 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.

5. The method according to any one of claims 1 to 4, wherein: The analysis code blocks include a plurality of blocks; the analysis results include an analysis result table; The target data table is analyzed by using the analysis code block to obtain the analysis result, including: Determine 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 block and the result indicator in the analysis result table; Using the target analysis code block, aggregate the indicator value 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.

6. The method according to claim 5, 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 at least one indicator value of each 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 index value of each of at least one of the indicators to be analyzed is aggregated using the aggregation code statement in the target analysis code block to obtain the index value of the result indicator.

7. The method according to any one of claims 1 to 6, further comprising: In response to a request for increasing the analysis dimension of the analysis result, determining a target result indicator of the increased 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.

8. 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 interactive behavior data; and The analysis module is used to analyze the target data table using the analysis code block to obtain the analysis result, wherein the target data table is obtained by associating multiple data tables.

9. The device according to claim 8, wherein: The processing module comprises: a query matching submodule, configured to determine a target query code block that matches a data indicator in the data table from a plurality of the query code blocks, wherein a mapping relationship exists between the query code block and the data indicator 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.

10. The device according to claim 8 or 9, wherein: The source data includes a plurality of source data sub-tables; The device also includes: An aggregation determination module, used 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, used to determine the index mode of each of the plurality of source data sub-tables based on the data dimensions of each of the plurality of source data sub-tables; A generation module is used to generate a query code block having a mapping relationship with the data indicators in the data table based on the respective indexing modes of the plurality of source data sub-tables and the aggregation relationship.

11. The device according to any one of claims 8 to 10, further comprising: A test module, configured to process 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 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.

12. The device according to any one of claims 8 to 11, wherein: The analysis code blocks include a plurality of blocks; the analysis results include an analysis result table; The analysis module comprises: An analysis matching submodule, used to determine a target analysis code block that matches a result indicator in the analysis result table from a plurality of the analysis code blocks, wherein a mapping relationship exists between the analysis code block and the result indicator in the analysis result table; an aggregation submodule, configured to aggregate the indicator value 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; 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.

13. The device according to claim 12, wherein: The aggregation submodule includes: an extraction unit, configured to extract at least one indicator value of each of the indicators to be analyzed from the target data table by 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.

14. The device according to any one of claims 8 to 13, 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.

15. 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 7.

16. 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 7.

17. 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 7.

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