Data analysis method and device, computer equipment and readable storage medium

Abstract

The invention relates to a data analysis method and device, computer equipment and a readable storage medium. The method comprises the following steps: acquiring a merging result table; the merging result table comprises a historical data analysis result obtained based on the user basic data and the historical transaction detail data; storing the incremental transaction data updated in real time based on the transaction incremental statistical table, and performing data analysis on each piece of incremental transaction data in the transaction incremental statistical table to obtain a real-time data analysis result; updating the real-time data analysis result into a merging result table; the merging result table comprises a total data analysis result; the merging result table is used for querying a full-amount data analysis result. By adopting the method, the timeliness of the data analysis result can be ensured.

Description

Technical Field

[0001] This application relates to the field of big data technology, and in particular, to a data analysis method, apparatus, computer device, computer-readable storage medium, and computer program product. Background Art

[0002] In the big data era, by analyzing the transaction data set of the business system through a data analysis method and obtaining an analysis result, it can help users understand the current business situation, enabling users to optimize the sales strategy according to the analysis result and increase the business volume.

[0003] The current data analysis method batches and obtains the full-volume historical transaction detail data set before the current time and the user information of each user from the business system every day. Then, the computer device analyzes the transaction situation of each user based on the user information and the historical transaction detail data set to obtain a historical data analysis result.

[0004] However, the current data analysis method only performs data analysis based on the historical transaction detail data set, and the obtained historical data analysis result has a lag. Therefore, the timeliness of the current data analysis method is poor. Summary of the Invention

[0005] Based on this, it is necessary to provide a data analysis method, apparatus, computer device, computer-readable storage medium, and computer program product for the above technical problems.

[0006] In a first aspect, this application provides a data analysis method, including:

[0007] Obtain a merged result table; the merged result table contains a historical data analysis result obtained based on user basic data and historical transaction detail data;

[0008] Store real-time updated incremental transaction data based on a transaction increment statistical table, and perform data analysis on each incremental transaction data in the transaction increment statistical table to obtain a real-time data analysis result;

[0009] Update the real-time data analysis result to the merged result table; the merged result table contains a full-volume data analysis result; the merged result table is used to query the full-volume data analysis result.

[0010] In one of the embodiments, the obtaining the merged result table includes:

[0011] Batch collect the user basic data of each user and the historical transaction detail data of the previous day before the current time from the business system according to a preset time period;

[0012] Perform data analysis on each of the user basic data and historical transaction detail data to obtain historical data analysis results, and store the historical data analysis results based on the merged result table.

[0013] In one embodiment, the performing data analysis on each of the user basic data and historical transaction detail data to obtain historical data analysis results, and storing the historical data analysis results based on the merged result table includes:

[0014] Preprocess each of the user basic data to obtain a user label data table, and preprocess the historical transaction detail data to obtain a product transaction basic data table;

[0015] According to the analysis algorithms corresponding to each analysis index, perform data analysis on the user label data table and the product transaction basic data table to obtain each historical data analysis result of the day before the current time;

[0016] Based on each of the analysis indexes, store each of the historical data analysis results into the merged calculation result table.

[0017] In one embodiment, after storing each of the historical data analysis results into the merged calculation result table based on each of the analysis indexes, the method further includes:

[0018] Format the data in the transaction increment statistical table and format the real-time result table;

[0019] Clear the data in the user label data table and the product transaction basic data table.

[0020] In one embodiment, the storing the increment transaction data of real-time update based on the transaction increment statistical table includes:

[0021] When the transaction data of the business system is updated, collect each initial increment transaction data of real-time update, and store each of the initial increment transaction data into the message queue;

[0022] Read the initial increment transaction data from the message queue;

[0023] Preprocess the initial increment transaction data to obtain increment transaction data, and update the increment transaction data to the transaction increment statistical table.

[0024] In one embodiment, after updating the real-time data analysis result to the merged result table, the method further includes:

[0025] Construct a target query request based on the query template;

[0026] In response to the target query request, determine the target query result corresponding to the target query request in the merged result table;

[0027] According to the analysis designer, display the target query result.

[0028] In one embodiment, the target query request includes a target query period and a target analysis metric; the step of determining the target query result corresponding to the target query request in the merged result table in response to the target query request includes:

[0029] Determine the initial target data analysis results corresponding to the target analysis metric in the merged result table, and screen the target data analysis results within the target query period from the initial target data analysis results;

[0030] Perform a summation process or an averaging process on each of the target data analysis results to obtain a processed result;

[0031] Construct a target query result based on each of the target data analysis results and the processed result.

[0032] In a second aspect, the present application further provides a data analysis device, including:

[0033] An acquisition module, configured to acquire a merged result table; the merged result table contains historical data analysis results obtained based on user basic data and historical transaction detail data;

[0034] A storage module, configured to store real-time updated incremental transaction data based on a transaction increment statistical table, and perform data analysis on each incremental transaction data in the transaction increment statistical table to obtain real-time data analysis results;

[0035] An update module, configured to update the real-time data analysis results to the merged result table; the merged result table contains full-scale data analysis results; the merged result table is used to query the full-scale data analysis results.

[0036] In a third aspect, the present application further provides a computer device, including a memory and a processor, where the memory stores a computer program, and when the processor executes the computer program, the following steps are implemented:

[0037] Acquire a merged result table; the merged result table contains historical data analysis results obtained based on user basic data and historical transaction detail data;

[0038] Store real-time updated incremental transaction data based on a transaction increment statistical table, and perform data analysis on each incremental transaction data in the transaction increment statistical table to obtain real-time data analysis results;

[0039] Update the real-time data analysis result to the merged result table; the merged result table contains the full-scale data analysis result; the merged result table is used to query the full-scale data analysis result.

[0040] In a fourth aspect, the present application further provides a computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, the following steps are implemented:

[0041] Obtain a merged result table; the merged result table contains a historical data analysis result obtained based on user basic data and historical transaction detail data;

[0042] Store real-time updated incremental transaction data based on a transaction increment statistical table, and perform data analysis on each incremental transaction data in the transaction increment statistical table to obtain a real-time data analysis result;

[0043] Update the real-time data analysis result to the merged result table; the merged result table contains the full-scale data analysis result; the merged result table is used to query the full-scale data analysis result.

[0044] In a fifth aspect, the present application further provides a computer program product, including a computer program, and when the computer program is executed by a processor, the following steps are implemented:

[0045] Obtain a merged result table; the merged result table contains a historical data analysis result obtained based on user basic data and historical transaction detail data;

[0046] Store real-time updated incremental transaction data based on a transaction increment statistical table, and perform data analysis on each incremental transaction data in the transaction increment statistical table to obtain a real-time data analysis result;

[0047] Update the real-time data analysis result to the merged result table; the merged result table contains the full-scale data analysis result; the merged result table is used to query the full-scale data analysis result.

[0048] The above data analysis method, device, computer device, computer-readable storage medium, and computer program product obtain a merged result table; the merged result table contains historical data analysis results obtained based on user basic data and historical transaction detail data; store real-time updated incremental transaction data based on a transaction increment statistical table, and perform data analysis on each incremental transaction data in the transaction increment statistical table to obtain real-time data analysis results; update the real-time data analysis results to the merged result table; the merged result table contains full-volume data analysis results; the merged result table is used to query the full-volume data analysis results. By using this method, real-time updated incremental transaction data is stored in the transaction increment statistical table, and real-time data analysis results are determined based on the real-time incremental transaction data, making the data analysis results real-time. Updating the real-time data analysis results to the merged result table makes the updated merged result table have data analysis results for the entire period, avoiding the timeliness of the data analysis results and enhancing the timeliness of the merged result table. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] In order to more clearly illustrate the technical solutions in the embodiments of the present application or related technologies, the following will briefly introduce the drawings required for use in the description of the embodiments of the present application or related technologies. Obviously, the drawings in the following description are only some embodiments of the present application. For those of ordinary skill in the art, other related drawings can be obtained based on these drawings without creative efforts.

[0050] Figure 1 It is a schematic flowchart of the data analysis method in an embodiment;

[0051] Figure 2 It is a schematic flowchart of obtaining the merged result table in an embodiment;

[0052] Figure 3 It is a schematic flowchart of analyzing user basic data and historical transaction detail data in an embodiment;

[0053] Figure 4 It is a schematic flowchart of clearing the transaction increment statistical table in an embodiment;

[0054] Figure 5 It is a schematic flowchart of storing incremental transaction data based on the transaction increment statistical table in an embodiment;

[0055] Figure 6 It is a schematic flowchart of displaying the target query result in an embodiment;

[0056] Figure 7 It is a schematic diagram of the display interface of the query template in an exemplary embodiment;

[0057] Figure 8Schematic diagram of the display interface of the query template in another exemplary embodiment;

[0058] Figure 9 Display interface diagram of the query template in one embodiment;

[0059] Figure 10 Schematic diagram showing the target query result in the form of a line chart in one exemplary embodiment;

[0060] Figure 11 Schematic diagram showing the target query result in the form of a bar chart in one exemplary embodiment;

[0061] Figure 12 Schematic diagram showing the target query result in the form of a table in one exemplary embodiment;

[0062] Figure 13 Flow schematic diagram for determining the target query result in the merged result table in one embodiment;

[0063] Figure 14 Schematic diagram of the architecture of the data analysis method in one exemplary embodiment;

[0064] Figure 15 Structural block diagram of the data analysis device in one embodiment;

[0065] Figure 16 Internal structure diagram of a computer device in one embodiment. Detailed implementation manners

[0066] In order to make the objectives, technical solutions and advantages of the present application clearer and more understandable, the present application will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0067] In the big data era, by analyzing the transaction data set of the business system through the data analysis method to obtain the analysis result, it can help users understand the current business situation, so that users can optimize the sales strategy according to the analysis result and increase the business volume.

[0068] The current data analysis method batch obtains the full historical transaction detail data set before the current time and the user information of each user from the business system every day. Then, the computer device analyzes the transaction situation of each user from the user perspective based on the user information and the historical transaction detail data set to obtain the historical data analysis result. The historical data analysis result is realized through statistical reports or charts.

[0069] However, the current data analysis method only performs data analysis based on the historical transaction details dataset, and the obtained historical data analysis results have lag. Therefore, the timeliness of the current data analysis method is poor.

[0070] Moreover, the flexibility of the historical data analysis results displayed is poor, and the historical data analysis results cannot be screened from various dimensions.

[0071] The data analysis method provided by the embodiments of the present application stores the increment transaction data updated in real time through the transaction increment statistical table, and determines the real-time data analysis results based on the real-time increment transaction data, so that the data analysis results have real-time nature. The real-time data analysis results are updated to the merged result table, so that the updated merged result table has the data analysis results for the entire time period, making the timeliness of the merged result table better.

[0072] In addition, the data analysis method provided by the embodiments of the present application analyzes the historical transaction details data and the increment transaction data through the analysis algorithms corresponding to each analysis index, and obtains the data analysis results corresponding to the full amount of analysis indexes, providing a data basis for screening the target query results to be presented subsequently, and improving the flexibility of data display.

[0073] In one embodiment, as Figure 1 shown, a data analysis method is provided. The embodiments of the present application take the application of this method to a computer device as an example for illustration. The embodiments of the present application do not limit the execution device of the data analysis method, and include the following steps 102 to step 106:

[0074] Step 102, obtain the merged result table.

[0075] Among them, the merged result table contains the historical data analysis results obtained based on the user basic data and the historical transaction details data.

[0076] In implementation, a time period is preset in the computer device. The computer device batch collects the user basic data of all users and the historical transaction details data of the day before the current time from the business system according to the preset time period. Then, the computer device determines each historical data analysis result based on each user basic data and the historical transaction details data, and stores each historical data analysis result in the merged result table.

[0077] Specifically, the merged result table contains the historical data analysis results for the two days before the current time. The computer device batch-collects the user basic data of all users and the historical transaction detail data for the day before the current time from each subsystem of the business system according to the time period. Then, the computer device performs data analysis on each user basic data and historical transaction detail data based on each analysis metric to obtain each historical data analysis result. Then, the computer device updates each historical data analysis result to the merged result table according to each analysis metric.

[0078] Step 104: Store the increment transaction data updated in real time based on the transaction increment statistical table, and perform data analysis on each increment transaction data in the transaction increment statistical table to obtain the real-time data analysis result.

[0079] In implementation, the computer device collects each increment transaction data updated in real time in the business system through the message queue. Then, the computer device updates each increment transaction data to the transaction increment statistical table. Then, the computer device performs data analysis on each increment transaction data in the transaction increment statistical table according to the analysis algorithms corresponding to each analysis metric to obtain each real-time data analysis result.

[0080] Specifically, each increment transaction data updated in real time in the business system is pre-set in the computer device and stored in the message queue. Then, the computer device reads the increment transaction data from the message queue and stores the increment transaction data in the transaction increment statistical table. The computer device calls the data analysis interfaces corresponding to each analysis metric. Then, the computer device calls each analysis algorithm through each data analysis interface and analyzes and processes each increment transaction data through each analysis algorithm to obtain the real-time data analysis result corresponding to the analysis algorithm.

[0081] In an exemplary embodiment, the increment transaction data includes the object, time, amount, product name, and product type of the product transaction. The analysis metric is the total transaction volume. The computer device performs statistical processing on all the increment transaction data in the transaction increment statistical table according to the total volume statistical algorithm corresponding to the total transaction volume, respectively by product type, product name, and transaction object, to obtain the real-time total transaction volume of each product type, the real-time total transaction volume of each product, the real-time total transaction volume of each transaction object, and the comprehensive real-time total transaction volume.

[0082] Step 106: Update the real-time data analysis result to the merged result table.

[0083] Among them, the merged result table contains the full-volume data analysis result; the merged result table is used to query the full-volume data analysis result.

[0084] In implementation, the computer device updates each real-time data analysis result to the merged result table according to each analysis index, so that the merged result table contains the real-time data analysis result at the current time.

[0085] Specifically, each real-time data analysis result corresponds to each analysis index. The computer device queries in the merged result table whether there is a historical real-time data analysis result corresponding to the analysis index for each analysis index. If there is a historical real-time data analysis result, the computer device replaces the historical real-time data analysis result based on the real-time data analysis result corresponding to the analysis index. If there is no historical real-time data analysis result, the computer device stores the real-time data analysis result corresponding to the analysis index in the merged result table.

[0086] In an exemplary embodiment, taking the analysis index of total transaction volume as an example, the computer device determines whether there are historical real-time total transaction volumes of each product type, historical real-time total transaction volumes of each product, historical real-time total transaction volumes of each trading object, and historical comprehensive real-time total transaction volume corresponding to the total transaction volume in the merged data table. For example, if there is a historical comprehensive real-time total transaction volume in the merged result table, the computer device deletes the historical comprehensive real-time total transaction volume and adds the comprehensive real-time total transaction volume.

[0087] In another exemplary embodiment, a data merging and calculating module is provided in the computer device. The computer device associates the historical data analysis result and the real-time data analysis result corresponding to each analysis index according to each analysis index, and performs a merging calculation on the historical data analysis result and the real-time data analysis result corresponding to the analysis index to obtain a merged calculation result. Then, the computer device stores the real-time data analysis result and the merged calculation result in the merged calculation result table. The data analysis result and the merged calculation result data in the merged calculation result table are used as the data finally provided for application query and analysis.

[0088] In the above data analysis method, the incremental transaction data updated in real time is stored in the transaction increment statistical table, and the real-time data analysis result is determined based on the real-time incremental transaction data, so that the data analysis result has real-time performance. Updating the real-time data analysis result to the merged result table makes the updated merged result table have the data analysis result for the entire time period, avoids the timeliness of the data analysis result, and enhances the timeliness of the merged result table.

[0089] In an exemplary embodiment, as Figure 2 shown, the specific processing process of step 102 includes steps 202 to 204. Among them:

[0090] Step 202, batch collect the user basic data of each user and the historical transaction details data of the day before the current time from the business system according to a preset time period.

[0091] In implementation, a time period is preset in the computer device. According to the collection time, the computer device batch-collects the user basic data of all users and the historical transaction detail data of the day before the current time from the business system through a batch collection tool. The user basic data includes basic data such as the user name, user identification, and user-associated products of the user. The historical transaction detail data includes the historical transaction detail data from two days before the current time to the day before the current time.

[0092] In an exemplary embodiment, the time period is one day. If the current time is 00:00:01 on March 12, 2025, then the day before the current time is from 00:00:01 on March 11, 2025 to 00:00:00 on March 12, 2025. The computer device collects the user basic data of all users of the day before the current time from the business system through a batch collection tool. At the same time, the computer device collects the historical transaction detail data of the day before the current time from the business system. Among them, if the business system is a bank transaction system, then the bank transaction system includes systems such as a core system, a credit system, and a channel-end system.

[0093] In an alternative embodiment, if there is historical transaction data in the business system and the merged result table does not contain the historical data analysis results corresponding to the historical transaction data, then the computer device obtains the full amount of historical transaction detail data up to the current time and the user basic data of all users from the business system. The historical transaction detail data includes data such as the products of the transactions, product types, transaction times, and transaction objects.

[0094] Optionally, the time period is generally one day. The time period set by the computer device is determined according to the updated data volume of the transaction detail data in the business system. The embodiments of the present application do not limit the time period.

[0095] Step 204, perform data analysis on each user basic data and historical transaction detail data to obtain historical data analysis results, and store the historical data analysis results based on the merged result table.

[0096] In implementation, the computer device preprocesses each user basic data and each historical transaction detail data. Then, the computer device stores the preprocessed user basic data in the user label data table and stores the preprocessed historical transaction detail data in the product transaction basic data table. The computer device uses various analysis algorithms to perform data analysis on the product transaction data basic table and the user label data table to obtain historical data analysis results, and stores the historical data analysis results in the merged result table.

[0097] Specifically, each analysis index is preset in the computer device, and each analysis index corresponds to an analysis algorithm. The computer device performs data analysis on the product transaction data base table or the combination of the product transaction data base and the user label data table for each analysis index to obtain various historical data analysis results. Then, the computer device adds the various historical data analysis results to the merged result table.

[0098] In this embodiment, by collecting the historical transaction detail data of the day before the current time and performing data analysis on the historical data transaction detail data, historical data analysis results are obtained. Compared with obtaining all historical transaction data before the current time, the amount of data analysis is reduced, and the efficiency of the data analysis method is improved.

[0099] In an exemplary embodiment, as Figure 3 shown, the specific processing process of step 204 includes steps 302 to 306. Among them:

[0100] Step 302, preprocess each user's basic data to obtain a user label data table, and preprocess the historical transaction detail data to obtain a product transaction base data table.

[0101] In implementation, the computer device preprocesses each user's basic data according to the format of the user label data in the user label data table to obtain each user label data. Then, the computer device stores each user label data in the user label data table. At the same time, the computer device preprocesses each historical transaction detail data according to the format of the historical transaction data in the product transaction base data table to obtain each historical transaction data. Then, the computer device stores each historical transaction data in the product transaction base data table.

[0102] Specifically, a user label data table and a product transaction basic data table are preset in the computer device. The data in the user label data table and the product transaction basic data table is empty. The user label data table is used to store each user label data. Therefore, the format of the user label data is stored in the user label data table. The product transaction basic data table is used to store historical transaction data. Therefore, the format of the historical transaction data is stored in the product transaction basic data table. For each user basic data, the computer device extracts initial user label data from the user basic data, and performs data cleaning and format conversion on the initial user label data according to the format of the user label data to obtain user label data. Then, the computer device stores each user label data in the user label data table. At the same time, for each historical transaction detail data, the computer device extracts initial historical transaction data from the historical transaction detail data, and performs data cleaning and format conversion on the initial historical transaction data according to the format of the historical transaction data to obtain historical transaction data. Then, the computer device stores each historical transaction data in the product transaction basic data table.

[0103] In an exemplary embodiment, taking a user of a bank as an example, the user basic data includes the user's name, user identification, user gender, user contact information, user account identification, and user type. The user label data in the user label data table only includes the user's name, user identification, and user type. The computer device extracts the user's name, user identification, and user type from the user basic data of each user according to the user label data table to obtain initial user label data. The computer device performs data cleaning and format conversion on the initial user label data according to the format of the user label data to obtain user label data. Then, the computer device stores each user label data in the user label data table. Taking the historical transaction detail data as the data generated by the user's purchase of products in the bank as an example, the historical transaction detail data includes information such as the transaction object, transaction product, product type, transaction type, transaction amount, transaction time, merchant information, transaction quantity, payment method, payment status, order number, order status, and logistics status information. The historical transaction data includes the transaction object, transaction product, product type, transaction amount, transaction time, and merchant information. The computer device extracts the transaction object, transaction product, product type, transaction amount, transaction time, and merchant information from each historical transaction detail data according to the product transaction basic data table to obtain initial historical transaction data. The computer device performs data cleaning and format conversion on the initial historical transaction data according to the format of the historical transaction data to obtain historical transaction data. Then, the computer device stores each historical transaction data in the user label data table.

[0104] Optionally, the data included in the historical transaction data and user tag data is determined according to the analysis requirements, and the embodiments of the present application do not limit the historical transaction data and user tag data.

[0105] Step 304: According to the analysis algorithms corresponding to the respective analysis indicators, perform data analysis on the user tag data table and the product transaction basic data table to obtain the historical data analysis results of the day before the current time.

[0106] In implementation, the computer device calls the data analysis interfaces corresponding to the respective analysis indicators. Then, the computer device, through each data analysis interface, calls each analysis algorithm, and performs analysis processing on the product transaction basic data table or the product transaction basic data table and the user tag data table through each analysis algorithm to obtain the historical data analysis result corresponding to the analysis algorithm.

[0107] In an exemplary embodiment, the historical transaction data in the product transaction basic data table includes the transaction object, transaction product, product type, transaction amount, transaction time, and merchant information. The analysis indicator is the total transaction volume. The computer device, through the total volume statistical algorithm corresponding to the total transaction volume, performs statistical processing on the user tag table and the product basic transaction data table according to the product type, product name, and transaction object to obtain the historical transaction total volume of each product type, the historical transaction total volume of each product, the historical transaction total volume of each transaction object, and the comprehensive historical transaction total volume.

[0108] Optionally, the analysis indicators are determined according to the analysis requirements, and the embodiments of the present application do not limit the analysis indicators.

[0109] Step 306: Based on the respective analysis indicators, store the historical data analysis results in the combined calculation result table.

[0110] Among them, the combined result table stores the analysis data results by each time period and each analysis indicator.

[0111] In implementation, the computer device stores the historical data analysis results corresponding to each analysis indicator in the combined calculation result table according to each analysis indicator and the day before the current moment.

[0112] Specifically, the computer device uses the day before the current time as the analysis period for the historical data analysis results, and stores the historical data analysis results in the combined calculation result table according to each analysis indicator and the analysis period.

[0113] In this embodiment, data analysis is performed on the historical transaction detail data through the analysis algorithms corresponding to the respective analysis indicators to obtain the historical data analysis results, and the historical data analysis results are stored in the combined result table, providing a data basis for subsequent data queries.

[0114] In an exemplary embodiment, when storing the historical data analysis results into the consolidated calculation result table, it is also necessary to clean the data in each table. As Figure 4 shown, after step 306 is executed, the specific processing procedure of this data analysis method further includes steps 402 to 404. Among them:

[0115] Step 402, format the data in the transaction increment statistical table and format the real-time result table.

[0116] Among them, the real-time result table is used to store the real-time data analysis results.

[0117] In implementation, the computer device clears the incremental transaction data in the transaction increment statistical table and clears the real-time data analysis results in the real-time result table.

[0118] Specifically, the computer device deletes the full incremental transaction data in the transaction increment statistical table. At the same time, the computer device deletes the full real-time data analysis results in the real-time result table. Then, the computer device clears the initial incremental transaction data before the current time in the message queue.

[0119] Step 404, clear the data in the user label data table and the product transaction basic data table.

[0120] In implementation, the computer device clears the full user label data in the user label data table. At the same time, the computer device clears the full historical transaction data in the product transaction basic data.

[0121] In this embodiment, by clearing the data in the transaction increment statistical table, the real-time result table, the user label data table, and the product transaction basic data table, it is convenient for data analysis at a subsequent time, avoids data chaos, and improves the correctness of the data analysis method.

[0122] In an exemplary embodiment, as Figure 5 shown, the specific processing procedure of storing the real-time updated incremental transaction data based on the transaction increment statistical table in step 104 includes steps 502 to 506. Among them:

[0123] Step 502, when the transaction data of the business system is updated, collect the real-time updated initial incremental transaction data and store each initial incremental transaction data into the message queue.

[0124] In implementation, the computer device monitors the business system. When the computer device monitors that the transaction data of the business system is updated, the computer device collects the real-time updated initial incremental transaction data through a CDC (Change Data Capture) tool. Then, the computer device stores each initial incremental transaction data into the message queue.

[0125] In an exemplary embodiment, the computer device monitors whether the business system updates transaction data. When the computer device detects that the transaction data of the business system is updated, the computer device collects the newly updated initial incremental transaction data from the business system through a CDC tool and stores the initial incremental transaction data in a message queue.

[0126] Optionally, the CDC tool can be, but is not limited to, Debezium (an open-source distributed CDC platform) or Maxwell (an open-source CDC tool). The embodiments of the present application do not limit the CDC tool.

[0127] Step 504: Read the initial incremental transaction data from the message queue.

[0128] In implementation, the computer device reads the initial incremental transaction data from the message queue in the order of the message queue.

[0129] In an exemplary embodiment, the computer device includes a real-time data calculation engine. The incremental data calculation and consumption module of the real-time data calculation engine consumes the topic data in the message queue and loads the topic data into a transaction incremental statistical table.

[0130] Step 506: Preprocess the initial incremental transaction data to obtain incremental transaction data and update the incremental transaction data to the transaction incremental statistical table.

[0131] In implementation, the computer device preprocesses the initial incremental transaction data according to the format of the incremental transaction data in the transaction incremental statistical table to obtain incremental transaction data. Then, the computer device stores the incremental transaction data in the transaction incremental statistical table.

[0132] Specifically, a transaction incremental statistical table is preset in the computer device. The transaction incremental statistical table is used to store each incremental transaction data. Therefore, the format of the incremental transaction data is stored in the transaction incremental statistical table. The computer device extracts data, cleans data, and converts the format of the initial incremental transaction data according to the format of the incremental transaction data to obtain incremental transaction data.

[0133] In an exemplary embodiment, taking the initial incremental transaction data of a bank as an example of the initial incremental transaction data, the initial incremental transaction data includes information such as the transaction object, transaction product, product type, transaction type, transaction amount, transaction time, merchant information, transaction quantity, payment method, payment status, order number, order status, and logistics status information. The incremental transaction data includes the transaction object, transaction product, product type, transaction amount, and transaction time, and merchant information. The computer device extracts the transaction object, transaction product, product type, transaction amount, transaction time, and merchant information from the initial incremental transaction data according to the format of the incremental transaction data, and performs data cleaning and format conversion on the extracted transaction object, transaction product, product type, transaction amount, transaction time, and merchant information to obtain the incremental transaction data. Then, the computer device adds the incremental transaction data to the transaction increment statistical table.

[0134] In this embodiment, by storing the initial incremental transaction data in the message queue and reading the initial incremental transaction data in the message queue, the orderly processing of the initial incremental transaction data in a high-concurrency scenario is realized, ensuring the reliability of the data analysis method. At the same time, it is ensured that each initial incremental transaction data can be processed, avoiding the omission of the initial incremental transaction data and improving the correctness of the data analysis method.

[0135] In an exemplary embodiment, the merged result table is also used for querying the data analysis results. As Figure 6 shown, after step 106 is executed, the specific processing process of the data analysis method further includes steps 602 to 606. Among them:

[0136] Step 602, construct a target query request based on the query template.

[0137] In implementation, the query template is preset in the computer device. Among them, the query template includes various transaction object tags, product cycles, various analysis indicators, and transaction object screening templates. The computer device determines the target transaction object tag among the various transaction object tags and determines the target analysis indicator among the various analysis indicators. Then, the computer device determines the target query cycle based on the product cycle and determines the target transaction object screening condition based on the transaction object screening template. The computer device constructs a target query request according to the target transaction object tag, target analysis indicator, target query cycle, and target object screening condition.

[0138] Specifically, the computer device displays a query template. The query template includes various transaction object tags, various analysis metrics, a product cycle, and a transaction object filtering template. The query personnel can freely select and trigger the transaction object tags among the various transaction object tags. In response to the triggering operation of the transaction object tags among the various transaction object tags, the computer device determines the triggered transaction object tag as the target transaction object tag. The query personnel select and trigger the analysis metrics to be queried among the various analysis metrics. The computer device determines the triggered analysis metric as the target analysis metric in response to the triggering operation of the analysis metric. A product cycle selector is set in the product cycle. The query personnel select and submit the target query cycle based on the product cycle selector. In response to the submission operation of the target query cycle, the computer device obtains the submitted target query cycle. The query personnel edit and submit the target object filtering condition in the transaction object filtering template. In response to the submission operation of the target object filtering condition, the computer device obtains the target object filtering condition submitted by the query personnel. The computer device constructs a target query request according to the target transaction object tag, the target analysis metric, the target query cycle, and the target object filtering condition.

[0139] In an exemplary embodiment, Figure 7 is a schematic diagram of the display interface of the query template in an exemplary embodiment. As Figure 7 shown, the schematic diagram of the display interface of the query template includes a first area, a second area, and a third area. The first area is used to display the target analysis metric (product metric) and the target transaction object tag (customer tag) that the query personnel have selected. The second display area is used to display the target query cycle, the transaction object filtering template, and the interval cycle. The interval cycle is the interval cycle between the target analysis results under the same target analysis metric. The customer filtering condition in the second display area is the transaction object filtering template. The transaction object filtering template includes 4 types of conditions. The first is the enumerated value type, including the equal or inclusion relationship. The computer device can perform data query according to the equal relationship or the inclusion relationship. The second is the amount and numerical type, including equal to, not equal to, less than, greater than, greater than or equal to, less than or equal to, and between these relationships. Based on this relationship, the query personnel enter data for query. The third is the pop-up box single-selection or multi-selection tree structure data, and the computer device performs query according to the inclusion relationship. The fourth is the date type, including equal to, not equal to, less than, greater than, greater than or equal to, less than or equal to, and between these relationships. Based on this relationship, the query personnel enter the data date for query. The third display area is the area where the analysis designer displays the target query result. Currently Figure 7 the first display area displays the target object tag. Figure 8 is a schematic diagram of the display interface of the query template in another exemplary embodiment. Figure 8 It also includes a first display area, a second display area, and a third display area. Figure 8The unique display area displays the target trading object label. Figure 9 It is a display interface diagram of a query template in an embodiment. Figure 9 It includes a first display area, a second display area, and a third display area. Figure 9 The second display area of displays a product cycle selector. The query user can select and submit a target query cycle based on this product cycle selector.

[0140] In an optional embodiment, when the query person triggers a query analysis task (the query person submits a target query request), the computer device receives the target query request. The computer device, through the interface call encapsulation module, converts the combined conditions (including the target trading object label, the target analysis metric, the target query cycle, and the target object screening condition) in the target query request into backend service interface data. The interface call encapsulation module calls the business service interface provided by the business service engine so that the business service interface performs data query according to the backend service interface data.

[0141] Step 604, in response to the target query request, determine the target query result corresponding to the target query request in the merged result table.

[0142] Among them, the target query request includes a target query cycle and a target analysis metric.

[0143] In implementation, the computer device, in response to the target query request, queries the target query result that meets the target query request in the merged result table.

[0144] Specifically, the computer device receives the target query request. Then, the computer device, according to the target query cycle and the target analysis metric, queries the target data analysis result that meets the target query cycle and the target analysis metric in the merged result table, and constructs the target query result based on the target data analysis result.

[0145] Step 606, display the target query result according to the analysis designer.

[0146] In implementation, an analysis designer is set in the computer device. The computer device, through the analysis designer, displays the target query result in multiple formats.

[0147] Specifically, each display format is displayed in the analysis designer. The query person can select and trigger the target display format among the various display formats. The computer device, in response to the trigger operation of the target display format, renders the target query result according to the target display format and displays the target query result according to the target display format.

[0148] In an exemplary implementation, the display formats included in the analysis designer are a line chart, a bar chart, and a table respectively. The query personnel can select and trigger the target display format among the display formats. If the target display format is a line chart, the computer device renders the target query result in the line chart format and displays the target query result in the form of a line chart, as Figure 10 shown. Figure 10 FIG. Figure 10 is a schematic diagram showing the target query result in the form of a line chart in an exemplary embodiment. If the target display format is a bar chart, the computer device renders the target query result in the bar chart format and displays the target query result in the form of a bar chart, as Figure 11 shown. Figure 11 FIG. Figure 11 is a schematic diagram showing the target query result in the form of a bar chart in an exemplary embodiment. If the target display format is a table, the computer device renders the target query result in the table format and displays the target query result in the form of a table, as Figure 12 shown. Figure 12 FIG. Figure 12 is a schematic diagram showing the target query result in the form of a table in an exemplary embodiment.

[0149] In the implementation, through the query template, the combined analysis of products and trading objects can be realized, and the data analysis results of each analysis index are presented, enabling the user to quickly play a role through each data analysis result and improving the decision-making efficiency.

[0150] In an exemplary embodiment, the target query request includes a target query period and a target analysis index; as Figure 13 shown, the specific processing process of step 604 includes steps 1302 to 1306. Among them:

[0151] Step 1302, determine each initial target data analysis result corresponding to the target analysis index in the merged result table, and screen each target data analysis result within the target query period from each initial target data analysis result.

[0152] Among them, the merged result table includes each historical data analysis result and each real-time data analysis result. The historical data analysis result and the real-time data analysis result are both data analysis results.

[0153] In the implementation, for each data analysis result in the merged result table, the computer device determines whether the analysis index corresponding to the data analysis result is the target analysis index. If the analysis index corresponding to the data analysis result is the target analysis index, the computer device determines the data analysis result as the initial target data analysis result. Then, the computer device determines whether the analysis period of the initial target data analysis result is within the target query period. If the analysis period of the initial target data analysis result is within the target query period, the computer device determines the initial target data analysis result as the target data analysis result.

[0154] In an exemplary embodiment, taking the target analysis indicator as the total transaction volume and the target query period as from 00:00:00 on March 10, 2025 to 00:00:00 on March 14, 2025 as an example, for each data analysis result in the merged result table, the computer device determines whether the analysis indicator corresponding to the data analysis result is the total transaction volume. For example, if the analysis indicator corresponding to the data analysis result is the total transaction volume, the computer device determines this data analysis result as the initial target data analysis result. If the analysis indicator corresponding to the data analysis result is the total transaction amount, the computer device filters out this data analysis result. Then, for each initial target data analysis result, the computer device determines whether the analysis period corresponding to the initial target data analysis result is within the period from 00:00:00 on March 10, 2025 to 00:00:00 on March 14, 2025. If the analysis period corresponding to the initial target data analysis result is within the period from 00:00:00 on March 10, 2025 to 00:00:00 on March 14, 2025, the computer device determines this initial target data analysis result as the target data analysis result.

[0155] Step 1304, perform a summation process or an averaging process on each target data analysis result to obtain a processing result.

[0156] In implementation, for each target data analysis result corresponding to each analysis indicator, the computer device performs an averaging process or a summation process on each target data analysis result according to this analysis indicator to obtain the processing result corresponding to this analysis indicator.

[0157] In an exemplary embodiment, if the analysis indicator is the total transaction volume, the computer device performs a summation process on each target data analysis result corresponding to the total transaction volume to obtain the processing result corresponding to the total transaction volume. If the analysis indicator is the average transaction volume, the computer device performs an averaging process on each target data analysis result corresponding to the average transaction volume to obtain the processing result corresponding to the average transaction volume.

[0158] Step 1306, construct a target query result based on each target data analysis result and the processing result.

[0159] In implementation, the computer device combines each target data analysis result and the processing result corresponding to each analysis indicator to obtain the initial target query result corresponding to this analysis indicator. Then, the computer device combines the initial target query results corresponding to each analysis indicator to obtain the target query result.

[0160] In an exemplary embodiment, the target query request includes a target transaction object tag, a target analysis metric, a target query period, and a target object filtering condition. The computer device filters the initial target data analysis results corresponding to the target analysis metric from the data analysis results of each merged data table. Then, the computer device filters the target data analysis results from each initial target data analysis result according to the target transaction object tag, the target query period, and the target object filtering condition.

[0161] In an exemplary embodiment, the target query request includes a target interval period. If the target interval period is monthly, the computer device performs a mean processing or a merging processing on the target data analysis results of each month for each analysis metric to obtain a processing result, and constructs a target query result based on each processing result.

[0162] In an alternative embodiment, the business service interface of the business service engine in the computer device invokes the processing logic of the SQL encapsulation processing unit to encapsulate the target query request into an SQL (Structured Query Language, a database language with various functions such as data manipulation and data definition) required for database query. The SQL encapsulation processing unit in the computer device invokes the processing logic of the data query control unit to obtain query analysis data, and obtains the target query result. The data query control unit in the computer device initiates an MPP database query transaction to obtain the target query result of the merged calculation result table, and displays the target query result through the front end.

[0163] In this embodiment, by the target query period and the target analysis metric of the target query request, the target data analysis results are filtered from the merged result table, and based on the target data analysis results, the target query result is constructed, realizing personalized data query and improving the flexibility of data query.

[0164] In an exemplary embodiment, Figure 14 is a schematic architecture diagram of the data analysis method in an exemplary embodiment. As Figure 14As shown in the figure, the business system (business batch data source) includes a core system, a credit system, and a channel-end system. The data batch processing scheduling engine in the computer device batch-collects the user basic data of each user and the historical transaction detail data of the day before the current time from the business system. Then, the computer device preprocesses the user basic data to obtain a user label data table (customer label data table), and preprocesses the historical transaction detail data to obtain a product transaction basic data table (customer product sales statistics basic data table). The computer device performs data analysis on the user label data table and the product transaction basic data table to obtain the historical data analysis results, and stores the historical data analysis results in a merged calculation result table (customer product sales analysis merged calculation result table). A data real-time calculation engine is set in the computer device. When the transaction data of the business system is updated, the computer device collects the initially incremented transaction data (on-time real-time data) that is updated in real time through CDC data collection, and stores the initially incremented transaction data in a message queue. Then, the data real-time calculation engine reads the initially incremented transaction data from the message queue. The data real-time calculation engine preprocesses the initially incremented transaction data to obtain incremented transaction data, and updates the incremented transaction data to a transaction increment statistics table (customer product sales statistics increment data table). The data real-time calculation engine performs data analysis on the incremented transaction data in the transaction increment statistics table to obtain real-time data analysis results, and stores the real-time data analysis results in a real-time result table (increment data calculation temporary result table). The computer device updates the real-time data analysis results in the real-time result table to the merged result table.

[0165] When the query personnel ( Figure 14After a user (in the system) triggers a query analysis task (the query person submits a target query request), the computer device receives the target query request. The computer device converts the combined conditions (including the target transaction object tag, the target analysis metric, the target query period, and the target object filtering condition) in the target query request into backend service interface data through the interface call encapsulation module. The interface call encapsulation module calls the business service interface provided by the business service engine, so that the business service interface executes data query according to the backend service interface data. The business service interface of the business service engine in the computer device calls the processing logic of the SQL encapsulation processing unit to encapsulate the target query request into an SQL (Structured Query Language, a database language with various functions such as data manipulation and data definition) required for database query. The SQL encapsulation processing unit in the computer device calls the processing logic of the data query control unit to obtain query analysis data and get the target query result. The data query control unit in the computer device initiates an MPP (Massively Parallel Processing Database, a database system designed specifically for processing large-scale data) database query transaction to obtain the target query result of the merged calculation result table, and displays the target query result through the front end.

[0166] By combining the use of a data real-time calculation engine and a data query analysis engine, the problem of being unable to accurately and real-time statistically analyze sales data in specific application scenarios is solved. Through the drag-and-drop data tags and customized analysis interface settings, the user's demand for self-service data analysis is solved, and the user experience is improved. Through the client processing engine, the query condition encapsulation and backend service interface conversion in a multi-dimensional analysis environment are realized, and the interface call for data analysis query can be quickly implemented.

[0167] It should be understood that although the steps in the flowcharts involved in the above-described embodiments are shown in sequence according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless there is a clear description in this article, the execution of these steps has no strict order limit, and these steps can be executed in other orders. Moreover, at least a part of the steps in the flowcharts involved in the above-described embodiments may include multiple steps or multiple stages. These steps or stages are not necessarily executed at the same time, but can be executed at different times. The execution order of these steps or stages is not necessarily sequential, but can be executed alternately or in turn with at least a part of other steps or steps or stages in other steps.

[0168] Based on the same inventive concept, an embodiment of the present application further provides a data analysis device for implementing the data analysis method involved above. The solution provided by this device for solving problems is similar to the solution described in the above method. Therefore, the specific limitations in one or more embodiments of the data analysis device provided below can refer to the limitations on the data analysis method in the foregoing, and will not be elaborated herein.

[0169] In an exemplary embodiment, as Figure 15 shown, a data analysis device 1500 is provided, including: an acquisition module 1501, a storage module 1502, and an update module 1503, where:

[0170] The acquisition module 1501 is configured to acquire a merged result table; the merged result table contains historical data analysis results obtained based on user basic data and historical transaction detail data.

[0171] The storage module 1502 is configured to store real-time updated incremental transaction data based on a transaction increment statistical table, and perform data analysis on each incremental transaction data in the transaction increment statistical table to obtain real-time data analysis results.

[0172] The update module 1503 is configured to update the real-time data analysis results to the merged result table; the merged result table contains full-scale data analysis results; the merged result table is used to query the full-scale data analysis results.

[0173] In an exemplary embodiment, the acquisition module 1501 includes:

[0174] A first collection sub-module, configured to batch-collect user basic data of each user and historical transaction detail data of the day before the current time from the business system according to a preset time period.

[0175] A first analysis sub-module, configured to perform data analysis on each user basic data and historical transaction detail data to obtain historical data analysis results, and store the historical data analysis results based on the merged result table.

[0176] In an exemplary embodiment, the first analysis sub-module includes:

[0177] A first processing sub-module, configured to preprocess each user basic data to obtain a user label data table, and preprocess the historical transaction detail data to obtain a product transaction basic data table.

[0178] A second analysis sub-module, configured to perform data analysis on the user label data table and the product transaction basic data table according to the analysis algorithms corresponding to each analysis index to obtain each historical data analysis result of the day before the current time.

[0179] The first storage sub-module is used to store the historical data analysis results into the combined calculation result table based on each analysis index.

[0180] In an exemplary embodiment, the data analysis device 1500 further includes:

[0181] The formatting module is used to format the data in the transaction increment statistical table and format the real-time result table.

[0182] The clearing module is used to clear the data in the user label data table and the product transaction basic data table.

[0183] In an exemplary embodiment, the storage module includes a first storage sub-module and a third analysis sub-module. Among them, the first storage sub-module includes:

[0184] The second acquisition sub-module is used to collect each initial incremental transaction data updated in real time when the transaction data of the business system is updated, and store each initial incremental transaction data into the message queue.

[0185] The first reading sub-module is used to read the initial incremental transaction data from the message queue.

[0186] The first update sub-module is used to preprocess the initial incremental transaction data to obtain incremental transaction data, and update the incremental transaction data to the transaction increment statistical table.

[0187] In an exemplary embodiment, the data analysis device further includes:

[0188] The first construction module is used to construct a target query request based on the query template.

[0189] The query module is used to determine the target query result corresponding to the target query request in the combined result table in response to the target query request.

[0190] The display module is used to display the target query result according to the analysis designer.

[0191] In an exemplary embodiment, the target query request includes a target query period and a target analysis index; the query module includes:

[0192] The determination module is used to determine each initial target data analysis result corresponding to the target analysis index in the combined result table, and screen each target data analysis result within the target query period from each initial target data analysis result.

[0193] The processing module is used to perform a summation process or an averaging process on each target data analysis result to obtain a processing result.

[0194] The second construction module is used to construct a target query result based on each target data analysis result and the processing result.

[0195] Each module in the above data analysis device can be implemented in whole or in part by software, hardware, or a combination thereof. Each of the above modules can be embedded in the processor of the computer device in hardware form or independent of it, or stored in the memory of the computer device in software form, so that the processor can call and execute the operations corresponding to each of the above modules.

[0196] In an exemplary embodiment, a computer device is provided. The computer device can be a terminal, and its internal structure diagram can be as Figure 16 shown. The computer device includes a processor, a memory, an input / output interface, a communication interface, a display unit, and an input device. Among them, the processor, the memory, and the input / output interface are connected through a system bus, and the communication interface, the display unit, and the input device are connected to the system bus through the input / output interface. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The input / output interface of the computer device is used to exchange information between the processor and external devices. The communication interface of the computer device is used to communicate with external terminals in a wired or wireless manner. The wireless manner can be achieved through WIFI, a mobile cellular network, near field communication (NFC), or other technologies. The computer program, when executed by the processor, implements a data analysis method. The display unit of the computer device is used to form a visually visible picture, which can be a display screen, a projection device, or a virtual reality imaging device. The display screen can be a liquid crystal display screen or an electronic ink display screen. The input device of the computer device can be a touch layer covering the display screen, or a button, a trackball, or a touchpad provided on the housing of the computer device, or an external keyboard, touchpad, or mouse, etc.

[0197] Those skilled in the art can understand that Figure 16 the structure shown in

[0198] merely shows the block diagram of some structures related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine some components, or have different component arrangements.

[0199] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored, and when the computer program is executed by a processor, the steps in the foregoing method embodiments are implemented.

[0200] In one embodiment, a computer program product is provided, including a computer program, and when the computer program is executed by a processor, the steps in the foregoing method embodiments are implemented.

[0201] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data for analysis, stored data, displayed data, etc.) involved in this application are all information and data that have been authorized by the user or fully authorized by all parties, and the collection, use, and processing of relevant data need to comply with relevant regulations.

[0202] Those of ordinary skill in the art can understand that all or part of the processes in the methods of the above embodiments can be completed by instructing relevant hardware through a computer program. The computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above methods. Among them, any reference to a memory, database, or other medium used in the embodiments provided in this application can include at least one of non-volatile memory and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetoresistive random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM), etc. The databases involved in the embodiments provided in this application can include at least one of relational databases and non-relational databases. Non-relational databases can include distributed databases based on blockchain, etc., without limitation. The processors involved in the embodiments provided in this application can be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, data processing logics based on quantum computing, artificial intelligence (AI) processors, etc., without limitation.

[0203] The technical features of the above embodiments can be combined arbitrarily. For the sake of brevity of description, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, it should be considered to be within the scope recorded in this application.

[0204] The above-described embodiments merely represent several implementation manners of the present application. The description thereof is relatively specific and detailed, but it should not be construed as a limitation to the patent scope of the present application. It should be noted that for those of ordinary skill in the art, without departing from the concept of the present application, several modifications and improvements can still be made, and these all fall within the protection scope of the present application. Therefore, the protection scope of the present application shall be subject to the appended claims.

Claims

1. A data analysis method, characterized in that, The method includes: Obtain a merged result table; the merged result table contains historical data analysis results obtained based on user basic data and historical transaction detail data; Store real-time updated incremental transaction data based on a transaction increment statistical table, and perform data analysis on each incremental transaction data in the transaction increment statistical table to obtain real-time data analysis results; Update the real-time data analysis results to the merged result table; the merged result table contains full-volume data analysis results; the merged result table is used to query the full-volume data analysis results.

2. The method according to claim 1, wherein The obtaining of the merged result table includes: Batch collect the user basic data of each user and the historical transaction detail data of the day before the current time from the business system according to a preset time period; Perform data analysis on each of the user basic data and the historical transaction detail data to obtain historical data analysis results, and store the historical data analysis results based on the merged result table.

3. The method according to claim 2, wherein The performing of data analysis on each of the user basic data and the historical transaction detail data to obtain historical data analysis results, and storing the historical data analysis results based on the merged result table includes: Preprocess each of the user basic data to obtain a user label data table, and preprocess the historical transaction detail data to obtain a product transaction basic data table; According to the analysis algorithms corresponding to each analysis index, perform data analysis on the user label data table and the product transaction basic data table to obtain each historical data analysis result of the day before the current time; Based on each of the analysis indexes, store each of the historical data analysis results into a merged calculation result table.

4. The method according to claim 3, characterized in that After storing each of the historical data analysis results into the merged calculation result table based on each of the analysis indexes, the method further includes: Format the data in the transaction increment statistical table and format the real-time result table; Clear the data in the user label data table and the product transaction basic data table.

5. The method according to claim 1, wherein The storing of the real-time updated incremental transaction data based on the transaction increment statistical table includes: When the transaction data of the business system is updated, collect each real-time updated initial incremental transaction data, and store each of the initial incremental transaction data into a message queue; Read the initial incremental transaction data from the message queue; Preprocess the initial incremental transaction data to obtain incremental transaction data, and update the incremental transaction data to the transaction increment statistical table.

6. The method according to claim 1, wherein After updating the real-time data analysis results to the merged result table, the method further includes: Construct a target query request based on a query template; In response to the target query request, determine a target query result corresponding to the target query request in the merged result table; Display the target query result according to an analysis designer.

7. The method according to claim 6, wherein The target query request includes a target query period and a target analysis index; The in response to the target query request, determining a target query result corresponding to the target query request in the merged result table includes: Determine the initial target data analysis results corresponding to the target analysis indicators in the combined result table, and screen the target data analysis results within the target query period from the initial target data analysis results; Perform a summation process or an averaging process on the target data analysis results to obtain a processing result; Construct a target query result based on the target data analysis results and the processing result.

8. A data analysis device, characterized in that, The device includes: An acquisition module, configured to acquire a combined result table; the combined result table contains historical data analysis results obtained based on user basic data and historical transaction detail data; A storage module, configured to store real-time updated incremental transaction data based on a transaction increment statistical table, and perform data analysis on each incremental transaction data in the transaction increment statistical table to obtain real-time data analysis results; An update module, configured to update the real-time data analysis results to the combined result table; the combined result table contains full-volume data analysis results; the combined result table is used to query the full-volume data analysis results.

9. A computer device, comprising a memory and a processor, the memory storing a computer program, characterized in that, When the processor executes the computer program, the steps of the method according to any one of claims 1 to 7 are implemented.

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

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