Data processing method and device, electronic equipment and storage medium

Abstract

The embodiment of the invention provides a data processing method and device, electronic equipment and a storage medium, and the method comprises the steps: obtaining transaction process data corresponding to a target transaction, and obtaining a target index and index configuration data of the target index based on the transaction process data, and obtaining a target data source corresponding to the target transaction based on preset data source configuration data, obtaining an index calculation result of the target index from the target data source based on the index configuration data, and analyzing the index calculation result according to the index configuration data to obtain an index analysis result of the target transaction.

Description

Technical Field

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

[0002] For data analysis, data collection, mining, statistical analysis, real-time monitoring and visualization are essential steps. In the current field of data analysis, with the explosive growth of data volume and the continuous increase in analysis needs, these processes have become more and more complicated. Modern data analysis not only needs to process massive amounts of data, but also faces challenges in data diversity and real-time performance. In order to perform these operations effectively, data R&D teams often need to invest a lot of time in writing and optimizing code. Especially in the automation of data processing and analysis, algorithm optimization, and system integration, it is crucial to write efficient code and develop robust analysis tools, which further increases the complexity and time consumption of development work. Summary of the invention

[0003] The main purpose of this specification is to provide a data processing method, device, electronic device and storage medium, aiming to save research and development time, reduce research and development costs, and improve analysis efficiency. The technical solution is as follows:

[0004] In a first aspect, an embodiment of this specification provides a data processing method, including:

[0005] Acquire transaction process data corresponding to the target transaction, and acquire a target indicator and indicator configuration data of the target indicator based on the transaction process data;

[0006] Acquire a target data source corresponding to the target transaction based on preset data source configuration data;

[0007] Acquire the indicator calculation result of the target indicator from the target data source based on the indicator configuration data;

[0008] The indicator calculation result is analyzed according to the indicator configuration data to obtain the indicator analysis result of the target transaction.

[0009] In a second aspect, an embodiment of the present specification provides a data processing device, including:

[0010] A transaction acquisition unit, configured to acquire transaction process data corresponding to a target transaction, and acquire a target indicator and indicator configuration data of the target indicator based on the transaction process data;

[0011] A data acquisition unit, configured to acquire a target data source corresponding to the target transaction based on preset data source configuration data;

[0012] An indicator acquisition unit, configured to acquire an indicator calculation result of the target indicator from the target data source based on the indicator configuration data;

[0013] An analysis unit is used to analyze the indicator calculation result according to the indicator configuration data to obtain the indicator analysis result of the target transaction.

[0014] In a third aspect, an embodiment of the present specification provides an electronic device, the device comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the computer program implements the steps of the above method when executed by the processor.

[0015] In a fourth aspect, an embodiment of the present specification provides a storage medium having a computer program stored thereon, and the computer program implements the steps of the above method when executed by a processor.

[0016] In a fifth aspect, an embodiment of this specification provides a computer program product, including: a computer program, when the computer program is executed by a processor of an electronic device, the processor can at least implement the method described in the first aspect.

[0017] In the embodiments of this specification, by obtaining the transaction process data corresponding to the target transaction, obtaining the target indicator and the indicator configuration data of the target indicator based on the transaction process data, obtaining the target data source corresponding to the target transaction based on the preset data source configuration data, obtaining the indicator calculation result of the target indicator from the target data source based on the indicator configuration data, and analyzing the indicator calculation result according to the indicator configuration data to obtain the indicator analysis result of the target transaction. For the target transaction that needs to be analyzed for data, there is no need for code development, and the indicator is confirmed according to the transaction process data of the target transaction, and the indicator data collection and cleaning process can be managed through visual configuration to quickly obtain the indicator calculation result of the target indicator, and realize automated indicator analysis, thereby reducing the difficulty and threshold of data analysis. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] In order to more clearly illustrate the embodiments of this specification or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this specification. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0019] Figure 1 It is an application schematic diagram of a data processing method provided in an embodiment of this specification;

[0020] Figure 2It is a flowchart of a data processing method provided by an embodiment of this specification;

[0021] Figure 3 It is a flowchart of a data processing method provided by an embodiment of this specification;

[0022] Figure 4 It is a flowchart of a data processing method provided by an embodiment of this specification;

[0023] Figure 5 It is an indicator query flow chart of a data processing method provided in an embodiment of this specification;

[0024] Figure 6 It is a flowchart of a data processing method provided by an embodiment of this specification;

[0025] Figure 7 is an example schematic diagram of a data processing method provided in an embodiment of this specification;

[0026] Figure 8 is a structural schematic diagram of a data processing device provided in an embodiment of this specification;

[0027] Fig. 9 It is a structural schematic diagram of an electronic device provided in an embodiment of this specification. DETAILED DESCRIPTION

[0028] The following will be combined with the drawings in the embodiments of this specification to clearly and completely describe the technical solutions in the embodiments of this specification. Obviously, the described embodiments are only part of the embodiments of this specification, not all of the embodiments. Based on the embodiments in this specification, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this specification.

[0029] In the relevant technologies, for data analysis, data collection, mining, statistical analysis, real-time monitoring and visualization are essential steps. For example, an intuitive indicator dashboard is used to provide a visual display of indicators. You can use existing business intelligence tools (such as Tableau, Power BI, QlikView), define SQL queries to extract data from the database regularly, and apply visualization components on the front end to display indicators. The advantages of this method are simple research and development, rapid construction of indicator visualization systems, and a small number of indicators, which are easy to maintain; the disadvantages are low indicator reusability: indicators are based on dashboards and cannot exist independently without dashboards; a large number of indicators are difficult to visualize and maintain, and information such as indicator caliber and statistical dimensions are hidden in the SQL code, which is difficult to view and expand; the system is difficult to expand and cannot be analyzed based on unified indicators.

[0030] For another example, to build an indicator module based on a data warehouse, you can build a data warehouse and use ETL tools to collect and mine data. For transactions that require real-time response and real-time analysis of indicators, use a stream processing framework (such as Apache Kafka, Apache Flink, Apache Storm) to process real-time data streams. Store the formatted data in the data warehouse library. Build an indicator module, where each indicator corresponds to a SQL query for the data warehouse. Indicator reuse and data warehouse-based analysis and expansion can be achieved. The statistical caliber and dimensions of the indicators of this solution are hidden in the ETL tool or stream processing code. The development and caliber of each indicator requires code modification, which consumes R&D resources.

[0031] Based on the above problems, the embodiment of this specification provides a data processing method, which obtains the transaction process data corresponding to the target transaction, obtains the target indicator and the indicator configuration data of the target indicator based on the transaction process data, obtains the target data source corresponding to the target transaction based on the preset data source configuration data, obtains the indicator calculation result of the target indicator from the target data source based on the indicator configuration data, and analyzes the indicator calculation result according to the indicator configuration data to obtain the indicator analysis result of the target transaction. In this way, after obtaining the target transaction that needs data processing, it is only necessary to obtain the transaction process data of the target transaction, automatically generate the target indicator and the indicator configuration data according to the transaction process data, and obtain the indicator calculation result of the target indicator based on the data source pre-configured for the target transaction. In this way, non-technical personnel can also adjust the collection process by changing the configuration instead of the code, thereby greatly reducing the programming workload and improving the flexibility of data processing. Then, according to the indicator configuration data, the indicator analysis is automatically triggered to improve the efficiency of data analysis.

[0032] The data processing device provided in the embodiments of this specification can be a terminal device such as a mobile phone, a computer, a tablet computer, a smart watch or a vehicle-mounted device, or it can be a module in the terminal device for implementing the data processing method. Figure 1, an example schematic diagram of a data processing method is provided for an embodiment of the present specification. The data processing device may include an indicator module, a data source management module and an indicator analysis module. After the transaction process data of the target transaction is obtained, the target indicator and indicator configuration data of the target transaction can be determined according to the transaction process data, and the target indicator and indicator configuration data are stored in the indicator module. In this way, non-technical personnel can also control the indicator calculation process by changing the indicator configuration in the indicator module, which helps to reduce programming workload and improve the flexibility of indicator calculation. The target data source corresponding to the target transaction is determined by the data source configuration data in the data source management module. The indicator module generates a data collection execution task according to the target indicator and indicator configuration data, obtains the indicator calculation result of the target transaction from the target data source, and transmits the indicator calculation result to the indicator analysis module for indicator analysis to obtain the indicator analysis result of the target transaction.

[0033] The data processing method provided in this specification is described in detail below in conjunction with specific embodiments.

[0034] See also Figure 2 , which is a flow chart of a data processing method provided in the embodiment of this specification. Figure 2 As shown, the method of the embodiment of this specification may include the following steps S102-S108.

[0035] S102, acquiring transaction process data corresponding to the target transaction, and acquiring a target indicator and indicator configuration data of the target indicator based on the transaction process data;

[0036] In one embodiment of the present specification, the target transaction is a transaction that currently needs to be processed. The target transaction can be a transaction that has been processed or a newly added transaction. The user can create a target transaction or select a target transaction in the data processing system so that the data processing system can obtain the transaction process data corresponding to the target transaction. Transaction process data can also be called transaction behavior dynamic line, and transaction behavior refers to the various activities and interactive behaviors displayed by the user or system in the transaction process. For example, in an e-commerce platform, the user's browsing, searching, clicking and purchasing are all transaction behaviors. Dynamic lines usually refer to the user's movement and behavior path in an application, website or actual space. In data analysis, it can refer to the process or path of user behavior, such as the steps from entering the website to completing the purchase. The dynamic lines of the main transaction behaviors in software products are generally carefully designed by the product manager, which can be known and described in advance. Therefore, the transaction process data of each transaction can be obtained in advance. After the target transaction is obtained, the transaction process data of the target transaction is matched from the pre-acquired multiple transaction process data.

[0037] Based on the various transaction behaviors and behavior paths indicated in the transaction process data, the target indicator and the indicator configuration data of the target indicator can be determined. An indicator is a quantitative unit for measuring the performance of a system, process or transaction activity. In big data analysis, indicators can be used to measure data of various dimensions, such as transaction indicators such as sales, click-through rate, conversion rate, etc., or system performance indicators such as response time, error rate, etc. The indicator configuration data is the information required to collect the target indicator, for example, it can include the indicator name, indicator caliber, indicator statistical dimension, the relationship between indicators, the transaction scenario to which the indicator belongs, the indicator type, the creation time, etc.

[0038] S104, acquiring a target data source corresponding to the target transaction based on preset data source configuration data;

[0039] In one embodiment of the present specification, the preset data source configuration data includes a data source that can be collected, and the data source can be a tracking log, a MySQL transaction table, a message middleware, etc. The tracking log may correspond to the tracking log, so the data source configuration data can also include the event identifier of the tracking event, the field name, format, data type, etc. of the event attribute. In addition, the data source configuration data also includes data source configuration information for different transactions, which is used to determine the data sources corresponding to different transactions. It can be understood that the data sources required for different transactions may be different, so the target data source corresponding to the target transaction can be determined according to the data source configuration data. The preset data source configuration data can be pre-configured manually, that is, the behavior of the system is customized or adjusted through configuration rather than programming, such as a technician filling in the necessary configuration information in the configuration page, or setting the data source through a configuration file.

[0040] S106, acquiring an indicator calculation result of the target indicator from the target data source based on the indicator configuration data;

[0041] In one embodiment of the present specification, the indicator configuration data includes path data indicating the acquisition path of the target indicator data, so that the value corresponding to the target indicator can be found from the target data source, and the value of the target indicator can be directly used as the indicator calculation result. Optionally, the indicator configuration data includes an indicator calculation method, and the values ​​of each target indicator are calculated respectively according to the indicator calculation method to obtain the indicator calculation result.

[0042] S108, analyzing the indicator calculation result according to the indicator configuration data to obtain the indicator analysis result of the target transaction.

[0043] In one embodiment of the present specification, indicator analysis involves statistical analysis, pattern recognition, and trend prediction of the collected indicator data to facilitate decision making. It may include descriptive analysis, diagnostic analysis, predictive analysis, and normative analysis. The indicator configuration data is configured with an analysis algorithm for the target indicator. Different analysis algorithms can be configured for different indicators. In addition, some general analysis algorithms can also be configured for the indicators. For example, the more basic and common analysis methods are: qualitative analysis, identifying the transaction factors, market environment, technology trends, etc. that may affect the indicators, and describing their possible impacts through expert evaluation, research, interviews, etc.; quantitative analysis: collecting relevant data, and determining the degree of influence of different factors on the indicators through statistical analysis methods (such as regression analysis, variance analysis, time series analysis, etc.).

[0044] For example, when conducting a qualitative analysis of the indicator calculation results, it supports configuring the analysis conclusions obtained through expert evaluation, research, interviews, etc. on each indicator. Then, strongly correlated indicators or indicators with causal and combination relationships can share the analysis conclusions, so that users only need to configure the most core or most commonly used indicator qualitative conclusions to automatically derive the qualitative analysis conclusions of related indicators.

[0045] Another example is that when quantitatively analyzing the results of indicator calculations, algorithms such as regression analysis, variance analysis, and time series analysis are built into the program to monitor the indicator calculation results in real time. When the number of indicator calculation results reaches the algorithm analysis requirements, the analysis algorithm can be executed on the indicator to draw a conclusion of quantitative analysis. The total value, mean, maximum value, minimum value, median, mode ratio, etc. of the indicator can be calculated; the periodicity of the indicator can be analyzed based on the time series, and the same-month and year-on-year changes of the calculation period can be obtained. Variance analysis can be used to obtain the degree of dispersion of the indicator, identify outliers, and achieve comparisons between indicators of different categories or scales. Regression analysis can be used to predict the future of the indicator.

[0046] In the embodiments of this specification, by obtaining the transaction process data corresponding to the target transaction, obtaining the target indicator and the indicator configuration data of the target indicator based on the transaction process data, obtaining the target data source corresponding to the target transaction based on the preset data source configuration data, obtaining the indicator calculation result of the target indicator from the target data source based on the indicator configuration data, and analyzing the indicator calculation result according to the indicator configuration data to obtain the indicator analysis result of the target transaction. For the target transaction that needs to be analyzed for data, there is no need for code development, and the indicator is confirmed according to the transaction process data of the target transaction, and the indicator data collection and cleaning process can be managed through visual configuration to quickly obtain the indicator calculation result of the target indicator, and realize automated indicator analysis, thereby reducing the difficulty and threshold of data analysis.

[0047] See also Figure 3 , which is a flow chart of a data processing method provided in the embodiment of this specification. Figure 3 As shown, the method of the embodiment of this specification may include the following steps S202-S226.

[0048] S202, acquiring transaction process data corresponding to the target transaction from a pre-stored transaction process database;

[0049] In one embodiment of the present specification, a transaction process database may be created in advance, and transaction process data corresponding to different transactions may be stored in the transaction process database, so that when a transaction needs to be processed, the corresponding transaction process data may be quickly obtained.

[0050] S204, determining a target indicator and an indicator caliber and an indicator statistical dimension of the target indicator based on the process nodes in the transaction process data;

[0051] In one embodiment of the present specification, the transaction process data may include multiple process nodes. For example, the process node is a page set for the target transaction. Different indicators may be collected in different pages. The target indicator corresponding to the target transaction, as well as the indicator caliber and indicator statistical dimension of the target indicator can be determined from the data that can be obtained from the page. Among them, the indicator caliber refers to the accuracy of the indicator definition and the rules of the set. The caliber can determine how to calculate and report the indicator value to ensure the consistency and accuracy of the indicator. The indicator statistical dimension is a parameter or standard used to refine and classify data in data analysis. There can be different statistical dimensions for the same indicator. Among them, the indicator statistical dimension can be determined according to the indicator type of the target indicator determined from the transaction process data.

[0052] See also Figure 4 , Figure 4 A transaction flow diagram of a data processing method is provided for the embodiment of this specification. Figure 4 As shown in the figure, a flow chart can be used to visually describe a transaction process. For example, if the transaction is user shopping, the user shopping process includes from the user login page to the product details page. In the product details page, the user behavior can be to place an order directly, then jump to the order page; the user behavior is to add to the shopping cart, then jump to the shopping page, and in the shopping page, the user submits the order and jumps to the order page; in the shopping page and the order page, the user can also return to the product details page; after the payment is completed in the order page, it will jump to the payment result page. These are the transaction processes of user shopping. Each indicator can be summarized into a node of a transaction process, such as the number of users logged in is summarized into the user login page node; the number of product details page exposures and the number of people browsing the product details page are summarized into the product details page node.

[0053] It should be noted that if the target transaction is a transaction that has been processed, the corresponding indicator data (indicators and indicator configuration) has been stored in the indicator module, so there is no need to obtain the transaction process data and obtain the target indicators and indicator configuration data corresponding to the transaction process data. The indicator module allows quick access and reuse of existing indicators, avoiding duplication of work, saving development time and improving efficiency.

[0054] S206, generating indicator relationship data of the target indicator according to the relationship data of the process nodes;

[0055] In one embodiment of the present specification, the transaction process data also includes relationship data of process nodes. The relationship data of process nodes can be recursively pushed to the indicators included in these behavior nodes.

[0056] Specifically, the relationship data of the process nodes corresponds to the sequential relationship, causal relationship, combination relationship, etc. of the transaction behavior, and the relationship data between the corresponding indicators can include combination relationship, causal relationship, correlation relationship, and obtain indicator relationship data. The correlation relationship can be calculated by a linear correlation algorithm or a nonlinear correlation algorithm. For example, a marketing indicator may be positively correlated with a sales indicator, and customer satisfaction may be related to customer retention rate. However, it is not easy to directly draw correct conclusions about the combination relationship and causal relationship through an algorithm. In a feasible implementation, Figure 4 As shown in the transaction flow description, each transaction will have many user behavior actions at each node, so based on these user behavior actions, indicators that can explain the transaction can be created. Obviously, since the occurrence of transaction behaviors is sequential and causal, the indicators based on these behaviors are also sequential and causal. Figure 4 In the online shopping process described in the article, you must first open the shopping website to log in, select products, place orders, and pay before you can get the payment results. Then the indicators created based on these user behaviors, such as the number of users who log in, the number of users who select products, the number of users who place orders, the number of users who pay, and the number of users who are exposed to the payment result page, also have a sequence and causal relationship. Through these relationships, when an indicator fluctuates, we can determine which other indicators affect the fluctuation of the indicator, the size of the influence, and the direction of the influence, and deduce from qualitative analysis which operations can make the indicator better, so as to give suggestions to the operation personnel.

[0057] Optionally, after the transaction process is created and each node is bound to an indicator, an indicator funnel chart, a traffic Sankey chart, etc. can be automatically generated according to the sequence relationship and combination relationship of the indicators to reduce the workload of dashboard maintenance.

[0058] S208, generating indicator configuration data of the target indicator based on the indicator caliber, the indicator statistical dimension and the indicator relationship data;

[0059] In one embodiment of the present specification, the indicator configuration data of the target indicator is generated according to the indicator caliber, indicator statistical dimension and indicator relationship data determined above. Afterwards, the indicator configuration data can be stored in the indicator module.

[0060] S210, acquiring a target data source corresponding to the target transaction based on preset data source configuration data;

[0061] For details, please refer to the description of step S106 in the above embodiment of the specification, which will not be described in detail here. S212, obtaining the indicator value of the target indicator from the target data source based on the indicator caliber;

[0062] In one embodiment of the present specification, the indicator scope includes the source location of the indicator, such as a specific page and portal, etc., and the indicator value is obtained at the collection location corresponding to the target indicator in the target data source according to the indicator scope of the target indicator.

[0063] S214, generating an indicator calculation result of the target indicator based on the indicator statistical dimension and the indicator value;

[0064] In one embodiment of the present specification, the same indicator may have multiple statistical dimensions, and the indicator value of the target indicator can be statistically calculated according to different statistical dimensions to obtain the indicator calculation result of the target indicator. For example, from the user access log of the product details page, the indicator values ​​such as the number of user visits, the user's visit stay time, and the number of user purchases can be obtained. For the user's visit stay time, the statistical dimension of the stay time exceeding 10 seconds can be used to calculate the proportion of user visit stay time exceeding 10 seconds as the indicator calculation result of the target indicator.

[0065] S216, based on the dimension field of the indicator statistical dimension, obtaining a dimension-related indicator having the same dimension field from the target indicator;

[0066] In one embodiment of the present specification, a dimension field is provided corresponding to the indicator statistical dimension, and the dimension field is used to identify different indicator statistical dimensions. According to the dimension field, dimension-related indicators with the same indicator statistical dimension can be determined. For example, if both commodity A and commodity B have the dimension field of the number of purchasers, then commodity A and commodity B are dimension-related indicators.

[0067] S218, obtaining the indicator calculation result corresponding to the dimension-related indicator and the dimension field, and generating the indicator analysis result of the dimension-related indicator;

[0068] In one embodiment of the present specification, an indicator analysis result of a dimension-related indicator is generated based on the indicator calculation result corresponding to the dimension field and the dimension-related indicator. For example, if the number of people who purchased commodity A is 100 and the number of people who purchased commodity B is 10, an indicator analysis result can be generated: the number of people who purchased commodity B is much lower than that of commodity A. The specific form of the indicator analysis result can be set according to actual needs. By comparing the indicators of the same dimension group, a more intuitive indicator analysis result can be generated.

[0069] In one embodiment of the present specification, the statistical dimension of the indicator is saved in the indicator module to view the dimension, and the database connection information storing the calculation result of the indicator is saved. The unified management of the indicator by the indicator module can make the dimension fields with the same meaning of different indicators point to the same character identifier, so that it can be easily judged whether different indicators have the same dimension information based on this character identifier. It is also possible to clearly know the table name and field name where the dimension and indicator value of each indicator are stored in the database, providing information for dynamically generating the query sql of the data indicator. Secondly, write a script that can dynamically generate the indicator value query sql statement. The main function of the script is to generate the indicator value query sql according to the indicator query conditions entered by the user and the indicator storage information obtained from the indicator module, and execute sql to return the result of the indicator value. Finally, write a script for horizontal splicing of indicator values ​​in the same dimension. The main function of the script is to group different indicator value results according to the same dimension, and place the indicator values ​​of the same dimension group in an array for return.

[0070] See also Figure 5 , Figure 5 An indicator query flow chart of a data processing method is provided for the embodiments of this specification. When an indicator query is required, in order to configure different indicators in a data report, after the report query operation is initiated, each indicator dynamically generates its own query SQL statement based on the user input conditions (such as the indicator selected by the user or the transaction selected by the user) and the indicator configuration data obtained from the indicator module. After waiting for the query results of all indicators to be returned, the indicator value same-dimensional horizontal splicing script is used to group the indicators according to the same dimension, and then return to the report for display.

[0071] S220, acquiring a corresponding indicator threshold value based on the indicator statistical dimension, and comparing the indicator calculation result with the indicator threshold value to obtain a comparison result;

[0072] In one embodiment of the present specification, corresponding indicator thresholds may be set for different indicator statistical dimensions of the target indicator, and the comparison result can be obtained by comparing the indicator calculation result and the indicator threshold. For example, the indicator calculation result is greater than or less than or equal to the indicator threshold, and for another example, the indicator calculation result is ten times the indicator threshold. The indicator threshold is an expected threshold (key threshold) set for different statistical dimensions of the target indicator, which can be manually set by the operator or obtained from historical data statistics, without specific limitation.

[0073] S222: Generate an indicator analysis result of the target transaction based on the comparison result.

[0074] In one embodiment of the present specification, the indicator analysis result of the target transaction is generated based on the comparison result. In this way, the change trend between the indicator value and the threshold value can be monitored in real time. When a change that does not meet the requirements occurs, an alarm can be actively triggered to prompt the transaction operator. At the same time, the indicator analysis results obtained by running other analysis algorithms such as qualitative analysis, quantitative analysis, and data exploration analysis of the indicator can be provided. The operator can quickly grasp the transaction operation status without the need for data research and development, and make corresponding adjustments.

[0075] S224, determining the change trend data of the indicator calculation result under each indicator statistical dimension;

[0076] In one embodiment of the present specification, based on the index calculation results, change trend data can be generated, that is, the distribution of each index itself (normal distribution) is automatically calculated to obtain the change trend data of the index calculation results under different index statistical dimensions. For example, the index calculation results are 10 visits from 10:00 to 10:01, 10 visits from 10:02 to 10:03, 8 visits from 10:04 to 10:05, etc., and the change trend data of the number of visits is generated.

[0077] S226: Generate an indicator analysis result of the target transaction based on the change trend data.

[0078] In one embodiment of the present specification, the target object indicator analysis result is generated based on the change trend data of the target indicator. When the indicator value is abnormal, an alarm is actively sent to a pre-set transaction group or an email is sent to notify the relevant transaction personnel. For example, if the change trend of the number of visits to a newly launched product has been maintained at a low value and has not increased, an exposure abnormality alarm can be generated to prompt the relevant transaction personnel.

[0079] Optionally, after determining the target indicator, you can also automatically generate the indicator real-time monitoring configuration to automatically determine the target indicator's change trend data, realize automatic monitoring of the indicator, and reduce operational difficulty. Of course, you can also manually modify the indicator real-time monitoring configuration, such as manually modifying the real-time monitoring name, effective time, filter dimension, trigger condition, notification method, noise reduction, etc.

[0080] In an embodiment of the present specification, transaction process data corresponding to a target transaction is obtained from a pre-stored transaction process database, a target indicator and an indicator scope and an indicator statistical dimension of the target indicator are determined based on process nodes in the transaction process data, indicator relationship data of the target indicator is generated based on relationship data of the process nodes, indicator configuration data of the target indicator is generated based on the indicator scope, the indicator statistical dimension and the indicator relationship data, a target data source corresponding to the target transaction is obtained based on preset data source configuration data, an indicator value of the target indicator is obtained from the target data source based on the indicator scope, an indicator calculation result of the target indicator is generated based on the indicator statistical dimension and the indicator value, after obtaining the indicator calculation result, a dimension-related indicator having the same dimension field can be obtained from the target indicator based on the dimension field of the indicator statistical dimension, the indicator calculation result corresponding to the dimension-related indicator and the dimension field can be obtained, and an indicator analysis result of the dimension-related indicator can be generated; it is also possible to obtain a corresponding indicator threshold value based on the indicator statistical dimension, compare the indicator calculation result with the indicator threshold value to obtain a comparison result, and generate an indicator analysis result of the target transaction based on the comparison result; or, determine change trend data of the indicator calculation result under each indicator statistical dimension, and generate an indicator analysis result of the target transaction based on the change trend data. By analyzing the transaction process data, the target indicators of the target transaction, the caliber of the target indicators and the indicator statistical dimensions can be automatically generated according to the process nodes, and the indicator relationship data can be determined according to the node relationship data of the process nodes, so that the data required for collecting indicators can be obtained, and the efficiency of indicator collection can be improved; the indicator values ​​of the target indicators are counted according to the indicator statistical dimensions, which can make the display of indicator data more intuitive and convenient for operators to analyze. In addition, three indicator analysis methods are provided. By comparing indicators with the same dimensions, comparing indicator thresholds and calculating their own change trends, the automatic analysis of indicators is realized, which reduces the difficulty and threshold of data analysis.

[0081] See also Figure 6 , which is a flow chart of a data processing method provided in the embodiment of this specification. Figure 6 As shown, the method of the embodiment of this specification may include the following steps S302-S308:

[0082] S302, obtaining data source configuration data, wherein the data source configuration data at least includes connection information of a data source table, data warehouse connection information, and data cleaning conversion logic;

[0083] In one embodiment of the present specification, the connection information of the data source table refers to the information of the database or file system where the data to be accessed is located. This includes the database type (such as MySQL, Hadoop HDFS, etc.), server address, port number, database name, username, password, etc. This information allows the application or ETL (extraction, transformation, loading) tool to correctly connect to the data store and read data from it. Data warehouse connection information refers to the location and related authentication information of the target data warehouse. It usually contains the type of data warehouse (such as Amazon Redshift, Snowflake, etc.), information of the server or cloud service provider, and necessary authentication credentials. This information is used to correctly write the processed data into the data warehouse to ensure that the data can be used by subsequent analysis tools. Data cleaning conversion logic refers to a series of operations that need to be performed during the transfer of data from the source system to the data warehouse. These operations may include but are not limited to data formatting, data validation, deduplication, data type conversion, data aggregation, data standardization, etc.

[0084] By applying these logics, data quality can be ensured and the data can be made more suitable for analytical purposes. For example, data cleaning can remove erroneous or incomplete records, while data transformation can make the data structure conform to the requirements of the data warehouse.

[0085] Specifically, the data source configuration data can be set by the user in the visualization configuration page.

[0086] S304, determining a target data source corresponding to the target transaction based on the connection information of the data source table;

[0087] In one embodiment of the present specification, when a specific target transaction is given, it is necessary to determine which specific data source the indicator acquisition operation of the target transaction should be performed on based on the connection information in the pre-defined data source table. By pre-defining a mapping rule or logic, this rule associates different transactions with corresponding data sources, so that the transaction identifier or transaction type of the target transaction queries the data source table to find the corresponding record. For example, if the target transaction is "obtain sales data", then this operation should point to the SalesDB data source.

[0088] S306, acquiring the indicator calculation result of the target indicator from the target data source based on the indicator configuration data and the data cleaning conversion logic;

[0089] In one embodiment of the present specification, the rules or parameters for calculating specific transaction indicators are defined in the indicator configuration data. These configuration data may include information such as the calculation formula of the indicator, the required data fields, the time range, etc. Through the indicator configuration data, it is possible to know what raw data is required to calculate a certain indicator and how to process the data. After identifying the target data source containing the raw data required to calculate these indicators, the data fields specified in the indicator configuration are extracted from the target data source, and the extracted data is cleaned and converted as necessary according to the data cleaning and conversion logic to ensure the quality of the data. The calculation logic in the indicator configuration data is applied to obtain the final indicator calculation result.

[0090] S308: Store the indicator calculation result in the data warehouse corresponding to the data warehouse connection information.

[0091] In one embodiment of the present specification, the storage location of the indicator calculation result is determined according to the connection information of the data warehouse, and then the indicator calculation result is stored in the determined data warehouse.

[0092] See also Figure 7 , Figure 7 The present invention provides an example schematic diagram of a data processing method. Figure 7 As shown in the figure, it is the page for creating a data source and configuring the data cleaning and transformation steps. When creating a data source, you can configure the data source name, data source identifier, data source table, data warehouse meta table, etc., and Figure 7 A data conversion step and the result display of the data example conversion are shown in the figure. The commonly used data cleaning and conversion logic can be implemented using Java code and pre-set in the configuration page. If you encounter a special data format that needs to be converted, you can write a conversion script by setting a custom conversion method. Then click the Submit Flink Task button, which will automatically convert into Flink task code and submit it to the Flink computing engine for execution. That is, the indicator calculation is implemented through the integrated stream and batch Flink task. The execution of the two tasks does not affect each other and the processing logic can be guaranteed to be completely consistent. Therefore, it will not cause delays in real-time tasks.

[0093] Optionally, metadata such as indicator configuration data and data source configuration data can be written into the database, which can reduce the workload of operation and maintenance. Compared with manual maintenance, it can also ensure the accuracy of the maintained data information and improve data research and development efficiency. In addition, the automatic writing method does not require the maintenance of ETL, OLAP, BI, etc. All indicator information calculations and data collection and conversion are configured through visual pages. The realization of automated indicator analysis greatly reduces the difficulty and threshold of data analysis.

[0094] In an embodiment of the present specification, by obtaining data source configuration data, the target data source corresponding to the target transaction is determined based on the connection information of the data source table, the indicator calculation result of the target indicator is obtained from the target data source based on the indicator configuration data and the data cleaning conversion logic, and the indicator calculation result is stored in the data warehouse corresponding to the data warehouse connection information. Through the data source configuration data, the relevant information of all data sources can be centrally managed and maintained. When the data source or its connection information needs to be changed, it only needs to be modified in one place, which reduces the possibility of errors, and this method makes the system easier to expand. By defining the indicator configuration data and the data cleaning conversion logic, the automation of data processing can be realized, the need for manual intervention can be reduced, and the processing speed can be improved. In addition, when the transaction requirements change, the indicator configuration data and the data cleaning conversion logic can be adjusted to quickly adapt to the new requirements without large-scale reconstruction of the entire system.

[0095] The following will be combined with the attached Figure 8 , the data processing device provided in the embodiments of this specification is introduced in detail. Figure 8 The data processing device in the present invention is used to execute the data processing device in the present invention. Figure 2-Figure 7 For the convenience of explanation, only the part related to the embodiment of this specification is shown. For the specific technical details not disclosed, please refer to this specification. Figure 2-Figure 7 The embodiment shown.

[0096] See also Figure 8 , which shows a schematic diagram of the structure of a data processing device provided by an exemplary embodiment of the present specification. The data processing device can be implemented as all or part of the device through software, hardware or a combination of both. The device 1 includes a transaction acquisition unit 11, a data acquisition unit 12, an indicator acquisition unit 13 and an analysis unit 14.

[0097] The transaction acquisition unit 11 is used to acquire transaction process data corresponding to the target transaction, and acquire the target indicator and indicator configuration data of the target indicator based on the transaction process data;

[0098] A data acquisition unit 12, configured to acquire a target data source corresponding to the target transaction based on preset data source configuration data;

[0099] An indicator acquisition unit 13, configured to acquire an indicator calculation result of the target indicator from the target data source based on the indicator configuration data;

[0100] The analyzing unit 14 is used to analyze the indicator calculation result according to the indicator configuration data to obtain the indicator analysis result of the target transaction.

[0101] Optionally, the first matching module 12 is specifically used to obtain the request content and the response content in the traffic data;

[0102] Detecting the request content and the response content according to the sensitive information detection rule;

[0103] The traffic data corresponding to the request content containing sensitive information and / or the response content containing sensitive information is confirmed as sensitive traffic data.

[0104] Optionally, the transaction acquisition unit 11 is specifically configured to acquire the transaction process data corresponding to the target transaction from a pre-stored transaction process database;

[0105] Determine the target indicator and the indicator caliber and indicator statistical dimension of the target indicator based on the process nodes in the transaction process data;

[0106] Generate indicator relationship data of the target indicator according to the relationship data of the process nodes;

[0107] Generate indicator configuration data of the target indicator based on the indicator scope, the indicator statistical dimension and the indicator relationship data.

[0108] Optionally, the indicator acquisition unit 13 is specifically used to acquire the indicator value of the target indicator from the target data source based on the indicator caliber;

[0109] Generate an indicator calculation result of the target indicator based on the indicator statistical dimension and the indicator value. Optionally, the analysis unit 14 is specifically used to obtain a dimension-related indicator with the same dimension field from the target indicator based on the dimension field of the indicator statistical dimension;

[0110] Obtain the indicator calculation results corresponding to the dimension-related indicators and the dimension fields, and generate indicator analysis results of the dimension-related indicators.

[0111] Optionally, the analysis unit 14 is specifically configured to obtain a corresponding indicator threshold value based on the indicator statistical dimension, and compare the indicator calculation result with the indicator threshold value to obtain a comparison result;

[0112] Generate an indicator analysis result of the target transaction based on the comparison result.

[0113] Optionally, the analysis unit 14 is specifically used to determine the change trend data of the indicator calculation result under each indicator statistical dimension;

[0114] Generate an indicator analysis result of the target transaction based on the change trend data.

[0115] Optionally, the data acquisition unit 12 is specifically used to acquire data source configuration data, and the data source configuration data at least includes connection information of a data source table, data warehouse connection information, and data cleaning conversion logic;

[0116] Determine the target data source corresponding to the target transaction based on the connection information of the data source table;

[0117] Optionally, the indicator acquisition unit 13 is specifically used to acquire the indicator calculation result of the target indicator from the target data source based on the indicator configuration data and the data cleaning conversion logic;

[0118] The indicator calculation results are stored in the data warehouse corresponding to the data warehouse connection information.

[0119] It should be noted that, when the data processing device provided in the above embodiment executes the data processing method, only the division of the above functional modules is used as an example. In actual applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the device is divided into different functional modules to complete all or part of the functions described above. In addition, the data processing device provided in the above embodiment and the data processing method embodiment belong to the same concept, and the implementation process thereof is detailed in the method embodiment, which will not be repeated here.

[0120] The serial numbers of the embodiments of the present specification are for description only and do not represent the advantages and disadvantages of the embodiments. In some cases, the actions or steps recorded in the claims can be performed in an order different from that in the embodiments and still achieve the desired results. In addition, the processes depicted in the drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0121] The present specification also provides a storage medium having a computer program stored thereon. When the computer program is executed by a processor, the above-mentioned Figure 2-Figure 7 The data processing method of the embodiment shown in the figure can be specifically executed by referring to Figure 2-Figure 7 The specific description of the illustrated embodiment will not be repeated here.

[0122] Please refer to Fig. 9 , which shows a schematic diagram of the structure of an electronic device provided by an exemplary embodiment of this specification. The electronic device in this specification may include one or more of the following components: a processor 110, a memory 120, an input device 130, an output device 140, and a bus 150. The processor 110, the memory 120, the input device 130, and the output device 140 may be connected via the bus 150.

[0123] The processor 110 may include one or more processing cores. The processor 110 uses various interfaces and lines to connect various parts of the entire electronic device, and executes various functions and processes data of the terminal 100 by running or executing instructions, programs, code sets or instruction sets stored in the memory 120, and calling data stored in the memory 120. Optionally, the processor 110 can be implemented in at least one hardware form of digital signal processing (DSP), field programmable gate array (FPGA), and programmable logic array (PLA). The processor 110 can integrate one or a combination of a central processing unit (CPU), a graphics processing unit (GPU), and a modem. Among them, the CPU mainly processes the operating system, user pages, and applications; the GPU is responsible for rendering and drawing display content; and the modem is used to process wireless communications. It can be understood that the above-mentioned modem may not be integrated into the processor 110, but may be implemented separately through a communication chip.

[0124] The memory 120 may include a random access memory (RAM) or a read-only memory (ROM). Optionally, the memory 120 includes a non-transitory computer-readable medium (Non-Transitory Computer-Readable Storage Medium). The memory 120 may be used to store instructions, programs, codes, code sets or instruction sets. The memory 120 may include a program storage area and a data storage area, wherein the program storage area may store instructions for implementing an operating system, instructions for implementing at least one function (such as a touch function, a sound playback function, an image playback function, etc.), instructions for implementing the above-mentioned various method embodiments, etc. The operating system may be an Android system, including a system deeply developed based on the Android system, an IOS system developed by Apple, including a system deeply developed based on the IOS system or other systems.

[0125] The memory 120 can be divided into an operating system space and a user space. The operating system runs in the operating system space, and native and third-party applications run in the user space. In order to ensure that different third-party applications can achieve good operating results, the operating system allocates corresponding system resources to different third-party applications. However, the requirements for system resources in different application scenarios in the same third-party application are also different. For example, in the local resource loading scenario, the third-party application has higher requirements for disk reading speed; in the animation rendering scenario, the third-party application has higher requirements for GPU performance. The operating system and third-party applications are independent of each other, and the operating system often cannot perceive the current application scenario of the third-party application in a timely manner, resulting in the operating system being unable to perform targeted system resource adaptation according to the specific application scenario of the third-party application.

[0126] In order for the operating system to distinguish the specific application scenarios of third-party applications, it is necessary to open up data communication between third-party applications and the operating system so that the operating system can obtain the current scenario information of third-party applications at any time, and then perform targeted system resource adaptation based on the current scenario.

[0127] The input device 130 is used to receive input commands or data, and includes but is not limited to a keyboard, a mouse, a camera, a microphone, or a touch device. The output device 140 is used to output commands or data, and includes but is not limited to a display device and a speaker. In one example, the input device 130 and the output device 140 can be combined, and the input device 130 and the output device 140 are touch screen displays.

[0128] The touch display screen can be designed as a full screen, a curved screen or a special-shaped screen. The touch display screen can also be designed as a combination of a full screen and a curved screen, or a combination of a special-shaped screen and a curved screen, which is not limited in the embodiments of this specification.

[0129] In addition, those skilled in the art can understand that the structure of the electronic device shown in the above drawings does not constitute a limitation on the electronic device, and the electronic device may include more or fewer components than shown, or combine certain components, or arrange the components differently. For example, the electronic device also includes components such as a radio frequency circuit, an input unit, a sensor, an audio circuit, a WiFi module, a power supply, and a Bluetooth module, which will not be described in detail here.

[0130] exist Fig. 9 In the electronic device shown, the processor 110 may be used to call a computer application stored in the memory 120 and specifically perform the following operations:

[0131] Acquire transaction process data corresponding to the target transaction, and acquire a target indicator and indicator configuration data of the target indicator based on the transaction process data;

[0132] Acquire a target data source corresponding to the target transaction based on preset data source configuration data;

[0133] Acquire the indicator calculation result of the target indicator from the target data source based on the indicator configuration data;

[0134] The indicator calculation result is analyzed according to the indicator configuration data to obtain the indicator analysis result of the target transaction.

[0135] In one embodiment, when the processor 110 acquires the transaction process data corresponding to the target transaction and acquires the target indicator and the indicator configuration data of the target indicator based on the transaction process data, the processor 110 specifically performs the following operations:

[0136] Acquire transaction process data corresponding to the target transaction from a pre-stored transaction process database;

[0137] Determine the target indicator and the indicator caliber and indicator statistical dimension of the target indicator based on the process nodes in the transaction process data;

[0138] Generate indicator relationship data of the target indicator according to the relationship data of the process nodes;

[0139] Generate indicator configuration data of the target indicator based on the indicator scope, the indicator statistical dimension and the indicator relationship data.

[0140] In one embodiment, when the processor 110 obtains the indicator calculation result of the target indicator from the target data source based on the indicator configuration data, the processor 110 specifically performs the following operations:

[0141] Acquire the indicator value of the target indicator from the target data source based on the indicator caliber;

[0142] An indicator calculation result of the target indicator is generated based on the indicator statistical dimension and the indicator value.

[0143] In one embodiment, when the processor 110 analyzes the indicator calculation result according to the indicator configuration data to obtain the indicator analysis result of the target transaction, the processor 110 specifically performs the following operations:

[0144] Based on the dimension field of the indicator statistical dimension, obtaining dimension-related indicators having the same dimension field from the target indicator;

[0145] Obtain the indicator calculation results corresponding to the dimension-related indicators and the dimension fields, and generate indicator analysis results of the dimension-related indicators.

[0146] In one embodiment, when the processor 110 analyzes the indicator calculation result according to the indicator configuration data to obtain the indicator analysis result of the target transaction, the processor 110 specifically performs the following operations:

[0147] Acquire a corresponding indicator threshold value based on the indicator statistical dimension, and compare the indicator calculation result with the indicator threshold value to obtain a comparison result;

[0148] Generate an indicator analysis result of the target transaction based on the comparison result.

[0149] In one embodiment, when the processor 110 analyzes the indicator calculation result according to the indicator configuration data to obtain the indicator analysis result of the target transaction, the processor 110 specifically performs the following operations:

[0150] Determine the change trend data of the indicator calculation results under each indicator statistical dimension;

[0151] Generate an indicator analysis result of the target transaction based on the change trend data.

[0152] In one embodiment, when the processor 110 acquires the target data source corresponding to the target transaction based on the preset data source configuration data, the processor 110 specifically performs the following operations:

[0153] Acquire data source configuration data, wherein the data source configuration data at least includes connection information of a data source table, data warehouse connection information, and data cleaning conversion logic;

[0154] The target data source corresponding to the target transaction is determined based on the connection information of the data source table.

[0155] In one embodiment, when the processor 110 obtains the indicator calculation result of the target indicator from the target data source based on the indicator configuration data, the processor 110 specifically performs the following operations:

[0156] Acquire the indicator calculation result of the target indicator from the target data source based on the indicator configuration data and the data cleaning conversion logic;

[0157] The indicator calculation results are stored in the data warehouse corresponding to the data warehouse connection information.

[0158] In the embodiments of this specification, by obtaining the transaction process data corresponding to the target transaction, obtaining the target indicator and the indicator configuration data of the target indicator based on the transaction process data, obtaining the target data source corresponding to the target transaction based on the preset data source configuration data, obtaining the indicator calculation result of the target indicator from the target data source based on the indicator configuration data, and analyzing the indicator calculation result according to the indicator configuration data to obtain the indicator analysis result of the target transaction. For the target transaction that needs to be analyzed for data, there is no need for code development, and the indicator is confirmed according to the transaction process data of the target transaction, and the indicator data collection and cleaning process can be managed through visual configuration to quickly obtain the indicator calculation result of the target indicator, and realize automated indicator analysis, thereby reducing the difficulty and threshold of data analysis.

[0159] Further, by obtaining the transaction process data corresponding to the target transaction from a pre-stored transaction process database, determining the target indicator and the indicator caliber and indicator statistical dimension of the target indicator based on the process nodes in the transaction process data, generating the indicator relationship data of the target indicator according to the relationship data of the process nodes, generating the indicator configuration data of the target indicator based on the indicator caliber, the indicator statistical dimension and the indicator relationship data, obtaining the target data source corresponding to the target transaction based on the preset data source configuration data, obtaining the indicator value of the target indicator from the target data source based on the indicator caliber, and generating the indicator calculation result of the target indicator based on the indicator statistical dimension and the indicator value, after obtaining the indicator calculation result, the dimension-related indicator with the same dimension field can be obtained from the target indicator based on the dimension field of the indicator statistical dimension, the indicator calculation result corresponding to the dimension-related indicator and the dimension field can be obtained, and the indicator analysis result of the dimension-related indicator can be generated; it is also possible to obtain the corresponding indicator threshold based on the indicator statistical dimension, compare the indicator calculation result with the indicator threshold to obtain the comparison result, and generate the indicator analysis result of the target transaction based on the comparison result; or, determine the change trend data of the indicator calculation result under each indicator statistical dimension, and generate the indicator analysis result of the target transaction based on the change trend data. By analyzing the transaction process data, the target indicators of the target transaction, the caliber of the target indicators and the indicator statistical dimensions can be automatically generated according to the process nodes, and the indicator relationship data can be determined according to the node relationship data of the process nodes, so that the data required for collecting indicators can be obtained, and the efficiency of indicator collection can be improved; the indicator values ​​of the target indicators are counted according to the indicator statistical dimensions, which can make the display of indicator data more intuitive and convenient for operators to analyze. In addition, three indicator analysis methods are provided. By comparing indicators with the same dimensions, comparing indicator thresholds and calculating their own change trends, the automatic analysis of indicators is realized, which reduces the difficulty and threshold of data analysis.

[0160] Furthermore, by obtaining the data source configuration data, the target data source corresponding to the target transaction is determined based on the connection information of the data source table, the indicator calculation results of the target indicators are obtained from the target data source based on the indicator configuration data and the data cleaning conversion logic, and the indicator calculation results are stored in the data warehouse corresponding to the data warehouse connection information. Through the data source configuration data, the relevant information of all data sources can be centrally managed and maintained. When the data source or its connection information needs to be changed, it only needs to be modified in one place, which reduces the possibility of errors, and this method makes the system easier to expand. By defining the indicator configuration data and the data cleaning conversion logic, data processing can be automated, the need for manual intervention can be reduced, and the processing speed can be improved. In addition, when the transaction requirements change, the indicator configuration data and the data cleaning conversion logic can be adjusted to quickly adapt to the new requirements without large-scale reconstruction of the entire system.

[0161] In addition, an embodiment of the present specification provides a computer program product, wherein the computer program product includes a computer program, and when the computer program is executed by a processor of an electronic device, the processor can at least implement the above-mentioned Figures 1 to 7 The methods provided in the illustrated embodiments.

[0162] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiments can be implemented by instructing the relevant hardware through a computer program, and the program can be stored in a computer-readable storage medium, and when the program is executed, it can include the processes of the embodiments of the above-mentioned methods. The storage medium can be a disk, an optical disk, a read-only memory (ROM) or a random access memory (RAM), etc.

[0163] The above disclosure is only the preferred embodiment of this specification, which certainly cannot be used to limit the scope of rights of this specification. Therefore, equivalent changes made according to the claims of this specification are still within the scope covered by this specification.

Claims

1. A data processing method, the method comprising: Acquire transaction process data corresponding to the target transaction, and acquire a target indicator and indicator configuration data of the target indicator based on the transaction process data; Acquire a target data source corresponding to the target transaction based on preset data source configuration data; Acquire the indicator calculation result of the target indicator from the target data source based on the indicator configuration data; The indicator calculation result is analyzed according to the indicator configuration data to obtain the indicator analysis result of the target transaction.

2. The method according to claim 1, wherein the step of obtaining transaction process data corresponding to the target transaction and obtaining the target indicator and indicator configuration data of the target indicator based on the transaction process data comprises: Acquire transaction process data corresponding to the target transaction from a pre-stored transaction process database; Determine the target indicator and the indicator caliber and indicator statistical dimension of the target indicator based on the process nodes in the transaction process data; Generate indicator relationship data of the target indicator according to the relationship data of the process nodes; Generate indicator configuration data of the target indicator based on the indicator scope, the indicator statistical dimension and the indicator relationship data.

3. The method according to claim 2, wherein obtaining the indicator calculation result of the target indicator from the target data source based on the indicator configuration data comprises: Acquire the indicator value of the target indicator from the target data source based on the indicator caliber; An indicator calculation result of the target indicator is generated based on the indicator statistical dimension and the indicator value.

4. The method according to claim 2, wherein the step of analyzing the indicator calculation result according to the indicator configuration data to obtain the indicator analysis result of the target transaction comprises: Based on the dimension field of the indicator statistical dimension, obtaining dimension-related indicators having the same dimension field from the target indicator; Obtain the indicator calculation results corresponding to the dimension-related indicators and the dimension fields, and generate indicator analysis results of the dimension-related indicators.

5. The method according to claim 2, wherein analyzing the indicator calculation result according to the indicator configuration data to obtain the indicator analysis result of the target transaction comprises: Acquire a corresponding indicator threshold value based on the indicator statistical dimension, and compare the indicator calculation result with the indicator threshold value to obtain a comparison result; Generate an indicator analysis result of the target transaction based on the comparison result.

6. The method according to claim 2, wherein the step of analyzing the indicator calculation result according to the indicator configuration data to obtain the indicator analysis result of the target transaction comprises: Determine the change trend data of the indicator calculation results under each indicator statistical dimension; Generate an indicator analysis result of the target transaction based on the change trend data.

7. The method according to claim 1, wherein obtaining the target data source corresponding to the target transaction based on preset data source configuration data comprises: Acquire data source configuration data, wherein the data source configuration data at least includes connection information of a data source table, data warehouse connection information, and data cleaning conversion logic; Determine the target data source corresponding to the target transaction based on the connection information of the data source table; The obtaining the indicator calculation result of the target indicator from the target data source based on the indicator configuration data includes: Acquire the indicator calculation result of the target indicator from the target data source based on the indicator configuration data and the data cleaning conversion logic; The indicator calculation results are stored in the data warehouse corresponding to the data warehouse connection information.

8. A data processing device, comprising: A transaction acquisition unit, configured to acquire transaction process data corresponding to a target transaction, and acquire a target indicator and indicator configuration data of the target indicator based on the transaction process data; A data acquisition unit, configured to acquire a target data source corresponding to the target transaction based on preset data source configuration data; An indicator acquisition unit, configured to acquire an indicator calculation result of the target indicator from the target data source based on the indicator configuration data; An analysis unit is used to analyze the indicator calculation result according to the indicator configuration data to obtain the indicator analysis result of the target transaction.

9. An electronic device, comprising: Processor and memory; The memory stores a computer program, wherein the computer program is suitable for being loaded by the processor and executing the steps of the method according to any one of claims 1 to 7.

10. A storage medium storing a computer program, wherein the computer program, when executed by a processor, implements the steps of the method according to any one of claims 1 to 7.

11. A computer program product comprising: A computer program, when executed by a processor of an electronic device, causes the processor to perform the steps of the method as claimed in any one of claims 1 to 7.

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