Processing method, system and equipment for data configuration release and medium

Abstract

The invention discloses a data configuration release processing method, system and device and a medium, and the method specifically comprises the steps: configuring a development environment according to a configuration management system, setting an index configuration table in the development environment, generating a corresponding SQL statement according to the index configuration table, and sending the SQL statement to a server; the index configuration table is used for recording index information and dimension information required by each business report; establishing a change registration form, and writing a change record into the change registration form every time the configuration of the index configuration table is changed; performing automatic proofreading on the change configuration data submitted by different developers, identifying difference conflicts of the change configuration data, and forming a change proofreading table; and issuing the change configuration data to a pre-issuing environment for automatic testing, and issuing the change configuration data to a production environment after the automatic testing is passed. According to the method, the problem of configuration data management in multi-user collaborative development is effectively solved, and the efficiency and stability of report development and delivery are improved.

Description

Technical Field

[0001] The present invention relates to the field of data processing technology, and in particular to a processing method, system, device and medium for data configuration release. Background Art

[0002] In current data query and analysis service platforms, such as the Tiansuan platform, data report configuration development is the core link for rapid report delivery. The Tiansuan platform allows business developers to define the indicators, dimensions, and fact tables required for reports through non-code-level data configuration through configuration development, and then generate the calculation logic SQL of the report indicators, execute the SQL and return the results. This method greatly improves the efficiency of report development and meets the needs of rapid business response.

[0003] However, the current configuration development process faces a series of problems and risks in actual operation. First, after developers develop configurations in the development library of the Tiansuan platform, they publish them to the pre-release environment for further verification after basic verification. In this process, developers directly operate on the database, lacking a systematic change management and verification mechanism, which can easily lead to configuration errors due to improper operation or negligence, and then abnormal problems will occur after the report is launched.

[0004] Secondly, in the current configuration release process, the verification of SQL statements and data mainly relies on manual operations. This not only increases the complexity and workload of operations, but also easily introduces human errors. Especially when the demand development cycle is long, recording and tracking each configuration change becomes particularly cumbersome, greatly increasing the difficulty of data management and maintenance.

[0005] Furthermore, the current process lacks automated testing and verification, which means that the stability and reliability of configuration changes cannot be fully guaranteed. Once there is a problem with the configuration change, it is often not discovered until after it goes online, which not only affects the normal operation of the business, but may also damage the accuracy and integrity of the data. Summary of the invention

[0006] The purpose of the present invention is to provide a processing method, system, device and medium for data configuration release, which effectively solves the configuration data management problem in multi-person collaborative development, improves the efficiency and stability of report development and delivery, and provides strong support for configuration optimization and risk control, so as to solve at least one of the above-mentioned prior art problems.

[0007] In a first aspect, the present invention provides a method for processing data configuration release, the method specifically comprising:

[0008] Configure the development environment according to the configuration management system, set an indicator configuration table in the development environment, generate corresponding SQL statements according to the indicator configuration table, and the indicator configuration table is used to record the indicator information and dimension information required for each business report;

[0009] Establish a change registration table, and write the change record into the change registration table every time a configuration change occurs in the indicator configuration table;

[0010] Automatically proofread the changed configuration data submitted by different developers, identify the differences and conflicts of each changed configuration data, and form a change proofreading table;

[0011] Publishing the changed configuration data to a pre-release environment for automated testing, and then publishing the changed configuration data to a production environment after passing the automated testing;

[0012] Check whether the changed configuration data released to the production environment meets expectations. If not, restore to the previous version of the configuration data through the rollback function.

[0013] In a second aspect, the present invention provides a data configuration publishing processing system, the system specifically comprising:

[0014] A first processing module is used to configure a development environment according to a configuration management system, set an indicator configuration table in the development environment, and generate a corresponding SQL statement according to the indicator configuration table, wherein the indicator configuration table is used to record indicator information and dimension information required for each business report;

[0015] The second processing module is used to establish a change registration table, and write the change record into the change registration table every time a configuration change occurs in the indicator configuration table;

[0016] The third processing module is used to automatically proofread the changed configuration data submitted by different developers, identify the differences and conflicts of each changed configuration data, and form a change proofreading table;

[0017] A fourth processing module is used to publish the changed configuration data to a pre-release environment for automated testing, and then publish the changed configuration data to a production environment after passing the automated testing;

[0018] The fifth processing module is used to detect whether the changed configuration data released to the production environment meets expectations. If not, the configuration data of the previous version is restored through a rollback function.

[0019] In a third aspect, the present invention provides a computer device, comprising: a memory and a processor and a computer program stored in the memory, and when the computer program is executed on the processor, a processing method for data configuration release as described in any one of the above methods is implemented.

[0020] In a fourth aspect, the present invention provides a computer-readable storage medium having a computer program stored thereon, and when the computer program is executed by a processor, the processing method for data configuration release as described in any one of the above methods is implemented.

[0021] Compared with the prior art, the present invention has at least one of the following technical effects:

[0022] 1. The present invention effectively solves the configuration data management problem in multi-person collaborative development, improves the efficiency and stability of report development and delivery, and provides strong support for configuration optimization and risk control.

[0023] 2. The present invention realizes systematic management and automatic generation of report configuration information by setting an indicator configuration table in a development environment and generating corresponding SQL statements according to the indicator configuration table, thereby improving the accuracy and efficiency of configuration.

[0024] 3. The present invention establishes a change registration table, records the change information each time the configuration is changed, and automatically proofreads the change configuration data submitted by different developers, effectively avoiding configuration errors caused by improper operation or negligence, and improving the reliability and stability of the configuration.

[0025] 4. The present invention realizes automated verification and testing of configuration changes by publishing the changed configuration data to a pre-release environment for automated testing, and then publishing it to a production environment after passing the test, thereby reducing the risk after going online.

[0026] 5. The present invention ensures business continuity and data integrity by detecting whether the changed configuration data released to the production environment achieves the expected effect, and restoring to the previous version of the configuration data through a rollback function if it does not achieve the expected effect. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0028] Figure 1 It is a flowchart of a processing method for data configuration release provided by the first embodiment of the present invention;

[0029] Figure 2 It is a flowchart of a processing method for data configuration release provided by the second embodiment of the present invention;

[0030] Figure 3It is a flowchart of a processing method for data configuration release provided by the third embodiment of the present invention;

[0031] Figure 4 It is a flowchart of a processing method for data configuration release provided by the fourth embodiment of the present invention;

[0032] Figure 5 is a flowchart of a processing method for data configuration release provided by a fifth embodiment of the present invention;

[0033] Figure 6 It is a flowchart of a processing method for data configuration release provided by the sixth embodiment of the present invention;

[0034] Figure 7 is a flowchart of a processing method for data configuration release provided by the seventh embodiment of the present invention;

[0035] Figure 8 is a flowchart of a method for processing data configuration release provided by an eighth embodiment of the present invention;

[0036] Fig. 9 It is a structural diagram of a data configuration publishing processing system provided by an embodiment of the present invention;

[0037] Fig.10 It is a structural schematic diagram of a computer device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0038] In the following description, specific details such as specific system structures, technologies, etc. are provided for the purpose of illustration rather than limitation, so as to provide a thorough understanding of the embodiments of the present application. However, it should be clear to those skilled in the art that the present application may also be implemented in other embodiments without these specific details. In other cases, detailed descriptions of well-known systems, devices, circuits, and methods are omitted to prevent unnecessary details from obstructing the description of the present application.

[0039] It should be understood that when used in the present specification and the appended claims, the term "comprising" indicates the presence of described features, wholes, steps, operations, elements and / or components, but does not exclude the presence or addition of one or more other features, wholes, steps, operations, elements, components and / or combinations thereof.

[0040] It should also be understood that the term “and / or” used in the specification and appended claims refers to any and all possible combinations of one or more of the associated listed items, and includes these combinations.

[0041] As used in the specification and appended claims of this application, the term "if" can be interpreted as "when" or "uponce" or "in response to determining" or "in response to detecting", depending on the context. Similarly, the phrase "if it is determined" or "if [described condition or event] is detected" can be interpreted as meaning "uponce it is determined" or "in response to determining" or "uponce [described condition or event] is detected" or "in response to detecting [described condition or event]", depending on the context.

[0042] In addition, in the description of the present application specification and the appended claims, the terms "first", "second", "third", etc. are only used to distinguish the descriptions and cannot be understood as indicating or implying relative importance.

[0043] References to "one embodiment" or "some embodiments" etc. described in the specification of this application mean that one or more embodiments of the present application include specific features, structures or characteristics described in conjunction with the embodiment. Therefore, the statements "in one embodiment", "in some embodiments", "in some other embodiments", "in some other embodiments", etc. that appear in different places in this specification do not necessarily refer to the same embodiment, but mean "one or more but not all embodiments", unless otherwise specifically emphasized in other ways. The terms "including", "comprising", "having" and their variations all mean "including but not limited to", unless otherwise specifically emphasized in other ways.

[0044] In the embodiment of the present application, the execution subject of the process includes a terminal device, which includes but is not limited to: a server, a computer, a smart phone, a tablet computer, and other devices capable of executing the method disclosed in the present application. Figure 1 A schematic flow chart of a method for processing data configuration release disclosed in the first embodiment of the present invention is shown, and the details are as follows:

[0045] S101, configure a development environment according to a configuration management system, set an indicator configuration table in the development environment, and generate corresponding SQL statements according to the indicator configuration table, wherein the indicator configuration table is used to record indicator information and dimension information required for each business report.

[0046] In this embodiment, the indicator configuration table may include a primary key, an indicator name, a calculation formula, dimension information, an indicator description, etc., and an executable SQL statement is generated through the information of each indicator according to the SQL statement template predefined in the configuration management system.

[0047] S102, establishing a change registration table, and writing a change record into the change registration table each time a configuration change occurs in the indicator configuration table.

[0048] In this embodiment, on the basis of the indicator configuration table, a configuration change registration table is established to record the detailed information of each configuration change, including the changer, change time, change content, etc. Through the database trigger mechanism, the change record is automatically written into the change registration table when the configuration table changes, forming a complete configuration change history record, providing data support for subsequent problem location and rollback. Specifically, a change registration table is created, including the fields: log ID, configuration item ID, changer, change time, content before change, content after change, and operation type. Among them, the configuration item ID is used as a foreign key to associate with the configuration table, which is used to record the detailed information of each configuration change. In order to automatically capture the changes in the configuration table, a database trigger is created and bound to the configuration table. The trigger monitors INSERT, UPDATE, and DELETE events. When the trigger captures the change event of the configuration table, the corresponding change information is obtained according to the event type. For INSERT events, the ID, changer, change time, content after change, and operation type "new" of the newly added configuration item are recorded; for UPDATE events, the configuration content, configuration item ID, changer, change time, and operation type "modification" before and after the modification are recorded; for DELETE events, the ID, changer, change time, content before deletion, and operation type "deletion" of the deleted configuration item are recorded.

[0049] S103, automatically proofreading the changed configuration data submitted by different developers, identifying the differences and conflicts of each changed configuration data, and forming a change proofreading table.

[0050] In this embodiment, a configuration change proofreading and merging function module is developed to automatically proofread the configuration changes submitted by different developers, identify different configuration versions of the same indicator ID, and determine whether there is a conflict by comparing the differences in key information such as indicator meaning and calculation logic. If there is a conflict, the user is prompted to perform a manual merge to ensure the accuracy and consistency of the merged configuration data.

[0051] S104, publishing the changed configuration data to a pre-release environment for automated testing, and then publishing the changed configuration data to a production environment after passing the automated testing.

[0052] In this embodiment, before the configuration data is released to the online environment, the changed configuration data can be first released to the pre-release environment, and the correctness of the configuration data can be detected by the automated test script. Specifically, the configuration data changes are deployed to the pre-release environment using the Ansible automated deployment tool. After the deployment is completed, the version number of the configuration data in the pre-release environment is obtained. The automated test script is written using the JUnit test framework to perform correctness detection on the new version of the configuration data in the pre-release environment, including data format, numerical range and other aspects. The result report of the JUnit automated test is obtained and the test result data is analyzed. If the test result shows that there is an error, the error information is extracted, an error report is generated, and the error report is sent to the relevant developers through Jenkins, and the release process is suspended at the same time. If the test result shows that all test cases pass, the configuration data changes are marked as "pre-release test passed" in the version control system such as Git. The configuration data changes are pushed to the Jenkins queue to be released and enter the online release process. Then, the configuration data changes to be released are obtained from the Jenkins queue to be released, and its status in Git is verified to be "pre-release test passed". If the status check passes, the subsequent process continues; if the status check fails, the release is rejected and the rejection information is recorded in the release log. Use the Ansible automated deployment tool to deploy the configuration data changes that have passed the verification to the online environment. After the deployment is completed, obtain the version number of the configuration data in the online environment and record the release results in the release log. At the same time, mark the configuration data changes as "published" in Git.

[0053] S105, detecting whether the changed configuration data released to the production environment meets expectations, and if not, restoring to the previous version of configuration data through a rollback function.

[0054] In this embodiment, a version management mechanism is introduced to perform version control on the changes of configuration data. A unique version number is generated each time the configuration data is released. Different configuration data sets are identified and managed by the version number. The configuration data is backed up during the release process, and the corresponding relationship between the release version and the configuration data is recorded to provide support for subsequent rollback operations. Specifically, the version control system git is used to record the change history of the configuration data and generate a version library. Each time the configuration data change is submitted, git will automatically generate version difference information and record the version evolution process. The hash value of the configuration data content is calculated by the SHA-256 hash algorithm to obtain a unique version number, which is stored in the git repository in correspondence with the configuration data version. When the configuration data is released, the current configuration data version is obtained, and the corresponding version number is obtained from the git repository. A backup copy of the configuration data is created according to the version number, and the version number is associated with the backup copy and stored. If the same configuration data already exists in the backup library, it is prompted that the configuration data already exists, and the release process is terminated. The release time of the configuration data version is recorded using a timestamp, and the timestamp is stored in correspondence with the version number. Git itself supports recording submission time and can be used directly. Different versions of configuration data are managed using git's tree structure. Git's branching function can record parallel version evolution paths. When rolling back to a historical version, search for the configuration data corresponding to the target version number from the git repository. Get the current running version number and the target rollback version number, and load the configuration data copy corresponding to the target version number from the backup library. Use the diff tool to compare the difference between the current version configuration data and the rollback version configuration data, generate a difference report, and determine the changes in the configuration items. If the preset difference threshold exceeds the preset value, an alarm message is issued. Use the rollback version configuration data copy to replace the currently running configuration data, and update the current version number in the version management system to the rollback version number. Record the rollback operation information in the log, including the operation time, operator, original version number, rollback version number, and rollback reason.

[0055] As an application example of the above steps S101 to S105, refer to Figure 2, the first step is for developers to develop in the development environment configured by the Tiansuan platform, configure indicators and dimensions, generate SQL statements and perform query verification during development, configure the development environment and the business backend development environment to test whether the generated SQL statements meet the requirements. The second step is to register changes in the development library, record the detailed information of the changes, record the change name, the baseline proofreading library configuration ID, the change notes, the requirement link and other information. The third step is to proofread the changes, display the differences, confirm the differences, resolve conflicts, and record the unique key value of the table field, the unique key value of the table field modified for the first time, the operation type, etc. The fourth step is to perform the last verification in the pre-release environment to ensure that the function and data are verified correctly. The fifth step is to back up the data of the formal environment when publishing to the production environment to facilitate the subsequent rollback operation. The sixth step is to use the rollback function to roll back the data in an emergency to ensure the stability of the online environment. The rollback function implements transaction-level rollback and uses the delete+insert into select method to recover data. The data of the most recent changes will be merged to form a new change. Because the rollback only rolls back the production environment, the intermediate versions of the pre-release environment and the development environment when the production version is released need to be merged during the rollback to know the changes between the versions.

[0056] In some embodiments, Figure 3 As shown, configuring the development environment according to the configuration management system, setting an indicator configuration table in the development environment, and generating a corresponding SQL statement according to the indicator configuration table specifically include:

[0057] S1011, configuring the development environment using an automated deployment tool according to development environment configuration parameters preset in the configuration management system;

[0058] S1012, creating an indicator configuration table using a relational database in the development environment, and storing indicator information and dimension information required for each business report according to the indicator configuration table;

[0059] S1013, extracting the indicator name and calculation formula of each indicator record in the indicator configuration table, and generating an executable SQL statement through a predefined SQL statement template according to the indicator name and the calculation formula.

[0060] In this embodiment, according to the development environment configuration parameters preset in the configuration management system, an automated deployment tool is used to configure the target development environment. In the configured development environment, an indicator configuration table is created using a relational database, and the indicator information and dimension information required for each business report are defined and stored in the indicator configuration table according to business requirements. The indicator name and calculation formula corresponding to each indicator record are extracted from the indicator configuration table. According to the obtained indicator name and calculation formula, an executable SQL statement corresponding to the indicator is dynamically generated through a predefined SQL statement template. The generated SQL statements are organized and sorted according to the association relationship of the indicators, and a logical execution chain for the indicator calculation is constructed. In the development environment, according to the constructed indicator calculation logic chain, the generated SQL statements are executed in sequence to complete the calculation and storage of each indicator data. The calculated indicator data is associated with the corresponding dimension information to generate a result data set that meets the business report display requirements.

[0061] For example, the configuration management system presets the operating system of the development environment as CentOS7, the web server as Nginx, the database as MySQL5.7, and the corresponding version number, installation path, and other information. Using automated deployment tools, such as Ansible or Jenkins, the development environment can be automatically built according to these preset parameters, including operating system installation, software installation and configuration, etc. This avoids the errors and time costs caused by manual configuration and ensures the consistency of the development environment.

[0062] In the built development environment, you need to create an indicator configuration table. Assuming that you use the MySQL database, create a table named indicator_config. This table is used to store the indicator information and dimension information required for various business reports. For example, for an e-commerce platform, you may need to count indicators such as the total daily order amount, the number of new users, and the average user visit time. The names, calculation formulas, and related dimensions of these indicators are stored in the indicator_config table. The specific table structure can include fields such as indicator ID (primary key), indicator name, calculation formula, dimension information, and indicator description. For example, the indicator name is "Daily Order Amount", the calculation formula is "SUM(order_amount)", the dimension information is "Date", and the indicator description is "Total Amount of All Orders per Day". Storing this information in the database can facilitate subsequent indicator calculations and report generation.

[0063] Next, you need to extract the indicator name and calculation formula from the indicator configuration table. Assume that the indicator_config table has been created and some indicator information has been stored in the table. You can use SQL statements to query the table, such as `SELECTindicator_name, calculation_formulaFROMindicator_config`. This SQL statement returns the names and calculation formulas of all indicators. For example, the returned results may include "Daily Order Total", "SUM(order_amount)"; "Number of New Users", "COUNT(DISTINCTuser_id)", etc. Finally, generate executable SQL statements based on the extracted indicator name and calculation formula, as well as the predefined SQL statement template. Assume that the predefined SQL statement template is SELECT{calculation_formula}AS{indicator_name}FROMorder_tableWHEREdate={date}. Among them, {calculation_formula} and {indicator_name} are placeholders that need to be replaced, and {date} is a date parameter. Substitute the "daily order total" and "SUM(order_amount)" extracted earlier into the template, and set the date parameter to "20xx-xx-xx", then you can generate an executable SQL statement.

[0064] In some embodiments, Figure 4 As shown, each time a configuration change occurs in the indicator configuration table, a change record is written into the change registration table, specifically including:

[0065] S1021, obtaining configuration change information according to the indicator configuration table;

[0066] S1022, comparing the configuration change information with a preset change condition to determine whether the configuration change information satisfies the change condition, where the change condition includes a value range and a change range of each field;

[0067] S1023: If the configuration change information satisfies the change condition, write the configuration change information into the change registration table.

[0068] In this embodiment, configuration change information is obtained according to the indicator configuration table, and the change information is parsed into structured data such as field names and field values. The value range and change amplitude threshold of each field are obtained from the preset conditions, and change condition judgment rules are constructed. For each configuration change information, its field value is extracted to determine whether it meets the value range of the corresponding field. If the field value meets the value range, the change amplitude of the field is calculated to determine whether it exceeds the preset change amplitude threshold. If the value range and change amplitude of all fields meet the change conditions, the configuration change information is marked as meeting the conditions. For configuration change information that meets the conditions, key fields such as change time, change content, etc. are extracted to construct change log information. The change log information is written into the change registration table to complete the review and recording of the configuration change information, providing data support for subsequent analysis and tracing.

[0069] Exemplarily, the change information acquisition of the indicator configuration table indicator_config can be implemented through a scheduled task or trigger. For example, at regular intervals or after the indicator_config table is updated, a program is executed to obtain the change information. The change information usually includes the change time, change type (new, modified, deleted), change field, and new and old values. The specific implementation method can use the trigger function of the database, or write a scheduled script to query and compare the changes in the database. Assume that the indicator_config table has the following fields: indicator ID (indicator_id), indicator name (indicator_name), calculation formula (calculation_formula), dimension information (dimension_info), indicator description (indicator_description). The change condition preset is to control which changes need to be recorded in the change registration table. For example, only focus on the changes of indicator names and calculation formulas, and the changes are recorded only when they exceed a certain threshold. The specific change conditions can be configured according to actual needs. For example, it can be set that the indicator name change length exceeds 5 characters, or the calculation formula change exceeds 10 characters, before it is recorded in the change registration table. You can also set certain fields to not be allowed to change, such as the indicator ID. This can avoid recording unnecessary change information, reduce log storage space, and improve query efficiency.

[0070] To compare the configuration change information with the preset change conditions, you need to compare each field one by one. For example, if the old value of the indicator name is "Daily Order Amount" and the new value is "Daily Total Order Amount", the change length is 3, which is less than the preset threshold of 5 characters, and the change condition is not met. For another example, if the old value of the calculation formula is "SUM(order_amount)" and the new value is "COUNT(DISTINCTuser_id)", the change length exceeds 10 characters, which meets the preset conditions. This can accurately determine which changes need to be recorded.

[0071] The change registration table is used to record configuration change information that meets the change conditions. Assume that the change registration table is named indicator_config_changelog, and its fields include: change time (change_time), change type (change_type), indicator ID (indicator_id), change field (changed_field), old value (old_value), and new value (new_value). If the configuration change information meets the change conditions, it will be written into the change registration table. For example, suppose the indicator ID is 1, and the indicator name is changed from "Daily Order Total" to "Daily Order Total Amount", which meets the change conditions. Then insert a record into the indicator_config_changelog table, with the change time being the current time, the change type being "modify", the indicator ID being 1, the change field being "indicator_name", the old value being "Daily Order Total Amount", and the new value being "Daily Order Total Amount". This can fully record the important configuration change history, facilitating subsequent auditing and tracing.

[0072] In some embodiments, Figure 5 As shown, the automatic proofreading of the changed configuration data submitted by different developers to identify the differences and conflicts of each changed configuration data specifically includes:

[0073] S1031, obtaining multiple configuration change data of the same indicator ID in the indicator configuration table;

[0074] S1032, using a similarity algorithm to determine whether the indicator meaning description and calculation logic of each configuration change data are consistent, and obtaining a comparison result;

[0075] S1033: Determine whether there are any differences or conflicts between the various changed configuration data according to the comparison result.

[0076] In this embodiment, multiple configuration change data with the same indicator ID are obtained from the indicator configuration table, and the obtained data are stored in a temporary data set. For each configuration change data in the temporary data set, the indicator meaning description and calculation logic field are extracted. A text similarity algorithm, such as TF-IDF, Word2Vec, etc., is used to calculate the similarity between the indicator meaning descriptions of each configuration change data to obtain a similarity matrix. A code similarity algorithm, such as abstract syntax tree comparison, edit distance, etc., is used to calculate the similarity between the calculation logics of each configuration change data to obtain another similarity matrix. The indicator meaning description similarity matrix and the calculation logic similarity matrix are weighted and fused to obtain a comprehensive similarity matrix as the final comparison result. According to a preset similarity threshold, it is determined whether there is an element less than the threshold in the comprehensive similarity matrix. If so, it is determined that there is a difference conflict between the corresponding two configuration change data. The configuration change data with difference conflicts are marked, and the marking results are output for manual review and processing by subsequent business processes.

[0077] For example, to obtain multiple configuration change data of the same indicator ID in the indicator configuration table, you first need to determine the indicator ID. For example, to obtain the change data of the indicator ID "1", you need to filter out all records with indicator_id "1" from the indicator configuration change registration table indicator_config_changelog. These records contain all historical change information of the indicator, including change time, change type, change field, old value and new value, etc. This allows you to centrally view all change history of a certain indicator, which is convenient for subsequent analysis and comparison.

[0078] Using a similarity algorithm to determine whether the indicator meaning description and calculation logic of each configuration change data are consistent can help discover potential conflicts or errors. For example, suppose there are two versions of indicator descriptions, "Daily Order Total" and "Daily Order Total Amount". The two descriptions have the same semantics, but the expressions are slightly different, so their similarity is high. If the indicator description of another version is "User Activity", the similarity with the previous two versions is lower, and there may be a problem of inconsistent indicator meaning. The similarity judgment of calculation logic is similar, and the similarity of calculation formulas of different versions can be compared. Doing so can quantify the degree of difference between indicator descriptions and calculation logic of different versions, providing a basis for subsequent conflict judgment.

[0079] Assume that the calculation formula of the indicator "user retention rate" has undergone the following three changes:

[0080] Version 1: “COUNT(DISTINCTretained_users) / COUNT(DISTINCTtotal_users)”

[0081] Version 2: “COUNT(DISTINCTretained_users) / COUNT(total_users)”

[0082] Version 3: “(COUNT(DISTINCTretained_users) / COUNT(DISTINCTtotal_users))*100%”

[0083] The calculation logic of version 1 and version 2 is highly similar because their core calculation parts are the same, but the second version omits the deduplication operation of total_users. This difference may be caused by changes in business needs or adjustments to the statistical caliber of data, and its impact needs to be further analyzed. The calculation logic of version 1 and version 3 is also highly similar. Version 3 simply multiplies version 1 by 100% and converts the result into a percentage. This change usually does not affect the core meaning of the indicator, but only changes the way the result is presented. By comparing the similarity algorithm, these potential differences can be quickly discovered and a more in-depth analysis can be performed. Based on the comparison results, it is necessary to judge whether there are differences and conflicts between the various changed configuration data in combination with the specific business scenario and the preset threshold. If the similarity of the indicator description of the two versions is less than 0.8, or the difference in calculation logic leads to a significant change in the indicator result, it is considered that there is a difference conflict. For example, if the calculation formula of the "Daily Order Total" indicator is changed from "SUM(order_amount)" to "COUNT(DISTINCTorder_id)", the calculation logic of the two versions is very different, and the indicator results are completely different, so it can be judged that there is a conflict. Doing so can effectively identify potential problems caused by configuration changes and prevent erroneous indicator data from affecting business decisions.

[0084] For example, the indicator "order conversion rate" is defined as "number of users who place orders / number of visiting users". Suppose the calculation formula of this indicator has changed twice as follows:

[0085] First change: Modify the calculation formula to "Number of paid orders / Number of visiting users". Analysis: This change changed the meaning of the indicator from measuring the user's willingness to place an order to measuring the user's willingness to pay. Although both indicators are related to orders, they have different business meanings and uses, so there is a conflict.

[0086] Second change: The calculation formula was changed to "number of users who placed orders / number of unique visitors". Analysis: This change changed the statistical caliber of the denominator from the original number of all visiting users to the number of unique visitors. This may lead to changes in the indicator results, but it does not necessarily mean that the meaning of the indicator has changed.

[0087] It is necessary to determine whether this change constitutes a conflict based on the specific business scenario. For example, if the business is more concerned with the conversion rate of independent visitors, then this change is reasonable; on the contrary, if it is still necessary to count the conversion rate of all visiting users, then this change will be a conflict. Through the above analysis and examples, you can clearly understand how to obtain, compare and judge indicator configuration change data, and effectively identify potential conflicts and errors to ensure the accuracy and consistency of indicator data. This is crucial to ensure the reliability of data analysis results and the effectiveness of business decisions.

[0088] In some embodiments, Figure 6 As shown, the change configuration data is released to the pre-release environment for automated testing, and then the change configuration data is released to the production environment after passing the automated testing, specifically including:

[0089] S1041, publishing the changed configuration data to a pre-release environment, and performing automated testing on the changed configuration data using a test framework according to a preset automated test case to obtain an automated test result;

[0090] S1042: Determine whether the changed configuration data is a stable version based on the automated test result; if it is a stable version, publish the changed configuration data to a production environment.

[0091] In this embodiment, step 1: obtain the changed configuration data and the preset version configuration data. From the configuration database, obtain the latest changed configuration data and the preset version configuration data, and store them in the temporary data storage area. Step 2: compare the configuration data and generate a difference report. Use the data comparison algorithm to compare the changed configuration data obtained in step 1 with the preset version configuration data, generate a configuration difference report, and store it in the file storage server. Step 3: determine the configuration difference. If the configuration difference report is empty, execute step 7, otherwise, execute step 4. Step 4: deploy the changed configuration data to the pre-release environment. Obtain the changed configuration data from the temporary data storage area, deploy it to the pre-release environment, and record the deployment results to the log database. Step 5: perform automated testing. According to the preset automated test case, use the test framework to perform automated testing on the changed configuration data in the pre-release environment, obtain the automated test results, and store them in the test result database. Step 6: analyze the automated test results and determine the stability of the version. Analyze the automated test results obtained in step 5, and use statistical methods to calculate the test pass rate. If the test pass rate is greater than the preset threshold, it is determined that the changed configuration data is a stable version, and step 7 is executed, otherwise, the process ends. Step 7: Release the stable version configuration data to the production environment. Obtain the changed configuration data from the temporary data storage area, release it to the production environment, and record the release results in the log database.

[0092] Exemplarily, the modified indicator configuration data, such as the calculation formula of "user activity", is synchronized from the test environment to the database of the pre-release environment. Using a test framework to perform automated testing on the changed configuration data can improve test efficiency and accuracy. The test framework usually provides a set of standard test processes and tools that can automatically execute test cases, collect test results and generate test reports. For example, you can use the test framework to write automated test cases for the "user activity" indicator to verify whether the indicator calculation results under different user behaviors meet expectations. Test cases can cover various normal and abnormal situations, such as new user registration, old user login, user logout, etc. Each test case should have a clear expected result to compare with the actual result. The automated test results are an important basis for judging whether the changed configuration data is a stable version. If the automated test passes, it means that the changed configuration data runs stably in the pre-release environment and can be considered for release to the production environment. On the contrary, if the automated test fails, it is necessary to analyze the cause of the failure and repair and improve the changed configuration data. If the automated test case of the "user activity" indicator fails, it is necessary to check the calculation formula, data source and other related configurations to find out the cause of the test failure and make corresponding corrections. Assuming that all automated test cases for the "user activity" indicator pass and the test results are consistent with expectations, the changed configuration data of the indicator can be considered to be a stable version. At this point, the changed configuration data can be synchronized from the pre-release environment to the production environment. In order to ensure the stability of the production environment, it is usually necessary to choose an appropriate release time, such as releasing during a period of low user traffic. At the same time, a rollback plan must be prepared so that the previous version can be quickly restored when problems occur. For example, the calculation formula of the "order conversion rate" indicator is changed from "number of ordering users / number of visiting users" to "number of paid orders / number of visiting users". In the pre-release environment, you can simulate the user's ordering and payment behaviors, and use automated test cases to verify whether the new calculation formula can correctly calculate the order conversion rate. Assuming that the test results show that the new calculation formula works properly in various situations and the calculation results are consistent with expectations, the changed configuration data of the indicator can be considered to be a stable version and can be released to the production environment. On the contrary, if the test results show that the new calculation formula does not work properly in some cases, or the calculation results are inconsistent with expectations, you need to analyze the reasons for the failure. For example, the number of paid orders may be counted incorrectly because some order statuses are not processed correctly. In this case, you need to modify the calculation formula or related code and re-execute the automated test until the test passes. For example, after the calculation formula of the "user retention rate" indicator is modified, perform automated testing in the pre-release environment. The test case simulates the retention behavior of different user groups, such as new users, old users, and users in different regions.Assume that the test results show that the retention rate calculation results for new users are consistent with expectations, but the retention rate calculation results for old users are low. This may be due to changes in the statistical caliber of old users' data or changes in the behavior patterns of old users. In this case, it is necessary to further analyze the cause and adjust the calculation formula or data source to ensure the accuracy of the indicator results. Changes can only be released to the production environment after the problem is resolved and retested.

[0093] In some embodiments, Figure 7 As shown, the changing configuration data is released to the production environment, and then further includes:

[0094] S1043, determining an initial release node set of the production environment according to a preset phased release strategy, wherein the initial release node set includes a plurality of initial release nodes;

[0095] S1044, publishing the changed configuration data to the initial publishing node, and using a Z-score anomaly detection algorithm to determine whether abnormal fluctuations occur in key business indicators of the initial publishing node;

[0096] S1045, if an abnormal fluctuation occurs, the configuration data of the initial release node is rolled back to the configuration data of the previous version, otherwise the release node range of the changed configuration data is gradually expanded in the production environment according to a preset time interval and step size.

[0097] In this embodiment, step 1: obtain the preset grayscale release strategy, including the number of initial release nodes, time interval, step size and other information, and determine the initial release node set. Step 2: Publish the changed configuration data to the determined initial release node set, and record the configuration version information of each node for subsequent rollback operations. Step 3: Use the Z-score anomaly detection algorithm to obtain real-time data of key business indicators of the initial release node from the monitoring system. Formula: Z = (x-μ) / σ, where x represents the current indicator value, μ represents the historical indicator mean, and σ represents the historical indicator standard deviation. Step 4: If the absolute value of the Z-score is greater than the preset threshold, it is determined that the business indicator has abnormal fluctuations. The setting of the threshold is determined according to the fluctuations of specific business indicators and historical data. Step 5: If it is determined that the key business indicators of the initial release node have abnormal fluctuations, the configuration data of the abnormal initial node will be rolled back to the previous stable version according to the recorded configuration version information. Step 6: If the key business indicators of the initial release node do not fluctuate abnormally, select a portion of the remaining production environment nodes according to the preset time interval and step size, add them to the release node set, and publish the changed configuration data to the newly added nodes. Step 7: Repeat steps 3 to 6 until the changed configuration data is published to all production environment nodes. Monitor the key business indicators of all nodes, and roll back the configuration data of the corresponding nodes if abnormal fluctuations occur.

[0098] Exemplarily, according to the preset grayscale release strategy, determine the initial release node set of the production environment. For example, assuming that there are 100 nodes in the production environment, 10 nodes can be selected as the initial release nodes. The selection of these nodes can be based on a variety of factors, such as the load of the nodes, the distribution of user groups, etc. In order to more comprehensively evaluate the impact of changing the configuration data, the selected initial nodes should cover different user groups and usage scenarios as much as possible. For example, a part of the nodes from different regions and different operators can be selected to cover a wider user group.

[0099] Then, the changed configuration data is published to the 10 initial release nodes. This step requires specific technical means, such as using automated deployment tools to synchronize configuration data to the target node. It should be noted that the release process needs to avoid affecting users as much as possible. For example, it can be released during a period of low user traffic, or a rolling release method can be used to update nodes one by one. Next, the Z-score anomaly detection algorithm is used to determine whether the key business indicators of the initial release node have abnormal fluctuations. The Z-score algorithm is a commonly used anomaly detection method that determines whether the data is abnormal by calculating the deviation between the data point and the average value. Taking the "user activity" indicator as an example, assume that the average value of this indicator on the initial release node is 1000 and the standard deviation is 100. If the "user activity" of a node drops to 800, its Z-score is (800-1000) / 100=-2. According to the preset threshold, if the absolute value of the Z-score is greater than a certain value (for example, 3), it is considered that the indicator has abnormal fluctuations. In addition to "user activity", other key business indicators need to be monitored, such as order conversion rate, user retention rate, etc. The Z-score threshold of each indicator can be adjusted according to historical data and business needs. For example, for indicators that have a greater impact on the business, a lower threshold can be set to detect anomalies earlier. If the Z-score anomaly detection algorithm determines that the key business indicators of the initial release node have abnormal fluctuations, the configuration data of these nodes need to be rolled back to the configuration data of the previous version immediately. The rollback operation needs to prepare a rollback plan in advance and conduct drills to ensure that the rollback process is fast and reliable. For example, the configuration data of the previous version can be backed up to a specified directory, and the configuration data can be restored to the previous version using an automated script during rollback. This can minimize the impact of abnormal fluctuations on users. If the key business indicators of the initial release node do not fluctuate abnormally, the release node range of the changed configuration data can be gradually expanded in the production environment according to the preset time interval and step size. For example, 10 release nodes can be added every 1 hour until all nodes are released. The settings of time interval and step size need to be adjusted according to the specific situation. For important business changes, you can set a longer time interval and a smaller step size so that you can release more cautiously. Gradually expanding the scope of release can effectively control risks and avoid large-scale failures that may be caused by releasing to all nodes at once. For example, suppose that the "order conversion rate" indicator does not show abnormal fluctuations on the initial 10 nodes. Next, you can add 20 release nodes every 2 hours. After releasing to 30 nodes, if the "order conversion rate" indicator is found to drop abnormally, you need to immediately roll back the configuration data of these 30 nodes and analyze the cause of the abnormality. It may be a bug in the new configuration, or it may be caused by other factors. After the problem is solved, you can re-release the grayscale.

[0100] In some embodiments, Figure 8 As shown, the detection of whether the changed configuration data released to the production environment meets expectations, and if not, restoring to the previous version of the configuration data through a rollback function, specifically includes:

[0101] S1051, comparing the changed configuration data with the configuration data of the previous version to identify the changed configuration items;

[0102] S1052, by monitoring the operating status of the production environment, analyzing and obtaining the impact value of the changed configuration item on each key performance indicator of the production environment;

[0103] S1053, comparing the impact value with a preset expected value range to determine whether the changed configuration data achieves the expected effect;

[0104] S1054: If the expected effect is not achieved, a rollback mechanism is triggered to restore the configuration data of the production environment to the configuration data of the previous version.

[0105] In this embodiment, step 1: obtain the current configuration data and the previous version of the configuration data. Calculate the difference between the two versions of the configuration data, identify the changed configuration items, and generate a set of changed configuration items. Step 2: Through the monitoring system, obtain the current values ​​of each key performance indicator of the production environment in real time, store them in the time series database, and form time series data. Step 3: According to the set of changed configuration items, analyze the historical change data of each configuration item and the historical data of the key performance indicators in the corresponding time period, and use a linear regression algorithm or a time series analysis algorithm to establish an association model between the configuration item change and the key performance indicator impact value. Step 4: Input the set of changed configuration items obtained in step 1 into the association model established in step 3, predict the impact value of the changed configuration item on the key performance indicator, and obtain a set of predicted impact values. Step 5: Obtain a preset expected value range, and compare the predicted impact value set obtained in step 4 with the preset expected value range. If the predicted impact value falls within the expected value range, it is judged that the changed configuration has achieved the expected effect. Step 6: If the comparison result of step 5 is that the predicted impact value does not fall within the expected value range, it is judged that the changed configuration has not achieved the expected effect, and the rollback mechanism is triggered. Step 7: After the rollback mechanism is triggered, an automated script is used to restore the configuration data of the production environment to the configuration data of the previous version, complete the configuration rollback operation, and record the rollback results to the log system.

[0106] For example, suppose the content of the configuration file of the previous version includes: {"timeout":1000, "max_connections":100, "log_level":"INFO"}, and the content of the new configuration file includes: {"timeout":2000, "max_connections":200, "log_level":"DEBUG"}. By comparison, it is easy to identify that the changed configuration items are timeout, max_connections, and log_level. Next, it is necessary to monitor the operating status of the production environment and analyze the impact of the changed configuration items on the key performance indicators of the production environment. Some key performance indicators can be selected for monitoring, such as interface response time, error rate, throughput, etc. Assume that the indicator of interest is the average interface response time. Before the configuration is changed, the value of this indicator is 50 milliseconds. After the configuration is changed, it is necessary to continuously monitor the changes in this indicator. Assume that after monitoring for a period of time, it is found that the average interface response time has changed to 30 milliseconds. Then, the impact of the changed configuration item on this indicator is -20 milliseconds. Then, the impact value needs to be compared with the preset expected value range to determine whether the changed configuration data has achieved the expected effect. Before changing the configuration, an expected value range needs to be set in advance. For example, it is expected that the average response time of the interface can be reduced by 10 to 30 milliseconds after the configuration change. The actual impact value is -20 milliseconds, which falls within the expected value range, so it can be considered that the changed configuration has achieved the expected effect. Assume that the indicator of concern is the error rate. Before the configuration change, the error rate was 0.1%. It is expected that the error rate can be reduced to below 0.05% after the configuration change. After the configuration change, it is found that the error rate has risen to 0.2%, which indicates that the changed configuration has not achieved the expected effect and even has a negative impact. If the changed configuration data does not achieve the expected effect, the rollback mechanism needs to be triggered to restore the configuration data of the production environment to the previous version of the configuration data, so that the loss can be stopped in time to avoid causing greater losses. In order to ensure the effectiveness of the rollback mechanism, the rollback plan needs to be prepared in advance and rehearsed. For example, the previous version of the configuration file can be backed up to a specified directory, and the configuration file can be restored to the previous version using an automated script during the rollback. Through the above measures, the risks brought by changing configuration data can be effectively controlled to ensure the stable operation of the production environment.

[0107] Reference Fig. 9 An embodiment of the present invention provides a data configuration publishing processing system 9, and the system 9 specifically includes:

[0108] The first processing module 901 is used to configure the development environment according to the configuration management system, set an indicator configuration table in the development environment, and generate a corresponding SQL statement according to the indicator configuration table, wherein the indicator configuration table is used to record the indicator information and dimension information required for each business report;

[0109] The second processing module 902 is used to establish a change registration table, and write a change record into the change registration table each time a configuration change occurs in the indicator configuration table;

[0110] The third processing module 903 is used to automatically proofread the changed configuration data submitted by different developers, identify the differences and conflicts of each changed configuration data, and form a change proofreading table;

[0111] The fourth processing module 904 is used to publish the changed configuration data to the pre-release environment for automated testing, and then publish the changed configuration data to the production environment after passing the automated testing;

[0112] The fifth processing module 905 is used to detect whether the changed configuration data released to the production environment meets expectations. If not, the configuration data of the previous version is restored through a rollback function.

[0113] It is understandable that if Figure 1 The contents of the data configuration release processing method embodiment shown in the figure are applicable to the data configuration release processing system embodiment. The functions specifically implemented by the data configuration release processing system embodiment are similar to those in the figure. Figure 1 The processing method for data configuration release shown in the embodiment is the same as that in the embodiment of the present invention, and the beneficial effects achieved are the same as those in the embodiment of the present invention. Figure 1 The beneficial effects achieved by the data configuration publishing processing method embodiment shown are also the same.

[0114] It should be noted that the information interaction, execution process and other contents between the above-mentioned systems are based on the same concept as the embodiment of the method of the present invention. Their specific functions and technical effects can be found in the method embodiment part and will not be repeated here.

[0115] Those skilled in the art can clearly understand that, for the convenience and simplicity of description, only the division of the above-mentioned functional units and modules is used as an example for illustration. In practical applications, the above-mentioned function allocation can be completed by different functional units and modules as needed, that is, the internal structure of the system can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiment can be integrated in a processing unit, or each unit can exist physically separately, or two or more units can be integrated in one unit. The above-mentioned integrated unit can be implemented in the form of hardware or in the form of software functional units. In addition, the specific names of the functional units and modules are only for the convenience of distinguishing each other, and are not used to limit the scope of protection of this application. The specific working process of the units and modules in the above-mentioned system can refer to the corresponding process in the aforementioned method embodiment, which will not be repeated here.

[0116] Reference Fig.10 The embodiment of the present invention further provides a computer device 10, comprising: a memory 1002 and a processor 1001 and a computer program 1003 stored in the memory 1002. When the computer program 1003 is executed on the processor 1001, a processing method for data configuration release as described in any one of the above methods is implemented.

[0117] The computer device 10 may be a computing device such as a desktop computer, a notebook, a PDA, or a cloud server. The computer device 10 may include, but is not limited to, a processor 1001 and a memory 1002. Those skilled in the art will appreciate that Fig.10 This is only an example of the computer device 10 and does not constitute a limitation on the computer device 10 . The computer device 10 may include more or fewer components than shown in the figure, or a combination of certain components, or different components. For example, it may also include input and output devices, network access devices, etc.

[0118] The processor 1001 may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor, etc.

[0119] In some embodiments, the memory 1002 may be an internal storage unit of the computer device 10, such as a hard disk or memory of the computer device 10. In other embodiments, the memory 1002 may also be an external storage device of the computer device 10, such as a plug-in hard disk, a smart memory card (SmartMedia Card, SMC), a secure digital (Secure Digital, SD) card, a flash card (Flash Card), etc. equipped on the computer device 10. Further, the memory 1002 may also include both an internal storage unit of the computer device 10 and an external storage device. The memory 1002 is used to store an operating system, an application program, a boot loader (Boot Loader), data, and other programs, such as the program code of the computer program, etc. The memory 1002 may also be used to temporarily store data that has been output or is to be output.

[0120] An embodiment of the present invention further provides a computer-readable storage medium on which a computer program is stored. When the computer program is executed by a processor, the processing method for data configuration release as described in any one of the above methods is implemented.

[0121] In this embodiment, if the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the present application implements all or part of the processes in the above-mentioned embodiment method, which can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a computer-readable storage medium. When the computer program is executed by the processor, the steps of the above-mentioned various method embodiments can be implemented. Among them, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form. The computer-readable medium can at least include: any entity or device that can carry the computer program code to the camera / terminal device, recording medium, computer memory, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), electric carrier signal, telecommunication signal and software distribution medium. For example, a USB flash drive, a mobile hard disk, a disk or an optical disk. In some jurisdictions, according to legislation and patent practice, computer-readable media cannot be electric carrier signals and telecommunication signals.

[0122] In the above embodiments, the description of each embodiment has its own emphasis. For parts that are not described or recorded in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0123] Those of ordinary skill in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0124] In the embodiments disclosed in the present application, it should be understood that the disclosed devices / terminal equipment and methods can be implemented in other ways. For example, the device / terminal equipment embodiments described above are only schematic. For example, the division of the modules or units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0125] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

Claims

1. A method for processing data configuration release, characterized in that: The method specifically comprises: Configure the development environment according to the configuration management system, set an indicator configuration table in the development environment, generate corresponding SQL statements according to the indicator configuration table, and the indicator configuration table is used to record the indicator information and dimension information required for each business report; Establish a change registration table, and write the change record into the change registration table every time a configuration change occurs in the indicator configuration table; Automatically proofread the changed configuration data submitted by different developers, identify the differences and conflicts of each changed configuration data, and form a change proofreading table; Publishing the changed configuration data to a pre-release environment for automated testing, and then publishing the changed configuration data to a production environment after passing the automated testing; Check whether the changed configuration data released to the production environment meets expectations. If not, restore to the previous version of the configuration data through the rollback function.

2. The method according to claim 1, characterized in that The configuring the development environment according to the configuration management system, setting an indicator configuration table in the development environment, and generating a corresponding SQL statement according to the indicator configuration table specifically include: Use automated deployment tools to configure the development environment according to the development environment configuration parameters preset in the configuration management system; Creating an indicator configuration table using a relational database in the development environment, and storing indicator information and dimension information required for each business report according to the indicator configuration table; The indicator name and calculation formula of each indicator record in the indicator configuration table are extracted, and an executable SQL statement is generated through a predefined SQL statement template according to the indicator name and the calculation formula.

3. The method according to claim 1, characterized in that The step of writing the change record into the change registration table each time a configuration change occurs in the indicator configuration table specifically includes: Acquire configuration change information according to the indicator configuration table; Comparing the configuration change information with a preset change condition to determine whether the configuration change information satisfies the change condition, wherein the change condition includes a value range and a change range of each field; If the configuration change information meets the change condition, the configuration change information is written into the change registration table.

4. The method according to claim 1, characterized in that: The automatic proofreading of the changed configuration data submitted by different developers to identify the differences and conflicts of each changed configuration data specifically includes: Obtain multiple configuration change data of the same indicator ID in the indicator configuration table; Use a similarity algorithm to determine whether the indicator meaning description and calculation logic of each configuration change data are consistent to obtain a comparison result; It is determined whether there are differences or conflicts between the various changed configuration data according to the comparison result.

5. The method according to claim 1, characterized in that: The step of publishing the changed configuration data to a pre-release environment for automated testing, and then publishing the changed configuration data to a production environment after the automated testing is passed, specifically includes: The changed configuration data is released to the pre-release environment, and according to the preset automated test cases, the test framework is used to perform automated testing on the changed configuration data to obtain automated testing results; According to the automated test result, it is determined whether the changed configuration data is a stable version. If it is a stable version, the changed configuration data is published to the production environment.

6. The method according to claim 5, characterized in that The step of publishing the changed configuration data to the production environment further includes: According to a preset grayscale release strategy, determine an initial release node set of a production environment, wherein the initial release node set includes multiple initial release nodes; Publishing the changed configuration data to the initial publishing node, and using a Z-score anomaly detection algorithm to determine whether abnormal fluctuations occur in key business indicators of the initial publishing node; If an abnormal fluctuation occurs, the configuration data of the initial release node is rolled back to the configuration data of the previous version; otherwise, the release node range of the changed configuration data is gradually expanded in the production environment according to a preset time interval and step size.

7. The method according to any one of claims 1 to 6, characterized in that The detection of whether the changed configuration data released to the production environment meets expectations, and if not, restoring to the previous version of the configuration data through a rollback function, specifically includes: Comparing the changed configuration data with the configuration data of the previous version to identify the changed configuration items; By monitoring the operating status of the production environment, the impact value of the changed configuration item on each key performance indicator of the production environment is analyzed and obtained; Compare the impact value with a preset expected value range to determine whether the changed configuration data achieves the expected effect; If the expected effect is not achieved, a rollback mechanism is triggered to restore the configuration data of the production environment to the configuration data of the previous version.

8. A data configuration publishing processing system, characterized in that: The system specifically comprises: A first processing module is used to configure a development environment according to a configuration management system, set an indicator configuration table in the development environment, and generate a corresponding SQL statement according to the indicator configuration table, wherein the indicator configuration table is used to record indicator information and dimension information required for each business report; The second processing module is used to establish a change registration table, and write the change record into the change registration table every time a configuration change occurs in the indicator configuration table; The third processing module is used to automatically proofread the changed configuration data submitted by different developers, identify the differences and conflicts of each changed configuration data, and form a change proofreading table; A fourth processing module is used to publish the changed configuration data to a pre-release environment for automated testing, and then publish the changed configuration data to a production environment after passing the automated testing; The fifth processing module is used to detect whether the changed configuration data released to the production environment meets expectations. If not, the configuration data of the previous version is restored through a rollback function.

9. A computer device, characterized in that: include: A memory and a processor and a computer program stored in the memory, when the computer program is executed on the processor, implements the processing method for data configuration release as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that: A computer program is stored thereon, and when the computer program is executed by a processor, the processing method for data configuration release according to any one of claims 1 to 7 is implemented.

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